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Support for the 2014 Olympic Games in Sotchi Pierre Eckert MeteoSwiss, Geneva

Support for the 2014 Olympic Games in Sotchi Pierre Eckert MeteoSwiss, Geneva COSMO WG4 coordinator « Interpretation and applications » COSMO General meeting, September 2010. Plan of the session. General introduction (P. Eckert) Postprocessing / statistical downscaling

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Support for the 2014 Olympic Games in Sotchi Pierre Eckert MeteoSwiss, Geneva

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  1. Support for the 2014 Olympic Games in Sotchi Pierre Eckert MeteoSwiss, Geneva COSMO WG4 coordinator « Interpretation and applications » COSMO General meeting, September 2010

  2. Plan of the session • General introduction (P. Eckert) • Postprocessing / statistical downscaling • Some methods used in Vancouver 2010 • Experiences from Torino 2006 (M. Milelli) • Input from Roshydromet (I. Rosinkina, G. Rivin,…) • Know-how in postprocessing • Planed organisation / setup of measurements • Elements of discussion (for further treatment) • Setup of 0.5-2 km model, incl. assimilation • Implementation of a probabilistic model (EPS) • Connection with demonstration project • Role of the COSMO w.r. to other collaborations • Definition of WG4 working packages

  3. Enhanced observational network; Nowcasting tools; Regional data assimilation; High-resolution NWP models and EPS; Meso-scale verification system; Means of NWP output interpretationand delivery (new parameters and products, visualization etc); postprocessing; Training Primary meteorological needs for Sochi-2014: 3

  4. Postprocessing • Derived fields: pressure levels, PV, radar reflectivity,… • Generation of products: TV, Internet,… • Diagnostics: turbulence, icing, snowfall limit,… • Local adaptation, downscaling • Statistical downscaling (correction of model with observations) • Blending (mixture of model output and observations (gridded), INCA,…) • Downstream models (1d, 2d, 3d,…)

  5. Perfect Prog, MOS Use two sets of historical data: • The predictand = the local element you want to predict: temperature at Sotchi, occurrence of fog on the downhill slope,… • The predictors = a bunch of model parameters: pressure, instability indices, 850 hPa temperature, winds,…It is allowed to take recent observations of the predicand as predictor. Correlations (regression, discriminance,…) between the predictand and the predictors are computed. Often the predictors are selected by significance. Kalman filtering is probably a subclass

  6. Non linear methods • The same data sets can be treated with non linear methods • Neural networks • Boosting • … • Instead of defining hyperplanes in the predictor space, arbitrary shapes can be found. • The selection of predictors, the choice of an optimal separation surface and the computation of coefficients is called “learning”

  7. Classification • A set of fixed meteorological situations is defined. • Every country has several such classifications • They are usually correlated to sensible weather (in the situation 7b, the sun is shining in 90% of the corresponding days,…), in situation SWa there is an 80% chance to get hill fog over the downhill slope,…

  8. Classification and interpretation Luganorain > 10 mm/24h Luganorain > 1 mm/24h %

  9. Analogs • This method looks for the n situations in the past which are closest to a given forecast according to some distance. • A statistics on the weather elements corresponding to these n situations is then made. • As with the classifier, it is possible that the closest situation is far away from the presented situation.

  10. Statistical Adaptation for COSMO COSMO General Meeting 2010 Vanessa Stauch

  11. calibration with Kalman Filter >> recursive estimation of forecast error (prediction – correction) >> requires online observations >> can be used quasi-instantaneously (no large historical database) >> cannot predict fast changes (assumption of persistent error for each fcst) >> suitable for a subset of parameters (normally distributed errors)

  12. Kalman Filter @ MeteoSwiss operational: T2m, TD2m for COSMO-LEPS mean COSMO-7 COSMO-2 IFS in preparation: FF10m, TW2m, RH2m for COSMO-LEPS mean COSMO-7 COSMO-2 IFS

