The design of a millimeter-wave Mouse-Antenna Sun-Tracking radiometer at W band (MASTRad89), capable of measuring both the downwelling emitted atmospheric brightness temperature and the associated extinction over an estimated dynamic range greater than 30 dB, is discussed. The MASTRad89 instrument at W band operates with two equal antennas having the same beam width and an offset from each other by an angle of about 10° (named “mouse antenna” system). A dedicated efficient solar tracking system allows to follow the apparent movement of the Sun by means a programmed controller. The two narrow-beam antennas share the same radiofrequency superheterodyne front-end chain to carry out simultaneous measurements at W band of both Sun and out-of-the-Sun atmospheric brightness temperature. Using a post-processing software, MASTRad89 can provide the atmospheric path attenuation at W band in any weather conditions, overcoming the saturation problem due to rain which is typical of conventional microwave radiometers.

Using Sentinel-1 satellite data, differential interferometric synthetic-aperture-radar (DInSAR) retrieval techniques at C band are presented to estimate snowpack depth, combined with SAR backscattered data for wet snow discrimination and a physically based snowpack model. Optical satellite data from satellite multispectral imagers are used for snow extent mapping. The processing chain is tested in central Apennines (Italy), using several validation sites where in-situ snow measurements are daily available during the winter 2018–19. The potential of using analytical and statistical inversion algorithms, trained by forward SAR and snowpack model simulations of the same area, is discussed. Results, in terms of error bias, standard deviation and correlation between estimated and in situ snow data, are illustrated pointing out critical issues due to coherence loss.

The forthcoming spaceborne ice cloud imager (ICI) millimeter/submillimeter-wave radiometer is designed to support climate monitoring and ice clouds representation in weather and climate models. The assessment of the correct pointing of each ICI channel is of undeniable importance to deliver high-quality products. Nevertheless, the ICI channels have a limited chance to sample the surface features due to the strong atmospheric gas absorption. Only for channels within 183–325 GHz, few locations worldwide show the sufficiently dry environmental conditions allowing for an occasional sampling of surface landmark targets. In this work, for the first time, we investigate the possibility of exploiting distinctive atmospheric signatures, namely, those generated by water vapor masses and deep convective clouds, for absolute and relative geolocation validation purposes. The main idea behind the proposed approach is: 1) to georeference a pivotal channel at 183 GHz, exploiting the synergy of infrared and microwave collocated observations (absolute geolocation) and 2) to test the relative pointing accuracy of all the other ICI channels with respect to the pivotal one (relative geolocation). Observations of the Special Sensor Microwave Imager/Sounder (SSMIS), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), and radiative transfer simulations are used to pursue the goals. Results show that water vapor mass (WVM) atmospheric targets can achieve an absolute point accuracy for the lower ICI channels of the order of 5.1 km (i.e., 32% of the 16-km footprint size). Conversely, when dealing with the relative pointing accuracy of higher ICI channels, the expected pointing accuracy is smaller than 4.1 km (i.e., 25% of the footprint size).

The forthcoming spaceborne Ice Cloud Imager (ICI) radiometer has 11 channels in the millimeter (mm) and sub-mm wave range from 183 up to 664 GHz. At some of these frequencies, the atmosphere is very opaque due to strong gaseous and cloud extinction, precluding the observation of the surface. We aim at investigating how to evaluate the ICI channels geolocation error using surface landmark targets. The most transparent ICI channels, i.e., those around 183.3 ± 7.0 GHz at vertical (V) polarization (ICI-1) and around 243.2 ± 2.5 GHz (ICI-4) at horizontal (H)/ V polarization, are considered. Starting from a previous work, we extend the database of the surface landmark targets to cover boreal and austral dry seasons at various latitudes. For testing the geolocation approach, we use satellite Special Sensor Microwave Imager/Sounder (SSMIS) available data at 183.31 ± 6.6 GHz at H-polarization during 2017, obtaining an overall mean error of about 5.0 km and standard deviation of about 2.2 km, well within the ICI geolocation error assessment specifications. Since no imagers are available at 243 GHz, we extrapolate SSMIS data to 243.2 ± 2.5 GHz using a model-based neural-network approach, named Blended Artificial-neural-network Microwave Imager Simulator (BAMIS). The latter is trained by radiative-transfer simulations and global-scale atmospheric reanalyses data as well as SSMIS data. Results confirm that the proposed approach can be successfully exploited for ICI-4 geolocation error assessment at 243.2 ± 2.5 GHz, with results close to those obtained for the SSMIS 183.31 ± 6.6-GHz channel.

The weather-forecast based Radio Meteorological Operation Planner (RMOP) model for the dynamical link-budget design in satellite communications at Ka-band is described and validated. For the first time, actual received Ka-band data from a deep-space satellite mission (Hayabusa2 mission from JAXA and supported by ESA) were available for operational tests. RMOP-predicted link-budget parameters were delivered in real-time before each scheduled satellite transmission period. After each transmission, received data measured by the ground stations were exploited for the RMOP validation. The data-volume actually received (transmitted and lost) by Hayabusa2 was compared with the one that would have been obtained if the transmission had been configured according to RMOP predictions. The results prove that RMOP model is capable of receiving more than 100% of extra data-volume with respect to the classical link-budget design techniques while keeping data losses under control. A specific approach usable in case of rainy events is described, too. These outstanding results will pave the way to an operative use of the weather-forecast based RMOP model for future satellite missions.

Sun-tracking microwave radiometry (STMW) is a ground-based technique where the Sun is used as a beacon source to infer the atmospheric path attenuation in all-weather conditions. STMW shows an appealing potential for overcoming the difficulties to perform satellite-to-Earth radiopropagation experiments in the unexplored millimeterwave and submillimeter-wave frequency region, especially where experimental data from a beacon receiver are not available. The theoretical framework and the ad hoc procedures and data processing developed in the last 5 years will be presented, together with the estimate of the overall error budget. The application and experimental challenges during long-term deployments, such as the field campaign of the W-band radiometric study (WRad) funded by the European Space Agency (ESA), will be reviewed.

Satellite-based detection of volcanic eruptions, using infrared radiometric data from Low Earth Orbit (LEO) spectroradiometers, may lead to an ambiguous detection in the proximity of the volcanic vent during sub-Plinian volcanic events. The thermal-infrared (TIR) brightness-temperature difference signatures saturate because of the large tephra particle within the expanding plume. In this respect, the use LEO spaceborne millimeter-wave (MMW) radiometric observations can help since plumes at millimeter wavelength are less optically opaque than at micron ones. To demonstrate this synergy, we show the analysis of the 2014 Kelud eruption case study considering LEO measurements and detection algorithms based on TIR and MMW.

Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m-3, using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2= 0.52 and RMSE = 1.95 g/m3using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores.

The goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. We used SSMIS data at 183.31 GHz as a submillimeter proxy to identify the most suitable targets for geolocation error validation in very dry atmospheric conditions, as suggested by radiative transfer modeling. Using a yearly SSMIS data set, three candidates' landmark targets are selected: 1) high-altitude lakes and high-latitude bays using a coastline reference database and 2) Antarctic ice shelves using coastlines derived from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. Data processing is carried out by using spatial cross correlation methods in the spatial frequency domain and performing a numerical sensitivity analysis to contour displacement. Cloud masking, based on a fuzzy-logic approach, is applied to automatically selected clear-air days. The results show that the average geolocation error is about 6.2 km for mountainous lakes and sea bays and 5.4 km for ice shelves, with a standard deviation of about 2.7 and 2.0 km, respectively. The results are in line with SSMIS previous estimates, whereas annual clear-air days are about 10% for mountainous lakes and sea bays and 18% for ice shelves.

The morning of November 23, 2013, a lava fountain formed from the New South-East Crater (NSEC) of Mt. Etna (Italy), one of the most active volcanoes in Europe. The explosive activity was observed from two ground-based radars, the X-band polarimetric scanning and the L-band Doppler fixed-pointing, as well as from a thermal-infrared camera. Taking advantage of the capability of the microwave radars to probe the volcanic plume and extending the volcanic ash radar retrieval (VARR) methodology, we estimate the mass eruption rate (MER) using three main techniques, namely surface-flux approach (SFA), mass continuity-based approach (MCA), and top-plume approach (TPA), as well as provide a quantitative evaluation of their uncertainty. Estimated exit velocities are between 160 and 230 m/s in the paroxysmal phase. The intercomparison between the SFA, MCA, and TPA methods, in terms of retrieved MER, shows a fairly good consistency with values up to 2.4 x 106kg/s. The estimated total erupted mass (TEM) is 3.8 x 109, 3.9 x 109, and 4.7 x 109kg for SFA with L-band, X-band, and thermal-infrared camera, respectively. Estimated TEM is between 1.7 x 109kg and 4.3 x 109for TPA methods and 3.9 x 109kg for the MCA technique. The SFA, MCA, and TPA results for TEM are in fairly good agreement with independent evaluations derived from ground collection of tephra deposit and estimated to be between 1.3 ± 1.1 x 109and 5.7 x 109kg. This article shows that complementary strategies of ground-based remote sensing systems can provide an accurate real-time monitoring of a volcanic explosive activity.

An anomaly detection and estimation technique, using a pixel-based modified version of the Kalman temporal filter, is presented. To detect the anomalies of the observed variable, the proposed Kalman-based anomaly masking (KAM) algorithm relies on the background state models of the expected measurement cycle of each pixel in nominal (abnormal) conditions. If the measurement significantly deviates from its expected value as predicted by a priori state, an anomaly is identified. The KAM algorithm also provides an a priori estimate of the nominal scenario, exploiting the previous Kalman filter states. The product is an equivalent clear-air observation, expected to be measured in the absence of anomaly (e.g., in the absence of cloud coverage). The KAM algorithm exhibits a general applicability, since its estimates are empirically computed from pixel-based models and its thresholds can be set independently of the area of interest. An application of the KAM algorithm to clear-air nominal scenarios is shown using multispectral imagery from the geostationary Spinning Enhanced Visible and Infrared Imager, having 12 visible-infrared channels and repeat cycle of 15 min, on board of the Meteosat satellite. The area of interest covers the whole West Africa for a test period of three months (December 2015 until February 2016). This results in a massive amount of processed pixels (i.e., 1530 × 880 pixels for 96 timeslots per day). A validation of the clear-air KAM algorithm is presented by intercomparing the detection results with the well-known EUMETSAT cloud mask product. The validation shows constant percentages of matching around 90% over the entire period of analysis.

