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Dr. Sid-Ahmed Boukabara Contributions from:

A Physical Approach for a Simultaneous Retrieval of Sounding, Surface, Hydrometeor and Cryospheric Parameters from SNPP/JPSS ATMS. Dr. Sid-Ahmed Boukabara Contributions from: K. Garrett, C. Grassotti , F.Iturbide -Sanchez, W. Chen, Z. Jiang,

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Dr. Sid-Ahmed Boukabara Contributions from:

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  1. A Physical Approach for a Simultaneous Retrieval of Sounding, Surface, Hydrometeor and Cryospheric Parameters from SNPP/JPSS ATMS • Dr. Sid-Ahmed Boukabara • Contributions from: • K. Garrett, C. Grassotti, F.Iturbide-Sanchez, W. Chen, Z. Jiang, • S. A. Clough, X. Zhan, F. Weng, P. Liang, Q. Liu, T. Islam, V. Zubko, and A. Mims 1

  2. Products, Performance and Validation Overview and Description of the Physical Algorithm 2 1 3 Algorithm Future Enhancements

  3. Products, Performance and Validation Overview and Description of the Physical Algorithm 2 1 3 Algorithm Future Enhancements

  4. MiRS OverviewMicrowave Integrated Retrieval System Megha-Tropiques SAPHIR/MADRAS S-NPP ATMS • Inversion Process • Consistent algorithm across all sensors • Uses CRTM for forward and jacobian operators • Use forecast, fast regression or climatology as first guess/background • Assimilation/Retrieval • All parameters retrieved simultaneously • Valid globally over all surface types • Valid in all weather conditions • Retrieved parameters depend on information content from sensor frequencies MetOp-A AMSU/MHS MetOp-B AMSU/MHS MiRS NOAA-18 AMSU/MHS NOAA-19 AMSU/MHS DMSP F16 SSMI/S DMSP F17 SSMI/S DMSP F18 SSMI/S TRMM TMI GPM GMI GCOM-W1 AMSR2

  5. MiRS General Overview Radiances Rapid Algorithms (Regression) Advanced Retrieval (1DVAR) Vertical Integration & Post-processing 1st Guess selection MIRS Products

  6. 1D-Variational Retrieval/Assimilation Comparison: Fit Within Noise Level ? Yes Simulated Radiances Measurement & RTM Uncertainty Matrix E Update State Vector Forward Operator (CRTM) New State Vector Geophysical Mean Background Geophysical Covariance Matrix B Measured Radiances MiRS Algorithm T<p> Q<p> CLW<p> RWP<p> GWP<p> Tskin Emiss Solution Reached No Jacobians Initial State Vector Forecast Field (1D-Assimilation Mode) Climatology (Retrieval Mode)

  7. Products, Performance and Validation Overview and Description of the Physical Algorithm 2 1 3 Algorithm Future Enhancements

  8. MiRS Products Overview Radiances Vertical Integration and Post-Processing Rapid Algorithms (Regression) Advanced Retrieval (1DVAR) Vertical Integration & Post-processing TPW RWP IWP CLW 1st Guess Vertical Integration Temp. Profile 1DVAR Outputs Humidity Profile -Sea Ice Concentration -Snow Water Equivalent -Snow Pack Properties -Land Moisture/Wetness -Rain Rate -Snow Fall Rate -Wind Speed/Vector -Cloud Top -Cloud Thickness -Cloud phase Liq. Amount Prof selection Post Processing (Algorithms) Ice. Amount Prof MIRS Products Rain Amount Prof Emissivity Spectrum Core Products Skin Temperature Official Products Products being investigated • Cloud Profile • Rain Profile • Atmospheric Ice Profile • Snow Temperature (skin) • Sea Surface Temperature • Effective Snow grain size • Multi-Year (MY) Type SIC • First-Year (FY) Type SIC • Wind Speed • Soil Wetness Index • Temperature profile • Moisture profile • TPW (global coverage) • Surface Temperature • Emissivity Spectrum • Surface Type • Snow Water Equivalent (SWE) • Snow Cover Extent (SCE) • Sea Ice Concentration (SIC) • Cloud Liquid Water (CLW) • Ice Water Path (IWP) • Rain Water Path (RWP) • Rainfall rate

  9. MiRS Temperature Sounding Assessment - NWP 500 hPa Maps Clear Sky Temperature Bias/Stdv Precipitating Sky Temperature Bias/Stdv

  10. MiRS Water Vapor Sounding Assessment - NWP 500 hPa Maps Clear Sky Water Vapor Bias/Stdv Precipitating Sky Water Vapor Bias/Stdv

  11. MiRS Sounding Intercomparisons - RAOB Collocations July – September 2013 (~25,000 pts)

  12. Application:Hurricane Rapid Intensification • MiRS/ATMS T,RH profiles used to compute (case of Hurricane Leslie, 2012): • Radial-height cross section • Temperature Anomaly • 500-800mb averaged values These are fed to : - Maximum Potential Intensity (MPI) algor. MPI is then fed to : - Rapid Intensification Index (RII) algor. Slide courtesy of Galina Chirokova and Mark DeMaria

  13. MiRS Total Precipitable Water Assessment – NWP Summary of TPW Performance Comparison vs. ECMWF Comparison vs. Radiosonde

