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

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