A Variational Approach to NWP Preprocessing and Quality Control K. Garrett1, S.-A. Boukabara2, Q. Liu3 18th International TOVS Study Conference March 23, 2012 Toulouse, France 1. Riverside Technology, Inc 2. Joint Center for Satellite Data Assimilation 3. University of Maryland, College Park Motivation Motivation Assimilation of satellite data is fundamental to forecast initialization/accuracy Best representation of analysis fields requires: Knowledge of how the guess, conventional and satellite data can best be utilized The types of scenes (sfc/atm) which have the ability of being assimilated and ensuring unwanted observations are filtered out ? Satellite radiance observations contain information to 6-hr help Forecast characterize the GDAS Analysis GFS surface and atmosphere before assimilation An assimilation radiance preprocessor can be utilized in this context GDAS Analysis 23 GHz Emissivity and Tskin 2 Contents Outline 1 The 1DVAR Algorithm 2 Preprocessor – QC/Geophysical Fields 3 Radiance Assimilation Impact 4 Summary 3 Overview of Overview the 1DVAR Algorithm Physical algorithm (1DVAR) for microwave sensors (MiRS) MiRS applies to imagers, sounders, combination Cost to extend to new sensors greatly reduced MiRS uses the CRTM as forward operator (leverage) Applicable on all surfaces and in all-weather conditions Operational for N18, N19, Metop-A and F16/F18 SSMI/S On-going / Future: Extension operations to Metop-B, NPP/ATMS and Megha-Tropiques (MADRAS and SAPHIR) Get ready for the JPSS and GPM sensors. Extend MiRS to Infrared Remote Sensing (CRTM is already valid) 4 1D-Variational Retrieval/Assimilation The 1DVAR Algorithm Measured Radiances Vertical Integration and Post-Processing MiRS Algorithm Temp. ProfileProfile Temp. TPW Yes Solution RWP Simulated Radiances Contribution Function Within Noise Level ? Humidity Profile Reached IWP Vertical -1 -1 -1 T T G = (K Se K + S CLW Integration a ) K Se No Liq.Humidity Amount Prof Profile Jacobians 1DVAR Outputs Initial State Vector Comparison: Fit Ice. Amount Prof Update State Vector -Sea Ice Concentration Measurement -Snow Water Equivalent Averaging Kernel Rain Amount Prof & RTM -Snow Pack Properties Uncertainty -Land Moisture/Wetness Post ForwardEmissivity Operator Spectrum New State Vector -Rain Rate Matrix E Processing (CRTM) -Snow Fall Rate (Algorithms) Emissivity Spectrum -Wind Speed/Vector Skin Temperature -Cloud Top -Cloud Thickness Geophysical -Cloud phase Core Products A = (GK) Mean Background Climatology (Retrieval Mode) Geophysical Covariance Matrix B 5 Contents 1 The 1DVAR Algorithm 2 Preprocessor – QC/Geophysical Fields 3 Radiance Assimilation Impact 4 Summary 6 Preprocessor QC Convergence is reached everywhere: all surfaces, all weather conditions including precipitating, icy conditions A radiometric solution (whole state vector) is found even when precip/ice present. With CRTM physical constraints. T −1 m m 2 ϕ = Y − Y (X ) ×E × Y − Y (X ) Previous version (1 attempt) Current version (2 attempts) (assume non-scattering atmosphere) (assume scattering from precip) 7 Geophysical Fields Preprocessor-CLW MiRS Cloud Liquid Water MiRS - GFS GFS 8 Preprocessor-Emissivity MiRS N18 retrieved emissivity at 31 GHz ascending node Evolution of emiss before, during and after rain 2010-10-20 2010-10-21 2010-10-22 CPC real-time 24-hour precipitation CPC Figures courtesy http://www.cpc.necp.noaa.gov 9 Contents 1 The 1DVAR Algorithm 2 Preprocessor – QC/Geophysical Fields 3 Radiance Assimilation Impact 4 Summary 10 Radiance Assimilation Impact Assimilation Impact Comparison of O-B using MiRS retrieved CLW versus NWP CLW for cloud screening ~6000 pts after filtering out cloudy scenes 1 day Ocean only 11 Assimilation Impact (O-B) AMSU 23 GHz AMSU 50 GHz AMSU 31 GHz AMSU 89 GHz 12 Assimilation Impact (O-B) MHS 89 GHz MHS 183 ±1 GHz MHS 157 GHz AMSU 183 ±3 GHz 13 Contents 1 The 1DVAR Algorithm 2 Preprocessor – QC/Geophysical Fields 4 Impact on Radiance Assimilation 5 Summary 14 Summary MiRS is a generic retrieval/assimilation system (N18, N19, Metop-A, DMSP F16/18 SSMIS) In retrieval mode, MiRS can be used as an NWP (assimilation) preprocessor MiRS LWP has shown to produce reasonable O-B (improvement over using guess fields) Future work will include investigating the use of other QC metrics and retrieved fields to filter/parameterize DA For more detailed information about the MiRS project, visit: mirs.nesdis.noaa.gov (more validation data, publication list and software package) 15 BACKUP SLIDES 16 And… ATMS Impact Experiment GDAS (Hybrid ENKF) high resolution ATMS experiment Acknowledgements: A. Collard J. Jung E. M. Devaliere 17 Mathematical Basis: The 1DVAR Algorithm Cost Function Minimization Cost Function to Minimize: 1 1Jacobians T −1 m & TRadiance J( X) = (X − X 0 ) × B × (X − X 0 ) + (Y − Y( X)) × E −1 × (Y mSimulation − Y( X)) from Forward Operator: CRTM 2 2 ∂J( X) = J(' X) = 0 To find the optimal solution, solve for: ∂X y( x) = y( x )+ K x − x Assuming Linearity 0 0 This leads to iterative solution: −1 Δ = B−1+ KnTE−1Kn KnTE−1 Ym − Y( X n ) + KΔ nX n X n+1 Δ X More efficient (1 inversion) −1 T K BK T + E Ym − Y( = BK n n n n+1 X n ) + KΔ n X n Preferred when nChan << nParams (MW) 18