daily to seasonal operational flood forecasting tom hopson, ncar and adpc peter webster, cfab and...

Daily to Seasonal Operational Flood Forecasting

Tom Hopson, NCAR and ADPCPeter Webster, CFAB and Georgia TechA. R. Subbiah, ADPC

Overview:Bangladesh flood forecastingI. Overview of daily to seasonal weather forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errorsIV. Future Work: Dartmouth Flood Observatory

Utility of a Three-Tier Forecast SystemSEASONAL OUTLOOK: Long term planning of agriculture, water resource management & disaster mitigation especially if high probability of anomalous season (e.g., flood/drought)

30 DAY FORECAST: Broad-scale planning schedules for planting, harvesting, pesticide & fertilizer application and water resource management (e.g., irrigation/hydro-power determination). Major disaster mitigation resource allocation.

1-10 DAY FORECAST: Detailed agriculture, water resource and disaster planning. E.g., fine tuning of reservoir level, planting and harvesting.

forecast products for hydrologic applicationsSeasonal -- ECMWF System 3- based on: 1) long predictability of ocean circulation, 2) variability in tropical SSTs impacts global atmospheric circulation- coupled atmosphere-ocean model integrations- out to 7 month lead-times, integrated 1Xmonth- 41 member ensembles, 1.125X1.125 degrees (TL159L62), 130kmMonthly forecasts -- ECMWF- fills in the gaps -- atmosphere retains some memory with ocean variability impacting atmospheric circulation- coupled ocean-atmospheric modeling after 10 days- 15 to 32 day lead-times, integrated 1Xweek- 51 member ensemble, 1.125X1.125 degrees (TL159L62), 130kmMedium-range -- ECMWF EPS- atmospheric initial value problem, SSTs persisted- 6hr - 15 day lead-time forecasts, integrated 2Xdaily- 51 member ensembles, 0.5X0.5 deg (TL255L40), 80kmShort-range -- RIMES- 26-member Country Regional Integrated Multi-hazard Early Warning System (RIMES) WRF Precipitation Forecasts- 3hr - 5 day lead-time, integrated 2X daily- 9km resolution

Meeting with John Pace 2829 May 2008 NCAR, Boulder, CONCAR/RAL - National Security Applications Program*

Greater accuracy of ensemble mean forecast (half the error variance of single forecast)Likelihood of extremesNon-Gaussian forecast PDFsEnsemble spread as a representation of forecast uncertaintyMotivation for Generating Ensemble Discharge Forecasts (from ensemble weather forecasts)

Overview:Bangladesh flood forecastingI. Overview of daily to seasonal forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errorsIV. Future Work: Dartmouth Flood Observatory

Seasonal rainfall prediction for 2006An example of seasonal predictions of precipitation issued in JFMA 2006 (left) and MJJA 2006 (right), to be compared with the observed rainfall (dotted line) and climatology (dashed line).The seasonal forecasts correctly indicate months in advance higher than normal rainfall.

Overview:Bangladesh flood forecastingI. Overview of daily to seasonal forecast productsII. Seasonal forecasting: Bangladesh CFAB exampleIII. Short-term forecasting: Bangladesh CFAB example1. Where does good predictability derive?1. precipitation forecast bias removal2. multi-model river forecasting3. accounting for all error: weather and hydrologic errorsIV. Future Work: Dartmouth Flood Observatory

CFAB Project: Improve flood warning lead time Problems:

Limited warning of upstream river discharges

Precipitation forecasting in tropics difficultGood forecasting skill derived from:1. good data inputs: ECMWF weather forecasts, satellite rainfall2. Large catchments => weather forecasting skill integrates over large spatial and temporal scales3. Partnership with Bangladeshs Flood Forecasting Warning Centre (FFWC)=> daily border river readings used in data assimilation scheme

1) Rainfall InputsRain gauge estimates: NOAA CPC and WMO GTS0.5 X 0.5 spatial resolution; 24h temporal resolutionapproximately 100 gauges reporting over combined catchment24hr reporting delay

Satellite-derived estimates: NASA TRMM0.25X0.25 spatial resolution; 3hr temporal resolution6hr reporting delaygeostationary infrared cold cloud top estimates calibrated from SSM/I and TMI microwave instruments

3)Satellite-derived estimates: NOAA CPC CMORPH0.25X0.25 spatial resolution; 3hr temporal resolution18hr reporting delay precipitation rain rates derived from microwave instruments (SSM/I, TMI, AMSU-B), but cloud tracking done using infrared satellites

