Assessing short range ensemble streamflow forecast
approaches in small to medium scale watersheds
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
AGU Fall Meeting
December 17, 2014 -- Moscone Center, San Francisco, CA
Outline
• Background: US short range ensemble prediction
• Study Question and Strategy
• Results
• Conclusion & future work
NCAR
RAL/HAP
NCAR
RAL/HAP
NWS Ensembles
NWS RFCs are now
producing
experimental/operation
al short range
ensemble forecast
products
The two major
techniques are:
• HEFS
• MMEFS
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Meteorological
Ensemble Forecasts
Hydrometeorological
Observations
Meteorological
Ensemble
Forecast
Generation and
Calibration
Ensemble Forecast
Verification
HEFS
Data Assimilation
Hydrologic,
Hydraulic, Water
Management
Simulation
Hydrologic
ensemble forecast
calibration (postprocessing)
Product
Generation
Ensemble Forecast
Products
MMEFS Implementation
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RAL/HAP
MMEFS
NCAR
RAL/HAP
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
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MMEFS flow forecast example
NCAR
RAL/HAP
NCAR
Hydrologic Ensemble Forecast Service RAL/HAP
• 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
7
GEFS Reforecasts
NCAR
RAL/HAP
Multi-year hindcast enables use of past performance for forecast
calibration and verification
Current
forecast
from T. Hamill presentation
Atmospheric Pre-Processor: calibration
NCAR
RAL/HAP
Based on model joint distribution between single-valued forecast and
verifying observation for each lead time
Archive of observed-forecast pairs
PDF of Observed
Joint distribution
Sample Space
NQT
Observed
Joint distribution
Correlation (X,Y)
0
X
Forecast
PDF of Forecast
PDF of Fcst STD Normal
Y
Model Space
Observed
Y
PDF of Obs. STD Normal
NQT
X
Forecast
9
NQT: Normal Quantile Transform
Schaake et al. (2007), Wu et al. (2011)
Multi-time-scale calibration
PCP
NCAR
RAL/HAP
• Calibration of
meteorological ensembles
applies for a broad array of
events (forecast lead,
period)
Event forecasts are merged into
input timeseries for flow forecasts
Sultan R, WA
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CONUS Precipitation Variation
NCAR
RAL/HAP
Western US terrain influences create more spatially heterogeneous
precipitation and temperature fields than in Eastern US
Precipitation,
1971-2000
11
Study Questions
NCAR
RAL/HAP
• 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?
Calibrated
correlation
HEFS Precip Forecast Skill (J. Brown)
California
Colorado
exceedence
Raw
from T. Hamill presentation
Forecasting Approach
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′WY
X=predictors, Y=predictand
• To predict new date, multiply betas with new inputs X0,
ŷ =βX0
Forecast Study Basins
•
•
NCAR
RAL/HAP
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 intercomparison studies
Case Study Website
http://www.ral.ucar.edu/staff/wood/case_studies/
Results
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
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RAL/HAP
Watershed temperature forecast example
NCAR
RAL/HAP
0
-10
GEFS-LWR
GEFS-Raw
-20
degrees Celsius
10
20
• Crystal River, 1997
• 7-day lead
• Raw GEFS and GEFS-LWR versus observations
0
100
200
day of water year
300
Watershed precipitation forecast example
NCAR
RAL/HAP
• Crystal River, 1997
• 1-day lead
• Raw GEFS and GEFS-LWR versus observations
10
5
0
mm
15
20
GEFS-LWR
GEFS-Raw
0
100
200
day of water year
300
Results for Ensemble Means
Crystal River precipitation
3
MAE (mm)
2.5
2
1.5
1
PRCP-LWR
0.5
Prcp-APCP
0
1
2
3
4
Lead (days)
5
6
7
NCAR
RAL/HAP
Results for Ensemble Means
Row River precipitation
4
3.5
MAE (mm)
3
2.5
2
1.5
1
PRCP-LWR
0.5
Prcp-APCP
0
1
2
3
4
Lead (days)
5
6
7
NCAR
RAL/HAP
Findings and Future Directions
NCAR
RAL/HAP
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
Questions?
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