Precipitation measurements are crucial for improving climate models and weather forecasting, with satellite-based radar systems, such as the Dual Precipitation Radar (DPR) on board the Global Precipitation Measurement Core Observatory (GPM CO), playing a pivotal role in enhancing observational coverage, especially in remote regions. In November 2023, an orbit boost maneuver raised the GPM CO’s altitude from 400 km to 435 km, potentially influencing the quality of DPR data. This study evaluates the impact of this altitude increase on the accuracy of DPR-derived precipitation products by comparing data acquired before and after the orbit boost. We assess the performance of DPR measurements against ground-based laser disdrometer data collected in Italy between May 2018 and November 2024. The results highlight the effects of the altitude change on rainfall retrievals, contributing to a better understanding of how orbital adjustments can affect satellite-based precipitation observations.

Millimitere (mm) and sub-millimiter (sub-mm) radiometer observation of the atmosphere from space is an appealing topic given the variety of information obtainable. The exploitation of window frequencies and various gaseous absorption bands at 50/60, 118, 183 allow for a better representation of tropospheric temperature profiles, water vapor and cloud liquid contents, as well as for hail detection, and to some extent, rainfall and snowfall estimates. These observations have shown tangible impacts on numerical weather prediction and data assimilation, climate benchmarking, hydrometeorology, extreme weather nowcasting, and civil protection. Further benefits for ice cloud retrievals are expected from observations at higher frequency, such as 243 and 664 GHz channels foreseen in the upcoming EUMETSAT Polar System-Secon Generation (EPS-SG) Ice cloud imager (ICI) sensor [1] , [2] . The increase in frequency, and consequently the reduction in wavelength, from mm to sub-mm also gives the technological advantage of reduced size of the overall system, maintaining performances unchanged, thus making it easier to implement constellation of radiometers with the glaring benefit of incrementing the repetition time of the satellite overpasses. A precursor on this topic was proposed by Prof. Marzano in 2009 [3] with the FLOwer constellation of MM-wave RADiometers (FLORAD) mission. The FLORAD concept consisted in tree small satellites (<500 kg each) in a pseudo-stationary orbit (also termed as resonant or floreal orbit) to have a repetition rate of 1 hour over the Mediterranean area with a cross-track scanner sensor named FLOMIS (FLORAD microwave imager-sounder) with channels ranging from 90 to 230 GHz. Two evolutions of FLORAD were proposed later, adding radio occultation [4] or cloud radar [5] . Ten years later, technological progress allowed the deployment of a proof-of-concept radiometer on a cubesat (1.23 kg), named TEMPEST-D [6] , as well as TROPICS [7] , a six-radiometer constellation (5.34 kg each). These missions exploit satellites that are orders of magnitude smaller and cheaper than traditional satellites operated by federal agencies, revolutionizing the next-generations of Earth-observations [8] . In Europe, the ESA/EUMETSAT prototype satellite of the Arctic Weather Satellite (AWS) mission has been recently approved. The AWS Microwave Radiometer (MWR) is a 19 channel cross-track scanning radiometer consisting of a rotating antenna focusing the incoming radiation onto four feedhorns (one for each group of channels) and four receivers, covering the frequency range 50–325 GHz. The AWS will be the forerunner of the potential EPS-Sterna mission, a constellation of small (120 kg) polar-orbiting satellites based on the AWS, each carrying a single microwave radiometer providing frequent coverage of the Earth and full coverage of the polar zones with no gaps. The EPS-Sterna would complement the MetOp series as well as the US NOAA’s Joint Polar Satellite System by providing more frequent observations mainly for temperature and humidity sounding but also for improving precipitation monitoring at high latitudes.

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.

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.

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.

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.

In this work an analysis of the vertical profiles of radar reflectivity (VPZ) is carried out. This topic is particular important in ground based weather radar applications to reconstruct the rain precipitation close to the terrain level. Several case studies for a total of 1584 radar volumes are collected and processed. The qualitative and quantitative analysis show that VPZ reconstruction gives some improvements even thought its strength is variable and possibly subjected to external causes other than the quality of VPZ reconstruction.

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.

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.

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).

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.

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.

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.

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.

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.

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 study 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 study we evaluate the relative accuracy of X-band DSD retrieval against DSD estimates from S- band radar observations and in-situ disdrometer spectra. We present comparisons of various techniques for estimating DSD model parameters from attenuation-corrected X-band dual- polarization radar data. Coincident X-band (XPOL) and S-band (S-Pol) dual-polarized radar measurements from the International H2O experiment (IHOP) as well as coincident X-band polarimetric radar (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.

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 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.