STEPS-BE: an ensemble radar rainfall nowcasting system for urban hydrology in Belgium

Transcription

1 STEPS-BE: an ensemble radar rainfall nowcasting system for urban hydrology in Belgium Loris Foresti 1,2, Maarten Reyniers 2, Lesley De Cruz 2, Alan Seed 3 and Laurent Delobbe 2 with contributions from Daniele Nerini 1, Ioannis Sideris 1 and Urs Germann Radar, Satellite and Nowcasting division, MeteoSwiss, Locarno-Monti, Switzerland 2 - Radar and Lightning Detection group, Royal Meteorological Institute, Brussels, Belgium 3 - Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Australia

2 Loris swimming in the river on 1st of August

3 What is nowcasting? Nowcasting: Detailed description of the current weather along with forecasts obtained by extrapolation for a period of 0 to 6 hours ahead using the latest radar, satellite and observational data Why nowcasting? Aiding forecasters in warning the public for hazardous high-impact weather Reduction of fatalities and injuries Reduction of property damage Aviation and marine safety Construction and leisure industry Power and water management Thunderstorms evolve very fast NWP models cannot be run every 5 min NWP skill low in the first 2-3 hours Radar data assimilation not trivial

4 A Swiss example The Sihl catchment How much rain can be expected to fall in the Sihl catchment within the next few hours?

5 Extrapolation of radar images Average rainrate over the catchment obs forecast How confident can we be that the storm won t touch the Sihl catchment? 13:00 14:00 15:00 16:00 Radar extrapolation (Lagrangian persistence): X(t + 1, x) = X(t, x α) future = present + displacement Main source of uncertainty: unknown rainfall growth and decay

6 Forecast uncertainty? Go ensemble! Ensemble forecasting: generate a set of forecasts to give an indication of the range of possible future states of the atmosphere. Generate multiple scenarios of the future Sources of uncertainty: - Initial conditions uncertainty (limitation of observations) - Model uncertainy (limitation of the model and parametrizations) NWP: perturb the initial conditions and let the forecast errors grow day 1 day 5 Nowcasting: no time to run NWP Need to find pragmatic solution!

7 Wavelength [km] Wavelength [km] Stochastic perturbations R 8 20 Fourier filtering of white noise 512 ε 20 Wavelength [km] ε = FFT 1 R N 8 N 8 20 rainfall field X(t) Wavelength [km] stochastic source-sink term ε(t) X(t + 1, x) = X(t, x α) + ε(t, x α) future = present + displacement + random growth/decay

8 Ensemble radar extrapolation Ensemble member 1 «possible future 1» Ensemble member 2 «possible future 2» Average rainrate over the catchment 50 members obs forecast observation Hydrological model Courtesy of M. Zappa: 13:00 14: :00 16:00

9 STEPS: Short-Term Ensemble Prediction System Vieux & associates USA Aalto Bristol Uni Uni FMI Met Office RMI ZAMG MeteoSwiss Hydrometcentre Russia Shanghai WS NIED Japan Singapore WS South African WS BOM 2000 UT Sydney Monash Uni Weather radar Co NZ

10 1. Estimation of advection using optical flow on radar images 2. Spatial scaling (FFT decomposition of rain field) 3. Dynamic scaling (rainfall lifetime ~ spatial scale) 20 member ensemble nowcast = Lagrangian extrapolation + stochastic evolution of cascade (Hierarchy of AR(1) processes) STEPS principles km km Seed (2003) Bowler et al., QJRMS, km km km km km Schertzer and Lovejoy, JGR, 1989 Realistic Forecast space-time uncertainty: properties and scale-dependent lifetime of rainfall - Rainfall growth and decay features adapted in real-time - Evolution of velocity field - Evolution of mean areal rainfall

11 STEPS probabilistic nowcast +5 min +120 min % ensemble members >= 0.1 mm/hr +5 min +120 min

12 STEPS probabilistic nowcast +5 min +120 min % ensemble members >= 5.0 mm/hr +5 min +120 min

13 STEPS ensemble mean Average of ensemble members Deterministic quantitative rainfall nowcast Accounts for loss of predictability (unpredictable features are automatically smoothed out) +5 min +120 min

14 Verification of STEPS-BE nowcasts STEPS nowcasts min min Radar observations Forecast biases

15 Is the forecast uncertainty correctly represented? Equally likely ensemble members Ensemble spread around mean = ensemble mean RMSE STEPS-BE slightly underdispersive

16 Reliability of probabilistic forecast Reliability: agreement between forecast probability and observed frequency Resolution: ability of the forecast to distinguish situations with strictly different observed frequencies Sharpness: ability to forecast probabilities near 0 or 1

