Challenges of Nowcasting by George A. Isaac 1, Monika Bailey 1, Faisal Boudala 1, Stewart Cober 1, Robert Crawford 1, Ivan Heckman 1, Laura Huang 1, Paul Joe 1, Jocelyn Mailhot 2, Jason Milbrandt 2, and Janti Reid 1 1 Cloud Physics and Severe Weather Research Section, Environment Canada 2 Numerical Weather Prediction Research Section, Environment Canada Workshop on Use of NWP for Nowcasting Boulder, Colorado, October 2011

Introduction Results from Canadian Airport Nowcasting (CAN-Now) and Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) Funding from Environment Canada, NAV CANADA, SAR-NIF, Transport Canada

Canadian Airport Nowcasting (CAN-Now) To improve short term forecasts (0-6 hour) or Nowcasts of airport severe weather. Develop a forecast system which will include routinely gathered information (radar, satellite, surface based data, pilot reports), numerical weather prediction model outputs, and a limited suite of specialized sensors placed at the airport. Forecast/Nowcast products are issued with 1-15 min resolution for most variables. Test this system, and its associated information delivery system, within an operational airport environment (e.g. Toronto and Vancouver International Airports ). Isaac et al., 2011: The Canadian Airport Nowcasting System (CAN-Now, Submitted to Meteorological Applications Isaac et al., 2011: Decision Making Regarding Aircraft De-Icing and In-Flight Icing Using the Canadian Airport Nowcasting System (CAN-Now), SAE 2011 International Conference on Aircraft and Engine Icing and Ground Deicing, June 2011, Chicago, Illinois, USA

Main equipment at Pearson at the old Test and Evaluation site near the existing Met compound

CAN-Now Situation Chart

SNOW-V10 Science of Nowcasting Olympic Weather – Vancouver 2010 To improve our understanding and ability to forecast/nowcast low cloud, and visibility. To improve our understanding and ability to forecast precipitation amount and type. To improve forecasts of wind speed, gusts and direction. To develop better forecast system production systems. Assess and evaluate value to end users. To increase the capacity of WMO member states. Olympics Societal Benefits

The Winter Olympic Challenge Steep topography, highly variable weather elements in space and time 5 km Village Creekside

Model NameOrganizationCountry Spatial Resolution Temporal Resolution Available Times of Day Run (UTC) Length of Forecst (hours) General Description ABOMLAM1km Environment Canada Canada1 Km15 minEvery 15 minMax 6 h Adaptive Blending of Observation and Models using GEM LAM1k ABOMREG Environment Canada Canada15 km15 minEvery 15 minMax 6 h Adaptive Blending of Observation and Models using GEM Regional INTW Environment Canada Canada 1 and 15 km 15 minEvery 15 minMax 6 h INTegrated Weighted Model using LAM1k, GEM Regional and Observations LAM1k Environment Canada Canada1 km 30 s (Model), 15 min (Tables) 11 and 20 UTC19 h Limited-Area version of GEM model LAM2.5k Environment Canada Canada2.5 km 1 min (Model), 15 min (Tables) 06 and 15 UTC33 h Limited-Area version of GEM model REG Environment Canada Canada15 km 7.5 min (Model), 15 min (Tables) 00, 06, 12, 18 UTC 48 h Regional version of GEM (Global Environmental Multiscale) model Canadian Models Used in SNOW-V10

