Converging Remote Sensing and Data Assimilation through Data Fusion

Transcription

1 Converging Remote Sensing and Data Assimilation through Data Fusion Benjamin T. Johnson, Ph.D. NOAA/STAR/JCSDA benjamin.t.johnson@noaa.gov The Team: Sid Boukabara Kevin Garrett Eric Maddy Ling Liu Krishna Kumar Mitch Weiss

2 Motivation and Concept of the Data Fusion Tool Project Objectives Status and Examples Summary and Future Work

3 Motivation Leverage existing data assimilation tools to meet the needs of the remote sensing community Use all observations (conventional, satellite) Valid globally (all-weather, all surfaces) High spatial/temporal resolution Comprehensive suite of traditional products Added value QC and geophysical products (beyond traditional retrieval products) Consistent error characteristics Consistent dissemination/latency

4 Concept: Use of Data Assimilation for Data Fusion Traditional Data Fusion Unique algorithms for each instrument, product. Refresh is sporadic in space and time. May not be physically consistent with all observations (error characteristics depend on individual algorithms). Observation Remote Sens. Algorithms A 1 A 2 A 3 A 4 A n Radiative Transfer Model *No guarantee that that the parameters fused at the geophysical level fit the radiometric (or other) observations Products E 1 E 2 E 3 E 4 E n Data Fusion

5 Concept: Use of Data Assimilation for Data Fusion Use of Global Data Assimilation for Data Fusion Unified set of products from a single algorithm (3DVAR). Global output at specified frequency (well known data age). Data fusion done in observation space: By design are physically consistent with all observations. (well known error characteristics). Observation Background Adjustment (Remote Sens. Algorithms) Data Fusion Data Assimilation for Data Fusion Products E 1 E 2 E 3 E 4 E n *The geophysical parameters fit the radiometric (or other) observations by design of the variational assimilation system. = Radiative Transfer Model

6 1DVAR Observed - ECMWF Analysis (TPW) Weighted Background

7 Data Fusion Concept

8 Primary Project Objectives Hourly or sub-hourly integrated observation weighted analysis Global coverage, every minutes 13 km spatial resolution Comprehensive analysis, remote sensing and derived products (products, not forecasts) Full list on next slide Value-added products Analysis to analysis hourly Trends (SfcT, Ps, TPW, etc) Forecast like: Height, Vorticity, Stability Indices, etc Quality control information Convergence Data age AWIPS compatible formats (e.g. netcdf, nagrib)

9 Expected Outcome Phase 1 2/1/2016 Phase 2 4/15/2016 Phase 3 7/1/2016 Phase 4 9/15/2016 Phase 5 12/15/2016

10 Satellite Observations Obs. Locations Data Age Data Count Figures provided by Eric Maddy and Kevin Garrett

11 Satellite Coverage for Z Cycle Develop QC metrics to inform users about data quality; better interpretation of analysis fields What is volume of observations input? What is the age of the observations used to generate analysis? How well do analysis fields fit observations?

12 Cumulative Data Age

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14 Atmospheric River Case November, 2015 Analyzed Variables Map Animations Time Visualization

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16 48 hour time series of q, t, u, and v hourly analyses at sigma level 1

17 48 hour time series of q, t, u, and v hourly analyses at sigma level 1 (conversion to pressure surfaces pending)

18 48 hour time series of q, t, u, and v hourly analyses at sigma level 1 (conversion to pressure surfaces pending)

19 48 hour time series of q, t, u, and v hourly increments at sigma level 1

20 Atmospheric River Case 3D Visualization Examples (work in progress) November, 2015 Analyzed Variables Map Animations Time Visualization

21 48 hour time series of a temperature isosurface (2D isocaps at box intersection)

22 48 hour time series of specific humidity isosurfaces (2D isocaps at box intersection)

23 3D rotation of specific humidity isosurfaces at two different times

24 3 isosurfaces of Zonal Wind, with 2D underlay, isocaps.

25 CLW is not updated in 3DVAR GSI without additional inputs. MIIDAPS, when implemented, will provide retrieved cloud/precip.

26 Simultaneous variables: will eventually include ability to visualize observations, analyzed variables, and ancillary products.

27 Validation Preliminary comparisons with ECMWF Future Plans Figures provided by Ling Liu, Eric Maddy

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30 Northern Hemisphere

31 Tropic s

32 Southern Hemisphere

33 Create online science monitoring of data fusion output

34 Collaboration with Potential Users Meeting with WPC/OPC 2/11/2016 Feedback on use of satellite products Inundation of GEO data (GOES/H8) GEO sounder data heavily utilized (sounding/stability) LEO, GEO+LEO TPW/RR products High latencies and lack of some radiometric obs (e.g. SSMIS) Collaboration established on case studies to illustrate benefits of data fusion AR event Nov 2015 Pacific NW Mid Atlantic Blizzard Jan 2016

35 Current Work Focus Visualization and Performance Baseline Performance of variables vs. ECMWF 2D maps (Global, Super CONUS) 4D cube (multiple variables x,y,z,t) Processing/assessing case studies Science improvements Observation error tuning Background error tuning All-sky radiance assimilation Technical improvements Increased spatial resolution (> T882) Increase temporal resolution (sub-hourly). Coordinating with NCEP/EMC on GSI code profiling/optimization

36 Next Steps (Phase 2 +) Observation-Weighted All-Sky Analysis (OWA) 1DVAR (MIIDAPS) Active Sensors (Radar/Scats.) Surface Properties, Aerosols, Trace Gases

37 MIIDAPS & CASM An Overview

38 MIIDAPS :: A 1DVAR Preprocessor Multi-Instrument Inversion and Data Assimilation Preprocessing System Motivation: Increase the number and types of satellite radiometric observations assimilated in NWP **CrIS** S-NPP ATMS Benefits Consistent Quality Control Characterization of surface state NOAA-18 AMSU/MHS Characterization of atmospheric NOAA-19 AMSU/MHS state Linearization of state vector elements / background adjustment TRMM TMI Megha-Tropiques SAPHIR MIIDAPS Inversion Process **IASI** MetOp-A AMSU/MHS Inversion/algorithm MetOp-B AMSU/MHS consistent across all sensors All parameters included in state vector Uses CRTM for forward and Jacobian operators Valid over all surfaces/all-sky DMSP F16 SSMI/S conditions DMSP F17 SSMI/S Use DMSP forecast, F18 SSMI/S fast regression or climatology as first guess/background GPM GMI GCOM-W1 AMSR2 *MIIDAPS extended to the hyperspectral Infrared for IR only or IR+MW 1DVAR analysis 38

39 Community Active Sensor Module (CASM) A modification of CRTM to include the ability to forward model active radar in clouds and precipitation

40 Same as previous, but with Attenuation Corrected Z at Ku band. Ku Ku

41 Zc (Attenuation Corrected Z) at Ka band. Ka Ka

42 Acronym Contest NOAA/JCSDA/NCEP loves Acronyms, and we don t have one yet for this project. Submit your recommendation to me (verbally or via ). Grand Prize: A beer.