The second generation of the EUMETSAT Polar System (EPS-SG) programme will include two conical scanning radiometer imaging missions, the Microwave Imager (MWI, measuring in the microwave/millimetre spectrum) and the Ice Cloud Imager (ICI, measuring in the millimetre and sub-millimetre spectrum) that will be flown on the Metop-SG-B satellites. The preparatory activities for the validation of the MWI and ICI Level 1B (L1B) and Level 2 (L2) products during the Metop-SG-B commissioning phase are currently ongoing. Hereafter an overview of some of the Calibration/Validation (Cal/Val) tools implemented in-house at EUMETSAT and via external studies for the verification of the geolocation and radiometric requirements within the EUMETSAT End-Users Requirements Document (EURD) is provided. Focus has been given to coastline matchup geolocation analysis and the radiometric comparison of the MWI and ICI observed brightness temperatures with radiative transfer simulations based on atmospheric profiles from radiosonde sensors and ECMWF forecasts.

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.

This study proposes an Artificial Neural Network (ANN) algorithm for downscaling weather forecasts of some variables useful for agriculture in Southern Italy. Using the Weather Research and Forecasting (WRF) model at 1.2 km spatial resolution, the algorithm performs downscaling at 240 m resolution using an operation similar to bilinear interpolation, but with enhanced performance. To train the ANNs, a database was built using the WRF model in Large Eddy Simulation (LES) mode with 240 m grid spacing. Particular attention was paid to defining the architecture of the ANNs and selecting the inputs. The comparison of the algorithm’s performance against spline interpolation shows a reduction of the mean squared error (MSE) ranging from a minimum of 6% for solar irradiance to a maximum of 87% for surface pressure.

This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer - New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feedforward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the σ-IASI radiative transfer model to simulate IASI/IASI-NG radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth’s system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer—new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of “clear-sky” or “thin-cirrus” detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the$\sigma $-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus.

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.

Humidity profiles retrieved from a ground-based millimeter-wave radiometer located at McMurdo Station, Antarctica, and the West Antarctica Ice Sheet Divide are presented, and their suitability to study the humidity of the polar climate is assessed. The dry conditions of the Antarctic winter and spring are ideal for ground-based millimeter-wave measurements, and the retrievals appear to realistically reproduce the spatial and temporal variabilities of humidity at both sites. The radiometer has the ability to capture the daily variability of very low humidity (0.5–4 g/kg) in the low-to-mid troposphere with an uncertainty of 10%–20% during the Antarctic winter, spring, and summer. Despite the coarse vertical resolution (200–600 m in the first 4 km), the retrievals provide additional information with respect to the European Centre for Medium Range Weather Forecasts (ECMWF) profiles used as background information. The radiometer is also able to realistically identify the location and frequency of supersaturated layers with respect to ice in the mid troposphere. The occurrence of supersaturated layers is correlated with the occurrence of ice clouds identified by a cloud mask. Overall, results show that ground-based microwave and millimeter-wave radiometry is a viable complement to satellite observations to provide continuous information on the thermodynamic state of the low-to-mid troposphere at high latitudes.

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.

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.

Line shapes are influenced by the dependence of collision cross sections on molecular speeds, with resulting effect on the absorption of up to a few percents. Speed-dependence effects on the water line at 22.23 GHz and the oxygen line at 118.75 GHz are calculated for atmospheric conditions common in midlatitudes. The effect on water vapor profiles retrieved with a ground-based microwave radiometer shows an impact (in simulations) comparable in magnitude to those of measurement errors and of uncertainties in spectroscopic parameters.

The Advanced Technology Microwave Sounder (ATMS) is a cross-track scanning microwave (MW) radiometer useful to retrieve Temperature (T) and Water Vapor (WV) atmospheric vertical profiles. Using spatial and temporal coincidences between ATMS observations and two different datasets of vertical T and WV, a global training dataset for machine learning purpose was built for the whole 2016. For each ATMS Brightness Temperatures acquisition, 32 levels of T (between 10 and 1000 hPa), and 23 levels of WV (between 200 and 1000 hPa), are used to train an algorithm based on Random Forests regression technique. A single RF was trained for each level and atmospheric variable, using the evaluation on the Out of Bag error to optimize the number of random selection of the input variables at each node splitting step, the number of trees in each forest and the minimum leaf size parameter, to avoid overfitting problem and obtain an accurate retrieval. Considering that the sounding below the precipitation level becomes unreliable, the precipitation-affected observations were removed from the training dataset by means of a pre-screening test based on BT. The results show an overall ability of the algorithm to retrieve T and WV vertical profiles in line with expectations.

