Seasonal Forecasting and Hydrology at ECMWF

Transcription

1 Seasonal Forecasting and Hydrology at ECMWF Florian Pappenberger European Centre for Medium-Range Weather Forecasts FEWS User Meeting in Delft /42

2 Overview Background to ECMWF (quick) Ensemble Forecasting (quick) Seasonal Forecasting Forecast System Products Meteorological Skill Case Study: Bangaldesh Monthly Forecasting The new ECMWF system Case Study: Danube Trib, Romania, October 2007 TIGGE: New perspectives on Medium Range Forecasting Grand ensembles (TIGGE) Case Study: Danube Trib, Romania, October 2007 Case Study: Upper Severn (Wales), January, 2008 FEWS User Meeting in Delft /42

3 Background Convention establishing ECMWF entered in force on 1 st Nov 1975, having been ratified by the following 13 Member states: Denmark Spain Ireland Netherlands Switzerland Sweden Belgium Germany France Yugoslavia Austria Finland United Kingdom Recognition of importance and potential to improve medium-range weather forecasts with benefits to the European economy Protection and safety of population Development of meteorology in Europe / post university training Development of European industry in the field of data-processing Recognition that resources are needed on a scale exceeding those normally practicable at national level FEWS User Meeting in Delft /42

4 Background: Today ECMWF is an independent international organization, supported by 18 member states Co-operating agreements: 12 co-operating states Croatia Czech Republic Estonia Iceland Montenegro Hungary Lithuania Morocco Romania Slovakia Serbia Slovenia FEWS User Meeting in Delft /42

5 Background: More Information & contact your national representative FEWS User Meeting in Delft /42

6 Ensemble Forecasting: Why? Edward Lorenz ( ) The Lorenz (1963) attractor, the prototype chaotic model.. one flap of a sea-gull s wing may forever change the future course of the weather (Lorenz, 1963) FEWS User Meeting in Delft /42

7 Ensemble Forecasting: What is it? Temperature Initial condition Forecast time Forecast Complete description of weather prediction in terms of a Probability Density Function (PDF) FEWS User Meeting in Delft /42

8 Ensemble forecasting: A defining conversation (Peter Webster, January 2003) P.Webster: We hope to provide you with seasonal forecasts to help you plan your agricultural activities FARMER: That would be good. P.Webster: But we will not always be correct: Perhaps only 7 times out of 10. FARMER: (after some thought): That is fine. Only God knows 100% what will happen and you are not God! Right now, we guess each year and that means we are right as often as we are wrong. 7 out of 10 means I am ahead! FEWS User Meeting in Delft /42

9 Review Paper on operational flood forecasting & ensemble techniques Hannah Cloke (King s College London), hannah.cloke@kcl.ac.uk A review of the scientific drivers of the shift towards ensemble flood forecasting Discuss questions surrounding best practise Critique main case studies Review existing operational and pre-operational systems using ensembles in flood forecasting FEWS User Meeting in Delft /42

10 Seasonal forecasting at ECMWF In 1995 ECMWF started an experimental programme in seasonal forecasting. Successful predictions of the exceptional El Nino event of 1997 encouraged the Council to support the seasonal forecast activity. A range of seasonal products are issued routinely on FEWS User Meeting in Delft /42

11 Seasonal forecasting at ECMWF Observations Data Assimilation Coupled model Forecast Products Current state of the atmosphere Atmospheric model Coupler Current state of the ocean Ocean Model FEWS User Meeting in Delft /42

12 Seasonal forecasting: Products Seasonal outlook: (up to 7 months ahead) - Forecasts for Nino3, Nino3.4 and Nino4 - Spatial plots (ens.mean anomaly, terciles..) - Climagrams ( similar to Epsgrams) - Tropical storms Annual outlook: (up to 10 months ahead) - Forecasts for Nino3, Nino3.4 and Nino4 - Spatial plots (ens. mean anomaly, terciles..) - Tropical storms FEWS User Meeting in Delft /42