A multispectral temporal-based remote sensing technique based on a modified Kalman filter is presented for clear-air detection by using Geostationary visible-infrared radiometric passive measurements. The Kalman estimate relies on a model of the daily measurement cycle of the considered pixel in clear-sky conditions. If the measurement significantly deviates from the predicted value, an anomaly is detected, which is interpreted as a non-clear air scenario. The add-on value of such approach is to be able to provide a-priori estimates, making the algorithm applicable in a global way. The Meteosat Second Generation satellite has been used over a large sample area in West Africa and a test period of three months. An inter-comparison with respect to the EUMETSAT cloud mask product has been carried out showing promising results in terms of detecting clear-air scenarios and percentages of matching around 90% over the entire period.

The European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims at investigating new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites. An example of synergy is the ingestion of surface information derived from Sentinel data in numerical weather prediction models. The rationale is that Sentinels 1, 2 and 3 are able to provide high spatio-temporal resolution information on the surface boundary (as well as the atmosphere column) and that an inaccurate representation of the boundary conditions represents a major source of uncertainty for weather forecasts. For a profitable ingestion of EO data in numerical weather prediction models, a critical aspect is the choice of a suitable model. Once the numerical model is chosen, the problem of the selection of the Sentinel-derived surface variables that have to be ingested in the model has to be tackled. While some data, such as sea and land surface temperature, are directly available, other surface data, such as soil moisture, have to be retrieved. Being STEAM currently in its initial phase, this paper gives a general overview of the project and focuses on the first activities performed in its framework. In particular, it describes the rationale behind the choice of the Numerical Weather Prediction Model and the multi-temporal approach designed to retrieve soil moisture from Sentinel-1 data. Moreover, the first results of the ingestion of Sentinel derived soil moisture, land surface temperature and sea surface temperature data into the selected model are shown. These results concern an extreme weather event that occurred in Tuscany (central Italy) in September 2017.

A-train satellite data, acquired during the Calbuco volcano (Chile) sub-Plinian eruption in April 2015, are discussed to explore the complementarity of spaceborne observations in the microwave (MW), thermal infrared (TIR), and visible wavelengths for both near-source plume and distal ash clouds. The analysis shows that TIR-based detection techniques are not suitable near the volcanic vent where rising convective columns are associated with large optical depths. Detection and parametric estimates of near-source tephra mass loading and plume height from MW radiometric data, available 69 min after the eruption onset, are proposed. Results indicate a maximum plume altitude of about 21 km above the sea level and an ash mass of 3.65 × 1010kg, in agreement with mass values obtained from empirical formulas, but less than proximal- distal mass deposit of 1.86 × 1011kg. This discrepancy may be explained by extrapolating Advanced Technology Microwave Sounder-based estimates to 6 h, thus obtaining a total mass of about 1.90 × 1011kg. Distal volcanic cloud retrievals are derived from TIR imagery and results show a good agreement between Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) retrievals of total mass taking into account the overpass time shift. If only the overlapping pixels between MODIS and VIIRS are considered, the respective estimates are 1.90 × 109kg and 1.80 × 109kg. TIR radiometric estimates of distal ash cloud height and mass loadings are also compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar retrievals. For low-to-medium optically thick ash cloud, average Cloud-Aerosol Lidar with Orthogonal Polarization-derived mass loading is about 0.8 g/m2against 0.4 g/m2from VIIRS and 1.4 g/m2from MODIS.

An inversion methodology, named maximum-likelihood (ML) volcanic ash light detection and ranging (Lidar) retrieval (VALR-ML), has been developed and applied to estimate volcanic ash particle size and ash mass concentration within volcanic plumes. Both estimations are based on the ML approach, trained by a polarimetric backscattering forward model coupled with a Monte Carlo ash microphysical model. The VALR-ML approach is applied to Lidar backscattering and depolarization profiles, measured at visible wavelength during two eruptions of Mt. Etna, Catania, Italy, in 2010 and 2011. The results are compared with those of ash products derived from other parametric retrieval algorithms. A detailed comparison among these different retrieval techniques highlights the potential of VALR-ML to determine, on the basis of a physically consistent approach, the ash cloud area that must be interdicted to flight operations. Moreover, the results confirm the usefulness of operating scanning Lidars near active volcanic vents.

Satellite microwave and infrared radiometric imagery, acquired during the recent Calbuco eruption in April 2015, are discussed to demonstrate the complementarity of microwave (MW) and thermal infrared (TIR) spaceborne observations for volcanic plume and ash cloud monitoring. TIR brightness temperatures clearly saturate in the proximity of the volcanic vent of the sub-Plinian column due to high optical extinction of the plume at those wavelengths. The use of microwave sounding can retrieve information below the volcanic plume top height, even though satellite MW radiometers still suffer of a relatively poor spatial resolution (tens of kilometers) with respect to TIR imagers (few kilometers). Preliminary estimates of tephra mass loading and plume height from MW data are also discussed.

Sun-tracking millimeter-wave radiometry exploits the Sun as a beacon source by tracking it along its diurnal ecliptic. The atmospheric brightness temperature is measured by alternately pointing toward-the-Sun and off-the-Sun according to ad hoc switching strategy. By properly developing a retrieval algorithm, we can estimate the atmospheric path attenuation in all-weather conditions. The Langley method, based on elevation-scanning, is proposed to estimate the equivalent brightness temperature of the Sun, which is a critical step for precipitation extinction estimation. An application to available Sun-tracking radiometric measurements at V and W band in Rome (NY, USA) is shown, discussed and compared with the conventional technique using the clear-air approximation of the mean radiative temperature. Results show an appealing potential of Sun-tracking technique in order to exploit millimeter-wave radiometry for atmospheric retrievals even in a cloudy and rainy conditions.

During an eruptive event, the near-real-time monitoring of volcanic explosion onset and its mass flow rate (MFR) is a key factor to predict ash plume dispersion and to mitigate risk to air traffic. Microwave (MW) weather radars have proved to be a fundamental instrument to derive eruptive source parameters. We extend this capability to include an early-warning detection scheme within the overall volcanic ash radar retrieval methodology. This scheme, called the volcanic ash detection (VAD) algorithm, is based on a hybrid technique using both fuzzy logic and conditional probability. Examples of VAD applications are shown for some case studies, including the Icelandic Grímsvötn eruption in 2011, the Eyjafjallajökull eruption in 2010, and the Italian Mt. Etna volcano eruption in 2013. Estimates of the eruption onset from the radar-based VAD module are compared with infrasonic array data. One-dimensional numerical simulations and analytical model estimates of MFR are also discussed and intercompared with sensor-based retrievals. Results confirm in all cases the potential of MW weather radar for ash plume monitoring in near real time and its complementarity with infrasonic array for early-warning system design.

A new data processing chain has been applied to polarimetric radar observations at X band. The article describes the chain and provides an analysis of the quality of the polarimetric variables. Different algorithms have been studied based on simple Zh-R, on Zhand Zdr, on Kdpand Kdpand Zdr. Evaluation against rain-gauges, confirms the superiority of polarimetric algorithms. It also shows that the benefits brought by algorithms based on power variables, Zhand Zdrare critically dependent on the capability of a correct calibration.

The goal of this work is to show the possibility to combine three different calibration techniques to obtain a reliable monitoring of the radar system through the definition of a quality index concept. The fuzzy logic approach uses the idea to convert the calibration error in a so called linguistic variable defined as the impact of it on the parameter estimation. After an inference step, we obtain a quality matrix that represents the quality index of calibration on the observed variables in different part of the system (transmitting and receiving). This information can be extremely important for the remote monitoring and the realtime diagnosis of the radar system state. The output of the procedure is a diagnostic quality index, useful to establish where and when a technical intervention on the radar system is necessary. Results, using copolar and differential reflectivity, are shown for a C-band weather radar operating in Italy.

Recent spaceborne polarimetric Synthetic Aperture Radars (SARs) enable the complete characterization of target scattering and extinction properties. Several missions are operating at X band while there are plans and analyses for systems operating at higher frequencies, such as Ka band. Systems operating at these frequencies have interesting and distinctive applications in the field of geosciences such as Cartography, Surface deformation detection, Forest cover mapping and many others. However, the detected ground surface response can be affected by atmospheric effects in both signal amplitude and phase, especially in presence of atmospheric precipitations. In this work we will introduce a simulation framework developed to characterize how precipitating clouds affect spaceborne X- and Ka-band SARs systems. The proposed framework is able to simulate the polarimetric SAR ground responses in terms of Normalized Radar Cross Sections (NRCS) and complex correlation coefficient, both for realistic atmosphere-ground scenarios and for synthetic canonical ones. Some preliminary results will be shown and discussed.

Spaceborne scatterometer observations, especially at Ku-band, are affected by rain in several ways and these effects need to be corrected to avoid errors in wind retrievals. In this work we propose a model to derive the surface backscattering coefficient in presence of both wind and rain. Our approach consists in the development of an ocean surface wind wave spectrum accounting for two effects due to raindrops impact on the surface: the rain-induced wave damping and the generation of ring waves. The results show that this extended spectrum is able to model the ocean surface wave modifications due to rain so that it can be used for further study in physically representing the scatterometer observations in presence of both wind and rain.

The eruption of the Eyjafjallajökull volcano in April-May 2010 was continuously monitored by the Keflavík C-band weather radar. The Keflavík radar is located at a distance of about 156 km from the volcano vent, and it has sensitivity of about -5 dBZ at 2-km range resolution over the volcanic area. The time series of radar volume data, which was available every 5 min, is quantitatively analyzed by using the Volcanic Ash Radar Retrieval (VARR) technique. The latter is a physically based methodology that is applied to estimate ash-fall rate and mass concentration within each radar volume. The VARR methodology is here extended, with respect to the previous formulation, to provide an approximate estimate of both mean particle diameter and airborne tephra particle size distribution under some assumptions. Deposited tephra at ground is also extrapolated together with an estimate of the magma mass flow rate (MFR) at the volcano vent, derived from the implementation of the mass continuity equation in the radar reference system. The VARR-based retrievals are compared with those derived from a direct tephra sampling at the ground, experimentally carried out in terms of ash grain size and loading during the Eyjafjallajökull eruption activity on May 5-7, 2010. VARR-based particle diameter estimates may suggest that a sorting of airborne particles during the downwind transport is taking place without observing aggregation processes during the ash fall. VARR-derived daily ash mass loadings in the period between April 14 and May 10 are also evaluated with respect to integrated ground and model-based data in the Eyjafjallajökull area. VARR-retrieved MFRs are finally compared with corresponding values obtained from analytical 1-D eruption models, using radar-estimated plume height and radio-sounding wind fields. A fairly good agreement is obtained, thus opening the exploitation of weather radar retrievals for volcanic eruption quantitative studies and ash dispersion model initialization.

