Hydrologic Ensemble Prediction: Challenges and Opportunities
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1 Hydrologic Ensemble Prediction: Challenges and Opportunities John Schaake (with lots of help from others including: Roberto Buizza, Martyn Clark, Peter Krahe, Tom Hamill, Robert Hartman, Chuck Howard, Pedro Restrepo, D.J. Seo, Jutta Thielen, Zoltan Toth, Eric Wood, HEPEX SSG) Joint COST Action 731 Workshop and NetFAM Workshop on Uncertainty in High-Resolution Meteorological and Hydrological Models Vilnius, Lithuania April 27, 2006
2 Some Questions for the Workshop Why are Ensemble Forecasts Needed? How should ensemble weather forecasts be used in hydrological forecasting? How can uncertainties in hydrological models, model parameters and hydrological initial conditions be represented in hydrological ensemble prediction? What is the relative role of initial conditions and meteorological forecast skill in hydrological uncertainty? How does this vary with season and climate? What processes and tools are needed for forecasters to control the operation of a hydrological ensemble forecast system? What is the role of a human forecaster in ensemble prediction? How can hydrological ensemble forecasts be verified, also for big events, and what can be done to gain confidence that a given forecast system is reliable? How should uncertainty be communicated to decision makers and to the public? What interaction is needed between forecasters and users?
3 Why are Ensemble Forecasts Needed?
4
5 What is Weather and Climate Forecasts Needed to Make Ensemble Forecasts? Observations Hydrologic Ensemble Prediction System Hydrological Hydrological Models (& Regulation) Models Products and Services
6 Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts History Product Properties Products and Services Reliable hydrologic products
7 Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Atmospheric Ensemble Pre-Processor Single-value and ensemble forecasts Reliable hydrologic inputs Hydrological Requirements PreProcessor Science Strategy AHPS PreProcessor Products and Services Reliable hydrologic products
8 Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Reliable hydrologic inputs Obs Land Data Assimilator Hydrologic Ensemble Processor Hydrological Models (& Regulation) Ensemble initial conditions Ensemble forecasts Automatic Data Assimilation is Essential Requirement of this System Hydrology Questions Reliable hydrologic products Products and Services
9 Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Ensemble forecasts Product Generator NCRFC Examples Products and Services Reliable hydrologic products
10 Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Atmospheric Ensemble Pre-Processor Single-value and ensemble forecasts Reliable hydrologic inputs Obs Land Data Assimilator Hydrologic Ensemble Processor Hydrological Hydrological Models (& Regulation) Models Ensemble initial conditions Ensemble forecasts Integration User Evaluation Product Generator Products and Services Reliable hydrologic products
11 Hydrological Ensemble Prediction Experiment (HEPEX) HEPEX aims to demonstrate how to produce reliable hydrological ensemble forecasts that can be used with confidence to make decisions for emergency management, water resources management and the environment
12 Initial Workshop ECMWF - March 8-10, Participants 16 Countries Users [NY Power, BC Hydro, Quebec Hydro, EDF (France), Mekorot (Israel), WMIG (Canada), CddHoward (Canada), SMHI (Sweden), BGF (Germany), ] Meteorologists Hydrologists
13 Three Basic Elements of HEPEX Testbed Projects Supporting Data Sets Components of the Community Hydrologic Prediction System (CHPS)
14 HEPEX Organization
15 3. TIGGE TIGGE (the THORPEX Interactive Grand Global Ensemble) is a framework for international collaboration in development and testing of ensemble prediction systems. TIGGE could lead to: An enhanced international collaboration between operational centres and universities A deeper understanding of the contribution of observation, initial and model uncertainties to forecast error, and the design of more valuable ensemble systems The developments of new methods of combining ensembles from different sources and of correcting for systematic errors (biases, spread over-/underestimation) Buizza et al: Recent Developments of the ECMWF EPS (JRC, 9 Sept 2005) - 27
16 Thank You HEPEX Workshop Boulder July 19-22, 2005
17 Properties of Existing Products Heterogeniety Assessment Model Bias Skill and reliability of probabilities Scale dependency Implications Problems Examples Examples Examples
18 Source: MODIS Parameterizations Much of the weather occurs at scales smaller than those resolved by the weather forecast model. Model must treat, or parameterize the effects of the sub-gridscale on the resolved scale. Tom Hamill Next
