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Operational Seasonal Forecasting for Bangladesh: Application of quantile-to-quantile mapping

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. Quantile-to-Quantile Mapping: seasonal forecasting of

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Operational Seasonal Forecasting for Bangladesh: Application of quantile-to-quantile mapping

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

  2. Overview: Seasonal forecasting Quantile-to-Quantile Mapping: seasonal forecasting of precipitation and river discharge What leads to good discharge forecast skill? 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)

  3. Three-Tier Overlapping Forecast SystemDeveloped 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 20-25 DAY FORECAST: Forecast of average 5-day rainfall and river discharge 3-4 weeks in advance. Updated every 5 days. 1-10 DAY FORECAST: Forecast of rainfall and precipitation in probabilistic form updated every day. Considerable skill out to 5-days. Moderate skill 5-10 days.

  4. 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) 20-25 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.

  5. Seasonal Forecast Bias

  6. 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 Discharge: -- precipitation forecast cumulative PDF’s mapped to observed historic discharge cumulative PDF’s (similar approach used for 1 - 10 day forecasts)

  7. Quantile to Quantile Mapping For 1-, 2-, …, 6-month Precipitation Forecasts Model Climatology “Observed” Climatology Pmax Pmax Precipitation Pfcst Padj 25th 50th 75th 100th 25th 50th 75th 100th Quantile Quantile

  8. Quantile to Quantile Mapping For 1-, 2-, …, 6-month Discharge Forecasts Model Precip Climatology “Observed” Q Climatology Pmax Pmax Precipitation Pfcst Padj 25th 50th 75th 100th 25th 50th 75th 100th Quantile Quantile Optimal correlation: Brahmaputra discharge 11-day lagged; Ganges discharge: 21 day lagged

  9. 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

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

  11. 2) Precipitation Estimates 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 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

  12. Rain gauge estimates: NOAA CPC and WMO GTS

  13. Comparison of Precipitation Products: Rain gauge, GPCP, CMORPH, ECMWF

  14. Good comparison for all products at large spatial scales

  15. ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space Brahmaputra Catchment-avg Forecasts • 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

  16. Quantile to Quantile Mapping Done independently for 1-, 2-, …, 10-day forecasts Model Climatology “Observed” Climatology Pmax Pmax Precipitation Pfcst Padj 25th 50th 75th 100th 25th 50th 75th 100th Quantile Quantile

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

  18. Rank Histogram Comparisons Original Adjusted

  19. 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

  20. A Cautionary Warning about using Probabilistic Precipitation Forecasts in Hydrologic Modeling (Importance of Maintaining Spatial and Temporal Covariances for Hydrologic Forecasting) ensemble1 ensemble2 ensemble3 River catchtment A subC subB QC QB QA QA same For all 3 possible ensembles Scenario for smallest possible QA? No. Scenario for largest possible QA? No. Scenario for average QA?

  21. 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

  22. Thank You!

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