The extraordinary rainfall event that affected the center of Italy on 9thand 10thSeptember 2017 was studied by examining the synoptic analysis, radar network and rain gauges' measurements. The main precipitation event took place in the area around Livorno, where more than 200 mm of precipitation was recorded in 24 hours. The case study is analyzed using Weather Research and Forecasting (WRF) model and two algorithms based on satellite observations: the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique and the cloud Classification Mask Coupling of Statistical and Physics Methods (C-MACSP). The analysis shows that WRF is able to forecast the event, though with errors in actual structure, location, and time. For this reason, the combined use of different observational tools could support the WRF simulation to provide a better characterization of the event.

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.

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.

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.

In this work we report the main conclusions of the European Space Agency (ESA) Metawave project (Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects) for what concerns the Synthetic Aperture Radar Interferometry (InSAR) viewpoint. The Atmospheric Phase Screen (APS) estimated with the Permanent Scatterers (PS) technique in the test sites of Roma and Como has been compared with MM5 simulations, Meris Water Vapor (WV) data and GPS Zenith Wet Delays (ZWD). The experiment shows that, even though MM5, Meris and GPS data detect the similar absolute amount of WV, their accuracy is not enough to compensate the InSAR sensitivity to the WV spatial variability. In particular, Numerical Weather Prediction models look promising for correcting long WV spatial wavelengths in presence of topography; GPS measurements reveal the best performances toward short WV spatial wavelengths, while Meris data in the analyzed area show generally poor reliability.

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.

Ground-based microwave radiometer profilers in the 20–60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecasting during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events ($\sim$235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied “quasi-operationally,” i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5$\hbox{g/m}^{3}$for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8$ \hbox{kg/m}^{2}$, with small bias$(-0.1\ \hbox{kg/m}^{2})$and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within$\sim$0.60 km.

Spaceborne interferometric synthetic aperture radar (SAR) (InSAR) imaging has been used for over a decade to monitor tectonic movements and landslides, as well as to improve digital elevation models. However, InSAR is affected by variations in round-trip propagation delay due to changes in ionospheric total electron content and in tropospheric humidity and temperature along the signal path. One of the largest sources of uncertainty in estimates of tropospheric path delay is the spatial and temporal variability of water vapor density, which currently limits the quality of InSAR products. This problem can be partially addressed by using a number of SAR interferograms from subsequent satellite overpasses to reduce the degradation in the images or by analyzing a long time series of interferometric phases from permanent scatterers. However, if there is a sudden deformation of the Earth's surface, the detection of which is one of the principal objectives of InSAR measurements over land, the effect of water vapor variations cannot be removed, reducing the quality of the interferometric products. In those cases, high-resolution information on the atmospheric water vapor content and its variation with time can be crucial to mitigate the effect of wet-tropospheric path delay variations. This paper describes the use of a ground-based microwave radiometer network to retrieve 3-D water vapor density with fine spatial and temporal resolution, which can be used to reduce InSAR ambiguities due to changes in wet-tropospheric path delay. Retrieval results and comparisons between the integrated water vapor measured by the radiometer network and satellite data are presented.

A 1-D variational (1DVAR) retrieval technique has been developed for obtaining temperature and humidity profiles from observations of the Ground-Based Scanning Radiometer (GSR) operating at millimeter-wavelengths. The GSR was deployed in two Arctic experiments held at the Atmospheric Radiation Measurement Program in Barrow, Alaska. Temperature and humidity profiles retrieved with the 1DVAR technique are compared with simultaneous radiosonde observations (RAOBs) during the Radiative Heating in Underexplored Bands Campaign (February–March 2007). Examples and statistical results are presented and discussed to demonstrate the achieved retrieval accuracy and vertical resolution. The 1DVAR retrievals based on GSR observations improve the NWP background up to 5 km, particularly in the lower 3 km. The present implementation achieved an root-mean-square (rms) error with respect to RAOB within 1.5 K for temperature and 0.10 g/kg for humidity profiles of up to 5 km in height, with 2.9 and 2.0 degrees of freedom for signal, respectively. Using the interlevel covariance definition of the vertical resolution, the 1DVAR retrievals showed a$ < 1$-km vertical resolution of up to 5 km for both temperature and humidity profiles. The integrated water vapor obtained from the retrieved humidity profiles showed an rms accuracy within 0.10$\hbox{kg/m}^{2}$, with small bias$( < 0.01\ \hbox{kg/m}^{2})$and excellent correlation (0.96).