A new algorithm called self-consistent with optimal parameterization (SCOP) for attenuation correction of radar reflectivities at low elevation angles is developed and evaluated. The SCOP algorithm, which uses optimal parameterization and best-fitted functions of specific attenuation coefficients and backscattering differential phase shift, is applied to X-band dual-polarization radar data and evaluated on the basis of radar observables calculated from disdrometer data at a distance of 35 km from the radar. The performance of the SCOP algorithm is compared with other algorithms [reflectivity-differential phase shift (ZPHI) and full self-consistent (FSC)] presented in the literature. Overall, the new algorithm performs similarly to ZPHI for the attenuation correction of horizontal-polarization reflectivity, whereas the FSC algorithm exhibits significant underestimation. The ZPHI algorithm tends to overestimate small rain-path attenuation values. All algorithms exhibit significant underestimation at high differential rain-path attenuation values, probably due to the presence of hail along the path of the radar beam during the examined cases. The new SCOP algorithm has the potential to retrieve profiles of horizontal and differential reflectivities with better accuracy than the other algorithms due to the low error of the parameterization functions used in it. Typical radar calibration biases and measurement noise are sufficient requirements to ensure low errors of the proposed algorithm. A real-time method to calibrate the differential reflectivity without additional measurements is also described.

An automatic method to distinguish water surfaces (either flooded or permanent water bodies) from artifacts caused by heavy precipitation and wet snow is designed to improve flood detection accuracy in X-band synthetic aperture radar (SAR) images. The algorithm implementing the proposed method, mainly based on image segmentation techniques and on the fuzzy logic, consists of two principal steps: 1) detection of regions (or segments) of low-radar backscatter that appear dark in a SAR image, and 2) classification of each detected segment. Ancillary data, such as a local incidence angle map, a land cover map, and an optical image (helpful to detect wet snow), are also used. Through the fuzzy logic, the algorithm integrates different rules for the detection of dark areas, as well as for their classification based on radiometric, geometrical and shape features extracted from the segmented SAR image and on the ancillary data. The algorithm is tested on the COSMO-SkyMed imagery of the severe weather event that hit Northwest Italy on November 2011. A comparison with measured data, provided by the weather radars belonging to the Italian radar national network, and with the ground precipitation, forecasted by a numerical weather prediction model routinely used within the framework of the EUMETSAT Hydrology Satellite Application Facility project, indicates that the algorithm produces reliable classification maps, being able to distinguish the rainfall signature on X-band SAR images from that of flooded areas.

The goal of this work is to show potentials and drawbacks of Dual Polarization measurements of volcanic plume from microwave ground-based X-band radar (DPX). Measurements of brightness temperature (BT) from the space-orbiting microwave radiometer are used as well and compared with DPX retrievals of total columnar content (TCC). The latter is estimated from the radar variables using the volcanic ash radar retrieval for dual-polarization X band systems (VARR-PX) algorithm whereas BT's have been acquired from the Special Sensor Microwave Imager/Sounder (SSMIS). Model simulations of volcanic plume evolution are generated to carry out comparisons with radar estimates of TCC. The Active Tracer High-Resolution Atmospheric Model (ATHAM) of eruption plume is used for this purpose. Results show that high- spatial-resolution DPX radar data identify an evident volcanic plume signature, even though the interpretation of the polarimetric variables and the related retrievals is not always easy, likely due to the possible formation of ash and ice particle aggregates, the radar signal depolarization induced by turbulence effects, and the partial filling of the radar beam. A forth degree polynomial relationship is in good agreement with BT - TCC measured samples with correlation of -0.71. The variability of TCC, described by the ATHAM simulations, seems to include the spatial and temporal variation of the radar retrievals.

Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivity-weighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3$\hbox{deg}\ \hbox{km}^{-1}$for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error).

The potential of satellite passive microwave sensors to provide quantitative information about near-source volcanic ash cloud parameters is assessed. To this aim, ground-based microwave weather radar and spaceborne microwave radiometer observations are used together with forward-model simulations. The latter are based on 2-D simulations with the numerical plume model Active Tracer High-Resolution Atmospheric Model (ATHAM), in conjunction with the radiative transfer model Satellite Data Simulator Unit (SDSU) that is based on the deltaEddington approximation and includes Mie scattering. The study area is the Icelandic subglacial volcanic region. The analyzed case study is that of the Grímsvötn eruption in May 2011. ATHAM input parameters are adjusted using available ground data, and sensitivity tests are conducted to investigate the observed brightness temperatures and their variance. The tests are based on the variation of environmental conditions like the terrain emissivity, water vapor, and ice in the volcanic plume. Quantitative correlation analysis between ATHAM/SDSU forward-model columnar content simulations and available microwave radiometric brightness temperature measurements, derived from the Special Sensor Microwave Imager/Sounder (SSMIS), are encouraging in terms of both dynamic range and correlation coefficient. The correlation coefficients are found to vary from -0.37 to -0.63 for SSMIS channels from 91 to 183 ± 1 GHz, respectively. The larger sensitivity of the brightness temperature at 183 ± 1 GHz to the columnar content, with respect to other channels, allowed us to consider this channel as the basis for a model-based polynomial relationship of volcanic plume height as a function of the measured SSMIS brightness temperature.

The sub-glacial Plinian explosive eruption of the Grímsvötn volcano on May 2011 was continuously monitored by the Keflavík C-band weather radar, located at a distance of about 260 km from the volcano vent. This work provides an analysis and quantitative interpretation by using these ground-based weather radar data and the Volcanic Ash Radar Retrieval (VARR) physically-based technique. The VARR methodology, herein briefly summarized, was applied to available radar time series to estimate the volume, mass and the plume maximum height, every 5 minutes. Deposited ash at ground was also retrieved from radar data by empirically reconstructing the vertical profile of radar reflectivity and estimating the near-surface ash fallout. The obtained results establish a further step towards the assessment of the VARR algorithm as an effective approach in the field volcanic ash cloud radar remote sensing.

This paper presents an investigation on the rainfall signature for two COSMO-SkyMed (CSK) satellite case studies. Both of them are relative to a severe precipitation weather event, occurred in northwestern Italy (close to Liguria region) on November 3-8, 2011. This event was monitored by using a number of CSK images provided by the Italian Space Agency (ASI). In this case CSK X-SAR data have been compared with the weather radar (WR) Italian Radar National Mosaic. A third case study is relative to Hurricane “Irene” event, occurred in Eastern United States (close to Delaware) on late August 2011. CSK X-SAR images are compared with respect to concurrent ground-based S-band NEXRAD weather radar reflectivities. The correlation of the precipitating cloud fields between CSK X-SAR and WR images is significant in all case studies. An application of a refined XSAR-based precipitation retrieval method is presented. The X-SAR surface response is estimated using ancillary data, such as land cover maps and a digital elevation model (DEM).

In this work, an automatic method to distinguish, in X-band SAR images such as those supplied by Cosmo-SkyMed, water surfaces (either flooded, or permanent water bodies) from artifacts due to heavy precipitation, is designed to improve flood detection accuracy. The method, mainly based on the fuzzy logic, consists of two main steps, i.e., the detection of low backscatter areas and the classification of each dark object present in the considered SAR image. The algorithm uses ancillary data, such as a local incidence angle map and a Land Cover map. Through the fuzzy logic, it integrates different rules for the detection of low backscatter areas (based on the standard deviation of the backscattering coefficient and on a well-established radar backscattering model), as well as different rules for the classification of the low backscatter (dark) areas (i.e., to distinguish water surfaces from artifacts) based on their geometrical and shape features and on both land cover and local incidence angle.

The main phase of the moderately sized November 2004 eruption of the Grímsvötn volcano, located in the center of the 8100 km2Vatnajökull glacier, was monitored by the Icelandic Meteorological Office C-band weather radar in Keflavík, 260 km west of the volcano. The eruption plume reached a height of 6-10 km relative to the vent. The distribution of the most distal tephra was measured in the autumn of 2004, while the deposition on the glacier was mapped in the summers of 2005 and 2006. The tephra formed a well-defined layer on the glacier in the region north and northeast of the craters. The total mass of the tephra layer is quantitatively compared with the retrieved values, obtained from an improved version of the volcanic ash radar retrieval (VARR) algorithm. VARR was statistically calibrated with ground-based ash size distribution samples, taken at Vatnajökull, and by taking into account both antenna beam occlusion and wind-driven plume advection. The latter was implemented by using a space-time image phase-based cross-correlation technique. Accuracy of the weather radar records was also reviewed, noting that a large variability in the plume height estimation may be obtained using different approaches. The comparisons suggest that, at least for this subglacial eruption, the surface tephra mass, estimated by using the VARR inversion approach, is in a fairly good agreement with in situ measurements in terms of spatial extension, distribution, and amount.

Spaceborne synthetic aperture radars (SARs) exhibit the appealing imaging feature of very high spatial resolution (on the order of meters). At frequency above C-band, the atmospheric effects, and particularly the signature of precipitating clouds, cannot be neglected on both amplitude and phase received signal. The impact of precipitation on SAR slant-view imagery is due to a combination of surface and volumetric backscattering, coupled with path attenuation and with a significant dependence on frequency, polarization, and spatial distribution of hydrometeors. The actual spatial resolution (on the order of hundreds of meters) of these effects is larger than the SAR nominal one due to the random nature of the moving distributed atmospheric target. This paper is devoted to the numerical forward modeling of SAR response at X-, Ku-, and Ka-bands due to precipitating clouds in order to better understand the physical correlation between SAR echo and precipitation. To this aim, a high-resolution mesoscale atmospheric numerical model is used to extract the 3-D distribution of liquid and ice hydrometeors. A detailed sensitivity analysis of SAR backscattering is carried out with respect to hydrometeor columnar and slant water contents, relative contribution of volumetric and surface scattering, incidence angle and ground inhomogeneity, polarimetric observables, and frequency scaling signatures. The numerical results show that the slant-view of SAR observations plays a determinant role and the use of a multifrequency polarimetric SAR may be very useful to characterize precipitation effects and, to a certain extent, retrieve its content at very high spatial resolution.

Weather radar retrieval, in terms of detection, estimation, and sensitivity, of volcanic ash plumes is dependent not only on the radar system specifications but also on the range and ash cloud distribution. The minimum detectable signal can be increased, for a given radar and ash plume scenario, by decreasing the observation range and increasing the operational frequency and also by exploiting possible polarimetric capabilities. For short-range observations in proximity of the volcano vent, a compact portable system with relatively low power transmitter may be evaluated as a suitable compromise between observational and technological requirements. This paper, starting from the results of a previous study and from the aforementioned issues, is aimed at quantitatively assessing the optimal choices for a portable X-band system with a dual-polarization capability for real-time ash cloud remote sensing. The physical-electromagnetic model of ash particle distributions is systematically reviewed and extended to include nonspherical particle shapes, vesicular composition, silicate content, and orientation phenomena. The radar backscattering response at X-band is simulated and analyzed in terms of self-consistent polarimetric signatures for ash classification purposes and correlation with ash concentration for quantitative retrieval aims. An X-band radar system sensitivity analysis to ash concentration, as a function of radar specifications, range, and ash category, is carried out in trying to assess the expected system performances and limitations.

