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Enhancing Weather Forecasting with Satellite Data Assimilation Research Activities in Atmospheric Science

Explore the latest research on utilizing satellite data assimilation for improved weather forecasting, including GPS data assimilation, cloud radiance assimilation, sea-ice data systems, and more. Learn about updates to the data assimilation system and its impact on precipitation forecasts.

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Enhancing Weather Forecasting with Satellite Data Assimilation Research Activities in Atmospheric Science

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  1. Research Activities in Satellite Data Assimilation at EC (Atmospheric Science & Technology) APSDEU 2008 Montreal Dr. Godelieve Deblonde Acting Manager, Data Assimilation and Satellite Meteorology Section Meteorological Research Division Atmospheric Science & Technology Directorate Environment Canada 10-12 October 2008

  2. Outline • Current global forecast model & updates to DAS (Data Assimilation System) • Future global forecast model & updates to DAS • GPS data assimilation (radio-occultation & ground-based) • Cloudy radiance assimilation • Sea-Ice data assimilation system • CaLDAS (SMOS/SMAP) • Chemical Data Assimilation • Canadian Space Agency Related Activities

  3. Current Global Forecast Model (Oct 2006) • GEM-meso -Main Features: • Horizontal resolution of 35 km, 58 vertical levels, model lid at 10 hPa (~ 30 km). • Parameterization of clouds and precipitation • Shallow convection with Kuo Transient • Deep convection with Kain-Fritsch (vs Kuo) • Modified Sundqvist scheme for large-scale condensation • Bougeault-Lacarrère for the turbulent mixing length (vs Blackadar) • Constant thermodynamic roughness length over water in the Tropics (vs Charnock everywhere) • ISBA land surface scheme with sequential assimilation of soil moisture (based on OI) (vs Force-Restore) • Data Assimilation: new B statistics, 58 levels (still T108), 40% more efficient 4D-Var code, new SST and snow analyses

  4. Updates to Data Assimilation System (Dec 2007) • Radiative transfer and bias correction: • RTTOV7  RTTOV8.7 • Dynamic Bias correction for radiances (15 day running mean) • New vertical interpolator (model <-> RTTOV levels) • to be included in RTTOV9 • In clear skies: • AMSU radiances on scan edges • SSM/I radiances • AIRS radiances (~ 87 channels) • QuikSCAT retrieved wind vectors (KNMI)

  5. More observation additions in 2008 • Extended parallel testing (Jan-Feb 2008): • GPS- RO: CHAMP, COSMIC (both rising and setting occultations), GRACE • 3D-Var FGAT winter period (06/07) test completed. Now being tested in 4D-Var (winter and summer cases). • On the fly (March 2008): • Metop: AMSU-A, MHS, GRAS, ASCAT • SSM/I like channels of SSMIS

  6. Future global forecast model & updates to DAS GEM-Strato (i.e. better representation of the stratosphere) • Model lid at 0.1 hPa or 60 km (now is 10 hPa or 30 km) • 80 vertical levels (now is 58) • Radiation: correlated-k (Li et Barker) • Gravity Wave Drag Schemes: orographic & non-orographic • Parameterization for methane oxidation improves humidity fields in the stratosphere • New ozone climatology reduces temperature biases in the stratosphere but increases bias in the tropical tropopause • New background error statistics (better assimilation of radiances and GPS radio-occultation) • Additional AMSU (implies more predictors for bias correction) and AIRS channels • GPS RO up to 40 km (bending angle? Instead of refractivity) • SSMIS sounding channels? • Target: Fall 2008

  7. GPS RO data assimilation: 3D-Var FGAT/GEM-meso test results • CHAMP, COSMIC, GRACE (total of 8 satellites) • Refractivity profiles versus MSL height (Level 2) • Data thinning: • No more than 1 observation/vertical km (i.e. 1 out of 5) • Vertical correlation of observation error not included • Vertical Clipping: Use data • between 4 km and 30 km • at least 1 km above background model surface • Background Error Check • -0.05< (O-F6h)/F6h<0.05 in all the profile (after clipping) • No bias correction • Observation error is proportional to Forecast value • PERIOD: 20 December 2006 - 21 January 2007

  8. GPS RO Impact on 6h forecasts: Verified against Radiosondes • TRUTH=Radiosonde Temperature • Error forecasting radiosonde temperature observations reduced on average by 5-10% after assimilating RO. • Red areas: improves with RO • Blue areas: degrades with RO • Biggest Impacts: • NH UTLS region • SH & especially over Antarctica 10 hPa 100 hPa 1000 hPa

  9. GPS RO Impact on 0-5 day forecastsAnomaly Correlation: T at 500 hPa GPS No GPS

  10. GPSRO Impact on 0-5 day forecastsAnomaly Correlation: T at 100 hPa GPS No GPS

  11. Data Assimilation of Ground-based GPS Observations in the regional analysis and forecast system (15 km) NOAA/GSD GPS Network (2004 snapshot) 270 sites thinned (∆x = 100 km) to ~180 sites for data impact study radiosonde sites • GPS observations assimilated: • 1) ZTD (zenith Tropospheric Delay) • 2) surface met (Ps, Ts, RHs) from collocated or nearby stations • Observations available every 30-minutes • Or 9 per 6h window in 3D-Var FGAT • 42 cycles (Summer 2004) • 24 cycles (Winter 04/05)