  13. T2m predictions COSMO-7 COSMO-7 COSMO-7 COSMO-7 COSMO-2 COSMO-2 KFC7 KFC2 performance?

  14. benefit COSMO-2 vs COSMO-7? C2 vs C7 C2-KF vs C7-KF Differences between COSMO-2 and COSMO-7 with KF smaller but still significant. Bias in KF predictions totally removed =

  15. Short term Kalman filter for radiation Zurich, 01.07.-10.07.2007 solar heat gain for south orientation (derived from global radiation) Short-term correction: based on previous hour and every new obs » exploits temporal autocorrelation of the error with the Kalman filter » corrects a few hours only » also beneficial for temperature forecasts

  16. Calibration with multiple regression MOS >> estimation of multiple linear regression models >> requires large historical database (observations and forecasts) >> models can be „arbitrarily“ complicated (provided the data) >> possibly less adaptive than the KF (constant regression parameters) >> suitable for a larger subset of parameters (compared to KF)

  17. SHA CHA UEB PMA ORO GUE EVI COSMO-7 vs COSMO-2 rRMSE (%) für 1-24h, period 01.09.08 – 31.03.09

  18. SHA CHA UEB PMA ORO GUE EVI effect on MOS-postprocessing rRMSE (%) für 1-24h, period 01.09.08 – 31.03.09

  19. plans for COSMO-MOS >> development of a regression-based model output statistics system >> target parameter: wind speed and direction, sunshine duration, global radiation >> using information of COSMO-7, COSMO-2 and COSMO-LEPS >> project duration 08.2010 – 08.2012

  20. Summary • Statistical postprocessing profits from a better NWP input model • „dynamical downscaling“ does not replace statistical adaptation to local observations (in particular if results being verified against those) • Long time series of model forecasts and observations (≥ 2 years) are prerequisite for the development of a robust statistical model

  21. 1d, 2d, 3d models • It is also possible to feed 1d, 2d, 3d models forced by the 3d (4d) model. • Ex. Fog model: soil model, a lot of levels in the few 10’s of meters of the atmosphere, aerosols,… • Should ideally be incorporated into the full model, but can be expensive.

  22. Local 2d and 3d models for the 2010 Vancouver Olympic Games COSMO General Meeting 2010 Thanks to André Méthod, CMC

  23. Real-Time Experimental Land Surface System for the 2010 Vancouver Games Natacha Bernier Linying Tong and Stéphane Bélair with contributions from: Maria Abrahamowicz, Bernard Bilodeau, Marco Carrera, Nathalie Gauthier, Lily Ioannidou, Alain Patoine, et Sylvie Leroyer SLIDE 1

  24. Concept of external land surface modeling (again!) LOW-RES ATMOS MODEL 3D INTEGRATION ATMOSPHERIC FORCING at FIRST ATMOS. MODEL LEVEL (T, q, U, V) ATMOSPHERIC FORCING at SURFACE (RADIATION and PRECIPITATION) HIGH-RES External Land Surface Model 2D INTEGRATION With horizontal resolution as high as that of surface databases (e.g., 100 m) Computational cost of off-line surface modeling system is much less than an integration of the atmospheric model SLIDE 2

  25. Applications to the 2010 Vancouver Games: Two surface systems: “2D” and “Point” 1400 x 1800 computational grid (100-m grid size) Whistler Blackcomb Callaghan VAN Cypress Bowl VANCOUVER USA SLIDE 3

  26. Experimental real-time “2D” land surface system 00 UTC Initial time Surface analysis ATMOSPHERIC FORCING REG-15 12Z (12-18h) REG-15 00Z (6-18h) REG-15 12Z (6-12h) 24-h open-loop run 24-h open-loop run for next day Geophysical fields ANALYSIS / ASSIMILATION FORECAST 96-h forecast run REG-15 00Z (0-48h) Av. at 03Z GLB-33 00Z (48-96h) Av. at 06Z 48h SLIDE 4