Atmospheric water vapor is a crucial constituent affecting both climate change and hydrological cycle processes, whereas on the other hand, it has a significant impact on the electromagnetic signal propagation. Since the distribution of atmospheric water vapor strongly varies with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, mapping its spatial distribution is difficult due to the lack of meteorological instrumentation at an adequate spatial and temporal observation scale. For many geophysical applications, there is also the need to reconstruct spatial details of integrated precipitable water vapor from information available only at coarser spatial scales. Spatial downscaling approaches can play a significant role when high-resolution water vapor retrievals from relatively new sensors, like synthetic aperture radars, or from conventional sensors, like the infrared radiometers MEdium Resolution Imaging Spectrometer (MERIS) or Moderate Resolution Imaging Spectroradiometer (MODIS), are used in synergy to enhance the accuracy of integrated water vapor retrievals. In this context, this paper introduces some new methodological aspects to increase the spatial resolution of integrated precipitable water vapor observations using a statistical downscaling spectral approach. To highlight the potential and the usefulness of the proposed downscaling estimation procedure, collocated 250-m MERIS and 1-km MODIS acquisitions are used. Results reveal the ability of spectral downscaling to reproduce quite well the second-order statistical variability of the water vapor field at small spatial scales with a root-mean-square error comparable with conventional interpolation techniques.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as tectonic movements, landslide monitoring and digital elevation model extraction. One of its major limitation is the atmospheric effect, and in particular the high water vapour spatial and temporal variability which introduces an unknown delay in the signal propagation. This paper describes the general approach and some results achieved in the framework of an ESA funded project devoted to the mapping of the water vapour with the aim to mitigate its effect in InSAR applications. Ground based (microwave radiometers, radiosoundings, GPS) and spaceborne observations (AMSR-E, MERIS, MODIS) of columnar water vapour were compared with Numerical Weather Prediction model runs in Central Italy during a 15-day experiment. A dense network of GPS receivers was deployed close to Como, in Northern Italy, to complement the operational network in order to derive Zenith Total Delay as well as Slant Delay which can support InSAR processing. A comparison with Atmospheric Phase Screens (APS) derived from a sequence of Envisat multi pass interferometric acquisitions processed using the Permanent Scatters technique on the two test sites has been also performed. The acquired experimental data and their comparison give a valuable idea of what can be done to gather information on water vapour, which, besides InSAR applications, plays a fundamental role in weather prediction and radio propagation studies. The work has been carried out in the framework of an ESA funded project, named "Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects" (METAWAVE). This paper presents the general approach an the various methodologies exploited in the project, together with the overall intercomparison of the results. In deep details on the comparison with the InSAR APS maps derived by the PS technique, as well as on GPS receiver processing and water vapour tomography are reported in two companion papers.

Understanding the lunar physical properties has been attracting the interest of scientists for many years. This paper is devoted to a numerical study on the capability of retrieving the thickness of the first layer of regolith as well as the temperature profile behavior from satellite-based multifrequency radiometers at frequencies ranging from 1 to 24 GHz. To this purpose, a forward thermal–electromagnetic numerical model, able to simulate the response of the lunar material in terms of upward brightness temperature$(TB)$, has been used. The input parameters of the forward model have been set after a detailed investigation of the scientific literature and available measurements. Different choices of input parameters are possible, and their selection is carefully discussed. By exploiting a Monte Carlo approach to generate a synthetic data set of forward-model simulations, a physically based inversion methodology has been developed using a neural network technique. The latter has been designed to perform, from multifrequency$TB$'s, the temperature estimation at the lunar surface, the discrimination of the subsurface material type, and the estimate of the near-surface regolith thickness. Results indicate that, within the simplified scenarios obtained by interposing strata of rock, ice, and regolith, the probability of detection of the presence of discontinuities beneath the lunar crust is on the order of 84%. The estimation uncertainty of the near-surface regolith thickness estimation ranges from 11 to 81 cm, whereas for the surface temperature, its estimation uncertainty ranges from about 1.5 K to 3 K, conditioned to the choice of radiometric frequencies and noise levels.

A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, has been used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography, i.e., the difference between the antenna temperature calculated for a topographically variable surface and that computed for a flat terrain versus the parameters representing the relief, have been analyzed. The analysis has been mainly performed for a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two roughness conditions (smooth and rough soils) and considering L- and C-bands, i.e., those most suitable for soil moisture retrieval. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean values of the height and slope of the radiometric pixel, as well as to the standard deviations of the aspect angle and local incidence angle. Both a regression analysis and a neural-network approach have been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets. The prediction of the topography effects and their correction in radiometric images have turned out to be feasible, at least for the scenarios considered in this study.

A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, is used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography versus the parameters representing the relief have been analyzed for a test case concerning a mountainous area in the Alps by assuming bare soil and considering L- and C-bands. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean value of the height and of the slope of the radiometric pixel, as well as to the standard deviation of the aspect angle and of the local incidence angle. A regression analysis has been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets.

Dual-polarized weather radars are capable to detect and identify different classes of hydrometeors, within stratiform and convective storms, exploiting polarimetric diversity. Among the various techniques, a model-supervised Bayesian method for hydrometeor classification, tuned for S- and X-band polarimetric weather radars, can be effectively applied. Once the hydrometeor class is estimated, the retrieval of their water content can also be statistically carried out. However, the critical issue of X-band radar data processing, and in general of any attenuating wavelength active system, is the intervening path attenuation, which is usually not negligible. Any approach aimed at estimating hydrometeor water content should be able to tackle, at the same time, path attenuation correction, hydrometeor classification uncertainty, and retrieval errors. An integrated iterative Bayesian radar algorithm (IBRA) scheme, based on the availability of the differential phase measurement, is presented in this paper and tested during the International H2O Project experiment in Oklahoma in 2002. During the latter campaign, two dual-polarized radars, at S- and X-bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of the IBRA technique at X-band are discussed, and the impact of path attenuation correction is quantitatively analyzed by comparing hydrometeor classifications and estimates with those obtained at S-band. The overall results in terms of error budget show a significant improvement with respect to the performance with no path attenuation correction.

The microphysical and dynamical features of volcanic clouds, due to Plinian and sub-Plinian eruptions, can be quantitatively monitored by using ground-based microwave weather radars. In order to demonstrate the unique potential of this remote sensing technique, a case study of a subglacial volcanic eruption, occurred in Iceland in November 2004, is described and analyzed. Volume data, acquired by a C-band ground-based weather radar, are processed to automatically classify and estimate ash particle concentration. The ash retrieval physical-statistical algorithm is based on a backscattering microphysical model of fine, coarse, and lapilli ash particles, used within a Bayesian classification and optimal regression algorithm. A sensitivity analysis is carried out to evaluate the overall error budget and the possible impact of nonprecipitating liquid and ice cloud droplets when mixed with ash particles. The evolution of the Icelandic eruption is discussed in terms of radar measurements and products, pointing out the unique features, the current limitations, and future improvements of radar remote sensing of volcanic plumes.

Microphysical and dynamical features of volcanic ash clouds can be quantitatively monitored by using ground-based microwave weather radars. These systems can provide data for determining the ash volume, total mass, and height of eruption clouds. In order to demonstrate the unique potential of this microwave active remote-sensing technique, the case study of the eruption of Augustine Volcano in Alaska in January 2006 is described and analyzed. Volume scan data, acquired by a NEXRAD WSR-88D S-band ground-based weather radar, are processed to automatically classify and estimate eruptive cloud particle concentration. The numerical results of the coupled model Z-reflectivity from Active Tracer High resolution Atmospheric Model (ATHAM), including particle aggregation processes and simulation of radar reflectivity from the ATHAM microphysical model, are exploited to train the inversion algorithm. The volcanic ash radar retrieval based on the ATHAM algorithm is a physical-statistical approach based on the backscattering microphysical model of volcanic cloud particles (hydrometeors, ash, and aggregates), used within a Bayesian classification and optimal regression algorithm. A sensitivity analysis is carried out to evaluate the overall error budget. The evolution of the Augustine eruption is discussed in terms of radar measurements and products, pointing out the unique features, the current limitations, and future improvements of radar remote sensing of volcanic plumes.

Five spaceborne X-band synthetic aperture radars (X-SARs) are nowadays operating, and several more will be launched in the coming years. These X-SAR sensors, able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about a few hundred meters due to the moving-target degradation effects. This work is devoted to experimentally demonstrate this X-SAR capability, which can also be exploited to correct synthetic aperture radar (SAR) imagery for rainfall attenuation effects. Several case studies, selected from TerraSAR-X (TSX) overpasses over Europe and the southern U.S. in 2008, are qualitatively analyzed in terms of rainfall signatures. Visual validation of these rainfall SAR signatures is carried out by using available data from ground-based weather radars. A detailed data analysis for the case study of Hurricane ¿Gustav¿ on September 2, 2008, is carried out to assess a quantitative correlation among X-SAR response and near-surface precipitation rain rate. Two simplified empirical inversion algorithms, based on statistical regression and probability matching, are developed to retrieve rain rate from TSX cross-track ground-range measurements. The TSX-retrieved rain fields are compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (Alabama, U.S.), showing a root-mean-square error less than 15 mm/h and a correlation of about 0.7.

A numerical simulator of satellite microwave-radiometric observations of orographically complex scenes, at various frequencies and observation angles, has been developed. The Simulator of Topographic Artefacts in MIcrowave RAdiometry (STAMIRA) exploits the information on the relief, extracted from a digital elevation model, and has been applied to a test case concerning a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two kinds of roughness (smooth and rough soils). The 1-10-GHz range has been considered to determine scattering and emission of soil and a nonscattering atmosphere has been supposed. The simulations have shown the large impact of the rotation of the polarization plane and of the brightness-temperature enhancement occurring for facets illuminated by radiation from the surrounding elevated terrain with respect to flat surfaces which scatter atmospheric downward radiation only. By considering also the antenna-pattern integration and the dependence of surface emissivity on the local observation angle, we have found that, for our case study, the brightness temperature is larger than that measured observing a flat terrain at horizontal polarization. At vertical polarization, the opposite occurs. These differences are analyzed and quantified.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as monitoring tectonic movements and landslides or extracting digital elevation models. One of its major limitations is the atmospheric variability, and in particular the high water vapor spatial and temporal variability, which introduces an unknown delay in the signal propagation. On the other hand, these effects might be exploited, so as InSAR could become a tool for highresolution water vapor mapping. This paper describes the approach and some preliminary results achieved in the framework of an ESA funded project devoted to the mitigation of the water vapor effects in InSAR applications. Although very preliminary, the acquired experimental data and their comparison give a first idea of what can be done to gather valuable information on water vapor, which play a fundamental role in weather prediction and radio propagation studies.

