Evaluating Numerical Weather Prediction Model Precipitation Products Via Satellite Remotely Sensed Observations

Precipitation is a fundamental meteorological variable output from numerical weather prediction (NWP) models. It is a challenge to accurately evaluate the NWP precipitation at global scales, especially over open oceans and complex geophysical regions where the standard surface rain gauge measurements are limited or not available. Precipitation datasets estimated from satellite remote sensing instruments with advanced retrieval techniques currently provide high quality datasets to evaluate NWP rainfall products at different spatiotemporal scales around the globe between 60N-S latitudes. However, due to differences between NWP model grid spatial resolution and the field of view (FOV) of satellite remote sensors as well as their different temporal scales, caution must be made in order to properly explain their intercomparisons. In addition, sensitivity limitations of satellite remote sensors with respect to rain detection could lead to missing of some precipitation, which would further complicate intercomparison between the remotely sensed and NWP rainfall. This paper analyses the long term Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) precipitation datasets and the Global Precipitation Measurement (GPM) rainfall products, and demonstrates a better approach of evaluating NWP precipitation by comparison of their statistic properties. Finally, impacts of the potentially missed rainfall in remotely sensed precipitation datasets on evaluation of NWP rainfall products will be discussed.