Use of GPM GMI at theJoint Center for Satellite Data

AssimilationKevin Garrett1,2,3, Sid Boukabara1,2,

and Erin Jones1,2,3

1. NOAA/NESDIS/STAR2. Joint Center for Satellite Data Assimilation3. Riverside Technology, Inc.

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)

GMIIngest(L1C-R)

NWP(ECMWF

/GFS)CRTM

Quality Control Algorithms

GMIObs

GMISim

QCFilter

Collo

catio

n

Compare

Tune dEmiss Threshold

Apply dEmiss Threshold (remove scatter)

37h

Obs

Tb

37h Sim Tb

37h

Obs

Tb

37h Sim Tb

19 G

Hz d

TB

19 GHz dEmiss

19 G

Hz d

TB

19 GHz dEmiss

DevelopmentBUFR encodersReadersQCRT capability

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 NOAANumerical Weather Prediction

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 NWPGSI 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