In this paper, the design of a minisatellite FLOwer constellation (FC), deploying millimeter-wave (MMW) scanning RADiometers, namely, FLORAD, and devoted to tropospheric observations, is analyzed and discussed. The FLORAD mission is aimed at the retrieval of thermal and hydrological properties of the troposphere, specifically temperature profile, water-vapor profile, cloud liquid content, and rainfall and snowfall rate. The goal of frequent revisit time at regional scale, coupled with quasi-global coverage and relatively high spatial resolution, is here called pseudogeostationary scale and implemented through a FC of three minisatellites in elliptical orbits. FCs are built on compatible (resonant) orbits and can offer several degrees of freedom in their design. The payload MMW channels for tropospheric retrieval were selected following the ranking based on a reduced-entropy method between 90 and 230 GHz. Various configurations of the MMW radiometer multiband channels are investigated, pointing out the tradeoff between performances and complexity within the constraint of minisatellite platform. Statistical inversion schemes are employed to quantify the overall accuracy of the selected MMW radiometer configurations.

A simulation study to quantify the influence of the topography on the measurements performed by a satellite microwave radiometer is accomplished in this work. The Northern Italy region (including Alps) is considered and the information on the relief, extracted from a digital elevation model (DEM), is exploited. We have developed a simulator of satellite microwave radiometric observations of mountainous scenes operating at different frequencies and observation angles. The simulation shows that the changes in the local observation angle generally tend to decrease the antenna temperature. The effect of the rotation of the polarization plane attenuates (for horizontal polarization) or enlarges (vertical polarization) this decrease. Facets illuminated by radiation from surrounding elevated terrain enhance their brightness temperature with respect to surfaces which scatter atmospheric downward radiation. At horizontal polarization, this results in a general overestimation of the brightness temperature with respect to that measured observing a flat terrain. At vertical polarization, both under and overestimation may occur.

Recent research has demonstrated the value of polarimetric measurements for the correction of rain-path attenuation at X-band radar frequency and the estimation of rain parameters including drop-size distributions (DSD). The issue this paper is concerned with is to what degree uncertainties in attenuation correction can affect the estimation of DSD. Since attenuation-correction uncertainty enhances with rain path, our hypothesis is that DSD retrieval uncertainty at X-band may deteriorate with range. In this paper, we evaluate the relative accuracy of X-band DSD retrieval against DSD estimates from S-band radar observations andinsitudisdrometer spectra. We present comparisons of various techniques for estimating DSD model parameters from attenuation-corrected X-band dual-polarization radar data. Coincident X-band polarimetric-radar (XPOL) and S-band polarimetric-radar dual-polarized radar measurements from the International H2O Project experiment as well as coincident XPOL (MP-X) measurements over disdrometer during a typhoon storm case in Japan are used to assess the accuracy of the different DSD retrieval algorithms applied to X-band radar measurements.

In this paper, a Bayesian statistical approach for supervised classification and estimation of hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event.

Several spaceborne X-band synthetic aperture radar (X-SAR) systems were launched in 2007, and more will be launched in the current decade. These sensors may significantly augment the sensors that comprise the global precipitation mission (GPM) constellation. X-SAR rainfall measurements may be beneficial particularly over land where rainfall is difficult to measure by means of satellite microwave radiometers. Inversion techniques to quantitatively derive precipitation fields over land at high spatial resolution are developed and illustrated in this paper. These inversion algorithms are the model-oriented statistical (MOS) methodology and the Volterra integral equation (VIE) approach. Simplified rain-cloud models are used to train and test the inversion algorithms by evaluating the expected error budget. Two case studies, using data obtained from measurements of SIR-C/X-SAR in 1994 over Bangladesh and the Amazon, are introduced, and retrieved precipitation maps are discussed. Even though no validation of the precipitation estimates was possible, the obtained results are encouraging, showing physically consistent retrieved structures and patterns.

Hydrometeorological and radio propagation applications can benefit from the capability to model the time evolution of raindrop size distribution (RSD). A new stochastic vector autoregressive semi-Markov model is proposed to randomly synthesize (generate) the temporal series of the three driving parameters of a normalized gamma RSD. Rainfall intermittence is reproduced through a discrete semi-Markov process, modeled from disdrometer measurements using two-state analytical statistics of rain and dry period duration. The overall model is set up by means of a large set of disdrometer measurements, collected from 2003 to 2005 at Chilbolton, U.K. The driving parameters of the retrieved RSD are estimated using three approaches: the Gamma moment method and the 1D and 3D maximum-likelihood methods. Interestingly, these methodologies lead to quite different results, particularly when one is interested in evaluating RSD higher order moments such as the rain rate. The accuracy of the proposed RSD time-series generation technique is evaluated against available disdrometer measurements, providing excellent statistical scores.

A large data set of raindrop size distribution (RSD) measurements collected with the Joss-Waldvogel disdrometer (JWD) and the 2-D video disdrometer (2DVD) in the U.K., Greece, Japan, and the U.S. are analyzed and modeled. This work extends a previous effort devoted to the exploitation of U.K. data and the design of a stochastic procedure to randomly generate synthetic RSD intermittent time series. This study seeks to: (1) explore the differences of RSD-derived moments for distinct hydroclimate regions, ranging from tropics to subtropics and mid and northern latitudes; (2) compare the governing parameters of the normalized gamma RSD for both stratiform and convective events and perform a sensitivity analysis by using different best fitting techniques; (3) exploit the time-correlation structure of the estimated RSD parameters as the input of a vector autoregressive stationary model used to simulate time series (or horizontal profiles) of RSDs and, consequently, its moments as the rain rate and concentration; and (4) characterize the distribution of the inter-rain duration and rain duration to design a semi-Markov chain to represent the intermittency feature of the rainfall process in a climatological framework. This climatological analysis and the related stochastic RSD generation model may find useful applications within both hydrometeorology and radio propagation.

The potential of ground-based microwave weather radar systems for volcanic ash cloud detection and quantitative retrieval is evaluated. A prototype algorithm for volcanic ash radar retrieval (VARR) is discussed. Starting from measured single-polarization reflectivity, the statistical inversion technique to retrieve ash concentration and fall rate is based on two cascade steps: i) classification of eruption regime and volcanic ash category; ii) estimation of ash concentration and fall rate. An application of the VARR technique is finally shown taldng into consideration the eruption of the Grímsvötn volcano in Iceland on Nov. 2004. Volume scan data from a Doppler C-band radar, located at 260 km from the volcano vent, are processed by means of the VARR algorithm. Examples of the achievable VARR products are presented and discussed.

Numerous space-borne X-band Synthetic Aperture Radars (X-SAR) systems will be launched by European agencies in the coming decade commencing this year. Those X-SARs can measure precipitation over land, thereby significantly augmenting the sensors that comprise the Global Precipitation Mission (GPM). This will incur relatively little incremental cost because they have already been funded. X-SAR measurements are especially beneficial over land where rainfall is difficult to measure by means of microwave radiometers that depend on scattering by frozen hydrometeors associated with that rain. The improved horizontal resolution of the retrievals will match the higher spatial resolution of mesoscale and general circulation models that will become available in the coming decade.

Dual-polarized weather radars are capable to detect and identify different classes of hydrometeors, within stratiform and convective storms exploiting polarimetric diversity. A model- supervised Bayesian method for hydrometeor classification (BRAHC), tuned for S- and X- band, is described in this study. The critical issue of X-band radar data processing is the path attenuation correction, usually negligible at S-band. During the IHOP experiment (Oklahoma, 2002) two dual-polarized radars, at S- and X- bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of hydrometeor classification and water content estimates at S- and X- bands are discussed and the impact of path attenuation correction is quantitatively analyzed.

Given the rising and renewed interest towards the study of Moon the European space Agency (ESA) approved, in March 2006, the phase-A for the feasibility study of the European Student Moon Orbiter (ESMO) mission proposed by the Student Space Exploration & Technology Initiative (SSETI). The objective of the ESMO mission is to acquire images of the moon in stable orbit, transmit them to the Earth and then to actively involve European students in a real space program experience. In order to accomplish the ESMO mission objectives, a Micro Wave Radiometric Sounder (MiWaRS) has been selected as a possible choice for flying on board of the ESMO satellite. This work summarizes the preliminary results obtained during the phase-A for the MiWaRS with special attention to the description of the radiometric system design and its scientific objectives.

A large data set of rain drop size distribution (RSD) measurements collected with Joss-Waldvogel (JWD) and 2D video disdrometers (2DVD) in UK, Athens, Japan and USA are analyzed. The objective of this work are manifold: i) show the differences of a wide climatological DSD-derived moments; ii) retrieve from this disdrometer data set the driving parameters of the normalized gamma RSD and perform a sensitivity analysis of these results by using different best-fitting techniques; iii) exploit the correlation structure of the estimated RSD parameters as input of a vector autoregressive stationary model in order to simulate time series (or horizontal profiles) of RSDs and, consequently, of either rain rate or path attenuation; iv) characterize the distribution of the inter-rain duration (or dry periods: DP) and rain duration (or wet periods: WP) to design a simple semi-Markov chain to represent the intermittency feature of rainfall process. The overall stochastic procedure to randomly synthetize (or generate) RSD time series is named Vector Autoregressive Raindrop Markov Synthesizer (VARMS) model. This stochastic RSD generation tool may find useful applications both in hydro-meteorology and radio-propagation.

A simulation study to understand the influence of the topography on the land emissivity estimated by a satellite microwave radiometer is accomplished in this work. A mountainous area in the Alps (Northern Italy) is considered and the information on the relief, extracted from a digital elevation model (DEM), is exploited. We have simulated an observation of the area of interest performed by a satellite radiometer flying at 800 km of altitude and conically scanning the area with an angle of 53deg. We have considered the following frequencies: 23.8, 36.5 and 90.0 GHz, with the following spatial resolutions: 60times40, 37times29 and 15times13 km for the, 23-, 36-, and 90-GHz bands, respectively. The results indicate that the effect of the topography tends to lower the antenna temperature, thus implying an underestimation of the surface emissivity. This effect is larger at 90 GHz for which the maximum underestimation of the antenna temperature is in the order of 5 K.

