Operational Seasonal Forecasting for Bangladesh: Application of quantile-to-quantile mapping Tom Hopson Peter Webster Hai-Ru Chang Climate Forecast Applications for Bangladesh (CFAB)

Overview: Seasonal forecasting I.Quantile-to-Quantile Mapping: seasonal forecasting of precipitation and river discharge II.What leads to good discharge forecast skill? III.Precipitation products IV.Quantile-to-Quantile Mapping: shortterm forecasting of precipitation V.A warning about using Probabilistic Precip Forecasts in Q modeling (or: Importance of Maintaining Original Ensemble Spatial and Temporal Covariances)

Three-Tier Overlapping Forecast System Developed for Bangladesh SEASONAL OUTLOOK: “Broad brush” probabilistic forecast of rainfall and river discharge. Updated each month. Produced out to 6 months, currently most useful skill out 3 months DAY FORECAST: Forecast of average 5-day rainfall and river discharge 3-4 weeks in advance. Updated every 5 days DAY FORECAST: Forecast of rainfall and precipitation in probabilistic form updated every day. Considerable skill out to 5-days. Moderate skill 5-10 days.

Utility of a Three-Tier Forecast System SEASONAL OUTLOOK: Long term planning of agriculture, water resource management & disaster mitigation especially if high probability of anomalous season (e.g., flood/drought) 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 DAY FORECAST: Detailed agriculture, water resource and disaster planning. E.g., fine tuning of reservoir level, planting and harvesting.

Seasonal 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 observed historic discharge cumulative PDF’s (similar approach used for day forecasts)

Pmax 25th50th75th100th Pfcst Precipitation Quantile Pmax 25th50th75th100th Padj Quantile Quantile to Quantile Mapping For 1-, 2-, …, 6-month Precipitation Forecasts Model Climatology“Observed” Climatology

Pmax 25th50th75th100th Pfcst Precipitation Quantile Pmax 25th50th75th100th Padj Quantile Quantile to Quantile Mapping For 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 basins 2) Satellite-raingauge estimates 3) ECMWF forecast skill 4) Partnership with FFWC/IWM => Utilize good quality daily border discharge measurements near-real-time

-- Increase in forecast skill (RMS error) with increasing spatial scale -- Logarithmic increase 1) Spatial Scale

2) Precipitation Estimates 1)Rain gauge estimates: NOAA CPC and WMO GTS 0.5 X 0.5 spatial resolution; 24h temporal resolution approximately 100 gauges reporting over combined catchment 24hr reporting delay 2)Satellite-derived estimates: Global Precipitation Climatology Project (GPCP) 0.25X0.25 spatial resolution; 3hr temporal resolution 6hr reporting delay geostationary 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 resolution 18hr 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 all products 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 25th50th75th100th Pfcst Precipitation Quantile Pmax 25th50th75th100th Padj Quantile Quantile to Quantile Mapping Done independently for 1-, 2-, …, 10-day forecasts Model Climatology“Observed” Climatology

Point: Mapping preserves the spatial (and temporal) features of the precipitation forecast fields (i.e. preserves the spatial and temporal covariances)

OriginalAdjusted 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 Probabilistic Precipitation Forecasts in Hydrologic Modeling (Importance of Maintaining Spatial and Temporal Covariances for Hydrologic Forecasting) River catchtment A subB subC ensemble1ensemble2ensemble3 QBQB QCQC QAQA Scenario for smallest possible Q A ? No. Scenario for largest possible Q A ? No. Q A same For all 3 possible ensembles Scenario for average Q A ?

Conclusions Seasonal Forecasts currently have skill out to about 3 months Possible increased lead-time skill through new statistical approach “Downscaling” (and other methods) holds promise for increased discharge forecast skill Caution: monthly forecasts won’t necessarily forecast extreme daily flooding

Thank You!