Model NameOrganizationCountry Spatial Resolution Temporal Resolution Available Times of Day Run (UTC) Length of Forecst (hours) General Description CMA Chinese Meteorological Administration China 15 km & 3 km 1 hour00 and 12 UTC48 h & 24 hCMA GRAPES-Meso NWP model WDTUSL Weather Decision Technologies and NanoWeather USA pointwise or 100 m grid 02, 08, 14, 20 UTC48 h Surface layer model nested in NAM. Works particularly well in quiescent condistions.. WSDDM National Center for Atmospheric Research (NCAR) USA Radar Resolution 10 min (based on radar update) Every 10 min2 hours Nowcast based on storm tracking of radar echo using cross correlation and real-time calibration with surface precipitation gauges. ZAMGINCA Central Institute for Meteorology and Geodynamics (ZAMG) Austria1 km1 hourEvery hour18 hours The Integrated Nowcasting Through Comprehensive Analysis (INCA) system uses downscaled ECMWF forecasts as a first guess and applies corrections according to the latest observation. ZAMGINCARR Central Institute for Meteorology and Geodynamics (ZAMG) Austria1 km15 minEvery 15 min18 hours The precipitation module of INCA combines raingauge and radar data, taking into account intensity- dependant elevation effects. The forecasting mode is based on displacement by INCA motion vectors, merging into the ECMWF model through prescribed weighting. Other Countries Models Used in SNOW-V10

Google Map of Outdoor Venues for Sochi 2014 Olympics. A box 7 km in size is drawn on map. The tops of the mountains are approximately 2200m and the valley 600 m. The area is about 35 km inland from Black Sea.

Products During Olympics Each group produced a Table showing 24 hour forecast of significant variables for main venue sites (hourly intervals and 10 to 15 min intervals in first two hours). Similar to what forecasters produce A Research Support Desk was run during Olympics and Paralympics (virtual and on-site) providing real time support to forecasters. A SNOW-V10 Web site was created with many of the products (time series for sites, remote sensing products, area displays, soundings (gondola and others), and a very successful Blog.

Equipment on Whistler mountain provided good data for forecasters and help in understanding weather processes Harveys Cloud

Adaptive Blending of Observations and Models (ABOM) ABOM reduces to: Observation persistence when r = s = 0, w = 1 Pure observation trend when r = 0, s = 1, w = 0 Model + obs trend + persistence for all other r, s and w Change predicted by model Forecast at lead time p Current Observation Change predicted by obs trend Coefficients s and r are proportional to the performance over recent history, relative to obs persistence (w) and to Local tuning parameters (not yet optimized at all SNOWV10 sites) Updated using new obs data every 15 minutes Bailey ME, Isaac GA, Driedger N, Reid J Comparison of nowcasting methods in the context of high- impact weather events for the Canadian Airport Nowcasting Project. International Symposium on Nowcasting and Very Short Range Forecasting, Whistler, British Columbia, 30 August - 4 September 2009.

Temperature Mean Absolute Error: Averaged by Nowcast Lead Time to 6 Hours 1)Nowcast - Model crossover lead-time 2)Improvement over persistence

Background of INTW INTW refers to integrated weighted modelINTW refers to integrated weighted model LAM 1k, LAM 2.5k, REG 15k and RUC (CAN- Now) were selected to generate INTW Major steps of INTW generationMajor steps of INTW generation –Data pre-checking - defining the available NWP models and observations –Extracting the available data for specific variable and location –Calculating statistics from NWP model data, e.g. MAE, RMSE –Deriving weights from model variables based on model performance –Defining and performing dynamic and variational bias correction –Generating Integrated Model forecasts (INTW) Huang, L.X., G.A. Isaac and G. Sheng, 2011: Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy, Submitted to Weather and Forecasting

Aerials jump Spectator grandstand Preparation of field-of-play 2 hours before training…

Freestyle Skiing Ladies Aerials Final February 24, 2010 Notes by Mindy Brugman as posted on SNOW-V10 Blog The event….Last night at the Cypress aerial site it began with drizzle and was quite foggy… We walked to the bright lights of the venue. There was no precipitation at the venue the entire competition. The competition started at 1930 PST. The first pictures (not shown here) are taken at the start of the competition and you can see that the fog was much worse at the start. I stopped taking pictures since I could not see anything. Then it began to clear just near the end of the competiton – and I clicked a few pictures. You can detect a flying bug above the lights. Thats really the gold medal winner, Lydia Lassila, of Australia! Based on the event last night – I suspect ladies aerials may qualify as a new paralympic sport for the visually impaired.

