1 / 25

D.Kiktev, E.Astakhova, A.Muravyev, M.Tsyrulnikov

D.Kiktev, E.Astakhova, A.Muravyev, M.Tsyrulnikov. Perfomance of the WWRP project FROST-2014 forecasting systems: Preliminary assessments (FROST = Forecast and Research in the Olympic Sochi Testbed). WWOSC-2014, 16-21 August 2 014. PRESENTED by S. BELAIR (with additions).

keona
Download Presentation

D.Kiktev, E.Astakhova, A.Muravyev, M.Tsyrulnikov

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. D.Kiktev, E.Astakhova, A.Muravyev, M.Tsyrulnikov Perfomance of the WWRP project FROST-2014 forecasting systems: Preliminary assessments (FROST = Forecast and Researchin the Olympic Sochi Testbed) WWOSC-2014, 16-21 August 2014 PRESENTED by S. BELAIR (with additions)

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

  3. Goals of WMO WWRP RDP/FDP FROST-2014: • To develop a comprehensive information resource of alpine winter weather observations; • 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)

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

  5. 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

  6. Forecasting systems participating in RDP/FDP FROST-2014 Deterministic NWP: COSMO-RU with grid spacing 1km, 2.2km, 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) Nowcasting: ABOM, CARDS, INCA, INTW, MeteoExpert, Joint (Multi-system forecast integration)

  7. 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.

  8. 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: 15.1.2014-15.3.2014, 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.

  9. Role of spatial resolution for ensemble forecasts – continued COSMO-S14-EPS (7km grid spacing) vs COSMO-RU2-EPS (2km grid spacing) Verifications for ensemble mean Verification Period: 15.1.2014-15.3.2014 • T2m: Some positive effect of downscaling from 7 to 2 km resolution. • Wind Speed: No positive effect of dynamical downscaling was found.

  10. Some ensemble verifications: ROC Area, Brier Skill Score, and Brier Score for Precip > 0.01 mm/3h 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. Lead time COSMO-S14-EPS, NMMB-EPS and COSMO-RU2-EPS look most informative.

  11. ROCA, BSS, and BS scores for Precip > 5 mm/3h ROCA BSS BS COSMO-S14-EPS – red COSMO-RU2-EPS – orange LAEF-EPS – brown NMMB-EPS – black HARMON-EPS – blue GLAMEPS – green 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.

  12. 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) byCOSMO-Ru1 (1km grid spacing) RH at 2m: Forecast and observations 17.02.2014 . Camera shots from Gornaya Carousel-1500 11:00 UTC 11:30 UTC 12:00 UTC Wind and RH at 850 hPa. Forecast from 12 UTC 16.02.2014 11:00 UTC 12:00 UTC 13:00 UTC Biathlon Stadium Biathlon Stadium Biathlon Stadium

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

  14. List of other interesting cases

  15. It was not simple for forecasters to deal with such an amount of information under the operational time constraints => Integrated Forecast • 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 F(t) –integrated forecast (t – forecast time); O – last available observation; fi(t)– forecast of i-thparticipating forecasting system; α(t), βi(t) - weights; bi(t) - biasfori-thforecasting system

  16. FROST-2014 weather data feedfor 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 outlooksfor a current day and next 5 days;- Alert Warnings

  17. Forecasters’ subjective evaluation

  18. Forecasters’ subjective evaluation

  19. 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.

  20. Further steps • Enhanced quality control of the project observations archive. • Additional diagnostic tools and export facilities on the project web-site http://frost2014. • Open access for international research community. • Validation and intercomparison of the participating forecasting systems, case studiesand numerical experiments, assessments of predictability of various weather elements; • Update of developed technologies and transfer of positive experience into operational practice.

  21. 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)

  22. Thank you! Gratitude to all the participants ! http://frost2014.meteoinfo.ru

  23. Along with traditional verification measures some new scores were implemented. EDI - Extremal Dependence Index 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.) 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

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

  25. 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.

More Related