Perfomance of the WWRP project FROST-2014 forecasting systems: Preliminary assessments (FROST = Forecast and Research in the Olympic Sochi Testbed) D.Kiktev, E.Astakhova, A.Muravyev, M.Tsyrulnikov WWOSC-2014, August 2014 PRESENTED by S. BELAIR (with additions)

Items in this presentation: Brief reminder Online monitoring, evaluation (deterministic and ensemble) Interesting cases Integrated forecasting What’s next

Goals of WMO WWRP RDP/FDP FROST-2014: To improve and exploit: – high-resolution deterministic mesoscale forecasts of meteorological conditions in winter complex terrain environment; – regional meso-scale ensemble forecast products in winter complex terrain environment; – nowcast systems of high impact weather phenomena (wind, precipitation type and intensity, visibility, etc.) in complex terrain. To improve the understanding of physics of high impact weather phenomena in the region; To deliver deterministic and probabilistic forecasts in real time to Olympic weather forecasters and decision makers. To assess benefits of forecast improvement (verification and societal impacts) To develop a comprehensive information resource of alpine winter weather observations;

3 rd meeting of the project participants (10-12 April 2013) International participants of the FROST-2014 project COSMO, EC, FMI, HIRLAM, KMA, NOAA, ZAMG under supervision of the WWRP WGs on Nowcasting, Mesoscale Forecasting, Verification Research

Observational network in the region of Sochi - About 50 AMS; - C-band Doppler Radar WRM200; - Temperature/Humidity profiler – HATPRO; - Wind – Scintec-3000 Radar Wind Profiler; - Two Micro Rain vertically pointing Radars (MRR-2); - 4 times/day upper air sounding in Sochi

Forecasting systems participating in RDP/FDP FROST-2014 Nowcasting: ABOM, CARDS, INCA, INTW, MeteoExpert, Joint (Multi-system forecast integration) Deterministic NWP: COSMO-RU with grid spacing 1km, 2.2 km, 7km; GEM with grid spacing 2.5 km, 1 km, 0.25 km; NMMB – 1 km; HARMONIE – 1 km; INCA – 1 km Ensemble NWP: COSMO-S14-EPS (7km), Aladin LAEF (11km), GLAMEPS (11km), NNMB-EPS (7km ), COSMO-RU2-EPS (2.2km), HARMON-EPS (2.5km)

FROST-2014 Online Monitoring of Forecast Quality: Role of resolution - GEM-2.5 km vs GEM-1 km vs GEM-250 m Forecast mean absolute errors as a function of forecast lead time. Location: Mountain skiing finish (Roza-Khutor-7 station). Period: 15 Jan – 15 March Effect is not straightforward. It depends on meteorological variable, location, lead time etc.

Some examples of diagnostic verification: Role of spatial resolution. COSMO-S14-EPS (7km grid spacing) vs COSMO-RU2-EPS (2km grid spacing) Parameter: T2m, Location: Biathlon Stadium (1075m), Verification Period: , Verification approach: Nearest point COSMO-S14-EPS (7km grid spacing) COSMO-S14-EPS (7km grid spacing) COSMO-RU2-EPS (2km grid spacing) COSMO-RU2-EPS (2km grid spacing) Better shape on Q-Q-plots and higher variability for the downscaled ensemble forecasts.

Role of spatial resolution for ensemble forecasts – continued COSMO-S14-EPS (7km grid spacing) vs COSMO-RU2-EPS (2km grid spacing) StationBIAS (for 6/12/18hr lead time)Mean Absolute Error (for 6/12/18hr lead time) COSMO-S14-EPSCOSMO-RU2-EPSCOSMO-S14-EPSCOSMO-RU2-EPS Sledge (~700m) -1.3 / -2.0/ / -1.9 / / 2.2 / / 3.5 / 1.7 Freestyle (~1000m) -2.0 / -1.8 / / -0.7 / / 2.0 / / 2.4 / 1.7 Biathlon Stadium (~1500m) -1.4 / -1.3 / / 0.0 / / 1.8 / / 2.6 / 2.3 Mountain Skiing(start) (~2000m) 1.6 / 2.2 / / 0.2 / / 3.1 / / 2.2 / 2.6 T2m: Some positive effect of downscaling from 7 to 2 km resolution. Wind Speed: No positive effect of dynamical downscaling was found. Verifications for ensemble mean Verification Period:

