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.

The second generation of the EUMETSAT Polar System (EPS-SG) will include two conical scanning radiometer imaging missions, the Microwave Imager (MWI, measuring in the microwave/millimeter spectrum) and the Ice Cloud Imager (ICI, measuring in the millimeter and sub-millimeter spectrum) that will be flown on the EPS-SG B satellites. Recently, new advanced versions of MWI and ICI Level 1B (L1B) and Level 2 (L2) test data packages have been released by EUMETSAT. These test data are described in detail hereafter.

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

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

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

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

The authors propose an inversion scheme devoted to the retrieval of the atmospheric specific absorption coefficient based on data collected by a ground-based microwave radiometer. The technique processes elevation scans. The vertical plan is modelled by allocating M bins in the vertical directions. The forward modelling is defined by linearization of the radiative transfer equation around a reference model of the atmospheric parameter pattern. The goal is to estimate the atmospheric attenuation along the vertical path 3 km away from the radiometer location, where a transponder calibration device for the nadir looking RA-2 altimeter on board of the ENVISAT satellite is positioned. Estimation is performed through inversion of the brightness temperature values observed at different elevation angles, provided a proper parameterization of the model space. According to the agreement of the reference model to the actual conditions two cases are given: either the solution is acceptable or it is not reliable and a different inversion scheme is to be implemented. © 2012 IEEE.

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.

A method based on neural networks is proposed to retrieve integrated precipitable water vapor (IPWV) over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Water vapor values provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to train the network. The performance of the network was demonstrated by using a separate data set of AMSR-E observations and the corresponding IPWV values from ECMWF. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data during clear-sky conditions. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas. In addition, results were compared with the IPWV values obtained from in situ instruments, a ground-based radiometer, and a global positioning system (GPS) receiver located in Rome, and a local network of GPS receivers in Como. An rms agreement of 0.34 cm was found between the ground-based radiometer and the neural network retrievals, and of 0.35 cm and 0.40 cm with the GPS located in Rome and Como, respectively.

A multilayer neural network has been developed to retrieve IPWV over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) on board the Aqua satellite. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data for clear-sky. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas.

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 work a method based on neural networks is proposed to retrieve profiles of refractivity, temperature, pressure and humidity in the troposphere from GPS-LEO radio occultation. To overcome the constraint of temperature profile availability at each GPS occultation, we have trained a neural network with refractivity profiles as input computed from the geometrical occultation parameters of the CHAMP LEO satellite, while the outputs are the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary ECMWF data.

Two cloud water models that can be included in the radiative transfer to simulate brightness temperatures from radiosonde data are analyzed. We first evaluate and optimize the capability of the models to detect correctly the presence of clouds, by comparing the cloud base heights identified from the radiosoundings with those provided by a ceilometer. Then, the performances of the different cloud models are analyzed by comparing simulated brightness temperatures in the absence of scattering with those measured by a dual-channel microwave radiometer.

The passive calibration of the Radar Altimeter (RA) consists of characterizing the receiver gain by observing natural surfaces with known emission in the so-called noise-listen mode. It is based on the comparison between the simulated values of the brightness temperature impinging on the altimeter antenna, and the digital counts at the output of the altimeter receiver in the absence of echo. The proposed method aims to calibrate measurements of the backscattering coefficient performed by a spaceborne altimeter based on the assumption that the receiver gain is the main source of uncertainty. This paper focuses on the general approach undertaken to characterize the receiver and to simulate the brightness temperature at the top of the atmosphere observed by the Envisat RA-2. The simulations rely on emissivity models for land and sea, as well as on atmospheric radiation models supported by a continuous flow of online data used as model inputs. To assess the accuracy, the model outputs are compared with observations from calibrated radiometers, namely the Special Sensor Microwave/Imager and Tropical Rainfall Measuring Mission Microwave Imager, with particular attention to the low-frequency channels (10 and 19 GHz). The new method has been first tested on European Remote Sensing satellite data and has been subsequently adopted for Envisat RA-2 in the framework of the Envisat Calibration and Validation activities managed by European Space Agency. The evaluation of the receiver gain at both Ku-band and S-band is presented and compared to the preflight values, as well as to transponder calibration done for Ku-band. An error budget for the final estimates is also presented and discussed

Brightness temperatures computed from five absorption models and radiosonde observations were analyzed by comparing them with measurements from three microwave radiometers at 23.8 and 31.4 GHz. Data were obtained during the Cloudiness Inter-Comparison Experiment at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's (ARM) site in North-Central Oklahoma in 2003. The radiometers were calibrated using two procedures, the so-called instantaneous "tipcal" method and an automatic self-calibration algorithm. Measurements from the radiometers were in agreement, with less than a 0.4-K rms difference during clear skies, when the instantaneous method was applied. Brightness temperatures from the radiometer and the radiosonde showed a bias difference of less than 0.69 K when the most recent absorption models were considered. Precipitable water vapor (PWV) computed from the radiometers were also compared to the PWV derived from a Global Positioning System station that operates at the ARM site. The instruments agree to within 0.1 cm in PWV retrieval.

This work concerns the surface 'change detection' by means of differential digital elevation model (D/spl I.bar/DEM) techniques, based on laser scanning 3D data. The laser scanning 3D technology allows to generate an high precision DEM, that can be used to calculate the morphological ground surface variations at different acquisition time intervals. The basic operation has been to acquire several DEMs in the test area: through the subtraction of different DEMs we can obtain the temporal decorrelation of two geometrical parameters: volume and elevation vector. The validation is performed by the comparison with the GPS data. The integration with differential interferometric SAR systems will allow to investigate wide areas. So, when the alarm event has been individuated, the laser scanning 3D is used to generate a precision DEM reconstruction. The results show that with such technique it is possible to calculate the volume and elevation variations with a standard deviation less than a centimetre.

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.

This paper deals with a procedure to generate maps of the integrated precipitable water vapor (IPWV) over the Mediterranean area by using estimates from a global positioning system (GPS) network over land and from the Special Sensor Microwave/Imager (SSM/I) over sea. In particular, we investigate the application of the kriging geostatistical technique to obtain regularly spaced IPWV values. The horizontal spatial structure of water vapor retrieved by SSM/I is explored by computing variograms that provide a measure of dissimilarity between pairs of IPWV values for the region of interest. Because the water vapor density decreases with height, the GPS station elevation is accounted for in the interpolation procedure. In this respect, the potential of the kriging with external drift relative to the ordinary kriging is evaluated by applying a test based on the cross-validation approach. Case studies are presented and qualitatively compared to the corresponding Meteosat infrared images. A quantitative comparison with an independent source of information, such as IPWV computed from radiosonde observations and from European Centre for Medium-Range Weather Forecasts analysis, is also performed.

This work considers the use of a dual-frequency, ground-based microwave radiometer with scanning capabilities to investigate the behaviour of the atmosphere in terms of precipitable water vapour and integrated cloud liquid before precipitation events.

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.

This paper considers the monitoring of integrated precipitable water vapour (IPWV) exploiting measurements of Global Positioning System (GPS) ground-based receivers with the purpose of analysing the behaviour of such parameter in occasion of precipitation events. A statistical characterisation of atmospheric water vapour content retrieved by GPS at specific receiver locations will be proposed to improve the knowledge of water vapour field of precipitation systems.

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

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