Physical Inversion and Data Assimilation of Cloud and Rain-Affected Passive Microwave Observations

Transcription

1 International Precipitation Working Group (IPWG) Physical Inversion and Data Assimilation of Cloud and Rain-Affected Passive Microwave Observations Sid Ahmed Boukabara Senior Data Assimilation Scientist, NESDIS/STAR Deputy Director, Joint Center for Satellite Data Assimilation (JCSDA) Contributions from: K. Garrett, E. Jones, Eric Maddy, M. Chattopadhyay 1

2 Contents 1 Introduction & Concept 2 Approach Handling Cloudy/Rainy Satellite Data 3 Highlights & Applications 4 Summary & Conclusion 2

3 In 2007, MiRS Algorithm: 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 Goal Goal is to develop a physical retrieval that applies to all microwave sensors (conical, x-track, sounders, imagers), and be easy to extend to new ones. Operational for 8 satellites In 2014, a New generation System is being put together Goal is to build on MiRS heritage and develop a system with a dual use: Retrieval and Data Assimilation Pre-processing. Goal is also to generalize it to all sensors including IR and Hyperspectral IR Goal is to cross-fertilize DA and retrieval systems 3

4 Retrieval / Data Assimilation Variational approach to invert microwave sensors data in both retrieval and data assimilation: same principle In rainy and cloudy conditions many phenomena happen simultaneously and impact measurements: Scattering and absorption by the atmosphere and hydrometeors Surface properties (Emissivity and temperature) changing Over ocean, roughness of surfaces is impacted (changing emiss.) Goal is to extract products in cloudy/rainy conditions for the retrieval case Goal is to establish accurate initial conditions in data assimilation, to be fed to forecasts systems Necessary to adopt: Coupled approach (simultaneous inversion of all parameters) Maximize information content from IR, MW, Sounders, Imagers 4

5 Variational Retrieval/Assimilation Measured Radiances Initial State Vector Simulated Radiances Jacobians Forward Operator (CRTM) Comparison: Fit Within Noise Level? No Update State Vector New State Vector Yes Solution Reached Measurement & RTM Uncertainty Matrix E Climatology Forecast Field (Retrieval (1D-Assimilation Mode) Mode) Geophysical Mean Background Geophysical Covariance Matrix B 5

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 Cost Function to Minimize (similar between retrieval& assimilation): Ø = Œ º Variational Mathematical Basis Œ º - ø Ø - ø ( - ) ( - ) + ( - ) ( - ) œß To find the optimal solution, solve for: = = œ ß Jacobians & Radiance Simulation from Forward Operator: CRTM Assuming Linearity = + - Same Methodology applied to all parameters including hydrometeors Ø Œ º Œ ø œ œß This leads to iterative solution: = Ø + Ł ł Œº Ł ł ø œß 6

7 Non-Linearity Issue in Cloudy/Rainy TBs (TB Variation as a Fct of hydrometeors) v v Forward model assumed locally linear at each iteration. Nothing, in theory, prevents us from including hydrometeors in the state vector, along with T, Q, Emissivity, Tskin -TB variation vs. hydrometeors is non-linear but is locally linear, therefore compatible with variational inversion

8 Non-Linearity Issue in Cloudy/Rainy TBs (TB Variation as a Fct of X-Y) Cross section at 28 N follows red curve for 190 GHz. TB variation in time and space is highly non-linear and is discontinuous due to noncontinuity variations of hydrometeors in space, which is incompatible with variational assimilation 8

9 NATI O NAL O C EAN I C AN D ATM OS PH E R I C AD MI N I ST RATI O N Non-Linearity Issue in Cloudy/Rainy TBs (TB Variation as a Fct of Time) TB variation in time and space is highly non-linear and is discontinuous due to non-continuity variations of hydrometeors in time, which is incompatible with variational assimilation