19 1. The EPS performance has been continuously increasing Improvements over Europe have been slightly smaller, as can be detected by comparing the time evolution of the RPPS for 500 hpa geopotential height predictions over Europe (left) and NH (previous slide). RPSS RPSS - EU Z500 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Date d+3 d+5 d+7 Next Buizza et al: Recent Developments of the ECMWF EPS (JRC, 9 Sept 2005) - 5
20 1. Trends in BSS for d+4 probabilistic precipitation prediction EPS probabilistic predictions of precipitation over the NH between d+3 and d+4 have improved following the introduction of stochastic physics (Oct 1998) and the system upgrade from TL159 to TL255 in Nov Next Buizza et al: Recent Developments of the ECMWF EPS (JRC, 9 Sept 2005) - 8
21 1. Comparison of the ECMWF, MSC and NCEP EPSs (JJA02) Recent studies [2,9] have shown that, accordingly to many accuracy measures, the ECMWF EPS can be considered the most accurate singlemodel ensemble system. This is shown, e.g., by the comparison of the EV* of 10-member ensembles based on the ECMWF, MSC (Meteorological Service of Canada) and NCEP (National Centers for Environmental Predictions) EPSs [9] (Z500 over NH). * EV, the potential economic value, is the reduction of the mean expenses with respect to the reduction that can be achieved by using a perfect forecast [4,16]. (Source: Buizza et al [9]) Buizza et al: Recent Developments of the ECMWF EPS (JRC, 9 Sept 2005) - 10
22 A lot happens inside a grid box Rocky Mountains Tom Hammil, CDC Approximate size of one grid box in NCEP ensemble system Denver Source: accessmaps.com Next
23 Questions Can we accurately forecast the evolution of the pdf of the grid-box average weather? How do we downscale from a grid-box average to a particular river basin or subarea? Tom Hammil, CDC
24 Typical problems with current generation ensemble forecasts Would like to maximize pdf sharpness subject to calibration. But: Tom Hammil, CDC Ensemble forecasts are biased Ensemble mean different (systematic model error; improve the model or post-process to correct errors) Ensemble spread less than it ought to be (better initial conditions, higher-res forecasts, incorporating stochastic effects). Next
25 Forecast Input Requirements For each sub-basin and time step For all lead times 1hr to 1yr Ensemble inputs include: Precipitation Temperature Potential evaporation Freezing level Verification Next
26 Ensemble Temperature Forecast Next
27 Ensemble Precipitation Forecast Next
28 Ensemble Streamflow Forecast
29 Immediate AHPS Ensemble Precipitation Goals Create short & medium term precipitation ensembles for input to hydrologic models at basin scale. Use existing HPC deterministic forecasts (after modification by RFC HAS forecasters) (i.e. Maintain role of human forecaster for short-term forecasts). Add confidence Factor? Use GFS fixed ensemble mean forecasts up to 14 days (to extend lead time and improve preprocessor parameters) Next
30 AHPS PreProcessor Performance Objectives Preserve skill of the single-value forecasts (at all space and time scales) Remove forecast biases Produce reliable probabilities Account for space/time scale dependency Simple, efficient and robust Next
31 Precipitation Forecasts and Observations California January Day 1 GFS January Day 1 Ensemble Mean Forecast vs Observed (39.25N, W - N. California) NFDC1HL - January Day 1 RFC Forecast vs Observed 100 Observed (mm) Observed (mm) Ensemble Mean Forecast (mm) RFC Forecast (mm) GFS vs NDFC1HL Forecasts January Day 1 GFS Forecast vs NDFC1HL Observations January Day 1 RFC Forecast (mm) NFDC1HL Observation (mm) GFS Forecast (mm) GFS Ensemble Mean Forecast (mm) Next
32 Bias nfdc1huf Precipitation 350 GFS Precipitation S.V. Forecast Bias Raw Forecasts Day of year Forecast Period RFC Precipitation Ensemble Mean Forecast Bias GFS Precipitation Ensemble Mean Forecast Bias Ensemble Mean Day of year Day of year Forecast Period Forecast Period -2 RFC Forecasts GFS Forecasts Next
33 Continuous Rank Probability Skill Score nfdc1huf Precipitation 350 RFC Precipitation Ensemble CRP Skill Score GFS Precipitation Ensemble CRP Skill Score All Events Day of year Day of year Forecast Period Forecast Period 0 Wet Events Day of year RFC Conditional Precipitation Ensemble CRP Skill Score Forecast Period Day of year GFS Conditional Precipitation Ensemble CRP Skill Score Forecast Period RFC Forecasts GFS Forecasts
34 Corr. coeff. between ensemble mean forecast and observed precipitation R January; Day July; Day 1
35 AHPS PreProcessor Science Strategy Develop basic capability using existing single-value forecasts and observations Apply to specific RFC Sub-basin areas using limited RFC and HPC archives of QPF Expand to gridded regions include mult-scale properties Develop general Bayesian approach to using GFS ensemble forecasts Other approaches (e.g. analogs)?
