1. FY08 GOES-R3 Project Proposal Title Page - PowerPoint PPT Presentation

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1. FY08 GOES-R3 Project Proposal Title Page

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  1. 1. FY08 GOES-R3 Project Proposal Title Page • Title: JCSDA Preparation for Uses of GOES-R Data in NWP Models: Assimilation of MSG, GOES, and MODIS data in GFS • Project Type: 3 • Status: Renewal • Duration: 2 • Leads: • Fuzhong Weng, STAR/JCSDA • Other Participants: • Tong Zhu, CIRA, GSI assimilation • Xiaolei Zou, FSU, WRF/NAM assimilation • Ron Vogel, JCSDA, IR land emissivity • Greg Krasowski, JCSDA, data BUFR • Steve Lord, EMC, program coordination • John Derber, NCEP, consultant on GSI • Dennis Keyser, NCEP, consultant on data bufring

  2. 2. Project Summary • Compare the performance of several land surface emissivity data bases (NPOESS, NRL, JPL etc). NPOESS emissivity data base is currently used in CRTM • Improve surface typing from NOAH land surface model outputs to better match the CRTM infrared emissivity data base. • Develop an advanced infrared land emissivity model including vegetation canopy • Prepare the BUFR data of GOES imager/sounder and MSG SEVIRI for NCEP data assimilation tests • Demonstrate the impacts of MSG, GOES and MODIS data in GFS and WRF/NAM

  3. 3. Motivation/Justification • Supports NOAA Mission Goal(s): Weather & Water • Operational data assimilation systems have not been able to take advantage of GOES high temporal data because of a lack of advanced data assimilation capability • For GOES-R ABI simulation and assimilation, JCSDA Community Radiative Transfer Model (CRTM) needs more accurate components such as BRDF and IR emissivity models, accurate and fast gaseous, aerosol and cloud optical models IR Emissivity Module Ocean: Wind-induced roughness emissivity (Wu and Smith, 1997) Land: NPOESS reflectivity data base

  4. 4. Methodology • NWP Models and Data Sets • GFS and NAM model outputs • Noah Land Surface Model outputs • NPOESS emissivity data base • NRL index-based emissivity data base • JCSDA community radiative transfer model (CRTM) • Exploratory Research • Multispectral and hyperspectral emissivity data base • Study the leaf optical property model such as PROSPECT for thermal infrared applications (Jacquemoud et al, INRA/CSE) • Test of the SAIL-Thermique (Solar-Thermal) RTM for IR emissivity and its spectra of plant canopies (Albert Olioso et al., INRA/CSE)

  5. 5. Summary of Previous Results • A study is completed on comparing NPOESS, NRL and JPL data bases • some major discrepancies in mid-infrared emissivity • NRL emissivity data base produces more dynamic range in near IR • Several case studies are completed. AVHRR radiances were simulated over land using NPOESS vs. NRL LSE data bases. It is shown that • NRL emissivity shows significant improvement over NPOESS emissivity for AVHRR Band 3 for Mid Asia, N. Africa (dry regions, nighttime), • NRL emissivity does not show improvement for AVHRR Bands 4 and 5 (dry regions, nighttime), • NRL emissivity does worse than NPOESS in vegetated region in nighttime (all bands), but daytime may show improvement in Bands 4 and 5. • Preparation of BUFR data (mainly GOES vs MSG data) for NCEP data assimilations • GOES Clear-Sky Brightness Temperature (CSBT) data was completed

  6. 6. Expected Outcomes • Improved infrared LSE data bases (mid-infrared) will be implemented into CRTM for NWP radiance assimilation and ABI applications • New global IR LSE data base will be developed including correction of surface roughness • A physical infrared LSE model will be investigated for CRTM applications • Routine assimilations of geo water vapor and CO2 channels in GFS and NAM

  7. 7. Major Milestones • FY09 • New surface typing algorithm is developed from NOAH LSM outputs • Improved NPOESS LSE in CRTM through better typing • New global IR LSE retrieval algorithm including roughness parameter (currently, most data bases are derived using specular assumptions) • Complete evaluations of PROSPECT, Sail-Thermique RTM and other radiative transfer scheme for plan canopy IR emissivity calculation • MODIS and MSG SEVIRI data BUFR for NCEP applications • FY10 • Complete a physical infrared LSE model • Study the impacts of new LSE model on ABI radiance simulations • More BUFR data sets (e.g. MODIS, GOES) will be developed

  8. 8. Funding Profile (K) • Summary of leveraged funding

  9. 9. Expected Purchase Items • FY08 • (205K): STAR Scitech contract for 1.5 persons of 100% from Jul08 to Jun09 • FY09 • (250K): STAR Scitech contract for 2 persons of 100% from Jul09 to Jun10 • FY10 • (260K): STAR Scitech contract for 2 persons of 100% from Jul10 to Jun12