10 Methodology Adopted Background (from Fcst) Cloud and Rain Impacted Radiances (MW, IR) Current Operational Data Assimilation (3D or 4D VAR) MiiDAPS is an extension of the MiRS algorithm (operationally implemented) with: -Extension to IR sensors -Reconfigured to be used as pre-processor to data assimilation systems - Used for QC, rain, cloud, t, q, dyn. emiss, Tskin, ice, inversion Background (from Fcst) Cloud and Rain Impacted Radiances (MW, IR) Suggested MiiDAPS 1DVAR Preprocessing (pt by pt. Local linearity assumption valid) GSI (or other) GSI Data Assimilation (3D or 4D VAR) All points with cloud- or rain- impact have been pre-processed 10

11 So How does MIIDAPS Work? Same mature science used in MiRS algorithm (already operational since 07 for cross-track and conical MW) Extension made to IR sensors (CrIS, AIRS, IASI). Plans to extend to Active sensors Coupled approach (emissivity part of the state vector) therefore works over all surfaces (ocean, land) Uses CRTM as forward model (and therefore works whenever CRTM works) for a large number (all) sensors. Allows maximum information content extraction: simultaneous inversion of all parameters Outputs used as products or as pre-processing information for satellite data 11

12 Applicability of MIIDAPS Sensors- All MiRS functions are retained in MiiDAPS DMSP SSMIS F16/17/F18 AQUA GCOM-W AMSR-E, 2 Metop- A, B AMSU/MHS POES N18/N19 Dual use: retrieval and DA pre-processing All Sensors handled in CRTM (x-track, conical) The same executable, forward operator, covariance matrix used for all sensors Modular design : Applied Operationally (8) : Research Mode or Routine processing (8) NPP/JPSS ATMS Megha- Tropiques SAPHIR/MADRAS TRMM/GPM TMI, GMI IR sensors CriS, IASI, AIRS,..

13 Benefits of 1D+DA Approach Takes high-non-linearity outside of the DA Provide T, Q, TPW, in rainy/cloudy conditions for assimilation Provide a dynamic emissivity as boundary condition Universal tool for all satellite data for which CRTM is valid Excellent QC tool for cloud detection, rain detection, RFI, Adjust background displacement for storms, hurricanes, etc. Consistency of parameters is ensured through DA analysis 13

14 Importance of Covariance Matrix New Atmospheric Background Covariance Matrix based on ECMWF 60, and WRF simulations over tropic oceans performed during SON season Off-diagonal elements exist to constrain T, Q, Cloud, Rain and Ice variations within the minimization process The inverson/assimilation is based on exclusive use of radiometric information content (no cloud resolving systems imbedded): Therefore heavily relying on: -CRTM TBs accuracy -CRTM Jacobians Accuracy -Background Cov Matrx -Measurements correction -Good estimation of Obs errors Temperature and Water Vapor from ECMWF 60 Cloud liquid, Rain and Ice water from WRF

15 Highlights: TPW in Exclusively Rainy Conditions (ATMS) Performances of TPW in exclusively Rainy conditions over ocean surfaces present a good correlation, low bias and stdev. Angle dependence of MiRS TPW Perfs in Rainy Sky - Ocean Surfaces Positive/Negative contrasts in TPW differences indicate a potential spatial displacements of TPW features in NWP analyses. Not identical between GDAS and ECMWF Differences MiRS - GDAS No significant scan dependence in the TPW differences with NWP analyses MiRS TPW Perfs in Rainy Sky Ocean Surfaces vs GDAS

16 MiRS RR part of IPWG Intercomparison (N. America, S. America, Europe, S. Africa, Japan and Australia sites) No discontinuity at coasts (MiRS applies to both land and ocean) This is an independent assessment where comparisons of MiRS RR composites are made against radar and gauges data. Images taken from IPWG web site: credit to Daniel Villa & John Janowiak 16

17 Precipitation Climatology & Vertical Structure Rainfall Climatologies Hydrometeor Profiles/Vertical Cross Sections MiRS Monthly Averaged MiRS NOAA-18 Rainfall Rate for 2009 MiRS Hydrometeor Profiles Heritage Monthly Averaged MSPPS NOAA-18 Rainfall Rate for TRMM 2A12 Hydrometeor Profiles

