Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Bibliographical noteFunding Information:
The authors acknowledge the support from the National Aeronautics and Space Administration (NASA) through a Precipitation Measurement Mission award (NNX16AO56G) and a New Investigator Program award (80NSSC18K0742). Zeinab Takbiri acknowledges the support provided by the Minnesota's Discovery, Research, and Innovation Economy (MnDRIVE 2017) fellowship. Pierre-Emmanuel Kirstetter acknowledges support from the NASA Precipitation Science Program (NNX16AE39G) and from the GPM mission Ground Validation Program (NNX16AL23G). The contributions from F. Joseph Turk were performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. The GPM data (version 4) are provided courtesy of the NASA Precipitation Processing System at theGoddard Space Flight Center (https://pmm.nasa.gov/ data-access/). The MERRA-2 and MODIS data are from the Goddard Earth Sciences and Information Service Center (https://disc.sci.gsfc.nasa.gov/mdisc/) and the Land Processes Distributed Active Archive Center by the USGS (https://lpdaac.usgs.gov/data_access/data_pool). The authors would also like to thank Ryan Currier from the University of Washington for providing the data from the Weather Research and Forecasting Model over the Olympic Mountains.
© 2019 American Meteorological Society.
- Bayesian methods
- Data processing
- Remote sensing