Retrieving Surface Snowfall With the GPM Microwave Imager: A New Module for the SLALOM Algorithm

Abstract

In this study, we present a new module for the Snow retrievaL ALgorithm fOr gMi (SLALOM) that retrieves surface snowfall rate using Global Precipitation Measurement (GPM) Microwave Imager measurements together with humidity and temperature vertical profiles. This module, named Surface Snowfall Rate Module, is tuned using colocated surface snowfall observations of the Cloud Profiling Radar onboard CloudSat. Using this new module, the SLALOM algorithm is able to predict surface snowfall rate with a relative bias of −13%, a root‐mean‐square error of 0.08 mm/hr, and a correlation coefficient of 0.7. Surface Snowfall Rate Module is then used to retrieve snowfall rate for three case studies and to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation, and climatological analyses.

Cross-validation of active and passive microwave snowfall products over the continental United States

“…SLALOM seems to be less affected, nevertheless its accuracy is also degraded by a factor of 2 in terms of the MB (33 vs 61%) when supercooled droplets are present at the cloud top. Since SLW is mostly associated to low snow rates, the overall accuracy of the PMW products is not strongly affected, as shown in Rysman et al (2019).…”

“…The Snow retrievaL ALgorithm fOr gMi (SLALOM; Rysman et al 2018Rysman et al , 2019, developed at CNR-ISAC under the EUMETSAT Satellite Application Facility for Operational Hydrology and Water Management (H SAF) program, is a frozen-precipitation-only retrieval algorithm based on machine learning, primarily designed for the GMI. SLALOM inputs all 13 GMI channels together with ancillary variables describing the atmospheric conditions (e.g., ERA-Interim T2m, TPW, humidity profiles).…”

The NASA‐JAXA Global Precipitation Measurement mission – part I: New frontiers in precipitation

“…The second instrument on‐board the GPM‐CO, the GPM Microwave Imager (GMI), also supersedes its TRMM counterpart (the TRMM Microwave Imager, TMI) with the inclusion of four extra channels. The 166GHz vertical and horizontal polarisation (V & H) channels are used for detecting light precipitation beyond the tropics, and the 183.31 ± 3GHz and 183.31 ± 7GHz V channels are used for detecting light snow and rain over snowy land and ice clouds with smaller ice particles (Hou et al ., 2014; Rysman et al ., 2019).…”

Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

“…CloudSat potential for snowfall retrieval using ML is investigated for PMW [e.g., Microwave Humidity Sounder (MHS)] in several studies. The results indicate that the CloudSat-based PMW retrieval algorithms using ML can detect and estimate both intense and weak snowfall events with high accuracy (Adhikari et al 2020;Edel et al 2019;Rysman et al 2018Rysman et al , 2019.…”

“…Nonetheless, studies have shown that PMW data can offer great potentials for snowfall retrieval (e.g., Levizzani et al 2011;Liu and Seo 2013;You et al 2017;Panegrossi et al 2017;Meng et al 2017;Tang et al 2018;Edel et al 2019;Skofronick-Jackson et al 2019;Adhikari et al 2020). For example, Rysman et al (2018Rysman et al ( , 2019 have developed a CloudSat/CALIPSObased machine learning approach for snowfall detection and retrieval for the GPM Microwave Imager (GMI): the Snow Retrieval Algorithm for GMI (SLALOM). The authors have demonstrated that the use of CloudSat products for training machine learning models is very effective in exploiting GMI capabilities to reproduce snowfall climatology in good agreement with CloudSat.…”