13 Seasonal forecasting at ECMWF: Products 2m Temperature Mean sea level pressure Precipitation Sea surface temperature 850 hpa temperature 500 hpa geopotential Forecast is made available on the 15th of each month. FEWS User Meeting in Delft /42

14 Seasonal Forecasting: Products 2m Temperature Mean sea level pressure Precipitation Sea surface temperature 850 hpa temperature 500 hpa geopotential Forecast is made available on the 15th of each month. FEWS User Meeting in Delft /42

15 Seasonal Forecasting 2m temp. predictions for Southern Europe Forecast started in May 2007 FEWS User Meeting in Delft /42

16 Seasonal Forecasting: Any good? (anomaly correlation) Spring (MAM) Summer (JJA) Winter (DJF) Autumn (SON) Over Europe seasonal forecast skill is larger in spring! FEWS User Meeting in Delft /42

17 Seasonal Forecasting: Any good? ROCSS ROC diagram Event: T2m in upper tercile FEWS User Meeting in Delft /42

18 Seasonal Forecasting: Any good? We use different measures to estimate the skill. The skill is larger over the tropics. The skill is seasonal dependent. FEWS User Meeting in Delft /42

19 Outlook Long-term predictions over Europe are particularly difficult: At times during very large El Niños parts of Europe seem to be affected. However non-linearity of the atmosphere seem to play a relevant role over this region. The Atlantic Ocean influence on the weather over Europe is not yet well understood. FEWS User Meeting in Delft /42

20 Case study, Seasonal Forecasting: Bangaldesh The case study has been kindly provided by Peter Webster (School of Earth and Atmospheric Sciences & CFAN: Climate Forecast Application Network Georgia Institute of Technology, Atlanta, Georgia, USA) & Tom Hopson (NCAR) please him for any questions Forecasts can be found at (reference: Hopson & Webster, 2008 see review paper) FEWS User Meeting in Delft /42

21 Overview The Bangladesh situation: Overcoming the problem of no upstream discharge data from two of the largest basins Ganges and Brahmaputra forecasts for 2003 and 2004 Seasonal and medium range forecasts for The 2007 seasonal and 1-10 day forecasts and their use! Extensions Forecasts can be found at FEWS User Meeting in Delft /42

22 Data Issues o Hydrological streamflow data collected at the borders of India and Bangladesh o Satellite data and derived precipitation products from NASA and NOAA o ECMWF products (EPS and System 3) Question: Is it possible to produce forecasts with an absence of data from within the Brahmaputra and Ganges catchments? This turns out to depend on the length of the forecast and the size of the basin. FEWS User Meeting in Delft /42

23 Discharge Forecast Schemes: 10-day means: 40 members Daily: 51 members FEWS User Meeting in Delft /42

24 2004: Seasonal (1-6 months) Ganges/Brahma forecast probabilistic forecasts climatology observed (validation) + 1 sd -1 sd flood level May forecast for July flood FEWS User Meeting in Delft /42 Forecast was for 28% chance of flooding over entire month of July with risk reducing thereafter. 28% chance of flood for entire month translates to very high probability of shorter term floods. But diffiucult to differentiate between G&B. (More on downscaling later)

25 Seasonal Forecasts 2007 FEWS User Meeting in Delft /42

26 Forecast flood probability 1-4 months compared to i climatological flood probabilities of different duration 9 < climatological probability 1-2 x climatological probability 2-3 x climatological probability > 3 x climatological probability FEWS User Meeting in Delft /42

27 Monthly forecasting: The 2008 seamless VAREPS/monthly ensemble system Until 1 Feb 06, the EPS had day forecasts at T L 255L40 resolution On the 1 st of Feb 06, the 10-day EPS resolution was upgraded to T L 399L62 On the 12 th of Sep 06, the new Variable Resolution EPS (VAREPS) was introduced, and the ensemble forecast range was extended to 15 days On the 11 th of Mar 08 the 15-day VAREPS has been merged with the monthly forecast system: since then the 00 UTC forecasts use a coupled ocean model from day 10 Jan 06: 00 & 12 Z Feb 06: 00 & 12 Z Sep 06: 00 & 12 Z TL255L40 TL399L62 TL399L62 TL255L62 Mar 08: 12 Z 00 Z TL399L62 TL255L62 TL399L62 TL255L62 TL255L62 T=0 10 d 15 d 32 d FEWS User Meeting in Delft /42

