Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems. How to represent uncertain radar observations and weather forecasts

Transcription

1 Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems How to represent uncertain radar observations and weather forecasts A. M. Rossa, COST 731 Management Committee Chair ARPA Veneto, Centro Meteorologico di Teolo, Italy contributions from the COST 731 and MAP D-PHASE community BALTRAD End User Workshop 26 Ocober 2010, Aalborg, Denmark

2 Physics lab memories... Take a simple measurements Repeat it over and over... mean standard deviation Mean: expected value or simply the measure Standard deviation: the typical spread or error you have to expect Histogram = frequency distribution Error model = Gaussian/Normal You could not stop until...

3 ... you had your errorbars!

4 Propagation of uncertainy e.g. runoff model runoff Model sensitivities ΔQPE/F Δsoil moisture Δparameters

5 ... unfortunately radar are afflicted by uncertainties Before telling you more...

6 Chair country in red, others in blue COST Countries COST Countries: 24 Chair: IT AU, BE, CH, CY, CZ, DE, DK, ES, FI, FR, GR, HU, IE, IL, IT, LU, NL, NO, PL, PT, RO, SE, UK Non-COST institutions linked through reciprocal agreement (AU, NZ, SA, AR)

7 History sketched: COST 731 latest offspring of a series of COST Actions on radar COST 72 (79-84): Measurement of precipitation by radar COST 73 (86-91): Weather radar networking COST 75 (92-97): Advanced Weather Radar Systems COST 717 (99-04): Application of radar in NWP & hydrology Uncertainty in radar (and NWP) and cascading into hydrological models COST 731 Propagation of Uncertainty in Advanced Meteo- Hydrological Forecast Systems D-PHASE is substantial part of COST 731

8 Flood forecasting chain various research directions COST 731: Start Jun 2005, End Jun 2010 MC: 28 operational, 7 research institutions; WG experts mostly from research institutions observations observing systems e.g. radar meteo hydro NWC observations: radar, gauges, satellites meteorological nowcasting system nowcast WG-2 hydrological forecast process NWP WG-1 hydrological data runoff, discharge runoff, discharge Propagation of uncertainty from observing systems and NWP into hydrological models (M. Zappa, WSL; NWP U. + Nowcast Germann, MCH) Hydrological Verification Verification verification Propagation of uncertainty from observing NWP NWP data assimilation systems NWP (radars) analysis into forecast NWP process (Ch. Keil, forecast LMU; fields G. Haase, SMHI) WG-3 End users, Use of decision uncertainty makers in warnings and decision making (M.Bruen, UCD; P. Krahe, BfG) decision process decision, warning Warning evaluation

9 COST 731 & radar data quality Several levels of quality accounting: Human expert qualitative Indices: apply or not apply? or partly apply? application-dependent Quantify: probabilistic QPE, ensembles quantitative

10 COST 731 & radar data quality: apply or not apply? Quality description is a pre-requisite for systematic use hydrological forecasting and NWP assimilation, e.g. non-rain echoes: 6h accum clear sky Model response for CAPE 800J/kg + wind Rossa and Leuenberger, 2008, Met. Apps

11 COST 731 radar data quality: partly apply or blind spots: vertical cross section LHN time-integrated w height +50 Radar blind: - LHN dries model - induces circulation - modfies precip LHN drying -20 Rossa et al., 2009, AMS Radar

12 A radar quality map may look like this... An example of w (0-1) for the Swiss radar network: 3-month period, DJF and JJA period DJF JJA Can evolve in time (seasonality)

13 This is application-dependent and needs to be identified and designed in close collaboration with the end user!

14 A novel approach: radar ensemble or probabilistic precipitation estimation Urs Germann, MeteoSwiss M Berenguer (CRAHI), D Sempere-Torres (CRAHI), M Zappa (WSL) Thanks to: I Zawadzki, X Llort, G Lee, G Pegram, A Seed, E Cassiraga

15 Quantify radar data quality: think ensemble! vary sligthly Radar ensemble radar error can be used to generate ensemble of equaly likely realizations radar errors are correlated in x/t and hard to quantify several approaches: Rain map? Germann: climatology w.r.t. dense rain gauge network Sempere: real-time estimation by comparing two stages of QC (benchmark) Szturc: error parametrization with QI Pegram: separate noise from signal and stochastically model noise Germann et al., 2009, Quarterly Journal

16 3 ensemble members (example) perturbation fields (from stochastic simulation) + original field (unperturbed) -8 0 db 8 = ensemble members (perturbed precipitation fields) mm in 1h mm in 1h

17 Radar ensemble for runoff modeling Radar ensemble Goal: import radar precipitation field into runoff model and keep track of uncertainties Ensemble of runoff Radar precipitation field 100 m 3 /s Runoff model 50 m 3 /s Time Germann et al., 2009, Quarterly Journal

18 Major flood event Verzasca river 5Sep08 7Sep08 Verzasca river, Lavertezzo 2605m, 186km Runoff m 3 /s Pincascia 44km 2 1 year data Radar deterministic Radar probabilisti c (ensemble) Raingauges deterministic Bias % As far as we know Scatter db st real-time experiment worldwide Major flood event of Verzasca river coupling radar ensemble with runoff model. Germann et al., 2009, Quarterly Journal

