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The High lAtitude sNow Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS) is a new machine-learning (ML)-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder (ATMS) observations that has been developed specifically to detect and quantify high-latitude snowfall events that often form in cold, dry environments and produce light snowfall rates. ATMS and the future European MetOp-SG Microwave Sounder offer good high-latitude coverage and sufficient microwave channel diversity (23 to 190 GHz), which allows surface radiometric properties to be dynamically characterized and the non-linear and sometimes subtle passive microwave response to falling snow to be detected. HANDEL-ATMS is based on a combined active–passive microwave observational dataset in the training phase, where each ATMS multichannel observation is associated with coincident (in time and space) CloudSat Cloud Profiling Radar (CPR) vertical snow profiles and surface snowfall rates. The main novelty of the approach is the radiometric characterization of the background surface (including snow-covered land and sea ice) at the time of the overpass to derive the multichannel surface emissivities and clear-sky contribution to be used in the snowfall retrieval process. The snowfall retrieval is based on four different artificial neural networks (ANNs) for snow water path (SWP) and surface snowfall rate (SSR) detection and estimate. HANDEL-ATMS shows very good detection capabilities, POD = 0.83, FAR = 0.18, and HSS = 0.68, for the SSR detection module. Estimation error statistics show a good agreement with CPR snowfall products for SSR >10-2 mm h -1 (RMSE = 0.08 mm h -1 , bias = 0.02 mm h -1). The analysis of the results for an independent CPR dataset and of selected snowfall events is evidence of the unique capability of HANDEL-ATMS to detect and estimate SWP and SSR also in the presence of extremely cold and dry environmental conditions typical of high latitudes. [ABSTRACT FROM AUTHOR]

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases. [ABSTRACT FROM AUTHOR]

Snowfall detection and quantification are challenging tasks in the Earth system science field. Ground- based instruments have limited spatial coverage and are scarce or absent at high latitudes. Therefore, the development of satellite-based snowfall retrieval methods is necessary for the global monitoring of snowfall. Passive Microwave (PMW) sensors can be exploited for snowfall quantification purposes because their measurements in the high-frequency channels (> 80 GHz) respond to snowfall microphysics. However, the highly non-linear PMW multichannel response to snowfall, the weakness of snowfall signature and the contamination by the background surface emission/scattering signal make snowfall retrieval very difficult. This phenomenon is particularly evident at high latitudes, where light snowfall events in extremely cold and dry environmental conditions are predominant. ML techniques have been demonstrated to be very suitable to handle the complex PMW multichannel relationship to snowfall. Operational microwave sounders on near-polar orbit satellites such as the Advanced Technology Microwave Sounder (ATMS), and the European MetOp-SG Microwave Sounder in the future, offer a very good coverage at high latitudes. Moreover, their wide range of channel frequencies (from 23 GHz to 190 GHz), allows for the radiometric characterization of the surface at the time of the overpass along with the exploitation of the high-frequency channels for snowfall retrieval. The paper describes the High lAtitude sNow Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS), a new machine learning-based snowfall retrieval algorithm developed specifically for high latitude environmental conditions and based on the ATMS observations. HANDEL-ATMS is based on the use of an observational dataset in the training phase, where each ATMS multichannel observation is associated with coincident (in time and space) CloudSat Cloud Profiling Radar (CPR) vertical snow profile and surface snowfall rate. The main novelty of the approach is the radiometric characterization of the background surface (including snow covered land and sea ice) at the time of the overpass to derive multi-channel surface emissivities and clear-sky contribution to be used in the snowfall retrieval process. The snowfall retrieval is based on four different artificial neural networks for snow water path (SWP) and surface snowfall rate (SSR) detection and retrieval HANDEL-ATMS shows very good detection capabilities - POD = 0.83, FAR = 0.18, and HSS = 0.68 for the SSR detection module. Estimation error statistics show a good agreement with CPR snowfall products for SSR > 10-2 mm h-1 (RMSE 0.08 mm h-1, bias=0,02 mm h-1). The analysis of the results for an independent CPR dataset and of selected snowfall events evidence the unique capability of HANDEL-ATMS to detect and estimate SWP and SSR also in presence of extreme cold and dry environmental conditions typical of high latitudes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]