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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 13-15, 2015.

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Presentation on theme: "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 13-15, 2015."— Presentation transcript:

1 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 13-15, 2015 JCSDA 13 th Annual Technical Review Meeting and Science Workshop on Satellite Data Assimilation 1 Overview of NESDIS Contributions to the JCSDA Activities 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)

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 13-15, 2015 Introduction 2  Strong interest in NESDIS in Accelerating/Optimizing use of satellite data  NESDIS funds multiple projects that impact satellite data assimilation  Activities in NESDIS in support of JCSDA include:  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  ATMS,SSMIS,GOES-R, HIMAWARI, AMSR2, ISS-RAPIDSCAT, GPM, SAPHIR, 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 with NWS through directed research for R2O  Effort planned to use same DA tool for data-fusion purposes:  Will link NESDIS to same DA system as the other partners for NESDIS-specific use

3 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 13-15, 2015 3 Contents O2R and Engaging the Community 4 Introduction 1 Accelerating/Optimizing Use of Satellite Data 3 Facilitating Using Satellite Data (Community Tools) 2

4 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 13-15, 2015 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 On-going/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)

5 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 13-15, 2015 CLBLM 5  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  2 nd Stage of implementation (CDR in summer 2015)

6 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 13-15, 2015 CSEM: Sea Ice Snow Canopy 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)

7 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 13-15, 2015 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 –Valid for MW, IR, Geo, Polar, etc 7 O-A(MIIDAPS) O-A(Oper.) Goal is to have a community QC tool for satellite data assimilation pre-processing: Bias: -0.2 StdDev: 0.43 Points Passing QC (slide courtesy of K. Garrett)

8 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 13-15, 2015 Community Satellite data Thinning and Representation Optimization Tool (CSTROT)  Objective of CSTROT:  Develop a new thinning scheme to optimize satellite data usage in GSI data assimilation for both global and regional modeling systems.  CSTROT Functions:  Thinning options: using Standard Deviation using regression by skipping points  Representation options: Random points Closest point Averaging  Nested domain options: by target regions by domain size CSTROT is an “intelligent” thinning tool to optimize satellite data selection in DA. Thinning of AMSU-A (N15+N19+Metop-A) Ch-2 Tb ( 0006 UTC 23 July, 2013 ) Two domain areas Two target regions Higher density in higher variation regions associated with cloudy, frontal system, moisture tongue. Specified Auto detected The tool will allow an optimal information content extraction while optimizing computation time

9 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 13-15, 2015 9 Contents O2R and Engaging the Community 4 Introduction 1 Accelerating/Optimizing Use of Satellite Data 3 Facilitating Using Satellite Data (Community Tools) 2

10 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 13-15, 2015 SNPP ATMS Assimilation Optimization S-NPP ATMS assimilated operationally at NCEP since May 2012 GDAS/GFS upgrade (L+6 months) Next generation MW sounder should have some positive impact on NWP forecast.  Additional channels (1 51 GHz, 2 183 GHz)  Scan geometry (Nyquist sampling, wider swath) Forecasts with ATMS (and no AMSU) show similar performance to forecasts with no ATMS (with AMSU) Optimization Results 10% more observations per sounding channel, per cycle, with new QC Current Assimilation Reduction in Tropical Cyclone Track Forecast Error Statistically significant increase in SH Anomaly Correlation (500 mb). Neutral NH. Assimilation Improvements Enhance data thinning/data selection from coarse (skip 5 out of every 6 FOVs) to intelligent thinning (CSTROT). Optimize spatial averaging (maintain high resolution for sounding/water vapor channels). Implement dedicated ATMS Quality Control routine based on 1DVAR preprocessor to increase the number and quality of assimmilated observations.

11 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 13-15, 2015 GMI Data Assimilation – Coordination with GMAO (Preliminary but Encouraging Results)  JCSDA began optimizing the assimilation of GMI data in GDAS. –A new QC subroutine has been developed to filter out cloud and precipitation contaminated observations from GMI data for clear sky data assimilation. –Bias correction, observation errors, and QC routines continue to be optimized, and forecast impacts are being assessed. Anomaly Correlation 850 hPa Wind, N. Hemisphere: Satellite data assimilated. Forecast 20140801 - 20140814 Preliminary assessment shows the assimilation of GMI data changing forecast hurricane tracks and the skill scores of forecast variables. Results are mixed, sample sizes thus far have been small. Work is ongoing to isolate and qualify forecast impacts. Forecast Hurricane Tracks, Hurricane Julio, with and without GMI Assimilated: No satellite data assimilated. Forecast period 20140804 - 20140808 ControlExperiment – GMI Assimilated

