use of gpm gmi at the joint center for satellite data assimilation
DESCRIPTION
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) assimilatedTRANSCRIPT
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