  27. Experimental real-time “point” land surface system 00 UTC Initial time Surface analysis ATMOSPHERIC FORCING SCREEN-LEVEL OBS + MODEL FORCING 24-h background run 24-h background run for next day Snow obs Geophysical fields ANALYSIS / ASSIMILATION FORECAST 96-h forecast run REG-15 00Z (0-48h) Av. at 03Z GLB-33 00Z (48-96h) Av. at 06Z 48h SLIDE 5

  28. Two-dimensional snow analysis against surface observations Close relationship with height of observations and of model outputs, ... but not always... (Bernier et al. 2010, part I) SLIDE 6

  29. Verification of “point” snow analysis at VOC VOC Blackcomb Mt. Base REG-OP (15 km) OBS “POINT” 2D-100m LAM-OP (2.5 km) 2008 As could be expected, “point” system is right on target (because of the asssimilation of surface snow data) Atmospheric forcing (e.g., precipitation phase) is of crucial importance for the 2D system (without assimilation of surface snow obs) SLIDE 7

  30. List of products • Last 10 days meteograms (forcing + screen-level diagnostics from surface system) • Last 10 days surfacegrams (surface prognostic variables – focus on snow conditions) • Next 4 days meteograms (forcing + screen-level diagnostics from surface system) • Next 4 days surfacegrams (surface prognostic variables – focus on snow conditions) SLIDE 11

  31. Examples of Meteograms and “Surfacegrams” SLIDE 12

  32. Whistler 15 km 2.5 km Vancouver 1.0 km A. Erfani, B. Denis, A. Giguère, N. McLennan, A. Plante, L. Tong, Environment Canada / MSC/ Development S. Bélair, M. Charron,J. Mailhot, R. McTaggart-Cowan, J. Milbrandt Environment Canada / Meteorological Research Division High resolution Numerical Weather Prediction Systems for the Vancouver 2010 Winter Olympics and Paralympics Games

  33. High Resolution Prediction System - cascading • Available to forecasters: • by 7:00 a.m. local • (for the morning briefing) • by 12:00 noon local • (for afternoon briefing)

  34. Customized output package • Based on Olympic forecasters’ feedback: - products, display format,… • Easy display (Weather Viewer) • Comprehensive list of model outputs: - 2D maps, time series at stations, vertical soundings and cross-sections • Products available for evaluation by support desk and briefings

  35. Customized output package 2D maps: • Screen-level potential temperature • Screen-level relative humidity • 10-m winds • Wind gusts (gust estimates, minimum, maximum) • Standard deviations of 10-m wind speed and direction • Accumulated precipitation types (liquid / freezing / snow / frozen) • Precipitation accumulation (liquid / solid / total) • Precipitation rate (liquid / solid / total) • Snow/liquid ratio • Cloud cover (high/ mid/ low) • Cloud base height • Visibility (through fog, rain, snow) • Freezing level (0C isotherm) • Snow level • Wind chill factor

  36. Customized output package Cloud cover: Low, mid, high Wind gusts Low level winds Surface winds Cloud base Windchill Dewpoint, RH Temperature Visibility: fog, rain, snow, resultant 2-D maps (1 km) QPF by type: cumul. , instant. QPF: /1h, /3h,/6h, cumulative

  37. Customized output package Clouds and visibility Wind and Gusts T, Td etc. General Wx Meteograms (1 km) Snow Precipitation PCP Rates

  38. Customized output package General weather: low-level temperature, cloud cover, total precipitation, wind speed and direction 1-km LAM model Callaghan Valley (VOD)

  39. High Resolution Prediction System - Multi-model Meteograms Precipitation Amounts Rates

  40. Thank You!

  41. Plan of the session • General introduction (P. Eckert) • Postprocessing / statistical downscaling • Some methods used in Vancouver 2010 • Experiences from Torino 2006 (M. Milelli) • Input from Roshydromet (I. Rosinkina, G. Rivin,…) • Know-how in postprocessing • Planed organisation / setup of measurements • Elements of discussion (for further treatment) • Setup of 0.5-2 km model, incl. assimilation • Implementation of a probabilistic model (EPS) • Connection with demonstration project • Role of the COSMO w.r. to other collaborations • Definition of WG4 working packages

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