12 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 13-15, 2015 Other sensors being demonstrated 12  SNPP OMPS (for ozone DA, in planning stage for FY15)  SNPP VIIRS (for aerosols DA, in planning stage for FY15)  HIMAWARI-8 AHI (Dry run for GOES-R ABI).  GCOMW AMSR2  SMAP (for soil moisture DA, in planning stage for FY15)  Megha-Tropiques SAPHIR  ISS-RAPIDSCAT Scatterometer

13 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 13-15, 2015 13 Contents O2R and Engaging the Community 4 Introduction 1 Accelerating/Optimizing Use of Satellite Data 3 Facilitating Using Satellite Data (Community Tools) 2

14 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 13-15, 2015 JCSDA NOAA /NESDIS Activities (V. Scientist Program –FY14 Cycle) 14 Visit (Title)Host Institution (Beneficiary) Area of benefitV. Scientist Parent Institution Migration of a wind-wave data assimilation scheme to NOAA systems NWSOcean Data Assimilation Naval Service, Argentina HYBRID data assimilation for the Indian Ocean NWSOcean Data Assimilation Indian Met. Service (India) Exploring Mathematical cutting edge techniques in satellite data assimilation NESDISCRTM & Inversion In Cloudy/Precipitating Conditions Georgetown University (US)

15 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 13-15, 2015 O2R/R2O Environment  Goal: Accelerates the use of satellite data in NWP centers  How: Build a solid O2R environment for a successful R2O  Ensures resources are in place: supercomputer – S4&JIBB-, a software integration team, etc.  Operations-consistency  Both JIBB and S4 are scheduled to be upgraded  In progress: –Extension of O2R to include Ocean DA (NCODA, HYCOM) –Extension of O2R to include Land Systems (LIS). –Extension of O2R to include NAM 15 Scientific efforts in satellite DA in academia, CIs Scientific efforts in satellite DA funded by partner agencies Scientific efforts in satellite DA in research community JCSDA’s own DA Activities NWP Operational Centers Ongoing Baseline Improvement in NWP centers Objective: Improvements in Forecast skills Diverse R&D activities Select projects for Transition Example of S4 Disk Space Usage% More than ~ 50 users on both JIBB and S4 (total more than ~100). Mixture of NOAA and Researchers

16 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 13-15, 2015 Supporting the JCSDA External Research Project # Proposal Number(s)TitleInstitutionPI 1 12,10 Modernization of the Community line-by-line models and CRTM-OSS Implementation Atmospheric & Environmental Research (AER) Jean-Luc Moncet, PI Eli Mlawer, co-PI 2 15 Improvement and validation of JCSDA’s Community Radiative Transfer Model (CRTM) Optical Properties Texas A&M University Ping Yang, PI 3 4 Evaluation and Improvement of Land Surface States and Parameters to Increase Assimilation of Surface-Sensitive Channels and Improve Operational Forecast Skill University Corporation for Atmospheric Research Michael Barlage, PI Xubin Zeng, co-PI 4 5 Assimilation of All-Sky Microwave and Infrared Satellite radiances: from research to Operations National Center for Atmospheric Research University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies Thomas Auligne, PI Selected Projects through the NOAA Federally-Funded Opportunity (FFO) –FY13 The FY15 External Research Opportunity target time: Sept-Oct 2014. Status: Identification of gaps and priorities.

17 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 13-15, 2015 Demonstrate NOAA DA System As a Data Fusion Tool Use of GSI as a Data Fusion Tool (Satellite, Conventional, ground based, Airborne, etc) SDRs (Polar) Metop, N19, NPP, DMSP IR, MW SDRs (Geo) GOES, GOES-R, MSG, Ground- Baaed Data Radar Conventional Data Airborne Data GPS Data Environment Analysis – Geophysical products (Data Fusion) Common Data Assimilation & Data Fusion Tool - Combine DA and RS Expertise - Highly flexible to serve as - Platform for O2R/R2O - Complete Analysis (atmosphere, cryosphere, ocean, land, hydrometeors, etc) “SA” AWIPS Environment Analysis – Geophysical products (Data Fusion) NG Mode (In NWS): - Closely tied to Forecast Model, - Every 6 hours “NG” SA Mode (In NESDIS): - Data Fusion of all sensors, -Every hour globally Forecaster 17 This should be (and will require) a coordination between JCSDA, STAR, OSGS, OSD and NWS

18 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 13-15, 2015 18 Questions?

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