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Recent developments on the NWP suite of Environment Canada Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal.

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Presentation on theme: "Recent developments on the NWP suite of Environment Canada Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal."— Presentation transcript:

1 Recent developments on the NWP suite of Environment Canada Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal Montréal (Québec) CANADA

2 Global Deterministic Prediction System Horizontal resolution: 33km 4DVar data assimilation system June 2009: extension into the stratosphere (top went from 10 hPa to 0.1 hPa)

3 Global Deterministic Prediction System Polar Stratosphere The major sudden stratospheric warming of January 2009 Horizontal resolution: 33km 4DVar data assimilation system June 2009: extension into the stratosphere (top went from 10 hPa to 0.1 hPa) Sources: M. Charron, S.-W. Son, P. Martineau

4 Global Deterministic Prediction System Decrease in false alarm rate for hurricanes (with modified convection)

5 Global Deterministic Prediction System Decrease in false alarm rate for hurricanes (with modified convection) From 2010/07/23 to 2010/10/12, 00Z and 12Z 164 cases up to 6 days; 82 cases from 6 to 10 days Comparison with NHC "best tracks“ Storm must last more than 24h Winds must be > 35 knots “Unequivocal“ false alarms per forecast Shaded areas indicate 5-95% confidence intervals from bootstrapping

6 “Strato-2b”: Upgrade to the global deterministic data assimilation systems (GDPS 2.2.0) Seminar October 15, 2010 Presented by Mark Buehner and Louis Garand

7 Strato 2b: additions to Strato 2a All additions and modifications first tested independently New observations - large increase in volume of assimilated obs: –IASI: 62 channels, sensitive to temperature below 150hPa –SSMIS: 7 SSMI-like channels sensitive to humidity and surface wind speed (over the ocean) –GEORAD: 5 geostationary satellites (previously only 2 GOES satellites), 1 water vapour channel assimilated using RTTOV –AIRS: assimilate upper level channels previously rejected within 30° of both poles –Reduced horizontal thinning for all satellite radiance observations (250km  150km, except SSMI: 200km  150km) –Humidity from aircraft

8 Number of radiance observations assimilated February 1 st, 2009 (4 analyses): InstrumentPlatformStrato 2aStrato 2b% Change AIRSAQUA392 554659 751+ 68% IASIMetop-20500 783New AMSU-ANOAA-15121 875338 194+ 178% NOAA-18170 773472 474+ 177% AQUA119 805331 557+ 177% AMSUBNOAA-1514 76241 350+ 180% NOAA-1630 08284 341+ 180% NOAA-1732 96592 609+ 181% MHSNOAA-1834 67196 025+ 177% SSMIDMSP-1337 96560 761+ 60% SSMISDMSP-16039 330New GOES ImagerGOES-1111 81334 967+ 196% GOES-1210 02441 919+ 318% SEVERIMSG-2069 183New MVIRIMeteosat-7041 882New GMS MTSATMTSAT-1020 612New All Radiances:977 2892 925 788+ 199%

9 Strato 2b: additions to Strato 2a Improved treatment of satellite observations : –new unified obs error bias correction system for satellite radiances –FASTEM bug fix in RTTOV –RTTOV coefficients for AMSU-A with no zeeman effect –reduced time window for bias correction 15 days  7 days –several minor improvements and bug fixes related to AIRS Other improvements –new sea-surface temperature analysis (from B. Brasnett) –first MPI version of variational assimilation code (all steps related to observations are now parallelized) –new, parallelized post-processing of analysis increment (separate program: addanalinc)

10 Improved SST Analysis (Bruce Brasnett) Description of the observations used Description of the analysis method Assessment of analysis quality 1.Verification against independent data for several analyses (zonal average) 2.Verification against independent data (time series)

11 Page 11 – 10/10/2014 Description of data assimilated AMSR-E (passive microwave) retrievals from RSS gridded on a 0.25° grid (65,000 /day) NAVO (infrared) retrievals for NOAA-18, NOAA-19 and METOP-A (45,000 /day from each source) A/ATSR (infrared) retrievals from ESA (16,000 /day) Proxy SSTs based on the CMC ice analysis (9,000/ day) Ships (1500 /day) Drifters (1200 /day) Moored buoys (200 /day)

