1. Use of GPM GMI at the Joint Center for Satellite Data Assimilation

2. Preparation for GPM GMI Data Assimilation at NOAA 2 JCSDA Representation on GPM Science Team provided early access to GMI sample data. Consistent with JCSDA Mission: To improve and accelerate the use of satellite observations in Numerical Weather Prediction. Accomplished by 1) Preparing pre-assimilation tools for data quality assessment Community Observation Assessment Tools (COAT) GMI Ingest (L1C-R) NWP (ECMWF /GFS) CRTM Quality Control Algorithms GMI Obs GMI Sim QC Filter Collocation Compare Tune dEmiss Threshold Apply dEmiss Threshold (remove scatter) 37h Obs Tb 37h Sim Tb 37h Obs Tb 37h Sim Tb 19 GHz dTB 19 GHz dEmiss 19 GHz dTB 19 GHz dEmiss Development BUFR encoders Readers QC RT capability

3. 3 JCSDA Representation on GPM Science Team provided early access to GMI sample data. Consistent with JCSDA Mission: To improve and accelerate the use of satellite observations in Numerical Weather Prediction. Accomplished by 2) Preparing preprocessing algorithms for data assimilation Preparation for GPM GMI Data Assimilation at NOAA Multi-Instrument Inversion and Data Assimilation Preprocessing System (MIIDAPS) Development – Extending 1DVAR plumbing, tuning 1DVAR analysis, assessing output Figure. MIIDAPS retrieved Liquid Water Path (LWP) (left) and GFS 6hr forecast LWP valid 12Z 3 JUL 2014 (middle), for Hurricane Arthur event off the U. S. Southeast coast. Differnce in LWP files shown right. GFS forecast is collocated in space/time to GPM GMI observation points. Application – Use 1DVAR to resolve displacement between observations and background fields to increase the number of observations (e.g. precipitation-affected) assimilated

4. Applications to NOAA Numerical Weather Prediction 4 Forecasts tracks for Hurricane Julio (2014) from August 4 to August 8 with no satellite data assimilated (left), and with only GMI data assimilated (right). Both experiments assimilated conventional observations. The best track is shown in black. JCSDA Representation on GPM Science Team provided early access to GMI sample data. Consistent with JCSDA Mission: To improve and accelerate the use of satellite observations in Numerical Weather Prediction. Accomplished by 3) Preparing data assimilation systems for GMI use in NWP GSI application was extended to GPM GMI using proxy data for day 1 readiness of real data Preassimilation assessment helps to optimize use of GPM GMI in data assimilation system by: Defining observation errors/RTM uncertainty Characterizing biases Providing testbed for quality control routines which can be implemented in data assimilation GSI Observing System Experiments (OSEs) run to assess impact of GMI on global forecast Assimilating all channels over ocean/clearksy Assimilating where both GMI swaths are available Future work supports assimilation of observations in all-sky and over non-ocean August 4 August 5 August 6 August 7 August 8 JPSS Data Gap Mitigation – NOAA specifically targeting GPM GMI as a priority sensor in the JPSS Data Gap Mitigation Strategy. Capability to assimilate GMI brightness temperatures in NCEP GDAS/GFS transitioned for next operational upgrade ~Early 2016 No GMI With GMI Coordination between NESDIS, NCEP, and NASA to ensure near-real time data flow of GMI BUFR data into NWP

5. JCSDA Support of the Global Precipitation Mission Goal is to be ready for GPM GMI data assimilation on Day 1 of public release (accomplished through proxy data) Need for strong coordination between cal/val teams and JCSDA. JCSDA is a user much like an algorithm team, with tools (DA) available to assess instrument performance. Prepare DA tools: CRTM, Pre-assimilation analysis (COAT), 1DVAR preprocessing (MIIDAPS) JPSS Data gap mitigation – NOAA identifies GPM GMI as priority sensor to help offset potential forecast degradation from losing afternoon orbit coverage Use of satellite data in NWP – encourage application of GMI uncalibrated (1B) data 5