28 Monthly Forecasting:Skill FEWS User Meeting in Delft /42

29 Monthly Forecasting:Skill FEWS User Meeting in Delft /42

30 Monthly Forecasting:Skill FEWS User Meeting in Delft /42

31 Case study: Romania, October Indication in might work (2-3 weeks in advance) Only large catchments (e.g. Danube) Only area warning (e.g. middle Danube) far more tests needed & is it useful? In collaboration with the Romanian Water Authorities & European Flood Alert System FEWS User Meeting in Delft /42

32 TIGGE: New perspectives on Medium Range Forecasting TIGGE (the THORPEX Interactive Grand Global Ensemble experiment) is: A framework for international collaboration in development and testing of ensemble prediction systems A resource for many THORPEX research projects A prediction component of THORPEX Forecast Demonstration Projects A prototype future Global Interactive Forecast System It means: Multiple Ensemble systems of several forecast centres archived with a 2-3 day delay & 1x1 degree grid globally FEWS User Meeting in Delft /42

33 TIGGE: New perspectives on Medium Range Forecasting What is the point of even more ensembles??? Multi model will represent more sources of error (model error, data assimilation systems etc) and thus hopefully give a better representation of the forecast error Better representation of the forecast probability purely through numbers Allows for comparison between forecast centres and allows to pick the best one FEWS User Meeting in Delft /42

34 TIGGE: New perspectives on Medium Range Forecasting 2 Case studies Trib. Danube, Romania, October 2007 relatively large catchment & medium sized flood is it possible at all? (collaboration Romanian Water Authority & European Flood Alert System) Upper Severn, Wales, January 2008 medium sized catchment & small flood can multiple EPS systems compensate for deficiencies in representing the pdf of small catchments by large scale models (part of FREE project in collaboration with King s College London (lead, HannaH Cloke), EA (Richard Cross), Bristol University (Jim Freer), University of West Indies (Matt Wilson), University of Auckland (Glenn McGregor) FEWS User Meeting in Delft /42

35 TIGGE: New perspectives on Medium Range Forecasting Case studies Total of 231 weather forecasts FEWS User Meeting in Delft /42

36 TIGGE: Case Study I: Romania, Danube floods, October 2007 Different Ensemble prediction systems can create significant different patterns FEWS User Meeting in Delft /42

37 TIGGE: Case Study I: Romania, Danube floods, October 2007 FEWS User Meeting in Delft /42

38 TIGGE: Case Study I: Romania, Danube floods, October 2007 FEWS User Meeting in Delft /42

39 TIGGE: Case Study II: Upper Severn, Wales, January 2008 ± Montford (a) Severn Catchment located in the Midlands region of England Buildwas Bridgnorth Gauges in Upper Severn Severn Rivers Catchment km2 Catchment km2 Catchment km2 DEM meters High : Km (b) Montford-Buildwas river section Low : 30.5 Severn catchment (c) Digital Elevation Model (DEM) of the Upper Severn Catchment FEWS User Meeting in Delft /42

40 TIGGE: Case Study II: Upper Severn, Wales, January 2008 FEWS User Meeting in Delft /42

41 15 ammc (32+1) babj (14+1) cwao (20+1) ecmf (50+1) egrr (23+1) kwbc (20+1) 10 5 Area of inundation (km 2 ) rjtd (50+1) GE FP simulation using Q observe FP simulation using TIGGE forecasted discharges Day (Jan. 2008) FEWS User Meeting in Delft /42

42 Thank you for listening Any questions?? Contributions by Antje Weissheimer, Gianpaolo Balsamo ( both ECMWF), Hannah Cloke, Helen Yi He & Fredrik Wetterhall (all King s College London), Peter Webster (Georgia Tech), Jutta Thielen (Joint Research Centre of the European Comission, Ispra) FEWS User Meeting in Delft /42