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.

During the Radiative Heating in Underexplored Bands Campaign (RHUBC), held in February-March 2007, three millimeter-wave radiometers were operated at the Atmospheric Radiation Measurement Program's site in Barrow, Alaska. These radiometers contain several channels located around the strong 183.31-GHz water vapor line, which is crucial for ground-based water-vapor measurements in very dry conditions, typical of the Arctic. Simultaneous radiosonde observations were carried out during conditions with very low integrated-water-vapor (IWV) content (< 2 mm). Observations from the three instruments are compared, accounting for their different design characteristics. The overall agreement during RHUBC among the three instruments and between instruments and forward model is discussed quantitatively. In general, the instrument cross-validation performed for sets of channel pairs showed agreement within the total expected uncertainty. The consistency between instruments allows the determination of the IWV to within around 2% for these dry conditions. Comparisons between these data sets and forward-model simulations using radiosondes as input show spectral features in the brightness-temperature residuals, indicating some degree of inconsistency between the instruments and the forward model. The most likely cause of forward-model error is systematic errors in the radiosonde humidity profiles.

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.

The University of Colorado (CU) Center for Environmental Technology (CET) has developed a Ground- Based Scanning Radiometer (GSR) and deployed it during two important Arctic Experiments relevant to climate research. The first was the Arctic Winter Radiometric Experiment during March-April 2004 and the second was the Radiative Heating in Underexplored Bands Campaign (RHUBC) in February-March 2007. This paper summarizes some of the design details of the instrument and gives new results from both campaigns

Measurements of water vapor and clouds in the polar regions are difficult, because conventional instruments show little sensitivity (~1.3 K/mm) to low amounts. On the other hand, millimeter- and submillimeter-wavelength radiometry offers greatly enhanced sensitivity (up to 51.4 K/mm, depending upon frequency). For this reason, the National Oceanic and Atmospheric Administration's Physical Science Division designed a new instrument, the Ground-Based Scanning Radiometer (GSR), for continuous and unattended observations at millimeter and submillimeter wavelengths (50-380 GHz). The GSR was deployed for the first time during the Arctic winter radiometric experiment in March-April 2004. In this paper, we discuss the GSR calibration procedure, which allows for accurate measurements during clear and cloudy skies. Error-budget analysis and comparison with independent measurements show an absolute accuracy on the order of 1-2 K. Examples of multifrequency and multiangle GSR observations are illustrated, representing a valuable new data set for the study of water vapor, clouds, and atmospheric absorption models in the Arctic.

Ground-based observations at millimeter (mm) and submillimeter (submm) wavelengths were collected at the atmospheric radiation measurement program site at Barrow, AK, during the Arctic winter by a new 25-channel radiometer. A weighting function analysis is presented to demonstrate the enhanced sensitivity of mm- and submm-wave (50-400 GHz) radiometers to low vapor and liquid water contents with respect to conventional instruments such as the ones operating at centimeter (cm) wavelengths (20-30 GHz). In addition, based on measurements, we carried out a quantitative analysis of mm- and submm-wavelength sensitivity, yielding improvement factors from 1.5 to 69 for precipitable water vapor (PWV) and 3 to 4 for liquid water path (LWP) when compared to 20-30 GHz radiometers. Furthermore, using a simulated data set, we evaluate the effect of hydrometeor scattering: given the conditions occurring during the experiment, the scattering contribution is within the instrumental noise for most, but not all, of the considered channels. With the same data set, we demonstrate that in the dry conditions of the Arctic, a simple linear regression yields satisfactory results when applied on selected mm- and submm-wave channels. For a dual-channel combination, the expected accuracy is ~0.23 (0.007) mm for PWV (LWP), when using mm- and submm-wavelengths, whereas it is 0.37 (0.012) mm using cm-wave channels. When the retrieval is applied to real observations, the accuracy is found in agreement with theoretical expectations.

