Assessing short range ensemble streamflow forecast approaches in small to medium scale watersheds AGU Fall Meeting December 17, Moscone Center, San Francisco, CA Andy Wood Andy Newman, Martyn Clark NCAR Research Applications Laboratory, Boulder, CO Levi Brekke Reclamation Technical Services Center, Denver, CO Jeff Arnold Institute for Water Resources, Alexandria, VA

NCAR RAL/HAP Outline Background: US short range ensemble prediction Study Question and Strategy Results Conclusion & future work

NCAR RAL/HAP 43 NWS Ensembles Data Assimilation Meteorological Ensemble Forecast Generation and Calibration Hydrologic, Hydraulic, Water Management Simulation Hydrologic ensemble forecast calibration (post- processing) Product Generation Ensemble Forecast Verification Meteorological Ensemble Forecasts Hydro- meteorological Observations Ensemble Forecast Products HEFS NWS RFCs are now producing experimental/operation al short range ensemble forecast products The two major techniques are: HEFS MMEFS

NCAR RAL/HAP MMEFS Implementation

NCAR RAL/HAP 43MMEFS Multi-Met Model Ensemble Forecast System Technique development led at the RFC level Implemented experimentally in four Eastern US RFCs Uses real time short range met. ensembles from: NCEP Global Ensemble Forecast System (GEFS) North American Ensemble Forecast system (NAEFS) Short Range Ensemble Forecast System (SREF) Produces short range streamflow ensemble forecasts Run in automated fashion (no forecaster intervention) results are a part of regular office briefings are communicated to partners Downscaling Method: none -- interpolation of raw NWP precipitation and temperature output to watershed centroids

NCAR RAL/HAP MMEFS flow forecast example

NCAR RAL/HAP Hydrologic Ensemble Forecast Service 7 Produces short to seasonal length ensembles from several sources GEFS reforecast CFSv2 reforecast RFC deterministic Like MMEFS, is run in automated fashion Uses model ensemble mean precipitation and temperature

NCAR RAL/HAP GEFS Reforecasts Multi-year hindcast enables use of past performance for forecast calibration and verification from T. Hamill presentation Past forecast-observation pairs Current forecast

NCAR RAL/HAP 9 Atmospheric Pre-Processor: calibration Based on model joint distribution between single-valued forecast and verifying observation for each lead time X Y Forecast Observed 0 Joint distribution Sample Space PDF of ObservedPDF of Obs. STD Normal NQT Schaake et al. (2007), Wu et al. (2011) Forecast Observed Joint distribution Model Space X Y Correlation (X,Y) Archive of observed-forecast pairs PDF of Forecast PDF of Fcst STD Normal NQT NQT: Normal Quantile Transform

NCAR RAL/HAP 43 Calibration of meteorological ensembles applies for a broad array of events (forecast lead, period) Multi-time-scale calibration Sultan R, WA PCP Event forecasts are merged into input timeseries for flow forecasts

NCAR RAL/HAP CONUS Precipitation Variation 11 Western US terrain influences create more spatially heterogeneous precipitation and temperature fields than in Eastern US Precipitation,

NCAR RAL/HAP Study Questions Given spatial heterogeneity in western US weather, how well does GEFS perform at small catchment scales? Is it possible to extract more forecast skill using multiple atmospheric variables from GEFS rather than just precipitation and temperature? Raw Calibrated from T. Hamill presentation exceedence correlation CaliforniaColorado HEFS Precip Forecast Skill (J. Brown)

NCAR RAL/HAP GEFS reforecasts at daily time-step were downscaled to estimate catchment model input precipitation and temperature forecasts Technique: Locally-weighted regression (LWR) weights were specified using multivariate analog similarity -- PRCP: PWAT_entireatmosphere, TMP_2m, CAPE_surface, PRES_msl, APCP_surface, DSWRF_surface -- TAVG: TCOLC_entireatmosphere, TMP_2m, PRES_msl, APCP_surface, DSWRF_surface LWR: like simple MLR but introduces a weight matrix W when finding regression model parameters, ie, solving β=(X′WX)−1X′WYX=predictors, Y=predictand To predict new date, multiply betas with new inputs X0, ŷ =βX0 Forecasting Approach

NCAR RAL/HAP Forecast Study Basins For small water-resources oriented basins across CONUS, estimate forcings & implement hydrology models (Newman et al, 2015) This catchment dataset is being used for forecast method inter- comparison studies Case Study Website

NCAR RAL/HAP Results Illustrating with 2 basins Row River (OR), – ‘high skill’ Crystal River (CO), – ‘lower skill’ 11 member ensembles – control + 10 perturbations 1-7 day lead times

NCAR RAL/HAP Watershed temperature forecast example Crystal River, day lead Raw GEFS and GEFS-LWR versus observations GEFS-LWR GEFS-Raw

NCAR RAL/HAP Watershed precipitation forecast example Crystal River, day lead Raw GEFS and GEFS-LWR versus observations GEFS-LWR GEFS-Raw

NCAR RAL/HAP Results for Ensemble Means Crystal River precipitation

NCAR RAL/HAP Results for Ensemble Means Row River precipitation

NCAR RAL/HAP Findings and Future Directions Findings Downscaled GEFS reforecasts have substantial skill at leads 1-7d Lower skill in Intermountain West still at usable levels High skill in western US can support skillful hydrologic prediction Benefit of additional atmospheric variables appears slight Primary variables are most highly correlated with watershed meteorology The LWR improved MAE but not correlation Analog weightings may add noise that reduces correlation skill Use of primary GEFS forecast outputs alone appears warranted Future Directions More comprehensive assessment of LWR method performance Complete a benchmarking against HEFS met forecasts for study basins Assess flow forecasts based on LWR & HEFS Invitation to interested collaborators to inter-compare other downscaling approaches in study-basin set

21 Questions?