Recent Data Assimilation Activities at Environment Canada

Transcription

1 Recent Data Assimilation Activities at Environment Canada Major upgrade to global and regional deterministic prediction systems (now in parallel run) Sea ice data assimilation Mark Buehner Data Assimilation and Satellite Meteorology Research

2 Upgrade to deterministic DA systems In a Parallel Run since June 2014, changes are mostly to DA: 4D-EnVar replaces 4D-Var Model and Analysis grids: Analysis increment: 50km instead of 100km grid spacing Unchanged for background and analysis Satellite radiance observations: Improved satellite radiance bias correction scheme: rely more on obs Additional AIRS/IASI channels assimilated, now 142 each Upgrade RTTOV8 to RTTOV10 Modified obs error stddev for all radiance observations [f x stddev(o-p)] Improved treatment of radiosonde (4D) and aircraft observations Assimilation of ground-based GPS data over North America Use of new global sea ice concentration analysis (3D-Var) 4D-IAU (instead of DFI) and recycling of physics variables (GDPS only) Use of Maestro sequencer for R/D/O Page 2 August-11-14

3 : Toward a Reorganization of the NWP Suites at Environment Canada 2014 implementation: Increasing role of global ensembles Global EnKF Global ensemble forecast (GEPS) Regional ensemble forecast (REPS) Background error covariances Global EnVar Global deterministic forecast (GDPS) global system Page 3 August Regional EnVar Regional deterministic forecast (RDPS) regional system

4 Forecast Results: GDPS vs GDPS Verification vs. ERA-Interim analyses 24h, Feb-March, 2011 Northern extra-tropics Tropics U RH U RH GZ T GZ T Page 4 August-11-14

5 Conclusions GDPS vs GDPS 3.0.0: Proposed vs. existing system overall improvement to forecasts Relative improvements larger at short lead times than at day 5 Improved surface scores, especially temperature during winter snow density fix Above ~20hPa, scores from EnVar alone similar to 3D- Var, worse than 4D-Var (benefit from raising EnKF model top to 0.1hPa?) Compensated by assimilation of 4D radiosonde obs that gives improvements up to 10hPa Page 5 August-11-14

6 Sea Ice Data Assimilation Project Collaboration between Meteorological Research and the Canadian Ice Service (CIS) Goal is to produce automated analyses of sea-ice conditions for the operational needs of the Meteorological Service of Canada: enhanced ability of CIS to deliver operational sea-ice products for marine navigation over larger area than currently possible (including new arctic METAREAs) improved NWP by supplying new sea-ice analyses for atmospheric models and for initializing coupled ice-ocean-atmosphere forecasts Assimilate numerous observation types using 3D-Var SSM/I ice concentration CIS daily ice charts Page 6 August-11-14

7 Regional Ice Prediction System RIPS 2.0: Additional assimilated observations: SSM/IS (DMSP ) ASCAT measure of anisotropy of 3 look angles related to ice concentration through linear forward model no data rejected based on air temp, instead rejected when wind speed is low A new analysis-error stddev field and correction procedure for grid points with high uncertainty Sea ice model CICE used to produce short-term forecasts up to 48 hours Page 7 August-11-14

8 Total impact of all changes over 2010 North of 65 N Additional assimilated observations: SSMIS and ASCAT Use of interpolated/extrapolated values to correct analysis where analysis error is high Large positive impact when verified against U.S. ice center s IMS manual ice extent analysis Page 8 August-11-14

9 Future Outlook Work towards improving Regional Ice Prediction System and Global Ice Analysis System: assimilate AMSR-2 retrieved ice concentration when available assimilate higher-resolution remote sensing data (AVHRR, VIIRS and eventually SAR) assimilate data related to ice thickness: starting with passive microwave (AMSR-2 and SMOS) and nighttime thermal infrared (AVHRR and VIIRS) Use of numerical sea-ice model within data-assimilation cycle on an expanded domain covering entire Arctic ocean Expand 3D-Var to ensemble of 4D-EnVar assimilation cycles to capture complex error covariances Page 9 August-11-14

10 Ensemble assimilation (with perturbed observations) Model: Perturbations to SST, Atmospheric and oceanic forcing, model parameters, etc. Ensemble forecasts Analyses Backgrounds Ensemble A Ensemble analyses Ensemble B Observations Obs, R Observations: Perturbations to observed ice concentration values consistent with expected observation error covariances Page 10 August-11-14

11 Comparing to CIS daily ice charts: 9 July 2011 (N.B. only over limited areas!) CIS daily ice chart ice concentration Mean of the ensemble of backgrounds RMSE of the ensemble mean Ensemble spread 50% 35% Page 11 August-11-14

12 Conclusions Initial ensemble strategy appears to give reasonable relationship between spread and error, however Effect of model bias difficult to capture with ensemble spread: fast ice (ice stuck to land) not sufficiently represented in model: ice incorrectly moves away from coast in all members errors in SST near the ice edge: ice incorrectly melts or forms in all members Now performing tests in which some members have dynamics or thermodynamics completely deactivated Also testing simple procedures to correct SST based on ice information Page 12 August-11-14

13 Additional slides follow Page 13 August-11-14

14 EnVar: a replacement for 4D-Var Overall, global EnVar analysis (~10 min) is ~6X faster than 4D-Var (~1 hr) on half as many cpus (320 vs 640), even though much higher resolution increments (50km vs 100km) Identical configuration of EnVar used for both global and regional systems (unified deterministic analysis) For practical and scientific reasons, decision made to replace 4D-Var with EnVar in GDPS/RDPS, if results at least as good as current 4D-Var Future improvements to EnKF will benefit deterministic forecasts incentive to increase overall effort on EnKF development Page 14 August-11-14

