Search

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]

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 development of precipitation retrieval techniques, as well as the quality assessment of satellite-based global precipitation estimates, can now benefit from the availability of unique cloud and precipitation observations by the two spaceborne radars: the Dual-frequency Precipitation Radar (DPR) on board the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory, and the NASA CloudSat Cloud Profiling Radar (CPR). The multi-year, quasi-global, and complementary DPR and CPR measurements offer a unique and extensive resource to analyze spaceborne microwave radiometer precipitation observational capabilities. This can be particularly useful in remote areas and/or where ground-based observations are sparse or not available and in conditions and regimes where passive MW precipitation retrieval is more challenging. A recent study (Panegrossi et al., 2017) illustrated the potential of the use of these observational datasets to improve snowfall detection and retrieval techniques for MW radiometers at higher latitudes. Recently, a new algorithm for snowfall detection and retrieval for the Global Precipitation Measurement (GPM) Microwave Imager (GMI) (SLALOM, Snow retrievaL ALgorithm fOr gMi) has been developed (Rysman et al., 2018). The algorithm is tuned and evaluated against coincident observations of the CPR, and enables the detection of supercooled water and the retrieval of snow water path (SWP). From a forward-looking perspective we are currently investigating the use of cross-track scanning radiometers, in particular the Advanced Technology MicroWave Sounder (ATMS), for snowfall detection and retrieval. It is worth noticing that ATMS sensor is currently carried by near-polar operational LEO satellites, and therefore it could be used for investigating the higher latitudes. In particular, the potential for snowfall detection of the several high-frequency channels, i.e. the five channels in the 183.3 GHz water vapor absorption band, is being investigated. These channels are very sensitive to snowfall and are less affected by the background surface. The results of a sensitivity study based on the analysis of snowfall events for which triple coincidences of CPR,ATMS, and GMI observations are available is presented. The events analyzed have been extracted from the 3B-CSATGPM dataset (Turk, 2018). A comparison of the different response of ATMS and GMI to the same snowfall event is carried out. In particular, the TBs measured by both radiometers and different channel combinations have been compared considering snowfall profiles obtained by CPR and environmental conditions (i.e., moisture and temperature profiles, and presence of supercooled droplets). Some intense snowfall events has been taken into account, such as an event that took place in Eastern Siberia in 2014/04/30, an event which took place on the Greenland coast 2014/09/21, and an event that took place in the Andes region in 2016/06/03.These studies pave the way towards the definition of future missions for snowfall global monitoring by combining a core satellite equipped with a multi-frequency radar in synergy with current and future MW radiometers (such as the EPS-SG Microwave Imager and Microwave Sounder). [ABSTRACT FROM AUTHOR]