  12. Impact on Precipitation Forecasts • Verification of 24h accumulations (00-24h, 12-36h, 24-48h) with rain gauge observations: SYNO network (North America) and SHEF network (USA) • Impact of GPS observations is mixed, but more positive than negative (both summer and winter) • Tendency for greater impact for higher amount thresholds • Impact generally greater for 12−36h and 24−48h forecasts (i.e. after initial precipitation spin-up period) • Forecasts produce less overall precipitation than control (lower PW) • Greatest regional impact observed for Central US SHEF region (summer) and Gulf region (winter) • GPS surface met data enhance positive impact for some regions (e.g. Central US)

  13. Results: T.S. Gaston (Precip.) 24−48h PR accumulations 30 Aug 2004 1200Z 119 mm CTRL OBS 190 mm 199 mm GPS • Better precipitation forecast in GPS

  14. Conclusions: Ground-Based GPS • Encouraging results obtained from data impact study with ground-based GPS observations (ZTD, surface met) and the EC regional analysis and forecast system • Positive impacts on forecast humidity (PW), precipitation, geopotential height (GZ), and surface pressure PS • Impacts on PW, GZ, PS are smaller and shorter-lived for winter case (drier); still some impact on precipitation • More definite positive impact on precipitation forecasts in Central US region in summer (due to upstream GPS sites) and Gulf region in winter (higher PW) • Positive impact also noted for forecast hurricane tracks and associated precipitation but contribution of GPS surface met data not determined • For some US regions, assimilation of collocated GPS surface met (Ps, Ts, RHs) with ZTD improves 12−36h and 24−48h precipitation verification over assimilation of ZTD alone • Paper accepted in MWR (Macpherson, Deblonde, Aparicio, and Casiti).

  15. Future: Ground-Based GPS • Test GPS data assimilation in 4D-Var global analysis system and new continental LAM 4D-Var analysis system (LAM system 4D-Var in fall 2009) • Proposed Canadian GPS meteorological network (pending funding) • at least 100 GPS sites in Canada currently exist • have developed capability to produce ZTD (with GAMIT software) and PW in-house for 30 GPS sites in Canada, but lack resources to do more.

  16. Assimilation of cloudy AIRS radiances • Current status on AIRS data assimilation in clear skies: • 87 channels to be assimilated operationally in fall 2007. • RTTOV-8 RTM. • Thinning : 250 km. • About 90,000 radiances per 6h. • Dynamic bias correction. Model top 10 hPa. • Near future: Assimilation of ~125 channels planned for fall 2008 (GEM-Strato) with model top at 0.1 hPa (~ 60 km). • Next step : Assimilate cloudy radiances

  17. Number of channels used from CO2 slicing Cloud affected radiances: a severe limitation

  18. Various approaches for cloudy infrared radiance assimilation • Use of cloud cleared radiances : • Processing delay, “cloud-cleared” radiances are not true observations • Full blown 4D-Var assimilation : • Very difficult : highly non linear problem, poor quality of the first guess • Simplified approach using effective cloud top and emissivity : • Approach chosen here

  19. Simplified approach to cloudy radiance modeling and assimilation • Cloudy radiance is computed assuming a single-layer cloud defined by an effective height Pc and emissivity Ne(n) : • Cloud emissivity depends on wavelength and phase. Mixed phase considered.

  20. Cloud emissivity model: summary • A full cloudy radiance spectrum can be simulated using only 4 cloud parameters : • The cloud top pressure Pc (gives also the cloud temperature Tc) • The effective cloud water path d • The cloud effective radius re (liquid phase) • The cloud effective diameter De (ice phase)

  21. Proposed 3D/4D-Var assimilation Addition to the state vector x of a local estimate of the 4 cloud parameters at each AIRS observation location z : local cloud effective parameter vector : 4 parameters for each AIRS FOV zb : cloud background state from CO2 slicing and climatology Hc cloudy observation operator combining RTTOV 8.7 and the cloud emissivity model

  22. Proposed 3D/4D-Var assimilation Number of radiances used • Significant increase in the number of AIRS radiances assimilated • Issues related to bias correction, observation error statistics of cloudy infrared radiances and data quality control

  23. Overview of the Sea-Ice Data Assimilation Project • Goal: to develop an automated ice analysis system for both Canadian Ice Service (CIS) and NWP operational requirements • Collaboration between CIS and Meteorological Research • CIS focus on ice data • MRD focus on assimilation system • Benefit from experience with variational and ensemble-based assimilation for NWP – use variational approach • Develop initial prototype analysis system using CIS ice-ocean model for Canadian east-coast