17 Reliability of probabilistic forecast Reliability: agreement between forecast probability and observed frequency Resolution: ability of the forecast to distinguish situations with strictly different observed frequencies Sharpness: ability to forecast probabilities near 0 or 1

18 Real-time STEPS-BE 20 member ensemble at 1 km 2 and 5 min resolutions up to +2h Times series of observed and forecast rain accumulations and probability at major cities and weather stations Detailed documentation section with case studies Feedback from forecasting office Continuous improvement of STEPS components Six S precipitation nowcasting workshop at MeteoSwiss, Oct. 2017

19 Conclusions The chaotic behaviour of the atmosphere and complex microphysics makes it impossible to provide a deterministic precipitation forecast (Edward Lorenz is still right ) The estimation (and verification) of the forecast uncertainty is as important as the forecast itself The predictability of weather changes in space and time STEPS provides an empirical treatment of the forecast uncertainty based on stochastic error models Need to engage with users (hydrologists, forecasters) to integrate the new probabilistic products into the warning systems Blend radar extrapolation with NWP to extend lead time beyond 2h Lesley

20 Part I Current research directions Non-stationary stochastic ensemble generator for radar rainfall fields Part II Radar-based analysis of orographic precipitation growth and decay Part III Seamless blending of radar and NWP ensembles using a Kalman filter NWP Radar extrapolation

21 Part I Non-stationary stochastic ensemble generator for radar rainfall fields Part II Radar-based analysis of orographic precipitation growth and decay Part III Seamless blending of radar and NWP ensembles using a Kalman filter NWP Radar extrapolation

22 Rainfields are often non-stationary Several local anisotropies are embedded in the global Fourier spectrum The stochastic perturbations will have the same properties everywhere over the forecast domain An elegant way to separate them is to compute local Fourier transforms!

23 Short-Space Fourier Transform Fourier analysis localized by Hanning window Local filtering of white noise Non-stationary perturbation field reproduces the local correlation structure (anisotropy and range) Hanning window Nerini et al., HESS (2017)

24 Probabilistic nowcasting with non-stationary noise Global noise perturbations Local noise perturbations

25 Part I Non-stationary stochastic ensemble generator for radar rainfall fields Part II Radar-based analysis of orographic precipitation growth and decay Part III Seamless blending of radar and NWP ensembles using a Kalman filter NWP Radar extrapolation

26 A complex Alpine orography!

27 Panziera et al. (2015) Orographic precipitation mechanisms Sketch: Kappenberger warm air

28 A winter orographic precipitation case Windward side: precipitation growth Leeward side: precipitation decay

29 Dependence of growth/decay with flow and freezing level height NW flow HZT 1-2 km NW flow HZT km SW flow HZT 1-2 km SW flow HZT km

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33 Nowcasting of growth and decay based on machine learning Predictors (inputs): (X, Y, Z) (U, V) HZT IMF (t) WAR (t) Geography Flow Airmass Precip now Machine learning (regression) Decision trees, neural networks, etc Predictand (output): IMF(t+1) / IMF(t) WAR(t+1) / WAR(t) Precip +1h

34 Part I Non-stationary stochastic ensemble generator for radar rainfall fields Part II Radar-based analysis of orographic precipitation growth and decay Part III Seamless blending of radar and NWP ensembles using a Kalman filter NWP Radar extrapolation

35 Radar extrapolation vs COSMO skill MAPLE vs COSMO-2 NowPrecip vs COSMO-1 Mandapaka et al. (2012) Cross-over time ~2-3 hours Nerini et al., internal report (2017)

36 Ensemble Kalman filter Kalman filter (KF): recursive combination of information in the presence of uncertainty Ensemble KF (EnKF): Monte Carlo approximation of KF (error covariance matrix derived from the ensemble) Local Ensemble KF (LEKF): spatial localization of EnKF to increase flexibility of the model solutions φ a = analysis φ f = COSMO forecast («background» to adjust) d = radar extrapolation («observation») P = covariance matrix of COSMO errors R = covariance matrix of nowcasting errors No forward operator needed (same variable and grid) Only measurement update step of Kalman (non recursive)

37 Radar vs COSMO ensemble

38 An example of EnKF blending Local SVD implementation of the EnKF (Evensen, 2003) using a 75 km influence radius Analysed ensemble becomes a local linear combination of the background ensemble

39 Verification of ensemble spread Stochastic radar ensemble COSMO time-lagged ensemble Local EnKF ensemble Need for: Variance inflation Non-gaussian variable Better conditioning in 0-2h range COSMO-E and more members