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

43

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

9

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

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

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

21

Questions?