  24. Assimilation capability: • Total ice concentration from: • CIS Daily ice charts (manual fusion/nowcast of multiple data sources) • RadarSAT image analyses (manual human analysis) • Nasa-Team-2 (NT2) ice concentration retrievals from SSM/I or AMSR-E data • ARGO Temperature-Salinity profiles • Sea Surface Temperature • Brightness temperatures (in progress, Mingrui Dai at CIS): • Using a simple radiative transfer model • Passive microwave data from AMSR-E

  25. East-coast prototype system • Run in experimental mode at CIS for winter 2007 using coupled ice-ocean model • Assimilated CIS daily ice charts and RadarSAT manual image analyses • Used to produce 24h and 48h numerical forecasts of sea-ice conditions • Results for 24h forecasts when assimilating various types of sea-ice observations (verification against CIS daily ice charts):

  26. Future plans: sea-ice data assimilation • Plans for data to be assimilated: • Passive microwave data: AMSR-E, SSM/I (with simple RTM) • Test approaches for assimilating AVHRR data (developed in Norway as part of OSI-SAF) • Explore approaches for assimilating RadarSAT data • Available oceanographic data • Improved background error covariances: • spatial/state dependent variances/correlations • Port the assimilation system to other models/regions: • Canadian Arctic archipelago region (IPY project) • Gulf of St. Lawrence (coupled ice-ocean-atmosphere model developed at RPN/IML)

  27. Data assimilation of radiances with soil moisture information • CaLDAS (Canadian Land Data Assimilation System) • off-line surface analysis system • 2D-Var (position and time -24h window) system to assimilate: • L-Band • ESA SMOS (soil moisture and ocean salinity) – L band radiances • Hydros was dropped-> SMAP (Soil Moisture Active Passive) • C-Band (AMSR-E – Radio Frequency Interference issues) • Infrared Tskin (e.g. GOES –clear sky only) • Screen level observations (Temperature and Humidity)

  28. Environment Canada’s Environmental Prediction System Including Hydrology Upper air Observations* 4DVar data assimilation GEM atmospheric model CaPA: Canadian Precipitation Analysis “ “ On On - - line” line” “ “ Off Off - - line” line” Surface Observations* mode mode mode mode SVAT scheme Routing model SMAP CaLDAS: Canadian Land Data Assimilation • Collaboration between: • Met. Res. Division • Clim. Res. Division • Hydromet. Arct. Lab • National Water Res. Ini. • Can. Met. Centre Environmental Community Surface and Hydrology System SOIL MOISTURE (Pietroniero, Fortin, Pellerin, Belair, NAESI project) *including remote sensing obs

  29. Best Estimation of Soil Moisture: Strategy Remote-sensing In-situ • Only mean to get global or continental soil moisture measurements • Representative of a certain area • Transfer models (emission, backscatter) • Thin layer of soil • Effect of vegetation and roughness • Profile information • Direct measurement • Sparse networks • Local heterogeneity but but LAND DATA ASSIMILATION Screen-level • Widespread measurements • High-frequency • Not a measure of soil moisture • Not uniformely distributed but Models • Physically-based evolution of soil moisture • High-resolution • Profile information • Not an observation • Errors from process representation, forcing, and geophysical fields but SOIL MOISTURE ANALYSIS

  30. Coupled Chemical-Dynamical Data Assimilation • Two year ESA (European Space Agency) contract under GSC (GEMS Space Component) • Jan 2005-Jan 2007 –extended to June 2007 • Awarded to EC in partnership with BIRA (Belgian Institute for Space and Aeronomy) and York University • Goal: Investigate the benefits and drawbacks of multivariate dynamical-chemical assimilation • GEM-BIRA (closely related to GEM-Strato) with 3D or 4D-Var • Horizontal Resolution of 1.5ox1.5o • BIRA stratospheric chemistry package • 57 constituents from the Ox, HOx, NOx, ClOx, BrOx families including few hydrocarbons • 194 photochemical reactions • Heterogeneous chemistry is fully resolved GEMS= Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data

  31. Space-based observations assimilated: • Vertical range: Lower mesosphere to upper troposphere • Verification data sources: TOMS, HALOE, WOUDC • Assimilation period(s): • 11 Aug. to 4 Sept. 2003, 11 Aug. to 31 Oct. 2003, 24 Feb. to 15 Mar., 2003

  32. Canadian Space Agency CSA led missions with data assimilation possibilities at EC • Northern latitudes quasi-geostationary observations • For now, Polar Communications and Weather Mission (partners: CSA, DND and EC). Would have an ABI (GOES-R) VIS/IR like instrument? In HEO orbit – Highly- elliptical earth orbit –2012-2013 launch? • Goal - Data assimilation: To assimilate retrieved polar AMV and radiances from some channels • CHINOOK (SWIFT, ARGO) – next CSA small satellite mission • 2012 launch • Primary load - SWIFT –Stratospheric Wind Interferometer for Transport Studies – measures winds & ozone from 15 to 55 km (PI from York U.) • Secondary load - ARGO? –GPS-radio-occultation instrument provided by Japan (PI from York U.) – Temperature in upper troposphere and lower stratosphere UTLS). • EC is considering assimilating data in NRT • SWIFT: 130 profiles per orbit • ARGO: 250 GPS RO profiles per day

  33. Thank you for your attention

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