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

Abstract

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes: the Dual-Frequency Precipitation Radar (DPR) retrieval and its single frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals, i.e., the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The 2C-SNOW retrieval has the highest Heidke Skill Score (HSS) for detecting snowfall among the products analysed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of two compared to MRMS. Large discrepancies (RMSE of 0.7 to 1.5 mm h-1) between space-borne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

“…The machine learning-based SLALOM algorithm exploits the multi-platform (GPM/CloudSat/CALIPSO) snowfall coincidence dataset, and is based on a random forest approach for snowfall and supercooled water detection, and multi-linear regression and gradient boosting for the SWP and SSR retrieval. Results showed very good skills of SLALOM compared to CPR and to ground-based observations, both in terms of surface snowfall occurrence and quantification [38]. SLALOM complements other studies using the CloudSat-GPM coincidence datasets for passive microwave snowfall retrieval algorithms based on machine learning approaches for AMSU/MHS [39,40].…”

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

“…Tb values at high-frequency channels may increase during snowfall events if clouds contain some liquid or supercooled water, which could completely mute the scattering contribution of snowfall [6,22,38,67,68]. Therefore, it is crucial to quantify the mixing effects of SWE scattering and thermal emission of LWP on the snowfall signal.…”

“…Passive microwave (PMW) retrieval of snowfall is one of the most challenging components of precipitation monitoring from space, with the largest error in precipitation retrieval often related to snowfall [1][2][3][4][5][6] over snow cover [7]. Snowfall emission is almost negligible due to the low dielectric constant of ice particles, especially over emissive land surfaces.…”

“…First, there is a likelihood that the snow cover and snowfall microwave signatures become very similar [2,35,36]. Second, the low emissivity of dry snow cover can significantly weaken the already weak snowfall scattering [6,[37][38][39] or vanish it completely.…”

Triple frequency radar retrieval of microphysical properties of snow

“…: transport, energy distribution networks) as well as private properties; on the other hand, snow accumulations and its eventual runoff is important for hydroelectric power generation and water resource management (Skofronick-Jackson et al, 2019). Snow cover plays a very important role in the climate system modifying the global and regional energy budget due to its high scattering albedo.Despite the undeniable importance of precipitation in the solid phase, there is large discrepancy between different snowfall accumulation estimates (Mroz et al, 2021b) which reflects a high degree of uncertainty of these products.To reduce the uncertainties related to the snow modeling, observational data are needed but these are still rare due to their cost and the remoteness of high-latitude regions where most of the snowfall occurs. Moreover, in-situ measurements at the ground are affected by problems like under-catch, wind-blown snow biases (Fassnacht, 2004) and they are only representative 1…”