Operational Seasonal ForecastingOperational Seasonal Forecastingfor Bangladesh:for Bangladesh:

Application of quantile-to-quantile mappingApplication of quantile-to-quantile mapping

Tom Hopson Peter Webster Hai-Ru ChangTom Hopson Peter Webster Hai-Ru Chang

Climate Forecast Applications for Bangladesh (CFAB)Climate Forecast Applications for Bangladesh (CFAB)

Overview:Seasonal forecasting

I. Quantile-to-Quantile Mapping: seasonal forecasting ofprecipitation and river discharge

II. What leads to good discharge forecast skill?III. Precipitation productsIV. Quantile-to-Quantile Mapping: shortterm forecasting

of precipitationV. A warning about using Probabilistic Precip Forecasts

in Q modeling (or: Importance of Maintaining OriginalEnsemble Spatial and Temporal Covariances)

Three-Tier Overlapping Forecast SystemThree-Tier Overlapping Forecast SystemDeveloped for BangladeshDeveloped for Bangladesh

Utility of a Three-Tier Forecast SystemUtility of a Three-Tier Forecast System

Seasonal Forecast BiasSeasonal Forecast Bias

Quantile-to-Quantile Approach to Remove Biases:applied to Seasonal Forecasts of Precipitation and Discharge

1) Precipitation:mapped to historic observed precipitation cumulative PDF’s

-- Brahmaputra, Ganges, and combined catchment-average values -- done independently on 1-mo, 2-mo, …, 6-mo forecasts

2) Discharge:-- precipitation forecast cumulative PDF’s mapped to observedhistoric discharge cumulative PDF’s

(similar approach used for 1 - 10 day forecasts)

Pmax

25th 50th 75th 100th

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Quantile

Pmax

25th 50th 75th 100th

Padj

Quantile

Quantile to Quantile MappingFor 1-, 2-, …, 6-month Precipitation Forecasts

Model Climatology “Observed” Climatology

Pmax

25th 50th 75th 100th

Pfcst

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cipi

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Quantile

Pmax

25th 50th 75th 100th

Padj

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Quantile to Quantile MappingFor 1-, 2-, …, 6-month Discharge Forecasts

Model Precip Climatology “Observed” Q Climatology

Optimal correlation:Brahmaputra discharge 11-day lagged;Ganges discharge: 21 day lagged

The Climate Forecast Applications Project CFAB

Good forecasting skill derived from:1) Spatial scale of the basins2) Satellite-raingauge estimates3) ECMWF forecast skill4) Partnership with FFWC/IWM => Utilize good quality daily border

discharge measurements near-real-time

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

-- Logarithmic increase

1) Spatial Scale

2) Precipitation Estimates

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

2) Satellite-derived estimates: Global Precipitation Climatology Project (GPCP)0.25X0.25 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 “CMORPH”0.25X0.25 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

Rain gauge estimates: NOAA CPC and WMO GTS

Comparison of Precipitation Products:

Rain gauge, GPCP, CMORPH, ECMWF

Good comparison for allproducts at large spatial scales

•Hydrology model initial conditions driven by near-real-time GPCP / CMORPH / Raingage precipitation• Ideally, observations would be statistically “just another ensemble member”•Approach: calculate historical NWP-climatology PDF and observation-climatology PDF for each grid using a “kernel” method•For each forecast ensemble, determine its quantile in model-space and extract equivalent quantile in observation-space

ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space

Brahmaputra Catchment-avg Forecasts

Pmax

25th 50th 75th 100th

Pfcst

Pre

cipi

tatio

n

Quantile

Pmax

25th 50th 75th 100th

Padj

Quantile

Quantile to Quantile MappingDone independently for 1-, 2-, …, 10-day forecasts

Model Climatology “Observed” Climatology

Point:Mapping preserves thespatial (and temporal)features of the precipitationforecast fields(i.e. preserves the spatialand temporal covariances)

Original Adjusted

Rank Histogram Comparisons

ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space Brahmaputra Adjusted Forecasts •Benefits:

--Gridded “realistic” forecast values--spatial- and temporal covariances preserved

•Drawbacks:--limited sample set for model-space PDF (2 yrs)--rank histograms show “under-variance”

Mean-Square-Error of the Ensemble-Mean shows skill out to 7-8 days

A Cautionary Warning about using ProbabilisticPrecipitation Forecasts in Hydrologic Modeling

(Importance of Maintaining Spatial and Temporal Covariancesfor Hydrologic Forecasting)

River catchtment A

subB

subC

ensemble1 ensemble2 ensemble3

QBQC

QA

Scenario forsmallest possibleQA? No.

Scenario forlargest possibleQA? No.

QA sameFor all 3 possibleensembles

Scenario foraverage QA?

ConclusionsConclusions

Seasonal Forecasts currently have skill out toSeasonal Forecasts currently have skill out toabout 3 monthsabout 3 months

Possible increased lead-time skill through newPossible increased lead-time skill through newstatistical approachstatistical approach

““Downscaling” (and other methods) holds promise for increased Downscaling” (and other methods) holds promise for increased discharge forecast skilldischarge forecast skill

Caution: monthly forecasts won’t necessarily forecast extremeCaution: monthly forecasts won’t necessarily forecast extremedaily floodingdaily flooding

Thank You!