We propose to extend our initial successful results in several ways so that at the end of three years the system for effective assimilation of imerg precipitation will be ready for operational testing…

This is a $119,999.99 grant awarded by the National Aeronautics and Space Administration (NASA) to the University of Maryland, College Park to extend their initial successful work in assimilating Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data. The key objectives are to: A) Compare IMERG and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data, B) Use a higher resolution Global Forecast System (GFS) model with assimilation of current observing systems, C) Test Gaussian transform for model precipitation based on ensemble, D) Assimilate precipitation in hurricanes using GPM radar observations, E) Implement Ensemble Forecast Sensitivity to Observations (EFSO) and proactive quality control to assimilate optimal IMERG observations, F) Compare impact of assimilating early vs. final IMERG estimates, G) Understand why TMPA is more useful over ocean than land, H) Explore using lightning information to improve convective precipitation assimilation, and I) Use EFSO to estimate IMERG/TMPA observation errors. This effort is aimed at preparing the IMERG precipitation assimilation system for operational testing and implementation. The contract has no set-aside designation.

We propose to extend our initial successful results in several ways so that at the end of three years the system for effective assimilation of imerg precipitation will be ready for operational testing for implementation. The tests we will perform include: a) use imerg and compare with tmpa. B) use a higher resolution GFS model with assimilation of current observing systems. C) test using a gaussian transform for the model precipitation based purely on the ensemble. D) assimilate precipitation in hurricanes, using GPM radar observations, to test whether the track and intensity forecasts are improved. E) implement efso and proactive QC to assimilate the optimal set of imerg observations. F) compare the impact of assimilating early imerg with final imerg estimates. G) understand why the tmpa observations are found (using efso) to be more useful over ocean than over land, and whether this is related to convective precipitation. H) explore whether using lightning information can improve the assimilation of convective precipitation. I) use efso to estimate imerg/tmpa observation errors.