N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N JCSDA Technical Review Meeting and Science Workshop May 21-23, 2014 JCSDA 12 th Annual Technical Review Meeting and Science Workshop on Satellite Data Assimilation 1 Overview of (some of) NESDIS Contributions to the JCSDA Presented by Sid Ahmed Boukabara Senior Data Assimilation Scientist, NESDIS/STAR Contributions from: NOAA NESDIS teams (DRT, JPSS/GOES-R PG), and JCSDA External Research Program (FFO)
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JCSDA 12 th Annual Technical Review Meeting and Science Workshop on Satellite Data Assimilation
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N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
JCSDA 12th Annual Technical Review Meeting and Science Workshop on Satellite Data Assimilation
1
Overview of (some of) NESDIS Contributionsto the JCSDA
Presented by
Sid Ahmed Boukabara
Senior Data Assimilation Scientist, NESDIS/STAR
Contributions from:NOAA NESDIS teams (DRT, JPSS/GOES-R PG), and JCSDA External Research Program (FFO)
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
Introduction
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NESDIS does not run a data assimilation system in real-time Strong interest in Accelerating/Optimizing use of satellite data NESDIS funds multiple projects that impact satellite data assimilation Approach:
Develop Tools needed to facilitate the use of satellite data CLBLM, CRTM & CSEM Satellite Data Thinning & Representation Optimization CMFT Centralized BUFRization Tool General Satellite QC Tool (MIIDAPS)
Accelerate/Optimize use of satellite data OSCAT, GPM, ATMS, SSMIS, GOES-R, Etc Proxy data for day-1 readiness Advance DA Science to allow more satellite data to be used (cloudy/rainy, ..)
Reach out to external research community Proving ground & Risk Reduction Programs FFO Visiting Scientists, Etc O2R Environment (S4 and JIBB Support and Upgrade)
Work closely with NWS through directed research for R2O
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014 3
Contents
O2R and Engaging the Community4
Introduction1
Accelerating/Optimizing Use of Satellite Data 3
Facilitating Using Satellite Data (Community Tools)2
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
CRTM Mission• Satellite radiance simulation and assimilation for passive MW, IR, & Visible sensors of NOAA,NASA,DoD satellites, and others (200 sensors) • Simulation of clear/cloudy/precipitating scenes, globally
CRTM Applications• Data assimilation in supporting of weather forecasting• Physical retrieval algorithm for products• Stability and accuracy monitoring of satellite observations• Education and Research: reanalysis, climate studies, air quality forecasting, and a radiative tool for students
CRTM Future Development• Acquarius, SMOS, SMAP, ..• CRTM for CMAQ• CRTM unapodized capability• CRTM for cloudy/rainy data assimilation
ATMS Ch. 4 (O-B) GDAS
CRTM
4
(slide based on Q. Liu presentation)
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
CLBLM
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An effort has been initiated to modernize the LBLRTM. Collaborative work between JCSDA and AER.
Features of CLBLM Modern coding standards Streamlined Interface (inputs, outputs and spectroscopy) Modular Easy to maintain and upgrade Parallel processing For all spectral regions
Status: Design/Planning Phase LBLRTM de-coding Early stage of implementation
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
CSEM:
Microwave
Physical Model
Database
Empirical Models
Vis Infrared
Physical Model
Database
Empirical Models
Sea Ice Snow
Can
opy
Desert
Ocean
Community Surface Emissivity Model
For all Spectral regions and for all surface types
Combination of models, LUTs, empirical
Centralizes all developments for the JCSDA emissivity effort
Fully Integrated with CRTM
Summary and Plans available. Feedback and suggestions welcome
(slide from M. Chen & F. Weng)
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
1DVAR Pre-Processing (MIIDAPS) Efforts are on going to:
– Use 1DVAR as a pre-processor to NWP for quality control purposes
– QC of satellite data, rain and ice detection, coast contamination,
– RFI for imagers, etc) – based on MiRS technology
(significant leverage)– Implement dynamically-
retrieved emissivity in the NWP to allow assimilation of surface –sensitive channels
– Assess assimilating sounding products in cloudy/rainy conditions
7
O-A(MIIDAPS)
O-A(Oper.)