ROCA BSS BS COSMO-S14-EPS – red COSMO-RU2-EPS – orange LAEF-EPS – brown NMMB-EPS – black HARMON-EPS – blue GLAMEPS – green Verification approach: 13 stations in the area of Krasnaya Polyana were clustered for matching to forecasts. Some ensemble verifications: ROC Area, Brier Skill Score, and Brier Score for Precip > 0.01 mm/3h COSMO-S14-EPS, NMMB-EPS and COSMO-RU2-EPS look most informative. Lead time

ROCA, BSS, and BS scores for Precip > 5 mm/3h For higher Precip threshold (w.r.t the low threshold): = COSMO-S14-EPS, NMMB, and HARMON-EPS become worse. = In contrast, LAEF and GLAMEPS become better. BSS ROCA BS COSMO-S14-EPS – red COSMO-RU2-EPS – orange LAEF-EPS – brown NMMB-EPS – black HARMON-EPS – blue GLAMEPS – green

Camera shots from Gornaya Carousel-1500 FROST-2014 experience demonstrates that direct forecast of visibility is a serious challenge. However, some results were encouraging. Example: 17 February 2014, 11:00-12:00 UTC (Biathlon venue) – Forecast of time slot for competitions during the 3-days period with low visibility. Forecast of wind direction and relative humidity (as proxy of visibility) by COSMO-Ru1 (1km grid spacing) Wind and RH at 850 hPa. Forecast from 12 UTC Biathlon Stadium 11:00 UTC 11:30 UTC12:00 UTC 11:00 UTC 13:00 UTC 12:00 UTC RH at 2m: Forecast and observations Biathlon Stadium

How was the window of good visibility on 17 February predicted by various systems?

List of other interesting cases Case Meteorological process/phenomenon Models’ behavior Impact on competitions Foehn Poor T forecast by most models at Biathlon Stadium (forecast errors negative: 1.4…3.7°С) Poor Max Wind Speed (Vmax) forecast by most models at Krasnaya Polyana (forecast errors negative: 3.5…7 m/s) Low visibility Postponed competitions at Laura and Extreme Park Cold front Good precipitation forecast by most models Foehn Poor T forecast by most models (negative forecast errors: °С, most markedly at height 1500 m) Cold front. Low visibility Tmax not good by most models (maximum T forecast at noon, whereas in reality maximum T occurred in the morning) Postponed skiing competitions at Roza Khutor “Weak process”. Precip. Poor precipitation forecast by most models at heights above 1500 m Cold frontPoor Vmax forecast (underestimation) by most models at heights above 1500 m

It was not simple for forecasters to deal with such an amount of information under the operational time constraints => Integrated Forecast F(t) – integrated forecast (t – forecast time); O – last available observation; f i (t) – forecast of i-th participating forecasting system; α(t), β i (t) - weights; b i (t) - bias for i-th forecasting system F. Woodcock and C. Engel: Operational Consensus Forecasts, Weather and Forecasting, 2005; L.X. Huang and G.A. Isaac: Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy, Weather and Forecasting, 2012

FROST-2014 weather data feed for the Olympic information system Integrated objective multi-model forecasts served as a first guess for preparation of the “official forecasts” for the Olympic information system. Web-editor was developed for forecasters for correction of objective forecasts. ATOS Requrements: - 1-hour update frequency; - Temporal resolution: for a current day – 1 hour; for subsequent days – 3 hours; - Forecast outlooks for a current day and next 5 days; - Alert Warnings