  14. Application: Blended TPW The blended TPW is available through AWIPS sectorsthat NWS forecasters use MiRS NPP ATMS data to be implemented once it becomes available from NESDIS OSPO MiRS from NOAA-18, -19, Metop-A are included in the blended TPW. Extension in progress (for more sensors, over sea-ice, snow, etc) Slide courtesy of the OSPO website: Effort led by CIRA: S. Kidder, J. Forsyth, L. Zhao and R. Ferraro

  15. MiRS Rainfall Rate Assessment – NCEP Stage IV Hourly Right: Rainfall rate skill scores between MiRS sensors (including ATMS) between December 2009 and January 2013 using NCEP Stage IV as reference. Bottom:Timeseries of MiRS ATMS Rainfall Rate over Hurricane Sandy. Bias Correlation POD False Alarm Ratio Maps of MiRS ATMS rainfall rate for January 30, 2013 (a) compared to NCEP Stage IV (b) Heidke Skill Score RMS 0.0 2.0 4.0 6.0 8.0 10.0

  16. MiRS Land Surface Temperature Assessment – Surfrad Simulated Performance Summary of LST Performance

  17. MiRS Surface Emissivity Assessment – Analytic Summary of Emissivity Performance

  18. MiRS Sea-ice Concentation Assessment – NASA Team Stdv Bias Right – Time series of MiRS SIC performance (NH) for various sensors (ATMS in Orange) Bottom – Monthly SIC from MiRS ATMS for 2013 Correlation Heidke Skill Score Maps of MiRS ATMS sea ice concentration for February 5, 2013 (left) compared to NASA Team algorithm (right) SENSOR SENSOR

  19. Products, Performance and Validation Overview and Description of the Physical Algorithm 2 1 3 Algorithm Future Enhancements

  20. Algorithm Future Enhancements • Stratification of background and covariances • Geographic • Seasonal • Diurnal • Upgrade to CRTM 2.1.3 • Improvements to hydrometeor retrievals • Extension to other sensors • AMSR2, GMI (research) • SAPHIR (operational) • Infrared (IASI, CrIS) • Implementation within the NCEP Global Forecast System (GFS) • 1DVAR Preprocessor to the GSI for PMW and Infrared radiances • Focus on increasing assimilation of surface sensitivity and all-sky observations http://mirs.nesdis.noaa.gov

  21. BACKUP

  22. Microwave Retrieval Historical Background & Context • Background: • NOAA/NESDIS/STAR has developed a flexible physical algorithm: the Microwave Integrated Retrieval System (MiRS) • Cost to extend to new sensors greatly reduced (avoids stove-piping) • MiRS can be applied to sounders, imagers and combinations • MiRS uses the CRTM as forward operator (leverage) • Applicable on all surfaces and in all-weather conditions • 1D-variational methodology (similar to NWP data assimilation) • Operational for N18,19,Metop-A,B, F16/F18 SSMI/S and SNPP/ATMS • Products from MiRS depend on Sensor: Atmospheric Sounding, Hydrological, Cryospheric, Oceanic and Land parameters • On-going: • Extension operations to Megha-Tropiques (SAPHIR) • Research extension to TRMM, GPM and GCOM-W AMSR-2. • Future: • Extend to FY-3 MWTS, MWHS and MWRI imager • Extend applications of MiRS (hydrometeors profiling): Active sensors. • Extend MiRS to Infrared Remote Sensing

  23. Mathematical Basis:Cost Function Minimization Jacobians & Radiance Simulation from Forward Operator: CRTM • Cost Function to Minimize: • To find the optimal solution, solve for: • Assuming Linearity • This leads to iterative solution:

  24. MiRS Rainfall Rate Assessment – CPC Daily Bias Correlation POD False Alarm Ratio Daily precipitation skill scores between MiRS sensors (including ATMS) between July 2009 and December 2012 using CPC as reference (right) Maps of MiRS ATMS daily rainfall for April 18, 2013 (a) compared to CPC (b) RMS Heidke Skill Score

  25. Note: All Parameters are Retrieved Simultaneously in MiRS Necessary Condition (but not sufficient) F(X) Fits Ym within Noise levels If F(X) Does not Fit Ym within Noise X is not the solution X is a solution X is the solution All parameters are retrieved simultaneously to fit all radiances together Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator

  26. All-Weather and All-Surfaces • Instead of guessing and then removing the impact of cloud and rain and ice on TBs (very hard), MiRS approach is to account for cloud, rain and ice within its state vector. • It is highly non-linear way of using cloud/rain/ice-impacted radiances. sensor • Sounding Retrieval: • Temperature • Moisture • Major Parameters for RT: • Sensing Frequency • Absorption and scattering properties of material • Geometry of material/wavelength interaction • Vertical Distribution • Temperature of absorbing layers • Pressure at which wavelength/absorber interaction occurs • Amount of absorbent(s) • Shape, diameter, phase, mixture of scatterers. • To account for cloud, rain, ice, we add the following in the state vector: • Cloud (non-precipitating) • Liquid Precipitation • Frozen precipitation Cloud-originating Radiance • To handle surface-sensitive channels, we add the following in the state vector: • Skin temperature • Surface emissivity (proxy parameter for all surface parameters) Upwelling Radiance Surface-reflected Radiance Absorption Downwelling Radiance Scattering Effect Surface-originating Radiance Scattering Effect Surface

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