18 Independent Validation (IPWG) Caution: algorithms perfs depend on how many sensors are used Monitor a running time series of statistics relative to rain gauges Intercomparison with other PE algorithms and radar MiRS composite uses Metop-A, N18, F16 Tightening of RTM uncertainty in June 2011 improves POD & Heidke HEIDKE SCORE RMSE POD

19 Preliminary Results Assimilating GMI Experiment details: GMI L1C-R data used. All channels 100 km thinning, ocean only. QC performed with legacy QC routines. Observation error determined by preliminary observation analysis: Experiment with GMI Channel Error Current work: Optimization of QC routines, extension of MIIDAPS to GMI. Finalization of observation assessment, and optimization of observation error and bias. Extend to cloudy/rainy data Control no GMI

20 Example of 10-Day Forecast (at day 6 of storm) 20

21 GFS 6hr Obs Displacement MIIDAPS GFS GFS-MIIDAPS MIIDAPS GFS GFS-MIIDAPS Top: SSMI/S 1DVAR LWP field, GFS 6hr LWP field, and difference (displacement) field over Hurricanes Iselle and Julio Bottom: November Same 2014 as top but for 200mb Temperature. IPWG, Shown Tsukuba, is large Japan displacement/magnitude for warm core of Hurricane Julio.

22 Summary & Conclusion 1D+DA Approach to handle cloud-, ice- and rain- impacted measurements, due to non-linearity Applicability to all sensors (MW, IR) as long as they are handled by CRTM. This applied to sounders and imagers and to x-track and conical sensors MiiDAPS is a dual-use generic retrieval/assimilation system Handling cloud/rainy radiances in MiiDAPS by varying hydrometeors in state vector. Handling surface-sensitive channels by varying emissivity Products Assessment suggests approach provides reasonable results (Compared to sondes, Radar, gauges, other algorithms) Adjustment of background fields features is one major benefit 22

23 BACKUP SLIDES BACKUP 23

24 A few Trends Foreseen (Motivating the Approach) - Scientifically, if it can retrieved, it can be analyzed. - Major Efforts ongoing to extend assimilation to all sensors - Coupled data assimilation is becoming a major focus: - Increased resolutions: assimilation for situational awareness -Data assimilation is a tool to perform data fusion or blending - Applies: MW, IR, Geo, Leo, x-track, conical, sounders, imagers - Cross-sensors intercalibration happens naturally inside the DA: - Capability to climate applications (re-analysis) - Data assimilation is becoming entry point for satellite data - Integration of technology, not necessarily retrieval assimilation 24

25 Merged SA and NG Product Generation (plans) SDRs (Polar) Metop, N19, NPP, DMSP IR, MW SDRs (Geo) GOES, GOES-R, MSG, Ground- Baaed Data Radar Conventional Data Airborne Data GPS Data SA Mode (In NESDIS): - Data Fusion of all sensors, -Every hour SA 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) Environment Analysis Geophysical products (Data Fusion) NG NG Mode (In NWS): - Closely tied to Forecast Model, - Every 6 hours AWIPS Forecaster 25

26 Proper handling of Surface signal MiRS N18 retrieved emissivity at 31 GHz ascending node Evolution of emiss before, during and after rain -Most channels sensitive to surface rainfall are also sensitive to surface. -The emissivity varies greatly and at short temporal scales when precipitation occurs Signal in TB is therefore a mixture of rain and emissivity signals (depending 37.0 V channel on the intensity of the precip) Es 19.35V channel -TB varies on a footprint by footprint basis, whose size varies CPC real-time 24-hour precipitation This suggests: Day in October (1) Not using fixed atlases for emissivity or physical models CPC Figures courtesy (2) Dynamically vary the emissivity along with rain, ice,cld