The term now cast in hydro meteorology reflects the need for timely and accurate predictions of risky environmental situations, which are related to the development of severe meteorological events at short time scales. The objective of this paper is to apply a fully neural-network approach to the rainfall field now casting from infrared (IR) and microwave (MW) passive-sensor imagery aboard, respectively, geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites. The multisatellite space-time prediction procedure, which is named Neural Combined Algorithm for Storm Tracking (NeuCAST), consists of two consecutive steps. First, the IR radiance field measured from a geostationary satellite radiometer (e.g., Meteosat) is projected ahead in time (e.g., 30 min); second, the projected radiance field is used in estimating the rainfall field by means of an MW-IR combined rain retrieval algorithm exploiting GEO-LEO observations. The NeuCAST methodology is extensively illustrated and discussed in this paper. Its accuracy is quantified by means of quantitative error indexes, which are evaluated on selected case studies of rainfall events in Southern Europe in 2003 and 2005.

A model-based fuzzy-logic method for hydrometeor classification using C-band polarimetric radar data is presented and discussed. Membership functions of the fuzzy-logic algorithm are designed for best fitting simulated radar signatures at C-band. Such signatures are derived for ten supervised hydrometeor classes by means of a fully polarimetric radar scattering model. The Fuzzy-logic Radar Algorithm for Hydrometeor Classification at C-band (FRAHCC) is designed to use a relatively small set of polarimetric observables, i.e., copolar reflectivity and differential reflectivity, but a version of the algorithm based on the use of specific differential phase is also numerically tested and documented. The classification methodology is applied to volume data coming from a C-band two-radar network that is located in north Italy within the Po valley. Numerical and experimental results clearly show the improvements of hydrometeor classification, which were obtained by using FRAHCC with respect to the direct use of fuzzy-logic-based algorithms that are specifically tuned for S-band radar data. Moreover, the availability of two C-band rainfall observations of the same event allowed us to implement a path-integrated attenuation correction procedure, based on either a composite radar field approach or a network-constrained variational algorithm. The impact of these correction procedures on hydrometeor classification is qualitatively discussed within the considered case study.

A spatial characterization of the midlatitude meso- scale rain fields from C-band radar measurements is performed by means of a systematic analysis and modeling of convective rain-cell bidimensional shapes and spatial correlation. A large rainfall dataset that is derived from an operational C-band dual-polarized radar, which is placed in S. Pietro Capoflume near Bologna (Italy), has been collected and analyzed for this purpose. Different models of convective rainy horizontal structures are described and compared. Special attention is devoted to the consolidated unimodal models (or unimodal patterns) like Gaucell with a Gaussian rain-rate profile and Excell with an exponential rain-rate profile, and the hybrid models like Hycell and Dexcell based on a proper combination of the previous unimodal models. The new hybrid model Dexell, which is introduced here, is an extension of the Hycell model, which is previously proposed in literature. A pixel-by-pixel model numerical integration is carried out in order to perform a homogeneous comparison between the rain-cell model and the measured features such as peak, average, root mean square, gradient average, and gradient deviation of rain rate. A maximum-likelihood algorithm, which is expressed in terms of the principal component of the previous rain-cell features, is introduced to estimate rain-cell pattern parameters from the available radar data. A detailed sensitivity analysis, which is devoted to find the best behavior in terms of root-mean-square error and correlation coefficient between the modeled and measured rain-cell features, is finally carried out.

The two-scale electromagnetic model is a well-established theory for simulating microwave polarimetric passive observations of a sea surface. A critical aspect is the long computational time that is required to run the forward model, which hampers the creation of large training databases or iterative simulations within retrieval algorithms. To tackle this problem, a neural network (NN) technique is proposed in this paper. In particular, we have adopted NNs to emulate a simulator named SEAWIND, which implements the two-scale model and was validated in previous works. Two training algorithms, including a regularized approach, have been considered and compared. The assessment of the proposed approach has been carried out by statistically comparing neural-network-derived simulations with SEAWIND-derived ones for two validation data sets comprising different climatic conditions, as well as by computing the azimuthal Fourier harmonic coefficients versus wind speed and atmospheric transmittance. Regressive model functions have also been used as benchmarks. This paper demonstrates the feasibility of an NN approach to efficient and effective modeling of sea-surface thermal emission and scattering.

The 38 papers in this special issue were originally presented at the 9th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad '06). These papers are organized into topical areas and applications, which are in the general order of the MicroRad technical sessions: Radiometer Calibration and RFI Mitigation (4); Synthetic Aperture Radiometry (3); Land and Vegetation (6); Ocean Salinity (5); Ocean Wind (4); Atmosphere (3); Temperature and Humidity Sounding (8); and Precipitation (5).

Forecast, detection and warning of severe weather and related hydro-geological risks is becoming one of the major issues for civil protection. The use of computational intelligence techniques such as artificial neural network and cellular automata algorithm can be suitable for such problems especially for real time forecasting system. Nowcasting (short-term forecasting) of extreme rainfall events is an example that invites to exploit remote sensing systems from satellites, such geostationary and low-orbit radiometers. A rainfall estimation algorithm based on artificial neural network has been developed for this purpose. Satellite data sources can be globally provided but at a quite coarse spatial resolution, therefore, the coupling of rain remote sensing data with regional raingauge networks is also essential for ensuring a calibration of remotely sensed rainfall fields in terms of ground effects. An overwhelming issue is the spatial integration of these rainfall data sources having different space-time resolution and variable accuracies. In this work a cellular automata based algorithm has been used to integrate these heterogeneous data. A flood forecast chain, developed at the Centre of Excellence for Remote Sensing and Hydro-Meteorology Modelling and based on coupled mesoscale atmospheric model and distributed hydrological model with in-situ and remote sensing data integration is presented, with the emphasis on the integration of numerical models and retrieval algorithms using integrated tools based on computational intelligence techniques.

The potential of ground-based microwave weather radar systems for volcanic ash cloud detection and quantitative retrieval is evaluated. The relationship between radar reflectivity factor, ash concentration, and fall rate is statistically derived for various eruption regimes and ash sizes by applying a radar-reflectivity microphysical model. To quantitatively evaluate the ash detectability by weather radars, a sensitivity analysis is carried out by simulating synthetic ash clouds and varying ash concentration and size as a function of the range. Radar specifications are taken from typical radar systems at S-, C-, and X-band. A prototype algorithm for volcanic ash radar retrieval (VARR) is discussed. Starting from measured single-polarization reflectivity, the statistical inversion technique to retrieve ash concentration and fall rate is based on two cascade steps, namely: 1) classification of eruption regime and volcanic ash category and 2) estimation of ash concentration and fall rate. Expected accuracy of the VARR algorithm estimates is evaluated using a synthetic data set. An application of the VARR technique is finally shown, taking into consideration the eruption of the Grinodotacutemsvoumltn volcano in Iceland on November 2004. Volume scan data from a Doppler C-band radar, which is located at 260 km from the volcano vent, are processed by means of the VARR algorithm. Examples of the achievable VARR products are presented and discussed

Ground-based microwave radar systems can have a valuable role in volcanic ash cloud monitoring as evidenced by available radar imagery. Their use for ash cloud detection and quantitative retrieval has been so far not fully investigated. In order to do this, a forward electromagnetic model is set up and examined taking into account various operating frequencies such as S-, C-, X-, and Ka-bands. A dielectric and microphysical characterization of volcanic vesicular ash is carried out. Particle size-distribution (PSD) functions are derived both from the sequential fragmentation-transport (SFT) theory of pyroclastic deposits, leading to a scaled-Weibull PSD, and from more conventional scaled-Gamma PSD functions. Best fitting of these theoretical PSDs to available measured ash data at ground is performed in order to determine the value of the free PSD parameters. The radar backscattering from spherical-equivalent ash particles is simulated up to Ka-band and the accuracy of the Rayleigh scattering approximation is assessed by using an accurate ensemble particle scattering model. A classification scheme of ash average concentration and particle size is proposed and a sensitivity study of ash radar backscattering to model parameters is accomplished. A comparison with C-band radar signatures is finally illustrated and discussed.

Physically based microwave spaceborne techniques for rainfall retrieval are usually trained by simulated cloud-radiation databases (CRDs) composed of cloud profiles and associated brightness temperatures (TBs). When generating the database, the evaluation of the associated modeling uncertainties is crucial for retrieval error estimation. However, this is extremely complex due to the large number of free parameters. In this work, a possible methodology for taking into account CRD-related modeling uncertainties is proposed. The methodology-fairly general-is here applied to a limited dataset (a cloud-model resolved numerical output of a tropical cyclone). The modeling errors are obtained from systematic TB sensitivity tests associated to several parameters: particle sizes, temperature, ice content, sea surface wind speed, viewing angle, footprint size, radiative transfer schemes, melting phase, and particle shape. TB uncertainties are eventually summarized in a modeling error covariance matrix representing the intrinsic variability of the generated CRD. For comparison with real observations, the TBs are simulated at the spatial resolution, viewing geometry and frequencies of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The matrix is evaluated with respect to TMI data in terms of an indicator called database matching index. Since they are based on a single case study and suffer from the lack of direct coupling of the radiative transfer with the cloud-resolving model, the provided results should not be considered an exhaustive evaluation of cloud-radiation modeling errors. Nevertheless, they may be considered a valuable starting point for error characterization, since extensions to larger databases could definitely improve modeling error budgets.

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values

The potential of ground-based multispectral microwave radiometers in retrieving rainfall parameters is investigated by coupling physically oriented models and retrieval methods with a large set of experimental data. Measured data come from rain events that occurred in the USA at Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site in Lamont, OK. Rain cloud models are specified to characterize both nonraining clouds, stratiform and convective rainfall. Brightness temperature numerical simulations are performed for a set of frequencies from 20 to 60 GHz at zenith angle, representing the channels currently deployed on a commercially available ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. A new statistical inversion algorithm, trained by synthetic data and based on principal component analysis is also developed to classify the meteorological background, to identify the rain regime, and to retrieve rain rate from passive radiometric observations. Rain rate estimate comparisons with simultaneous rain gauge data and rain effect mitigation methods are also discussed.

A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.

