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Assessing short range ensemble streamflow forecast approaches in small to medium scale watersheds
AGU Fall MeetingDecember 17, 2014 -- Moscone Center, San Francisco, CA
Andy WoodAndy Newman, Martyn Clark
NCAR Research Applications Laboratory, Boulder, COLevi Brekke
Reclamation Technical Services Center, Denver, COJeff Arnold
Institute for Water Resources, Alexandria, VA
NCARRAL/HAPOutline
• Background: US short range ensemble prediction
• Study Question and Strategy
• Results
• Conclusion & future work
NCARRAL/HAP
43
NWS Ensembles
Data Assimilation
Meteorological Ensemble Forecast
Generation and Calibration
Hydrologic, Hydraulic, Water
Management Simulation
Hydrologicensemble forecast calibration (post-
processing)
Product Generation
Ensemble Forecast Verification
Meteorological Ensemble Forecasts
Hydro-meteorological Observations
Ensemble Forecast Products
HEFSNWS RFCs are now producing experimental/operational short range ensemble forecast products
The two major techniques are:• HEFS• MMEFS
NCARRAL/HAPMMEFS Implementation
NCARRAL/HAP
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MMEFSMulti-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
NCARRAL/HAPMMEFS flow forecast example
NCARRAL/HAPHydrologic 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
NCARRAL/HAPGEFS Reforecasts
Multi-year hindcast enables use of past performance for forecast calibration and verification
from T. Hamill presentation
Past forecast-o
bservation pairs
Current forecast
NCARRAL/HAP
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Atmospheric Pre-Processor: calibrationBased on model joint distribution between single-valued forecast and
verifying observation for each lead time
X
Y
Forecast
Obs
erve
d
0
Joint distributionSample Space
PDF of Observed PDF of Obs. STD Normal
NQT
Schaake et al. (2007), Wu et al. (2011)
ForecastO
bser
ved
Joint distributionModel Space
X
YCorrelation (X,Y)
Archive of observed-forecast pairs
PDF of Forecast PDF of Fcst STD Normal
NQT
NQT: Normal Quantile Transform
NCARRAL/HAP
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• 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
NCARRAL/HAPCONUS Precipitation Variation
11
Western US terrain influences create more spatially heterogeneous precipitation and temperature fields than in Eastern US
Precipitation, 1971-2000
NCARRAL/HAPStudy 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 presentationexceedence
corr
elati
on
California Colorado
HEFS Precip Forecast Skill (J. Brown)
NCARRAL/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′WY X=predictors, Y=predictand
• To predict new date, multiply betas with new inputs X0, y =̂ βX0
Forecasting Approach
NCARRAL/HAPForecast 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
http://www.ral.ucar.edu/staff/wood/case_studies/
Case Study Website
NCARRAL/HAPResults
Illustrating with 2 basins• Row River (OR), 14154500 – ‘high skill’• Crystal River (CO), 09081600 – ‘lower skill’
• 11 member ensembles – control + 10 perturbations• 1-7 day lead times
NCARRAL/HAPWatershed temperature forecast example
• Crystal River, 1997• 7-day lead• Raw GEFS and GEFS-LWR versus observations
GEFS-LWRGEFS-Raw
NCARRAL/HAPWatershed precipitation forecast example
• Crystal River, 1997• 1-day lead• Raw GEFS and GEFS-LWR versus observations
GEFS-LWRGEFS-Raw
NCARRAL/HAPResults for Ensemble Means
Crystal River precipitation
NCARRAL/HAPResults for Ensemble Means
Row River precipitation
NCARRAL/HAPFindings 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
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Questions?
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