27 Surface Pressure (Hurricane Intensity) Inverted Sfc Pressure ECMWF Sfc Pressure Retrieved Surface Pressure vs. ECMWF for over Hurricane Sandy. Algorithm training and retrieval using simulated data. Surface Pressure Information could help in determining the intensity of storms and Hurricanes (along with warm core) 27

28 Importance of Simultaneous Retrieval If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator Necessary Condition (but not sufficient) If F(X) Does not Fit Y m within Noise F(X) Fits Y m within Noise levels X is not the solution X is a solution X is the solution All parameters are retrieved simultaneously to fit all radiances together (in EOF space: = Suggests it is not recommended to use independent algorithms for different parameters, since they don t guarantee the fit to the radiances 28

29 Example: MiRS Rainfall Rate from TMI data Comparison to official TMI/GPROF 2A12 TRMM-2A12 Rainfall Rate (GPROF) MiRS TRMM/TMI RR

30 MiRS/ATMS T,RH profiles used to compute (case of Hurricane Leslie, 2012): -Radial-height cross section - Temperature Anomaly mb averaged values 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 Hurricane Rapid Intensification 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

31 RI Forecast: GFS vs MiRS/ATMS Inputs The bias of RI index (between obs. and RII algorithm output) is 1.67 when MiRS/ATMS data is used as inputs and 1.87 when GFS I sused. Preliminary results for the RII forecast show up to 3.1% increase in Brier Skill Score with the use of MiRS/ATMS data, and for the center-fix algorithm up to 10% better center location as compared to the first guess position from the NHC realtime forecast positions. Slide courtesy of Galina Chirokova and Mark DeMaria

32 Data Assimilation Applications (MIIDAPS) Efforts are on going to: Use 1DVAR as a preprocessor to NWP for quality control purposes (QC of satellite data, rain and ice detection, coast contamination, RFI for imagers, etc) Implement dynamicallyretrieved emissivity in the NWP first guess/background (allow assimilation of surface sensitive channels) Assess assimilating sounding products in cloudy/rainy conditions 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) 32

33 A few Trends Foreseen (Motivating the Approach) - Satellite Measurements by nature are sensitive to all sorts of parameters (products). Scientifically, if it can retrieved, it can be analyzed. - Major Efforts ongoing to extend/improve assimilation to all sensors (active passive, RO, IR/MW, Lightning, etc), all situations (cloudy, rainy, ice-covered,..) - Coupled data assimilation is becoming a major focus: will lead to using DA analysis beyond NWP (to ocean, land, cryosphere, hydrometeors, etc) - Increased spatial and temporal resolutions will lead to usage of assimilation for situational awareness purposes (nowcasting and short term forecasting) - Data assimilation presents a natural tool to perform data fusion or blending of sort (MW and IR, Geo and Leo, x-track, conical, sounders, imagers) - Cross-sensors intercalibration happens naturally inside the DA: A unique analysis is produced out of the hundreds of measurements types. - This lesser sensitivity to calibration errors is naturally extending the capability to climate applications (re-analysis) Data assimilation is becoming the entry point for the usage of satellite data for diverse types of users (NWP, Short-term/nowcasting, climate, etc) - Integration of technology, not necessarily retrieval assimilation 33

34 Satellite Data is therefore able to invert/analyze: -Atmosphere (Temperature, moisture, aerosols, ) -Surface (ice, snow, land, ocean) -Hydrometeors (cloud, rain, suspended ice) All-Weather Radiative Transfer sensor Absorption Scattering Effect Scattering Effect Surface 34

35 MIIDAPS MIIDAPS Applies by design to sensors for which we have a forward operator (FO) X: State vector of Geophysical Parameters (T, Q, Tskin, etc) Forward Operator Y: State vector of Radiometric Measurements (MW, IR, etc) K: Jacobians dy/dx X (analysis) MIIDAPS Y + K MIIDAPS works for MW (imagers and sounders) and IR sensors. Work planned to extend to active FO