Performance of and Uncertainties in the Global Precipitation Measurement (GPM) Microwave Imager Retrieval Algorithm for Falling Snow Estimates

Friday, 19 December 2014: 8:15 AM
Gail Skofronick Jackson, NASA-GSFC, Greenbelt, MD, United States, Stephen J Munchak, NASA GSFC, Greenbelt, MD, United States and Benjamin T Johnson, University of Maryland Baltimore County / JCET, Bowie, MD, United States
Retrievals of falling snow from space represent an important data set for understanding the Earth’s atmospheric, hydrological, and energy cycles. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges and uncertainties remaining. This work reports on the development and early post-launch testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Observatory satellite launched in February 2014. In particular, we will report on GPM Microwave Imager (GMI) radiometer instrument algorithm performance with respect to falling snow detection and estimation.

Throughout 2014, the at-launch GMI precipitation algorithms, based on a Bayesian framework, have been used with the new GPM data. The Bayesian framework for GMI retrievals is dependent on the a priori database used in the algorithm and how profiles are selected from that database. Our work has shown that knowing if the land surface is snow-covered, or not, can improve the performance of the algorithm. Improvements were made to the algorithm that allow for daily inputs of ancillary snow cover values and also updated Bayesian channel weights for various surface types.

We will evaluate the algorithm that was released to the public in July 2014 and has already shown that it can detect and estimate falling snow. Performance factors to be investigated include the ability to detect falling snow at various rates, causes of errors, and performance for various surface types. A major source of ground validation data will be the NOAA NMQ dataset. We will also provide qualitative information on known uncertainties and errors associated with both the satellite retrievals and the ground validation measurements. We will report on the analysis of our falling snow validation completed by the time of the AGU conference.