Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Support for the 2014 Olympic Games in Sotchi Pierre Eckert MeteoSwiss, Geneva COSMO WG4 coordinator « Interpretation and applications » COSMO General meeting, September 2010

2 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 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 interpretation and delivery (new parameters and products, visualization etc); postprocessing;  Training Primary meteorological needs for Sochi-2014:

4 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch Perfect Prog, MOS Use two sets of historical data: 1.The predictand = the local element you want to predict: temperature at Sotchi, occurrence of fog on the downhill slope,… 2.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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch Classification and interpretation Lugano rain > 1 mm/24h Lugano rain > 10 mm/24h %

9 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Statistical Adaptation for COSMO COSMO General Meeting 2010 Vanessa Stauch

11 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch Kalman 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch T2m predictions COSMO-7 COSMO-2 KF C7 COSMO-7 KF C2 COSMO-7 performance?

14 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch benefit COSMO-2 vs COSMO-7? – RMSE All ANETZ stations 11.0 %4.0 % Low ANETZ stations 8.8 %5.2 % High ANETZ stations 13.0 %3.2 % – STD All ANETZ stations 12.3 %4.0 % Low ANETZ stations 12.7 %5.2 % High ANETZ stations 12.6 %3.2 % C2 vs C7C2-KF vs C7-KF = Differences between COSMO- 2 and COSMO-7 with KF smaller but still significant. Bias in KF predictions totally removed

15 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch Short term Kalman filter for 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 Zurich, solar heat gain for south orientation (derived from global radiation)

16 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch COSMO-7 vs COSMO-2 COSMO-7COSMO-2 (03) Chasseral (CHA) 56>44 Evionnaz (EVI) 90<99 Gütsch (GUE) 55>45 Oron (ORO) 53<59 Piz Martegnas (PMA) 51>45 Schaffhausen (SHA) 54>52 Uetliberg (UEB) 76>69 rRMSE (%) für 1-24h, period – CHA EVI GUE PMA SHA ORO UEB

18 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch effect on MOS-postprocessing COSMO-7 MOS COSMO-2 (03) MOS Chasseral (CHA) 34>32 Evionnaz (EVI) 78>77 Gütsch (GUE) 45>39 Oron (ORO) 59>47 Piz Martegnas (PMA) 69>42 Schaffhausen (SHA) 85>77 Uetliberg (UEB) 59>54 rRMSE (%) für 1-24h, period – CHA EVI GUE PMA SHA ORO UEB

19 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 –

20 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss 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 with contributions from: Maria Abrahamowicz, Bernard Bilodeau, Marco Carrera, Nathalie Gauthier, Lily Ioannidou, Alain Patoine, et Sylvie Leroyer Natacha Bernier Linying Tong and Stéphane Bélair SLIDE 1

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

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

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

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

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

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

30 Screen-level air temperature from the “2D” land surface system (Bernier et al. 2010, part I) SLIDE 8

31 Screen-level air temperature error distributions for the “2D” system (Bernier et al. 2010, part I) Removes bias, but just slightly better than a simple downscaling of the REG-15 and GLB-33 models (strong effect of orography, versus surface cover types) SLIDE 9

32 Objective evaluation of screen-level air temperature from the “Point” system (against “downscaled” REG-15 and GLB-33) (Bernier et al. 2010, part II) With assimilation (CaLDAS), same kind of improvement should be expected for the 2D system SLIDE 10

33 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

34 Examples of Meteograms and “Surfacegrams” SLIDE 12

1.0 km Whistler Vancouver 15 km 2.5 km High resolution Numerical Weather Prediction Systems for the Vancouver 2010 Winter Olympics and Paralympics Games 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 Prediction System - cascading Available to forecasters: by 7:00 a.m. local (for the morning briefing) by 12:00 noon local (for afternoon briefing)

Latest available program libraries: 1.Dynamics (GEM v_4.0.6): vertically-staggered grid (Charney-Phillips): solved intermittent grid-point storm problem caused by instability of semi-Lagrangian scheme at high resolution “growing” orography: reduction of shocks during the nesting adjustment 2.Physics (PHY v_5.0.4): – CCCmaRAD radiation scheme (solar + infrared) – Milbrandt-Yau double-moment microphysics scheme + prognostic snow density (solid-to-liquid ratio) Configuration of LAM-V10 NOTE: LAM-V10 is different from the quasi-operational “GEM-LAM-2.5” configuration

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 Customized output package

High Resolution Prediction System - New Features Some Improved physics and dynamics over “operational models”: geophysical fields with resolution of 90m (orography, surface roughness,…) Improved radiation scheme Milbrandt-Yau double-moment bulk microphysics Vertical staggering of 58 levels New model diagnostic outputs: visibility reduction due to hydrometeors (fog, rain, and snow) cloud base, melting level solid-to-liquid ratio for snow density diagnostics of surface wind gusts Customized output package: based on Olympic forecasters request and feedback; clear and easy to use; multi model display products for better model comparisons

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 Customized output package

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

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

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

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

High Resolution Prediction System - Multi- What worked well The ongoing collaborations between the operational forecasters and the research & development team forecasters Feedbacks ongoing training reasearch and support desk (SNOWV10) The state of the art science of the high resolution GEM Physics (Mildbrandt and Yau scheme, new radiation scheme etc) Dynamics (new vertically staggered coordinate system in GEM Customized outputs new diagnostic outputs (winds gust, visibility fields etc) 2 D display fields meteograms showing high resolution in time point forecasts Multi-model display fields (for comparisons and verifications)

High Resolution Prediction System - Multi- Needed improvements The horizontal and vertical resolution more detailed topography and geophysical fields better handling of the calculations in the vertical Higher resolution data assimilation

Thank You!

48 Sotchi Olympic Games, General Introduction ¦ COSMO General Meeting September 2010 Pierre.Eckert[at]meteoswiss.ch 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 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