15 : Toward a Reorganization of the NWP Suites at Environment Canada Current operational systems Global EnKF Global ensemble forecasts (GEPS) Regional ensemble forecasts (REPS) Global 4D-Var Global deterministic forecast (GDPS) global system Page 15 August Regional 4D-Var Regional deterministic forecast (RDPS) regional system

16 Forecast Results: GDPS vs GDPS Verification vs. (1D) Radiosondes 72h, Northern extra-tropics Feb-March, 2011 July-August, 2011 U U U U GZ T GZ T T-T d T-T d Page 16 August-11-14

17 New satellite radiance bias correction approach Current approach uses background state as reference state for bias correction, i.e. background state assumed unbiased New approach: Perform an extra 3D-Var analysis that assimilates only: radiosonde, GPS-RO, aircraft, AMV, surface obs, scatterometer This analysis is ONLY used as the reference state for computing the bias correction (instead of the background state) Allows conventional data and GPS-RO (instead of the forecast model) to have the last word for establishing the reference state for bias correction 24h global forecast vs ERA-interim New approach (uses 3D-Var analysis) Old approach (uses background state) GZ T Page 17 August-11-14

18 Upgrades to Processing and Assimilation of Radiosonde and Aircraft Data (Laroche and Sarrazin, W&F, 2013) Increased volume of data: selection of observations according to model levels. Revised observation error statistics. Horizontal drift of radiosonde balloon and acquisition time taken into account in both data assimilation and verification systems 4D. Bias correction scheme for aircraft temperature reports. Bias correction scheme for radiosonde temperature and humidity under development. Page 18 August Operational Proposed for both Radiosonde & Aircraft

19 Revision of AIRS/IASI channel selection cm-1 Spectral bands DVar DVar 2014 EnVar AIRS IASI AIRS IASI AIRS IASI T sounding (peak higher than 80mb) T sounding (80mb < peak < 150mb) T sounding (peak lower than 150mb) Surface, low peaking T, RH sounding Ozone sounding Surface and cloud properties Water vapor & temperature sounding CO column amount Temperature sounding (N 2 O band) Temperature sounding (CO 2 Band) Surface and cloud properties Total volume assimilated (Million/day) Page 19 August-11-14

20 Assimilation of GB-GPS Data over North America (Stephen Macpherson) Data available every 30 minutes. Network uses mostly existing (NGS geodesy) GPS site infrastructure with some additional sites installed and maintained by NOAA. GPS MET from nearby SYNO/METAR at ~50% of sites. zenith mapping function GPS Receiver Like NOAA wind profiler network, still a demonstration network although GPS PW data are assimilated operationally in NCEP regional models. FSL plans to transfer network to OPS. More GPS sites in Canada could be added (with assistance from Environment Canada). Page 20 August-11-14

21 Incremental Analysis Updates and Recycling in a Continuous Integration The analysis increment computed by EnVar (δ) is applied as δ/n at each of N steps during the assimilation window The sequential increment periods are a continuous integration, allowing for selective recycling to dramatically reduce spin-up Page 21 August-11-14

22 Reduction of Model Spin-Up as a Result of IAU and Recycling Total GDPS Total GDPS Conv. GDPS Conv. GDPS Both physics (precipitation; top-left) and dynamics (energy; bottom-left) spin-up are significantly reduced in the proposed configuration (IAU and recycling) This change in cycling yields an improved semi-diurnal tide signal (below) as noted in tropical scores Response to modifications should be more predictable with a spun up state IAU Cycle DF Cycle Globally-averaged 6-h precipitation (top; total and convective), and 0-hour energy spectra comparing IAU (red) and DF-based (blue) cycles. Page 22 August IAU Cycle DF Cycle Difference of 1000 mb heights in 24h forecast.

23 Regional Ice Prediction System: RIPS Main use: provide input for generation of CIS operational products (both manual and automated) Four analyses per day of ice concentration at 5 km resolution on rotated lat-lon grid Domain chosen to include new METAREAs and meet the needs of North American Ice Service (USA/Canada) Background state is previous analysis, persisted in time (no ice model) Also compute simple measure of uncertainty at each grid point Page 23 August-11-14

24 Assimilated data: Typical data coverage SSM/I ice concentration AMSR-E ice concentration No longer available since late 2011 CIS daily ice charts CIS image analyses Page 24 August-11-14

25 Impact of all changes (2010) Change in mean Proportion Correct Total: exp.4 exp.1 Page 25 August-11-14

26 Example of the results: background ensemble ice concentration variances Ice concentration (ensemble mean) Background ensemble variances 10 Mar, Jul, 2011 Page 26 August-11-14

27 RIPS 2.0 analysis vs. AVHRR observations A potential for higher resolution RIPS May 06, :00 UTC AVHRR May 06, :47 UTC Page 27 August-11-14

28 Characteristic Values (CV) of sea ice and open water for various regions AVHRR Ch1, June 2010 AVHRR Ch2, June 2010 Ice Open Water Ch1 and Ch2 reflectance are well separated for ice and open water. Page 28 August-11-14

29 Sea Ice ensemble-da Goal is to: provide an estimate of the uncertainty in a deterministic forecast provide a range of possible forecast scenarios improve how observations are assimilated by using improved background-error statistics obtained from ensembles Uncertainty in forecasts results from many poorly known sources in the forecast model and data assimilation system: imperfect model dynamics and physics limited spatial and temporal model resolution imperfect observations that are assimilated limited spatial and temporal coverage of assimilated observations imperfect estimates of observation and background uncertainty Page 29 August-11-14