12 Page 12 – 10/10/2014 Analysis Description Analysis variable is anomaly from climatology Updated once per day on a global, 0.2° grid Uses previous analysis as background (persistence) Method is statistical (optimal) interpolation Extensive quality control of observations Goal is to produce an analysis of the foundation SST (SST at a depth where there is no diurnal variation) Uses data from a variety of sources, in situ and satellite Biases of satellite retrievals are estimated and removed

13 Page 13 – 10/10/2014 Comparison of zonally averaged analysis error for 8 products based on independent Argo floats

14 Development of a global forecasting Yin-Yang model: orthogonal coordinates (easy representation of operators such as Gradient, Laplacian … ) no polar singularity same grid structure for Yin and Yang components easy to nest (as a LAM) easy to parallelize (domain decomposition method) = 2-way coupling of 2 LAMs Global Deterministic Prediction System Yin-Yang grid by Abdessamad Qaddouri (see Qaddouri et al. 2008 Appl. Num. Math.)

15 Regional Deterministic Prediction System northward extension of the core of the Canadian regional model March 2009: core grid of the regional model (15-km resolution) was extended over the Arctic regions + new radiative transfer scheme June 2009: implementation of 0600 UTC and 1800 UTC runs June 2009: extension into the stratosphere (model top went from 10 hPa to 0.1 hPa)

16 Regional Deterministic Prediction System Fall 2010: New RDPS will become operational, using limited-area model (red line in figure) 3D-Var assimilation system independent from the global system Project lead: L. Fillion

17 Observations Clipping

18 Specific aspects of the LAM analysis Kilometric-scale LAM configuration Uses bi-fourier representation (DFT-2D) – Analysis increments are bi-periodic on the extended computational grid. Arakawa-C grid. Uses a Rotated analysis grid (following same approach as in GEM). A fast wind-rotation operator is used at the end of the process of constructing the analysis increment before computing departures with innovation vector at each simulation of 3D-VAR Regional-continental configurationfor tranfer to CMC Uses Hemispheric-spectral representation Based on symmetrized NH/SH background-error fields and associated statistic file. No Balanced/Unbalanced splitting involved

19 The Standard NMC approach for control variables: Initial implementation in REG-LAM3D/4D - Construction of P b from  using Local Balance Equation: - Spectral form of Regression matrix to derive T b and p sb : -Vertical localization of correlations of CV compulsory in Stratospheric version (to limit impact of TOVS upper stratospheric channels onto low-tropospheric analysis increments… especially during summer)

20 “Spherique”  Oper Global-Spectral Approach with NMC Balance-splitting control variables “hemis”  New Hemispheric-Spectral approach Without NMC balance splitting

21 Canadian Precipitation Analysis (CaPA) project The objective of the CaPA project is to combine, through optimal interpolation, different sources of information on precipitation Ground stations (coop + synoptic stations)‏ Atmospheric model Satellite imagery RADAR

22 CaPA vs Ordinary Kriging CaPA Kriging Spatial structure of precipitation over Lake Superior determined by two observations in the Keweenaw peninsula (6h accumulation valid on 2006-10-13 at 0Z)‏ Buffalo airport

23 June-July-August 2010 (6h) CaPA and GEM vs Observations (SYNOP) Frequency Bias Indicator (FBI) Equitable Threat Score (ETS)

24 June-July-August 2010 (24h) CaPA and GEM vs Observations (SYNOP)

25 Dec. 2009 – Feb. 2010 (24h) New quality control for solid precipitations measurements from automated stations

26 The Gulf of St. Lawrence (GSL) Coupled System (forecast season, 2008) -5°C -15°C -25°C A dynamic representation of sea surface conditions improves the meteorological forecast locally –Operational regional forecasting system (GEM-Ops) has tendency to overestimate cold events in winter. ▪Due to overly high ice concentration and thickness –Dynamic ice cover in coupled model allows vast stretches of ice-free water to open up, buffering atmospheric temperatures Use of coupled model results in significantly improved forecasts all around the GSL Slide kindly provided by G. Smith (MRD/EC)