Goal is to have a community QC tool for satellite data assimilation pre-processing:extend the MIIDAPS to all Sensors (IR & MW, geo/Pol)
Bias: -0.2StdDev: 0.43
Points Passing QC
(slide courtesy of K. Garrett)
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
Community Satellite data Thinning and Representation Optimization Tool (CSTROT)
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Thinning & Representation is an important aspect of assimilating satellite data
An effort is on-going to optimize this aspect through a standalone tool (CSTROT)
Features include:– Different representations
(superobing / sampling)– Variability based thinning– Flexible (sampling rate)– Applicable to different observations
The tool could also serve as a super-sampling tool for ROI (for regional data assimilation) (Effort led by T. Zhu)
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
CMFT: Community Multi-Format Tool
Part of the O2R environment For Researchers who do not have access
to BUFR files. It is NOT a new tool. It is instead a tool that centralizes all
BUFR formatting kits that exist in a single, user-friendly tool for researchers to use.
Works from many input formats (HDF, ncdf, etc)
Outputs are in BUFR format (WMO flavor and NCEP flavor of BUFR)
Packaged to be user-friendly (GUI), but also applicable to operations (the GUI generates scripts that can run operationally)
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014 10
Contents
O2R and Engaging the Community4
Introduction1
Accelerating/Optimizing Use of Satellite Data 3
Facilitating Using Satellite Data (Community Tools)2
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N
JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
GPM Data Assimilation in JCSDA
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JCSDA is an early adopter of the GPM (GMI) data:
– Close interaction with GPM science team before launch
– High on priority list of the JCSDA executive team (tracked action item)
– Listed in the FY14 Directed research activities
Status:
– BUFRization of the L1B and L1C complete (for testing purposes)
– Close coordination with GMAO on-going– NOAA-NASA MOU in progress to coordinate on
cloudy radiance assimilation
Future Steps:– Assessment of GPM/GMI data on GSI/GFS– Assessment of GPM/GMI on GSI/HWRF– Continued coordination with NASA/GMAO – Close interaction with NWS/NCEP on GPM data
assimilation transition to NCEP GSI– Assess GPM data impact in active regions
(Rain, ice, cloud)(slide courtesy of E. Jones)
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014
OSCAT Data Assimilation Effort has led to parallel testing
of OSCAT DA in the next version of the GDAS system
Both NESDIS and KNMI OSCAT data were investigated
Pre-assessment was done characterize filtering, thinning, biases and observation error
Errors in wind direction was found to have a bigger effect on A/C than intensity
OSCAT has since failed in orbit Lessons learned will apply to
ASCAT and other scatt. data12
Impact assessment of the OSCAT scatterometer data assimilation. These plots represent the forecast impact (b) and verification results (a) of OSCAT winds experiments. They represent the change in anomaly correlation and RMS (increase or decrease) of the surface wind speed at 0.995 sigma level. The impact, globally, at 48 hours lead time is mixed, but overall positive. Plot courtesy of Li Bi, Riverside Inc, JCSDA Active Sensors data assimilation scientist.
a
b
(slide courtesy of L. Bi)
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014 13
AMSR-2 Data Assimilation Land and Atmospheric Activities
Assimilation of Satellite Soil Moisture Product from AMSR2 in NCEP Global Forecast System. M. Ek (NCEP) and Zhan (NESDIS). JCSDA Directed research funded project. This effort is JCSDA-facilitated. In this figure, the Noah LSM multiple year means and standard deviations are used to scale the surface layer soil moisture retrievals before assimilation
AMSR2 Soil Moisture (Reference)
GFS-EnKF: After AMSR2 Soil Moisture DA
GFS: Without AMSR2 Soil Moisture DA
JCSDA is actively working on assimilating AMSR-2 data:
– Close interaction between NOAA Scientists
– Listed in the FY14 Directed research activities
– Land applications (soil moisture)
– Atmospheric Application (moisture)
Status:
– Soil moisture data from AMSR-2 combined with other sensors producing SM
– Impact assessment performed – Moisture profiling information
content assessed
Future Steps:– Assessment of GPM/GMI data
on GSI– Continued coordination with
NASA/GMAO – Close interaction with
NWS/NCEP on GPM data assimilation transition to NCEP GSI trunk
Assessment of the vertical moisture information content in AMSR-2 data, showing tropospheric sounding capability, prior to assessing the impact in a GSI environment.
(courtesy K. Garrett, M. Ek, X. Zhan)
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JCSDA Technical Review Meeting and Science WorkshopMay 21-23, 2014 14
(slide courtesy of E. Maddy)
Unfiltered
Filtered
AMV Data Assimilation Activities A coordinated AMV Data Assimilation effort is being
initiated in the JCSDA through coordination between NESDIS, NWS, Navy, NASA and U. Wisconsin
Goal:– Maximize the impact of AMV in NOAA systems – Learn from Navy/NASA experience using AMVs– R2O transition of academia projects that were proven
to offer added value– Prepare for AMV from future sensors (Himawari-8,