4)Weather forecasts: ECMWF GCM 51-member ensemble weather forecasts at 1-day to 15-day forecast lead-times (nominal resolution about 0.5degree)

Comparison of Precipitation Products:

Rain gauge, GPCP, CMORPH, ECMWF

-- Increase in forecast skill(RMS error) with increasingspatial scale

-- Logarithmic increase2) Spatial Scale

Merged FFWC-CFAB Hydraulic Model SchematicPrimary forecast boundary conditions shown in gold:

Ganges at Hardinge Bridge

Brahmaputra at Bahadurabad3) Benefit: FFWC daily river discharge observations used in forecast data assimilation scheme (Auto-Regressive Integrated Moving Average model [ARIMA] approach)

Daily Operational Flood Forecasting Sequence

Statistically corrected downscaled forecasts

Generate forecasts

Update soil moisture states and in-stream flows

Calibrate model

Generate hindcasts

Generate forecasts

Distributed Model Hindcast/Forecast Discharge Generation

Generate hindcasts

Generate forecasts

Above-critical-level forecast probabilities transferred to Bangladesh

Convolve multi-model forecast PDF with model error PDF

Generate forecasted model error PDF

Generate hindcasts

Generate forecasts

Updated outlet discharge estimates

Calibrate multi-model

Updated distributed model parameters

Calibrate AR error model

Multi-Model Hindcast/Forecast Discharge Generation

Updated TRMM-CMORPH-CPC precipitation estimates

Forecast Trigger:

ECMWF forecast files

Lumped Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Generate hindcasts

Transforming (Ensemble) Rainfall into (Probabilistic) River Flow ForecastsRainfall ProbabilityRainfall [mm]Discharge ProbabilityDischarge [m3/s] Above danger level probability 36%Greater than climatological seasonal risk?

ECMWF 51-member Ensemble Precipitation Forecasts2004 Brahmaputra Catchment-averaged Forecastsblack line satellite observationscolored lines ensemble forecastsBasic structure of catchment rainfall similar for both forecasts and observationsBut large relative over-bias in forecasts5 Day Lead-time Forecasts=> Lots of variability

Pmax25th50th75th100thPfcstPrecipitationQuantilePmax25th50th75th100thPadjQuantileForecast Bias Adjustment done independently for each forecast grid(bias-correct the whole PDF, not just the median)Model Climatology CDFObserved Climatology CDFIn practical terms Precipitation 01mranked forecastsPrecipitation 01mranked observations

Bias-corrected Precipitation Forecasts Brahmaputra Corrected Forecasts Original Forecast Corrected Forecast => Now observed precipitation within the ensemble bundle

Daily Operational Flood Forecasting Sequence

Statistically corrected downscaled forecasts

Generate forecasts

Update soil moisture states and in-stream flows

Calibrate model

Generate hindcasts

Generate forecasts

Distributed Model Hindcast/Forecast Discharge Generation

Generate hindcasts

Generate forecasts

Above-critical-level forecast probabilities transferred to Bangladesh

Convolve multi-model forecast PDF with model error PDF

Generate forecasted model error PDF

Generate hindcasts

Generate forecasts

Updated outlet discharge estimates

Calibrate multi-model

Updated distributed model parameters

Calibrate AR error model

Multi-Model Hindcast/Forecast Discharge Generation

Updated TRMM-CMORPH-CPC precipitation estimates

Forecast Trigger:

ECMWF forecast files

Lumped Model Hindcast/Forecast Discharge Generation

Discharge Forecast PDF Generation

Generate hindcasts

Discharge Multi-Model ForecastMulti-Model-Ensemble Approach:

Rank models based on historic residual error using current model calibration and observed precipitation

Regress models historic discharges to minimize historic residuals with observed discharge

To avoid over-calibration, evaluate resultant residuals using Akaike Information Criteria (AIC)

If AIC minimized, use regression coefficients to generate multi-model forecast; otherwise use highest-ranked model => win-win situation!

2003 Model Comparisons for the Ganges (4-day lead-time)hydrologic distributed modelhydrologic lumped modelResultant Hydrologic multi-model

Multi-Model Forecast Regression Coefficients- Lumped model (red)- Distributed model (blue)

Significant catchment variationCoefficients vary with the forecast lead-timeRepresentative of the each basins hydrology-- Ganges slower time-scale response-- Brahmaputra flashier

Daily Operational Flood Forecasting Sequence

Statistically corrected downscaled