36 NCEP Global Ensemble Forecasts Next
37 Cumulative Distributions of Adjusted Ensemble Members Ensemble Member Climatologies Probability Obs Raw Adj Adj2 Syn Daily Precipitation (mm)
38 Uncertainty Analysis of Global Ensemble Precipitation Forecasts Talagrand Diagram July - Southeast U.S. - Day 1 Distribution-Adjusted Raw Ensemble 0.6 Relative Frequency Quartile
39 Effect of Spatial Scale on 24hr Forecast Skill (July 5 locations) Correlation between CDC ensemble mean forecast and observed precipitation July, Day Correlation coefficient Grid size (degrees) Next
40 Correlation Coefficient Precipitation Forecast vs Observation North Fork American River RFC Forecasts GFS Forecasts 350 GFS Precipitation Forecast vs Observation Correlation 1 Time of Year Day of year Forecast Period 0.3 Forecast Period Forecast Period
41 Hydrologic Model Uncertainty Issues What are the sources of uncertainty in hydrological models? How are they linked? What are the implications of hydrological models being imperfect representations of real hydrological systems? How can uncertainties in hydrological models, model parameters and hydrological initial conditions be represented in hydrological ensemble prediction? What is relative role of initial conditions and meteorological forcing? Next
42 ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number ESP Retrospective Ensemble Member Number Relative Importance of Initial Conditions vs Precipitation North Fork American River, CA Effect of Initial Conditions vs Forcing for 1-Month Forecast North Fork American River Effect of Initial Conditions vs Forcing for December 15 Forecasts North Fork American River Ensemble Number November 15 December 15 ESP Retrospective Ensemble Ensemble Number ESP Retrospective Ensemble Ensemble Number January 15 ESP Retrospective Ensemble Ensemble Number Day 1-4 Day 1-8 Ensemble Number Ensemble Number Day 1-16 Ensemble Number Ensemble Number Ensemble Number Ensemble Number Ensemble Number Ensemble Number Member Number February Member Number March Member Number April Day 1-32 Month 2 Month 3
43 Short-term Ensemble Prototype Smith River Mad River Salmon River Van Duzen River Navarro River American River (11 basins) Next
44 Ensemble Challenges Maintain spatial and temporal relationships across very large areas rainy + cold clear + warm snowing cloudy + hot Irrational outcomes Next
45 Forecaster Role Include forecaster skill in short-term inputs (QPF, temperature, etc.) Forecasters add value to short-term QPF. HPC adds value to models RFC adds value to HPC Next
46 Forecaster Role Include forecaster guidance of hydrologic model operation Hydrologic models require on-going tuning Forecasters commonly adjust or influence raw model output Next
47 Verification Must be able to measure performance of every element in the system Need probabilistic measures Must be useful to forecasters and model developers
48 HEPEX Testbed Projects Canada Great Lakes Europe EU-JRC Ispra, Pan-European Flood, Po River Brazil U.S. SE U.S. Western U.S. Bangladesh PreProcessing / Statistical Downscaling Hydrologic Uncertainty / Data Assimilation
49 Supporting Data Sets (Under Construction) GFS Fixed Ensemble (1979 present) Temperature and Precipitation Selected Regions Precipitation and Temperature Analyses Selected Regions Multi-scale Hydrological Basins Forcing Basin characteristics Streamflow Snow, soil moisture, satellite, etc TIGGE Ensembles CPC Products (for US) Other? (e.g. ECMWF, CMS, etc.)
50 Community Hydrologic Prediction System (CHPS) Federal agencies State, regional, and local cooperators Universities Private sector International organizations Algorithm Services Data Services Control Services Point Information (Stations, Grid Cells) Near- Surface Deep Soil Moisture Runoff Snow Water Equivalent Precipitation Evapotranspiration Vector Information (River Segments) Security Services Display Services Hydrology XML Consortium River Stage & Flow Surface Water Storage Water Supply Availability Flood Potential Water Quality
51 AHPS Outreach Customer Satisfaction Survey Next National Weather Service
52 Forecaster User Roles Potential users know what hydrologic ensemble predictions can do for them. Forecast developers know what users need Users and forecasters work together to develop forecast products Real-time Interaction is Essential
53 North Central River Forecast Center (NCRFC) Forecast Area Red River of the North Next
54
55 Probability Forecasts for Red River at Fargo, ND 90-day Maximum Flow 90-day Minimum Flow Expected Value Plot Ensemble Traces
56 Stage Exceedance Forecast for Fargo
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