1. Development of an Ensemble Gridded Hydrometeorological Forcing Dataset over the Contiguous United States Andrew J. Newman 1, Martyn P. Clark 1, Jason Craig 1, Bart Nijssen 2, Andrew Wood 1, Ethan Gutmann 1, Naoki Mizukami 1, Levi Brekke 3, and Jeff R. Arnold 4 1 National Center for Atmospheric Research, Boulder CO, USA 2 University of Washington, Seattle WA, USA 3 U.S. Department of Interior, Bureau of Reclamation, Denver CO, USA 4 U. S. Army Corps of Engineers, Institute for Water Resources, Seattle WA, USA

2. Outline Motivation Methodology Input station data Validation Examples Summary

3. Opportunities for hydrologic prediction Hydrological Prediction: How well can we estimate the amount of water stored? Accuracy in precipitation estimates Fidelity of hydro model simulations Effectiveness of hydrologic data assimilation methods Water Cycle (from NASA) Meteorological predictability: How well can we forecast the weather and climate? Opportunity: Characterize hydrologic and historical forcing uncertainty hydrological predictability meteorological predictability MotivationMethodologyInput DataSummaryValidationExamples

4. Hydrologic model uncertainties Characterize uncertainty in hydrologic model simulation ▫Uncertainty in historical model inputs  Probabilistic quantitative precipitation estimation ▫Uncertainty in model parameter choice and model structure ▫…leading to estimate of uncertainty in model states and fluxes …and uncertainty in initial conditions Reduce uncertainty in modeled hydrologic states ▫Probabilistic snow estimation from stations and satellites ▫Ensemble snow data assimilation methods  MotivationMethodologyInput DataSummaryValidationExamples

5. Step 1: Estimate probability of precipitation (PoP) via logistic regression, amount and uncertainty at each grid cell (locally weighted regression & residuals) observations Example over the Colorado Headwaters Ensemble Generation MotivationMethodologyInput DataSummaryValidationExamples Clark & Slater (2006), Newman et al. (2014, in prep)

6. Step 2: Synthesize ensembles from PoP, amount & error using spatially correlated random fields Example over the Colorado Headwaters observations Clark & Slater (2006), Newman et al. (2014, in prep) Ensemble Generation MotivationMethodologyInput DataSummaryValidationExamples

7. Development of Serially Complete Station Dataset Daily Global Historical Climate Network (GHCN) and SNOTEL observations for 1980-2012 USA, Canada, Mexico Following Eischeid et al. (2000), processed stations with >10 years of data and nearby stations that have 10 years of overlap with target station(s) Then, for each station: Fill 1-2 day temperature gaps with interpolation Fill all precipitation and > 2 day temperature with CDF matching using highest available rank correlation station MotivationMethodologyInput DataSummaryValidationExamples

8. 12,000+ stations with serially complete data Precipitation, temperature or both Development of Serially Complete Station Dataset MotivationMethodologyInput DataSummaryValidationExamples

9. Validation: Discrimination & Reliability MotivationMethodologyInput DataSummaryValidationExamples Discrimination (a-d, red & black lines) Want to maximize separation of event, non-event PDFs Reliability (e-h, blue & black lines) Want to be close to 1-1 line (predicted = observed probabilities)

10. Validation: Comparisons to other datasets MotivationMethodologyInput DataSummaryValidationExamples Maurer Maurer et al. (2002) NLDAS-2 Xia et al. (2012) Daymet Ver. 2 Thornton et al. (2014) NLDAS – Maurer Daymet - Maurer

11. Validation: Comparisons to other datasets MotivationMethodologyInput DataSummaryValidationExamples Maurer et al. (2002) Interpolation between observations increases precipitation occurrence Speckled pattern Grid points with obs Ensemble: SCRFs applied to PoP field more realistic PoP Reduces PoP Data differences responsible for PoP increases

12. Validation: Comparisons to other datasets MotivationMethodologyInput DataSummaryValidationExamples Minimal differences in mean temperature Except in intermountain west Maurer uses constant elevation lapse rate (-6.5 K km -1 ) Ensemble lapse rate derived from station data (including Snotel) Maurer shown to be too cold in higher elevations (e.g. Mizukami et al. 2014) Ensemble warmer than Maurer in high terrain

13. Example Output MotivationMethodologyInput DataSummaryValidationExamples Central US Flood of 1993 June 1993 total precipitation

14. Example Application MotivationMethodologyInput DataSummaryValidationExamples Snowmelt dominated basin in Colorado Rockies Example water year daily temperature (a) Snow water equivalent accumulation (b) Simple temperature index model (optimized for Daymet (green))

15. Summary MotivationMethodologyInput DataSummaryValidationExamples Developed first of its kind ensemble hydrometeorolgical dataset Required development of serially complete station dataset Both will be available for download at: http://ral.ucar.edu/projects/hap/flowpredict/subpages/pqpe.php Methodology follows Clark and Slater (2006) with modifications Ensemble has: Realistic probability of precipitation (PoP) Estimate of forcing uncertainty Better observation error estimates for data assimilation Propagation of forcing uncertainty into model states Useful for state uncertainty estimation Ensemble calibrations will allow for estimates of parameter uncertainty (impact of noise in parameter estimation)

16. Next Steps MotivationMethodologyInput DataSummaryValidationExamples Include downward shortwave and humidity in ensemble Improved downward shortwave radiation product (Slater et al., in prep) Stations across US at many elevations, climatic conditions Will allow for improved temperature/moisture – shortwave relationships Include ensemble into data assimilation system(s) Particle filter and ensemble Kalman filter Can we improve daily to seasonal streamflow prediction using automated systems?