1. Current and Future Use of Satellite Data in NWP at Environment Canada Satellite Direct Readout Conference 2011 Miami, USA David Bradley, Gilles Verner, Mike Manore Meteorological Service of Canada April 4-8, 2011

2. Context Environment Canada (EC) Meteorological Services of Canada (MSC) “providing weather and environmental predictions and services to reduce risks and contribute to the well-being of Canadians” –Operations (e.g. 24-7 forecasts and warnings, NWP operations) –Monitoring Networks (e.g. Upper Air, Surface, Climate, Water, Space-based) –Environmental Predictions and Services (e.g. Ice, Aviation, Military, Policy) –Science (e.g. Air Quality, Climate, Meteorological)

3. Canadian Meteorological Centre (CMC) Meteorological Research Division: Data Assimilation, Modeling, Cloud Physics CMC Development Division: Data Assimilation, Numerical Weather Prediction, Weather Elements, Scientific Applications IT Infrastructure (CIOB): Supercomputer, National Telecommunications, Network, User support CMC Operations: Analysis & Prognosis, Env. Emergency Response (VAAC), Air Quality, Implementation and Operational Services

4. Role of CMC and Regions in Weather Prediction NAV CANADA Dept. National Defence Public Marine Agriculture Private sector. CMC Supercomputer/Telecom Decoding, QC & Databasing Data Assimilation & Modeling Post-processing 5 EC Regions Warnings Forecasts Dissemination Services Data + Prod. Canadian Ice Service Aviation & Defense Services USERS Canadian Data International Data (GTS Washington, NESDIS, Eumetsat, UKMet, etc.) Research in NWP, Data Assimilation, Remote Sensing and AQ MRD Tech. transfer

5. DATA ACQUISITION COMPUTER ANALYSIS OF DATA COMPUTER FORECAST INTERPRETATION & DISSEMINATION Observations obtained from weather balloons, surface stations, ships, satellites, aircrafts, drifting buoys. Produce Values of atmospheric variables (temperature, winds, humidity & pressure) at mesh points. Run computer model of atmosphere. Provides forecast values of atmospheric variables at mesh points. Applications. Forecasts interpreted in terms of weather elements (e.g. sunny and cloudy periods), disseminated via media. THE MAKING OF A WEATHER FORECAST

6. Data Assimilation Process First Guess (6hr fcst) Analysis (Spatial QC) NWP model Error Statistics (Observation and Forecast) Data Acquisition Data Quality Control Cost MIN J(x) xaxa J(x i-1 ) Use to find x i

7. Unified Numerical Forecasting System

8. Main Uses of Observational Data at CMC CMC is a major user of observational data, both Canadian and foreign, main uses are: Radiosonde andSatellite data are of crucial importance Data Assimilation: Blending of observations with other information to generate initial conditions (the analysis) to run the NWP forecast models. Radiosonde and Satellite data are of crucial importance. Forecast Verification: Observations (upper air, surface, satellite) considered as truth (after QC), and used to verify the accuracy of forecasts (both model and Scribe) and perform diagnostic studies. Weather Element Forecast: Observations used in generation of statistical equations which are used to produce forecasts of weather elements, important input to SCRIBE and forecast system. Applications: EER (volcanic ash, spills, fires, etc.), air pollution and atmospheric chemistry, nowcasting, surface fields (SST, ice, snow, etc.)

9. Observing Systems used in Global DA Upper-air sites (TEMP, PILOT, DROP) Surface stations (SYNOP, ASYNOP,METAR) Buoys and ships Aircraft (BUFR, AIREP, AMDAR, ADS) Wind profilers (NOAA network) Polar-orbiting Satellites (NOAA-15,16,17,18,METOP-A; DMSP-F15; AQUA, TERRA;) Geostationary Satellites (GOES-11,12, Meteosat-7,9; MTSAT-1R)

10. Observations assimilated at CMC 200kmx200km/time step 7 MW channels U,V at 10 meter over ocean 250kmx250km/time step 87 IR channels SSM/I DMSP-13 QUIKSCAT, ASCAT AIRS (750 m) Vertical hourly U,V Profiler (NOAA Network) ~180 km boxes 11 layers, per time step U,V MODIS polar winds (Aqua, Terra, Global & DB) 1.5 o x 1.5 o 11 layers, per time step U,V (IR, WV, VI, 3.9μ channels) AMV’s (METEOSAT E-W, GOES E-W, MTSAT-1R) 2 o x 2 o 3-hourlyIM3 (6.7 µm) Water vapor channel GOES 11-12 250 km x 250 km per time step Ocean Land AMSU-A 4-14 6-14 AMSU-B / MHS 2-5 3-4 ATOVS NOAA 15-16-17-18-19, AQUA, METOP 1 o x 1 o x 50 hPa per time step U, V, T Aircraft (BUFR, AIREP, AMDAR, ADS) 1 report / 6hT, (T-T d ), p s, (U, V over water) Surface report (SYNOP, SHIP, BUOYs) 28 levelsU, V, T, (T-T d ), p s Radiosonde/dropsonde ThinningVariablesType 100kmx100km/time step GPSRO (COSMIC, GRACE, GRAS) Refractivity830km, per time step

11. Conventional Observations Radiosondes Surface reports Aircraft reports

12. Passive remote sensing observations (polar-orbiting satellites) AIRS SSM/I AMSU-A/B

13. Passive remote sensing observations GOES radiances AMVs

14. Active remote sensing observations GPS-RO Wind profilers Scatterometers

15. Data Quality Monitoring Meteorological Centres such as CMC that run Numerical Weather Prediction (NWP) models can monitor the performance of instruments (e.g. aircraft sensors used in AMDAR) on a continuous and near real-time basis Monitoring is based on observed minus first guess values (innovations), as well as data rejection statistics, extracted from the operational data assimilation system Monitoring is performed for individual platform, station, as well as by various programs (e.g. E-AMDAR, NOAA Satellites, etc). Time evolution of innovations, as well as their statistical distribution are extremely powerful and useful tools

16. Monitoring of AIRS radiance data

17. Analysis & Prediction at CMC Environmental Emergency applications – dispersion modeling –Nuclear and volcanic ash –Release of hazardous chemicals –National security issues

18. Challenges Data Access –Despite numerous dissemination channels –Unique solutions for each new observation/product Data Timeliness –Require data less than one hour old –Weather Waits For No Man.. or Satellite.. or Data Delivery System Maintaining a Super-Computer facility –Many modeling programs require access –Keeping up with computing advances Assimilation of new data –Takes a long time to assimilate new data –Human resources - Finding, hiring and keeping operational staff, researchers etc.