27 Regional Ensemble Prediction System First implementation at CMC of a regional ensemble prediction system in early 2011 – 20 members at 33 km grid spacing – Lead time between 48 and 72 h (TBD) – Important gain in QPF – Same grid as regional model A modified configuration of this system with grid extending to the Tropics is used in 2010 to provide help to the Haitian Met Services during the hurricane season.

28 Main differences with global EPS Global EPS Two surface scheme (Force-restore and ISBA) Four deep convection schemes – (Kain-Fritsch, Relaxed Arakawa- Schubert, 2 flavors of Kuo) Different parameter values for GWD and ABL schemes Radiation scheme: Fouquart/Bonnel+Garand radiation scheme Stochastic Kinetic Energy Backscatter Regional EPS Only ISBA Only Kain-Fritsch GWD and ABL: All members have same parameter values Li and Barker (correlated k) No SKEB

29 Regional Ensemble Prediction System

30 Global Ensemble Prediction System  Global EPS horizontal grid spacing from 100 to 66 km  Use new GEM dynamics with vertical Charney-Phillips staggering  Operational in early 2011 Continuous Rank Probability Score Summer 2008 against radiosondes Northern Hemisphere

31 Monthly Forecast System Monthly forecast system based on operational global EPS – Currently, based on seasonal forecast system, which is suboptimal due to the use of lagged initial conditions and low model resolution

32 Monthly Forecast System

33 2006 windows GEM 2.5 km domains The project started with 2 LAM window in 2006: LAM East, LAM West LAM Atlantic and Baffin were added in 2007 LAM west was expanded to the east to cover Alberta in support of the convection along the foothills (UNSTABLE) LAM Atlantic expanded in February of 2009

34 Experimental GEM-LAM window (summer 2008/9) LAM 2.5kmAugust-October 2008: Lancaster Sound: collaboration with CIS (Canadian Ice Services); help with maritime transport in Arctic waters; One 30 hrs run per day; similar exercise held during summer 2009 and 2010.

35 1.0 km Whistler Production of high-resolution LAM forecast for winter Olympics 3 nested LAM integrations twice daily from 0000 and 1200 UTC GEM Regional forecasts: LAM-15km → 2.5km → 1.0km Vancouver 15 km 2.5 km

36 Improving the LAM 2.5km (2011): (experimental) Improved physics: – geophysical fields (orography, surface roughness,…) using new GenPhysX and database at 90-m res; – CCCmarad radiation scheme (solar + infrared); – Milbrandt-Yau double-moment bulk microphysics (prognostic snow/liquid ratio, drizzle/rain, small/large hail); Numerics (changes to increase precision and efficiency of the model): – new vertical staggering (Charney-Phillips vertical grid – increased numerical stability and robustness); – hi-tech I/O and nesting (“hollow cube” nesting at higher time frequency); – ‘growing’ orography (reduction of noise generation during nesting adjustment); – vertical nesting (from the top): lower the model top / increase vertical resolution; New model diagnostic outputs: – visibility reduction due to hydrometeors (fog, rain, and snow); – cloud base, melting level, snow base; – solid-to-liquid ratio for snow density; – diagnostics of surface wind gusts and wind variances (speed + direction).

37 Future work for global model development Work on physical processes – Revised/improved radiation code – New microphysics scheme – Improving the planetary boundary layer scheme – Improving the representation of subgrid-scale orographic processes Work on initialization of forecasts – Shorten the adjustment / spin-up time scale – Use short-term forecasts (typically 6-h) to initialize the physical state of the model Clouds, vertical diffusion, more generally all variables of the physics bus – Expected to be useful for Nowcasting using NWP Data assimilation by improving the background state

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