INTW Forecasts of Visibility at VOG

Visibility LAM 1K including 24 hours before ladies aerials

Ceiling LAM 2.5K including 24 hours before ladies aerials

Total precip rate REG 15K including 24 hours before ladies aerials

NWP Model with Minimum MAE in CAN-Now for Winter Dec 1/09 – Mar 31/10 and Summer June 1/10 to Aug 31/10 Periods Based on First 6 Hours of Forecast

Winter period – Dec. 1, 2009 to Mar. 31, 2010 Summer period - June 1 to August 31, 2010

Variable LAMREGRUCINTW CYYZCYVRCYYZCYVRCYYZCYVRCYYZCYVR TEMP RH 66no WS no12.5 GUST no 53.5no1.5 Winter Summer Variable LAMREGRUCINTW CYYZCYVRCYYZCYVRCYYZCYVRCYYZCYVR TEMP no0.5 RH no11 WS no GUST no 5.5no2.2no0.54 Time (h) for Model to Beat Persistence 6h RUC Used Huang, L.X., G.A. Isaac and G. Sheng, 2011: Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy, Submitted to Weather and Forecasting

Observation – Model grid match Mean Absolute Errors for RUC 6h YVR NNYVR BIVAN 1PE Temperature (ºC) Wind Speed (m/s) Wind Direction (º) RUC temperature at CYVR July 2010 Compared data from 3 model points Consider season when selecting the best point CAN-Now: usually nearest grid point (with no water mask) J. Reid 2010

Heidke Skill Score: Multi- Categories Ktotal 1 N(F 1 ) 2 N(F 2 ) 3 N(F 3 ).. K N(F k ) total N(O 1 )N(O 2 )N(O 3 )N(O k )N Observed category Forecast Category j i Using: Calculate:

Categories Being Used in CAN-Now Analysis

Summary of HSS/ACC results ModelVariableOriginal Overall HSS / ACC Relaxed Timing (±60min) HSS / ACC REGCLD_BASE_HGT0.45 / / 0.61 PCP_RATE0.30 / / 0.62 VIS_FB0.28 / / 0.74 LAMPCP_RATE0.29 / / 0.67 VIS_FB0.26 / / 0.74 RUC6hCLD_BASE_HGT0.28 / / 0.52 PCP_RATE0.40 / / 0.81 VIS0.22 / / 0.60 CYYZ (Winter) J. Reid 2010 Scores dominated by long periods of non-events Relaxed timing: No clear changes, for better or worse Similar results at CYVR

Categories for SNOW-V10 HSS Analysis

LAM 1km - 40 LAM 2.5km - 23 GEM-15km - 7 NWP Model with Highest HSS Score During SNOW-V10

Summary RH predictions are poor, barely beating climatology. (Impacts visibility forecasts) Visibility forecasts are poor from statistical point of view. (also require snow and rain rates) Cloud base forecasts, although showing some skill, could be improved with better model resolution in boundary layer. Wind direction either poorly forecast or measured. There are many difficulties in measuring parameters, especially precipitation amount and type. Overall statistical scores do not show complete story. Need emphasis on high impact events. Selection of model point to best represent site is a critical process.

Summary (continued) Weather changes rapidly, especially in complex terrain, and it is necessary to get good measurements at time resolutions of at least min. CAN-Now and SNOW-V10 attempted to get measurements at 1 min resolution where possible. Because of the rapidly changing nature of the weather, weather forecasts also must be given at high time resolution. Verification of mesoscale forecasts, and nowcasts, must be done with appropriate data (time and space). Data collected on hourly basis are not sufficient. Nowcast schemes which blend NWP models and observations at a site, outperform individual NWP models and persistence after 1-2 hours.

Questions?