  10. 10. Major Accomplishments Developed and tested a new transmittance model (ODPS) and implemented into CRTM to improve GOES-R ABI simulations (variable trace gases, improved H2O/O3 absorptions) Improved the IR land emissivity data base for CRTM and other applications Implemented the new cross-calibration from WMO GSICS to GOES-12 imager channel 6 data, resulting smaller O-A and O-B Revised and coded several new GSI subroutines for ingesting MSG SEVIRI ASR/CSR data into GSI system. Prepared the BUFR data of GOES and MSG data for NCEP operations 10

  11. Fast and Accurate Gaseous Absorption ModelOptical Depth in Pressure Coordinate (ODPS) • We have developed a new transmittance model ODPS (Optical Depth in Pressure Space), and its tangent linear and adjoint components. By using the ODPS model, the new CRTM is more accurate and more efficient. • The new ODPS model can take up to six user input variable absorbers (H2O, CO2, O3, N2O, CO and CH4), while the Compact-OPTRAN model in current operational CRTM only allows two variable absorbers (H2O and O3). • The ODPS will still use Compact-OPTRAN concept to treat water line transmittance and keep the water vapor Jacobian smoothness. • The ODPS also considers the Earth curvature effect by adding altitude dependence to the zenith angle profiles.

  12. CRTM FWD/KM Model Speed Test

  13. New IR Land Surface Emissivity for CRTM • Emissivity spectrum for each of the 13 GFS land surface types • Spectrum based on material reflectances from JPL Spectral Library (grass, needle tree, soils, etc) Example: Savanna (broadleaf tree with groundcover) Emis = 1 – [(0.1*GrassRefl)+(0.1*DecidTreeRefl)+(0.8*DryGrassRefl)] • Seasonal dependency by adding Green Vegetation Fraction (L. Jiang, 2008) Emis = ( GVF * VegEmis) + ( (1 – GVF) * SoilEmis ) • Verified on independent days across seasons • Will be included in CRTM version 2.0 Weights determined by bias minimization between CRTM and GOES obs

  14. Improvement when using new IR emissivity in CRTM New emissivity shows improved bias across seasons GOES 3.9 um GOES 10.7 um

  15. CRTM – GOES Difference (Tb)July 21, 2008, Nighttime, 3.9 um CRTM run with current emissivity CRTM run with new emissivity smaller bias when using new emissivity

  16. GSI Assimilation Test of GOES-12 Imager GSI analysis results: O-A, O-B Ch-2 O-A Ch-3 O-A Ch-4 O-A • Currently GOES-11/12 Imager data is not assimilated in the GSI operational mode, because of large obs/simulation bias and low impacts. • In order to ingest MSG SEVIRI and future GOES-R ABI data into GSI system, we first turn on and test GOES-12 Ch2/3/4 bands in GSI analysis. • GOES-12 IMGR Ch-6 cannot be assimilated before of larger bias, new calibration correction is required

  17. Cross Calibration of GOES-12 Imager for GSI The GSICS studies have found that there are large biases of the GOES-12 Imager channel at13.3 µm because of contamination issue and Spectral Response Function Shift GSICS Xcalibration Algorithm is developed using AIRS/IASI as a standard Rad(c) = (Rad(o) – b ) / a, where a and b are regression coefficients from matched monthly GOES and Hyperspectral data sets Before Xcalibration bias= -2.55 K After Xcalibration bias= -0.11 K

  18. Time series of GOES-12 IMGR Ch6 Biasbefore and after correction

  19. Tests SEVIRI Clear Sky Radiance data • Hourly SEVIRI Clear sky Radiances (CSR) and All Sky Radiances (ASR) data for May 22-28, and November 22-28, 2008. The data has been converted to NCEP BUFR data format. • We have developed new GSI subroutines for reading ASR/CSR data and modified many old subroutines and parameters to ingest this new sensor into GSI system. • We are working on bias correction for the simulated radiance in GSI, and develop QC scheme for SEVIRI observations. CSR Ch-6 7.35 µm 11:45 UTC 05/22/ 2008 CSR Ch-5 6.25 µm 11:45 UTC 05/22/ 2008

  20. SEVIRI Water Vapor Channel Simulations from GFS Analysis • The CRTM simulations with GDAS input data are compared with SEVIRI Clear Sky Observations on May 22, 2008. • For Ch-5 6.25 µm WV band, the bias and RMS of brightness temperature are 0.89 K and 2.20 K, respectively. • For Ch-6 7.35 µm WV band, the bias and RMS of brightness temperature are 0.63 K and 1.64 K, respectively.

  21. 11. Plan for FY10 Complete new infrared LSE model and data sets to improve uses of GOES and MSG surface sensitive channels Study the impacts of new LSE model on GOES-R ABI radiance simulations Prepare BUFR MODIS data for NCEP data assimilation experiments Demonstrate impacts of GOES and MSG imaging data in GFS and NAM/WRF 21