19 August 2005 flood in Switzerland probability for 72h precipitation sum A. Walser, MeteoSwiss André Walser, MeteoSwiss

20 August 2005 flood in Switzerland HEPS: hydrological ensemble prediction system, based on COSMO-LEPS runoff at Hagneck (Aare, 5170 km 2 ), IT: , 00 UTC Observed runoff Ensemble range Simon Jaun, IACETH

21 is such information used in the real world? Business case: Construction of a new train station in Zurich requires river duct partially be closed Sihl at Zürich HB, June 16 th 2009 Photo: A. Ludwig

22 Outreach to applications Cost of opening the gates: 1 Mio CHF Damage if not opened: many billion CHF Flood wave to station: 2-6 hours Evacuation construction site: 2-4 hours Opening one gate: 1-2 hours

23 Verification: Brier Skill Scores (BSS) for COSMO-LEPS and COSMO7 reforecasts moderate flow high flow very high flow Probabilistic beats deterministic! Switzerland: M. Zappa et al.

24 Zurich rail project: a show case Verification: Brier Skill Scores (BSS) for COSMO- LEPS and COSMO7 reforecasts moderate flow high flow very high flow Probabilistic beats deterministic! Switzerland: M. Zappa et al.

25 Swedish WebHyPro System Two displays: - EPS/HBV based quantile plots - Areal flood alert map

26 Country Platform Status Chains End users Scale Forecast range CH Verzasca op. (e)qpe, (e)nwp, HYD scientific, hydro power PL IMGW, Szturc exp. (e)qpe, QPF, HYD scientific 300 case study CH Sihl op. QPE, (e)nwp, HYD, EU city, canton, railway, hydro power 330 hrs 5d E Besos exp. (e)qpe, HYD scientific Real 1020 case study E Catalunya op.? Catalan water agency, civil protection?? Many E Andalucia exp.? water agency??? LU Alzette exp. EPS, statistics, HYD scientific d? D Donau (Preview) op./exp. (e)nwp, HYD public, authorities, etc ^5 3d, 5d CH, Alps D-PHASE exp. QPE, TRT, many (e)nwp, many HYD large demonstr. project small to Alpine hrs 5d Systems CH GIN op. (e)nwp, HYD authorities End small-swiss hrs 5d B RMIB op EPS, HYD regional authorities 6 10^2 2 10^4 10d SF SYKE op. EPS (10,30,100d), HYD flood mitigation, hydro power, public > 1000 days, months, seasons & N NVE op. EPS, HYD??? S WebHyPro op. DET/EPS, HYD hydro power, public 10-10^5 10d F Safran-Isba-Modcou\ (Météo France) op.exp EPS, HYD Schapi (flood forecasting) >1000 km 1-10 days countries det. & prob. QPX on all smal l F Safran-Isba-Modcou (Météo France) exp EPS, HYD scientific Users > 1000 km Months seasons F AROME-Isba-TopModel (Météo-France) exp. EPS, HYD scientific <100km2 0-2 days scales NL KNMI op. EPS-based warning system waterboard 2000 Dutch 10d D BafG, Rhein exp. (e)nwp, HYD scientific 10^4 10^5 5d UK K. Beven????? E INM B. Orfila????? to large CZ Daniela Rezacova exp. NWP: COSMO + assim. radar, HYD: HYDROG scientific flash flood warning small CR catchments 10^3 1h 4h IR M. Bruen?????

27 The pieces that constitute D-PHASE Ensemble (7) and high-resolution deterministic (23) models for the atmosphere Coupling of atmospheric and hydrological (7) models in 40+ catchments (deterministic as well as ensemble systems) Nowcasting tools Centralised visualisation platform and data archive Involvement of forecasters and end users (47 institutions and nine participating countries) Objective verification (models) and subjective feedback (forecasters, end users) Distributed, real-time end-to-end forecasting system

28 Visualisation Platform (VP) Visualises (displays) all products and all alerts; main source of information for forecasters and end users. Alerts based on return periods: End users Key features: No data No alert 6 times a year Twice a year Every 10 years (Accumulation times: 03h, 06h, 12h, 24h, 48h, 72h) Same look and feel for all models (colour coding etc.) Full information is provided to the end user; the diversity of forecasts is visible Hierarchical layout allowing to zoom in from an overview to the details

29 More details: Which models alert? , ~ 15:00 local time

30 Atmospheric alerts: For which point in time? , ~ 15:00 local time

31 And how do the actual precipitation forecasts look like? Same plots (variables, colour coding, ) for all models , ~ 15:00 local time

32 In summary: no use in ignoring uncertainty Systematic treatment of uncertainty prerequisite for taking advantage of imperfect yet informative radar and weather forecast data. COST 731: acted as a door-opener and had significant end user involvement witnessed several integrated, end-to-end forecasts systems provided information and hands-on training on visualization platforms of probabilistic information based on huge amounts of observational and forecast data learnt to express radar uncertainty in directly usable forms, i.e. QPE ensembles demonstrated that probabilistic beats deterministic information

33 Thank you very much for your attention! Uncertainty does not mean that anything can happen, it just means you have to work harder to get what you are looking for.