Forecasters’ subjective evaluation Model Grid mesh size Ove rall usef ulne ss Forecast accuracy:Visu aliza tion (app eara nce) Timel iness and reliab ility Comments TPrec ip Win d Gust s Vis COSMO- Ru7 7 km The basic model for the forecasters. Reasonable precip fact. Overestimated precip intensity. Tmin, Tmax poor. Wind poor. dT/dt OK. COSMO- Ru2 2.2 km The basic model for the forecasters. In general better than Cosmo-Ru7. COSMO- Ru1 1.1 km Comments are contradictory. The majority of forecasters considered COSMO-Ru2 to be more useful than COSMO- Ru1. Some forecasters preferred Cosmo-Ru1 (helpful wind, humidity). Overestimates precip intensity. COSMO- S14-EPS 7 km Precip reasonable. Good tendencies. Wind poor. Was available well before the Olympics that was helpful to get used to this information. NMMB 1 km Good in T and Precip. Informative visibility NMMB- EPS 7 km Nice. Informative visibility. Precip reasonable. Tmin, Tmax poor GEM km Good precip, humidity. GEM-1 1 km Good precip, humidity. GEM m Good precip, humidity. Very detailed maps.

Forecasters’ subjective evaluation Model Grid mesh size Over all usefu lness Forecast accuracy:Visua lizati on (appe aran ce) Time lines s and relia bility Comments TPreci p Win d Gust s Vis GLAMEPS 11 km Informative tendencies. Issues with absolute values. GLAMEPS calibr.,freque nt update 11 km Interesting and helpful. HarmonEPS 2.5 km In general good in T and Precip, but there were problems with T in anticyclones and Foehn. Harmonie 1 km Good T, Precip. ALADIN LAEF 11 km Good Wind, including Vmax. Nice plots WRF 600 m Useful but late COSMO- Ru2-EPS 2.2 km Experimental

Project Social and Economic Impacts Socially significant project application areas: Education Understanding Transfer of technologies Practical forecasting – first guess for operational official forecasts. Integrated project forecasts were used as a first guess for the data feed to the Olympic information system.

Further steps Enhanced quality control of the project observations archive. Additional diagnostic tools and export facilities on the project web-site Open access for international research community. Validation and intercomparison of the participating forecasting systems, case studies and numerical experiments, assessments of predictability of various weather elements; Update of developed technologies and transfer of positive experience into operational practice.

What’s next… Workshop this Fall in Moscow (difficult to attend for some of the participants) Interesting case studies for the international community Joint paper? Lessons learned (after Vancouver and in preparation for upcoming events)

Thank you! Gratitude to all the participants !

Along with traditional verification measures some new scores were implemented. EDI - Extremal Dependence Index NOTES: - Pictures will refer to thresholds 0.01 and 3, and the last threshold at which any of the three EDI curves remains not interrupted in the 0-36h interval - The base rate has the following approximate values: P(0.01mm/3h)=0.3; P(1mm/3h)=0.2; P(2mm/3h)=0.15; P(3mm/3h)=0.1; P(4mm/3h)=0.055;P(5mm/3h)=0.05 EDI = (logF – logH) / (logF+logH) EDI is especially recommended for low base-rate thresholds, but it will give a good comparative estimate of accuracy for all thresholds (“Suggested methods for the verification of precipitation forecasts against high resolution limited area observations” by the JWGFVR (Laurie Wilson, Beth Ebert et al.)

COSMO-S14-EPS Highest threshold: 8 mm/3h Lower decision-making level Blue: EDI for 50% probability threshold Green: for 66% Red: for 90%

Conclusions, EDI Extremal Dependence Index, EDI, can be used for decision making, especially for rare events when other scores, such as PSS, approach zero. Constructing EDI for different probability decision levels (50, 66, and 90%) showed that the participated EPSs demonstrate skill for all these levels up to the following precipitation thresholds: COSMO-S14-EPS and NMMB-EPS – informative up to 8mm/3h; COSMO-RU2-EPS, Harmon-EPS, ALADIN LAEF - informative up to 6mm/3h; GLAMEPS – informative up to 4mm/3h. Sampling effects are evident for all the models, especially for higher thresholds of variables. It is not possible to single out “the best ensemble producing system”, but still some conclusions can be drawn.