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 large dataset of concurrent integrated precipitable water vapor (IPWV) estimates from ground-based microwave radiometers (MWRs), global positioning system (GPS) ground-receivers, and radiosonde observations (RAOBs) has been collected in five different sites in Central Italy. Both MWRs and GPS have shown a capability of accurate and continuous water vapor monitoring. These data are used to study the seasonal and spatial variability of IPWV. A comparison of these data with the IPWV field produced operationally by the nonhydrostatic Mesoscale Model (MM5), running at the University of L'Aquila/Center of Excellence (CETEMPS) is performed in order to find either model shortcomings and to corroborate the IPWV behavior highlighted by the measurements. Both measurements and model outputs span over a period of about one year allowing for a systematic statistical analysis for all the examined stations. The statistical analysis shows a good agreement between GPS and MWR data, whereas discrepancies are found between RAOBs and the other techniques. The IPWV shows the largest diurnal variability, approximately 3%, during the summer season. An overall good agreement is found between the forecasted and observed IPWV. The related statistical parameters show a very low bias (0.001 cm) with a good correlation coefficient (0.939). On the other hands, the seasonal analyses highlight a few discrepancies, mostly due to the MM5 difficulties in correctly forecasting the diurnal cycle.

A multiinstrument radiometric experiment was conducted on the North Slope of Alaska near Barrow, Alaska, during March 9 to April 9 2004. Initial radiometric and radiosonde data from this experiment are presented.

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.

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.

Microwave ground- and satellite-based sensing of atmospheric precipitable water vapor (PWV) is commonly based on the measurement of down/up welling brightness temperature (Tb) in the spectral region around the water vapor rotational line at 22.235 GHz. The atmospheric absorption/emission at these frequencies is dependent on the thermodynamic state of the atmosphere, which is usually unknown. The dependence on atmospheric profile can be limited by choosing the channel frequency near the so called "hinge points". However, the frequency allocation of such points is not exactly determined. Therefore, we considered three databases of atmospheric thermodynamic sets of profiles (pressure, temperature, humidity) collected in three contrasting environments (Arctic, mid-latitude, tropical), and processed them with four commonly used microwave absorption models, to get simulated ground based measurements in the 19-26 GHz range. We evaluated a variety of different inversion methods for single-channel PWV retrieval. Thus, we show spectra of PWV retrieval uncertainty for typical cases, and we discuss our choice of the frequency location for the hinge points around 22.235 GHz.

This paper concerns the validation of the atmospheric integrated precipitable water vapour (IPVW) product of the MERIS instrument on board of ENVISAT satellite. The validation is performed both at specific locations and over an extended area. The first comparison is performed with respect to the measurements of ground based instruments (microwave radiometers, GPS receivers, radiosoundings). The second assessment is based on IPWV maps of the Tyrrhenian area that are produced by geostatistical interpolation of the measurements of a network of GPS receivers (over land) and measurements of Special Sensor Microwave Imager Radiometer (over sea). The preliminary results show that the standard ESA algorithm for MERIS underestimates IPWV values both over land and sea backgrounds.

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.

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.

Based on radiometric and radiosonde observations made at the North Slope of Alaska, during March 1991, comparisons of measured and calculated brightness temperatures are made for frequencies ranging from 23.8 to 340 GHz. Three clean air absorption models of Liebe (1987 and 1993) and Rosenkranz (1998) are used.

We discuss the performances of a set of four microwave water vapor radiometers operating in the 20-30-GHz band during a field experiment, with an emphasis on calibration and achievable accuracy. The field experiment was conducted at the Department of Energy's Atmospheric Radiation Measurement Program's field site in north central Oklahoma, and was focused on clear-sky water vapor measurements by both radiometers and radiosondes. A comparison between two published radiometric tip curve calibration procedures is presented, and these procedures are applied to measurements from two nearly identical instruments placed a few meters apart. Using the instantaneous tip cal method of the Environmental Technology Laboratory, the brightness temperature measurements for the two identical instruments differed by less than 0.2 K over a 24-h period. Results from reference load cryogenic tests and brightness temperature cross comparisons have shown differences within 0.7 K. In addition, we compare radiometric measurements with calculations of brightness temperature based on the Rosenkranz absorption model and radiosonde observations. During the experiment, both Vaisala-type RS80 and RS90 humidity sensors were used. Our comparisons demonstrate the improvements achieved by the new Vaisala RS90 sensors in atmospheric humidity profiling, which reduce or eliminate the "dry bias" problem.

We discuss the performances of a set of four microwave radiometers during a field experiment, with an emphasis on calibration and achievable accuracy. A comparison between two alternative tip curve calibration procedures has been carried out. Additional results from reference load cryogenic tests and from brightness temperature cross-comparison are shown. Also, we compare radiometric measurements with radiosonde observations and we demonstrate the improvements achieved by the new Vaisala sensors to reduce or eliminate the "dry bias" problem.

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.