NOAA Satellite Snowfall Rate Algorithm and its Applications

The NOAA/NESDIS snowfall rate (SFR) dataset is a global, liquid equivalent snowfall rate estimation. It is derived from observations taken by passive microwave radiometers, including ATMS, MHS and GMI sensors, aboard eleven polar-orbiting satellites. The SFR algorithm consists of two main components: snowfall detection (SD) and rate estimation. Recently, the SD component has been adapted to machine learning (ML) models, which was trained against a large collection of in-situ observations. The XGBoost ML model shows the best performance, hence is implemented in the SFR operational retrieval system. The core of the second component, rate estimation, is a 1DVAR-based inversion model. It has been improved by using ML-based initial conditions. Lastly, the retrieved SFR values are further biased-corrected with a neural network (NN) model.

The SFR product provides observation-based winter precipitation estimates and it has applications in a number of fields: weather forecasting, hydrology, cryosphere, and climate etc. To support weather nowcasting with shorter latency, NOAA NESDIS and CISESS/University of Maryland has built a near real-time processing system that produces SFR from Direct Broadcast (DB) data. The latency of the SFR output from this system is less than 20 minutes, much shorter than the product from the NOAA operational data stream. The DB SFR is also merged with ground MRMS radar data to produce the merged-SFR (mSFR) product for weather forecast offices with improved spatial and temporal coverage. Both the SFR and mSFR products are reformatted for AWIPS at NASA SPoRT and provided to some NWS Offices in near real-time. The SFR product is also an important input to the NWS Climate Prediction Center (CPC) CMORPH2 dataset, which is a gridded global precipitation analysis. The SFR team, working with the Geographic Information Network of Alaska (GINA), University of Alaska Fairbanks, has also developed an Alaska regional SFR system (AKSFR) to provide situational awareness for nowcasting in the data-deprived Alaska region.