The 8th Specialist Meeting on Microwave Radiometry and \nRemote Sensing Applications (MicroRad04) was held on \nFebruary 24-27, 2004 in Rome, Italy. The coorganizers of the \nConference were Prof. N. Pierdicca of The University of Rome \n“La Sapienza” and Prof. F. S. Marzano of CETEMPS, University \nof L’Aquila. The meeting was an overwhelming success and \ncan be summarized by 162 submitted abstracts, 152 participants, \n85 oral presentations, and 42 interactive posters during four full \ndays of sessions. \nMicroRad04 was held at the Engineering College of the University \nof Rome “La Sapienza” situated in one of the most beautiful \nsites of the Eternal City, between the Colosseum, the archaeological \narea, and the early Christian Basilicas. It was the \nlatest of a series focusing on Microwave Radiometry and Remote \nSensing of the Environment. The very first one dates back \nto March 1983, when itwas organized and supported by the University \n“La Sapienza” of Rome, Italy, as a result of the initiative \nof Prof. G. d’Auria. The satisfactory outcome of the first \nmeeting stimulated an agreement among the participants to ensure \nthe continuity in the form of a periodic meeting, the second \nof which, supported by IROE-CNR in Florence, Italy, occurred \nin 1988. Since then, more regular meetings, every 30 months \napproximately, were scheduled and held in the U.S. (Boulder, \nCO, 1992; Boston, MA, 1996) and in Italy (Rome “Tor Vergata,” \n1994; Florence, 1999), alternately. In 2001, the meeting \nwas hosted by NOAA in Boulder, CO. \nThe MicroRad04 Meeting was organized by the Department \nof Electronic Engineering of the University “La Sapienza” of \nRome and was created as an open invitation to convene again \nin Rome, 20 years after the first meeting. The objective of MicroRad04 \nwas to set up a common forum to report and discuss \nrecent advances in the specific field of microwave radiometry, \nthus to gather all parties belonging to the research and industrial \ncommunity, active in projects and studies in microwave radiometry \nof atmosphere, ocean, and land. \nContributions on topics of primary interest were received, and \nthe papers were separated into both oral and interactive sessions. \nThe 15 sessions of the meeting were focused on classical \nand new advanced topics of environmental remote sensing \nby microwave radiometry, emphasizing the methodological, instrumental, \nand application point of views. Interdisciplinary and \nsensor synergy issues were also stimulated.

A new stable backward iterative technique to correct for path attenuation and differential attenuation is presented here. The technique named, neural network iterative polarimetric precipitation estimator by radar (NIPPER), is based on a polarimetric model used to train an embedded neural network, constrained by the measurement of the differential phase along the rain path. Simulations are used to investigate the efficiency, accuracy, and the robustness of the proposed technique. The precipitation is characterized with respect to raindrop size, shape, and orientation distribution. The performance of NIPPER is evaluated by using simulated radar volumes scan generated from S-band radar measurements. A sensitivity analysis is performed in order to evaluate the expected errors of NIPPER. These evaluations show relatively better performance and robustness of the attenuation correction process when compared with currently available techniques.

A reliable estimate of atmospheric and sea surface parameters from satellite microwave radiometric measurements requires the availability of a large dataset of simulated brightness temperatures to train the retrieval algorithms. The validity of the radiative forward model adopted to generate the synthetic brightness temperatures, for known atmospheric and surface conditions, represents a crucial point of any retrieval method. This work proposes an approach based on microwave sea emissivity and scattering model functions, derived from the two-scale sea surface model and on a radiative transfer scheme in a nonscattering atmosphere. For the latter, the author investigate about the possibility to adopt a simple cloud model derived from the outputs of a simulation of a stratiform event occurred in the Mediterranean basin. The simulation has been carried out by using a detailed microphysical time-evolving cloud model.

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne IR measurements on sufficiently limited subregions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the proposed MW-IR combined rainfall algorithm (MICRA), three case studies are discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature.

Operating a meteorological radar is generally a challenging task when in presence of a significant beam blockage as in complex orography. Apart from enhanced ground clutter, mountainous obstructions of the radar beam can significantly reduce the radar visibility and, thus, its monitoring capabilities. Self-consistent adaptive techniques to reconstruct vertical profiles of reflectivity (VPR) and near-surface rain-rate fields from high-elevation reflectivity bins are here proposed, compared, and tested for ranges up to 60 km. The methodology is based on statistical estimators trained by a large reflectivity volumetric datasets, classified into stratiform and convective rain regimes and resampled onto a uniform Cartesian grid by means of a modified Cressman technique. For what concerns reflectivity vertical profiles, two methods, respectively named statistical nonlinear reconstruction (NSR) and neural network reconstruction (NNR), are considered. The NSR method is based on the principal component analysis, applied to the radar dataset, in order to extract significant reflectivity-profile variance. A retrieval technique, based on a nonlinear multiple regression scheme, is then used to infer near-surface reflectivity from available high-altitude echoes at a given range. The NNR is based on a three-layer artificial neural network trained by means a feedforward backpropagation algorithm. For what concerns the near-surface rain retrieval, besides a power-law reflectivity-rain-rate (ZR) approach, a three-layer neural network technique is also set up in order to estimate surface rain rate from reconstructed VPR. The proposed reconstruction techniques are here illustrated by using volumetric data acquired by the C-band Doppler single-polarization radar, operated in L'Aquila, Italy. A case study, related to a rainfall event that occurred during fall 2000, is discussed. Using a test area within 60 km from the radar site and simulating the presence of beam obstructions, a comparison of NSR and NNR with conventional area average reconstruction techniques shows that the percentage improvement of both NSR and NNR approaches is significant, both for the error bias (by 30% to more than 50%, depending on altitude) and variance (by 10% to more than 20%). A sensitivity test indicates that the VPR reconstruction procedure is fairly robust to missing data, especially in terms of error bias. The comparison of estimated radar rainfall with rain gauge data measurement is also illustrated. The mean field bias closer to its optimal value and an error variance much smaller is obtained when neural network techniques are applied than with conventional ZR methods for both techniques of reconstruction. With respect to the latter, the obtained improvement is more than 40% in terms of root mean square error and is comparable when estimating near-surface rain rate using either NSR or NNR methods to reconstruct the reflectivity vertical profiles. Limitations, potential, and future developments of the proposed adaptive reconstruction techniques are finally discussed.

The capability of some inversion algorithms to estimate surface rain rate at the midlatitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. For this purpose, an extended database has been derived from coincident SSM/I images and half-hourly rain rate data obtained from a rain gauge network, placed along the Tiber River basin in Central Italy, during nine years (from 1992 to 2000). The database has been divided in a training set, to calibrate the empirical algorithms, and in a validation one, to compare the results of the considered techniques. The proposed retrieval methods are based on both empirical and physical approaches. Among the empirical methods, a regression, an artificial feedforward neural network, and a Bayesian maximum a posteriori (MAP) inversion have been considered. Three algorithms available in the literature are also included as benchmarks. As physical algorithms, the MAP method and the minimum mean square estimator have been used. Moreover, in order to test the behavior of the algorithms with different kinds of precipitation, a classification of rainy events, based on some statistical parameters derived from rain gauge measurements, has been performed. From this classification, an attempt to identify the type of event from radiometric data has been carried out. The purposes of this paper are to determine whether the use of an extended training set, referred to a limited geographical area, can improve the SSM/I skill in rain detection and estimation and, mainly, to confirm the validity of the physical approach adopted in previous works. It will be shown that, among all the estimators, the neural network presents the best performances and that the physical techniques provide results only slightly worse than those given by empirical methods, but with the well-known advantage of an easy application to different geographical zones and different sensors.

An algorithm to compute high-resolution atmospheric humidity profiling by synergetic use of microwave radiometer and Wind Profiler Radar (WPR) is illustrated. WPR data are input for the computation of the potential refractivity gradient profiles, and combined with radiometer estimates of potential temperature profiles, order to fully retrieve humidity gradient profiles. The algorithm makes use of recent developments WPR signal processing, computing the zeroth, first, and second moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data the spectral domain. On the radiometric side, we have used a multichannel microwave radiometer profiler (MWRP) which provides continuous estimates of tropospheric temperature and humidity profiles. Finally, the combined algorithm performances retrieving humidity profiles are tested with simultaneous radiosonde in situ measurements. The empirical sets of WPR and MWRP data were provided by the Atmospheric Radiation Measurement (ARM) Program. The synergy of microwave radiometer and wind profiler measurements shows encouraging results and significantly improves the spatial vertical resolution of atmospheric humidity profiles.

A part of the study of future climate changes is based on the forecast provided by Global Circulation Models (GCM). Testing GCM output for the past is therefore a major issue for climate studies. Different approaches are possible, based on comparison between numerical output and atmospheric and oceanic measurements. More recently, another approach has been proposed, which makes use of direct observations, such as outgoing long-wave radiance, instead of retrieved products. In order to accomplish this goal, it is necessary to obtain an equivalent set of data from numerical models and observations. High-resolution spectrally resolved outgoing long-wave radiance from the Earth-Atmosphere system has been measured in the last decades by satellite-borne interferometers. On the model side, we built an equivalent synthetic dataset by processing the output of a GCM with a Radiative Transfer Model (RTM) code. As suggested by, by comparing first and second order statistics from the synthetic and measured datasets of outgoing long-wave radiance spectra we are able to test the performances of a GCM in describing not only the main behaviour of the Earth-Atmosphere system, but also its variability and climate sensitivity.

In this work we study the differences in downwelling brightness temperature (Tb) as computed by using different microwave absorption models available in literature. By processing historical datasets of radiosonde observations with four among the most used models, we discussed the main differences between models in three contrasting environments: at tropical, mid and arctic latitudes. Furthermore, we compare model predictions withmpirical observationsak en in the spectral range 20-30 GHz, which is commonly used for ground- based estimates of atmospheric water vapor by microwave radiometers. Three independent radiometers are considered, for a total of seven channels, from 20.6 to 31.65 GHz. Simulated data are computed from simultaneous atmospheric thermodynamic profiles measured by balloon-borne sensors of the new generation (Vaisala RS90), which are believed to reduce substantially the so- called dry-bias. Thus, we show comparisons of Tb computed from RS90 measurements using four models with Tb observations from the MWR units, and discuss a possible choice between the considered models.

Ground-based multifrequency microwave radiometric measurements for different sets of frequency channels and precipitation regimes are analyzed. Simulation results are shown to illustrate the potential of the proposed models by selecting, for this study, a wide range of frequencies from 19.0 to 60 GHz, representing the frequency set currently available on the ground-based radiometric system. As a validation of the approach, we have analyzed rain events occurred in Boulder, Colorado. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. Rain estimates are also compared with available rain gauge data.

Spaceborne precipitation radars are usually designed to operate at attenuating wavelengths, mostly at X, Ku and Ka band. At these frequencies and above, convective rainfall clouds can cause severe attenuation. Moreover, raindrops and precipitating ice can give rise to appreciable multiple scattered radiations which apparently tends to enhance the nominal attenuated reflectivity. In order to properly describe radar observations in such conditions, apparent reflectivity has to be modeled taking into account both path attenuation and incoherent effects. To this aim, a general definition of volume radar reflectivity is introduced and a Monte Carlo model of backscattered specific intensity is implemented. Spaceborne apparent reflectivity due to multiple scattering is shown to be significantly different from the attenuated one for the near-surface layers of mature convection at Ku band and even for growing convection at Ka band. A discussion about this discrepancy is carried out at Ku band showing its possible impact for estimated rainrate profiles. If precipitation incoherent effects are formally treated as perturbation factors of the specific attenuation model, constrained single-frequency inversion techniques are shown to be suitable to minimize rainrate retrieval errors due to multiple scattering.

The objective of this paper is to investigate how the synergy between low-earth-orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is, at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be sufficiently accurate, to calibrate spaceborne IR measurements on limited sub-regions and time windows. The proposed methodology is focused on a new statistical approach, namely the multivariate probability matching (MPM). The MPM method is rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy.

The Bayesian Algorithm for Microwave Precipitation Retrieval (BAMPR) is a cloud resolving model (CRM)-based retrieval technique for estimating surface rainfall and precipitating cloud profiles by means of microwave radiometric measurements from space. In this paper is illustrated the version of BAMPR tailored for the multi-frequency microwave radiometer aboard of the Tropical Rainfall Measuring Mission (TRMM), called TRMM Microwave Imager (TMI). Since the inversion procedure is based on a cloud-radiation database (CRD) pre-generated by performing radiative transfer calculations on the CRM numerical outputs, the important aspect of CRD representativeness is described. Results of applying BAMPR to a selected event observed by TRMM are finally discussed.

Numerical simulations are used to investigate the sensitivity of C-Band rain retrieval to polarimetric radar observables. The simulator is based on a T-matrix solution technique, while the hydrometeor distribution have been characterized with respect to dielectric composition (water, ice, and mixed phase), raindrop size distribution (normalized gamma distribution), shape (ellipsoid with parameterized aspect ratio), and angle orientation. The self-consistent ZPHI approach is here adopted and the sensitivity analysis is performed in order to evaluate the expected errors of this method to radar observables. Since differential phase shift K/sub DP/ is affected by the spatial variation of the backscattering differential phase shift /spl delta/, a new neural-network estimation technique is applied to remove /spl delta/ effects on K/sub DP/ estimate. The performance of these correction procedures and the effects of an error bias on radar measurements is evaluated by using mono-dimensional Gaussian raincell models.

This paper concerns the remote sensing of atmospheric integrated precipitable water vapour (IPVW) using a Global Positioning System (GPS) network and the Special Sensor Microwave Imager Radiometer (SSM/I) in the Mediterranean area. A comparison of IPWV maps from the two different techniques is presented. Some preliminary attempts to develop a data assimilation method, are also proposed.

Nonlinear inversion algorithms are developed to invert ground-based radiometric measurements for different sets of frequency channels and precipitation regimes. Both statistical regression estimators and feedforward neural networks are applied and compared using synthetic data sets from 6 to 50 GHz. An experimental validation is carried out using data collected by the ITALSAT ground-station (near Rome, Italy) equipped with 3 beacons at 19.7, 39.6, and 49.5 GHz together with a multi-channel radiometer at 13.0, 23.8, and 31.6 GHz. Results in terms of comparison between measurements and predictions for a rain event are finally discussed.

The complementarity between Sun-synchronous microwave (MW) and geo-stationary infrared (IR) radiometry for rain detection and estimation is analyzed. A systematic analysis of different statistical integration methods is carried out in order to use MW-based rainrate estimates to calibrate IR measurements. Multivariate probability matching and nonlinear multiple regression algorithms are investigated in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the techniques proposed, three case studies are discussed: one focused over the Mediterranean area and two focused on a global scale. The analysis of these case studies has been extended to similar techniques, available in literature, for a comparative evaluation.

The passive calibration of the radar altimeter consists in characterising the receiver by observing natural surfaces with known emission in the so-called noise-sensing mode. The paper focuses on the general approach undertaken to simulate the brightness temperature at the top of the atmosphere observed by the Envisat Radar Altimeter (RA-2). It is based on emissivity models for land and sea as well as atmospheric radiation models supported by a continuous flow of on-line data used as model inputs.

The capability of some empirical algorithms to estimate surface rain-rate at mid-latitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. We propose three retrieval techniques based on a multivariate regression, a Bayesian maximum a posteriori inversion and on an artificial feed-forward neural network. Three algorithms available in literature are also included as benchmarks. The training data set is derived from coincident SSM/I images and half hourly rain-rate data obtained from a rain-gauge network, placed along the River Tiber basin in Central Italy, during 9 years (from 1992 to 2000). The work points out that an algorithm based on regression or a neural network is a good estimator of low precipitation, while it tends to underestimate high rain rates. The best results have been achieved with the Bayesian method.

Inversion algorithms for ground-based microwave radiometric retrieval of surface rain-rate, integrated cloud parameters, and slant-path attenuation are proposed and tested. The estimation methods are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures, representative of stratiform and convective rainy clouds. The discrete-ordinate method is used to solve the radiative transfer equation for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. Besides ordinary multiple regression, a variance-constrained regression algorithm is developed and applied to synthetic data in order to evaluate its robustness to noise and its potentiality. Selection of optimal frequency sets and polynomial retrieval algorithms for rainfall parameters is carried out and discussed. Ground-based radiometric measurements at 13.0, 23.8, and 31.7 GHz are used for experimentally testing the retrieval algorithms. Comparison with rain-gauge data and rain path-attenuation measurements, derived from the three ITALSAT satellite beacons at 18.7, 39.6, and 49.5 GHz acquired at Pomezia (Rome, Italy), are performed for two selected cases of moderate and intense rainfall during 1998.

A physical-statistical approach to simulate cloud structures and their upward radiation over the Mediterranean is described. It aims to construct a synthetic database of microwave passive observations matching the climatological conditions of this geographical region. The synthetic database is conceived to train a Bayesian maximum a posteriori probability inversion scheme to retrieve precipitating cloud parameters from spaceborne microwave radiometric data. The initial microphysical a priori information on vertical profiles of cloud parameters is derived from a mesoscale cloud-resolving model. In order to complement information from cloud models and to match simulations to the conditions of the area of interest, a new approach is proposed. Climatological constraints over the Mediterranean are derived on a monthly basis from available radiosounding profiles, rain-gauge network measurements, and colocated METEOSAT infrared measurements. In order to introduce the actual surface background in the radiative-transfer simulations, a further constraint is represented by the monthly average and variance maps of surface emissivity derived from Special Sensor Microwave Imager (SSM/I) clear-air observations. A validation of the forward model is carried out by comparing a large set of brightness temperatures measured by the SSM/I with the synthetic cloud radiative database to asses its representativeness and range of variability.

An intercomparison of microwave multiple scattering radiative transfer codes used in generating databases for satellite rainfall retrieval algorithms has been carried out to ensure that differences obtained from retrieval techniques do not originate from the underlying radiative transfer code employed for the forward modeling. A set of profiles containing liquid water and ice contents of cloud and rain water as well as snow, graupel and pristine ice were distributed to the participants together with a black box routine providing Mie single scattering, atmospheric background absorption and surface emissivity. Simulations were to be carried out for nadir and off-nadir (53.1/spl deg/) observation angles at frequencies between 10 and 85 GHz. Among the radiative transfer models were two-stream, multiple stream and Monte Carlo models. The results showed that there were two major sources of differences between the codes. 1) If surface reflection/emission was considered isotropic, simulated brightness temperatures were significantly higher than for specular reflection and this effect was most pronounced at nadir observation and over ocean-type surfaces. 2) Flux-type models including delta-scaling could partially compensate for the errors introduced by the two-stream approximation. Largest discrepancies occurred at high frequencies where atmospheric scattering is most pronounced and at nadir observation. If the same surface boundary conditions, the same multiple-stream resolution and the same scaling procedures are used, the models were very close to each other with discrepancies below 1 K.

A new approach is presented to determine atmospheric temperature profiles by combining measurements coming from different sources and taking into account evolution models derived by conventional meteorological observations. Using a historical database of atmospheric parameters and related microwave brightness temperatures, the authors have developed a data assimilation procedure based on the geostatistical Kriging method and the Kalman filtering suitable for processing satellite radiometric measurements available at each satellite pass, data of a ground-based radiometer, and temperature profiles from radiosondes released at specific times and locations. The Kalman filter technique and the geostatistical Kriging method as well as the principal component analysis have proved very powerful in exploiting climatological a priori information to build spatial and temporal evolution models of the atmospheric temperature field. The use of both historical radiosoundings (RAOBs) and a radiative transfer code allowed the estimation of the statistical parameters that appears in the models themselves (covariance and cross-covariance matrices, observation matrix, etc.). The authors have developed an algorithm, based on a Kalman filter supplemented with a Kriging geostatistical interpolator, that shows a significant improvement of accuracy in vertical profile estimations with respect to the results of a standard Kalman filter when applied to real satellite radiometric data.

A sensitivity analysis for airborne microwave passive and active retrievals of hydrometeor profiles with respect to melting-layer parameterizations is carried out using synthetic data. The parameterizations of the melting layer include the effects of snow density, particle size distributions of hydrometeors as well as different permittivity models for mixed-phase particles. The hydrometeor profiles are obtained from a two-dimensional cloud ensemble model simulating a convective-stratiform rainfall event over the East Mediterranean sea. The statistical analysis reveals that the Maxwell-Garnett mixing formulas with water matrix and ice inclusions may be chosen for graupel, while a new permittivity model from Meneghini and Liao is suitable for snowflakes. A new Bayesian inversion framework is set up for both airborne microwave radiometric, radar, and combined radar-radiometer retrievals of hydrometeor profiles. Using the cloud profiles as control training data set, a numerical analysis was carried out by testing the inversion algorithms on each melting model data set. Results are discussed in terms of estimate sensitivity, defined as the statistical deviation bounds of the retrieved profiles from the control case ones. Relatively high values of estimate sensitivity to the melting-layer parameterizations are found for all hydrometeor species, especially for low snow-density and Maxwell-Garnett dielectric model test cases. The need of including various melting-layer characterizations within a comprehensive training data set and its implications for model-based Bayesian retrieval algorithms is finally argued and numerically tested.

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

A retrieval technique for estimating rainfall rate and precipitating cloud parameters from spaceborne multifrequency microwave radiometers is described. The algorithm is based on the maximum a posteriori probability criterion (MAP) applied to a simulated data base of cloud structures and related upward brightness temperatures. The cloud data base is randomly generated by imposing the mean values, the variances, and the correlations among the hydrometeor contents at each layer of the cloud vertical structure, derived from the outputs of a time-dependent microphysical cloud model. The simulated upward brightness temperatures are computed by applying a plane-parallel radiative transfer scheme. Given a multifrequency brightness temperature measurement, the MAP criterion is used to select the most probable cloud structure within the cloud-radiation data base. The algorithm is computationally efficient and has been numerically tested and compared against other methods. Its potential to retrieve rainfall over land has been explored by means of Special Sensor Microwave/Imager measurements for a rainfall event over Central Italy. The comparison of estimated rain rates with available raingauge measurements is also shown.