Verification of a downscaling approach for large area flood prediction over the Ohio River Basin

Verification of a Verification of a downscaling approach for downscaling approach for large area flood large area flood prediction over the Ohio prediction over the Ohio River BasinRiver Basin

N. Voisin, J.C. Schaake and D.P. LettenmaierUniversity of Washington, Seattle, WA

AMS Annual Meeting, Phoenix AZ 11-15 Jan 2009

ObjectiveObjective

Predict streamflow and associated hydrologic variables, soil moisture, runoff, evaporation and snow water equivalent :◦ Applicable to large river basins, eventually

globally: spatial consistency, ungauged basins

◦ Using a fully distributed hydrology model◦ Using ensemble weather forecasts◦ Lead time up to 2 weeks

ObjectiveObjective

Forecast schematic

Hydrologic fcst (stream flow, soil moist., SWE, runoff )

Initial State

ECMWF EPS 50 ensemble members

2002-2008

BCSD with forecast calibration, 0.25 degree

Daily ERA-40 surrogate for near real time analysis

fields1979-2002

Atmospheric inputs VIC Hydrology

Model

Several years back Medium range forecasts (2 weeks)

Daily ECMWF

Analysis 2002-2008

BCSD to 0.25 degree

Hydrologic model spinup 0.25 degree

BCSD = Bias correction and statistical downscaling

Flow fcst calibration

ObjectiveObjective

Compare different downscaling techniques◦ Applicable at a global scale◦ For precipitation forecast◦ Improve or conserve the skill

OutlineOutline

1. Existing downscaling methods2. Analog technique and various

variations of it3. Forecast Verification at different

spatial and temporal scales:◦ Mean errors◦ Predictability, reliability◦ Spatial rank structure

1. Downscaling techniques

MOS (Glahn and Lowry 1972, Clark and Hay 2004)

Bias correction followed by spatial and temporal resampling for seasonal forecast (Wood et al. 2002 and 2004)

National Weather Service (NWS) Ensemble Precipitation Processor (EPP) ( Schaake et al. 2007)

Analog techniques ( Hamill and Whitaker 2006)

2. Analog technique

FCST day n1 degree

Retrosp. FCST dataset, +/- 45 days around day n1 degree resolution

OBS D DAYOBS D DAY

OBS D DAYOBS D DAY

OBS D DAYOBS D DAY

OBS D DAYOBS D DAY

OBS D DAYOBS D DAY

OBS D DAYOBS D DAY

OBSn

+/- 45 daysYear-1

FCST D DAYFCST D DAY






FCST n

+/- 45 daysYear-1

Corresp. Observation (TRMM)0.25 degree resolution

DownscaledFCSTday n

0.25 degree

3 methods for choosing the analog:-Closest in terms of RMSD, for each ensemble-15 closest in terms of RMSD, to the ensemble mean fcst-Closest in terms of rank, for each ensemble

( adapted from Hamill and Whitaker 2006)

5 degree

5 de

gree

2. Analog technique

Spatial domain for the analogChoose an analog for the entire domain (Maurer

et al. 2008): entire US, or the globe◦ Ensure spatial rank structure◦ Need a long dataset of retrofcst-

observation.

Moving spatial window (Hamill and Whitaker 2006):

◦ 5x5 degree window (25 grid points)

◦ Choose analog based on ΣRMSD, or Σ(Δrank)◦ Date of analog is assigned to the center grid

point

Ens. Mean Fcst, 20050713

4 closest analogs in the retrospective forecast dataset

Corresponding 0.25 degree TRMM for the analogs, Downscaled ensemble forecast members

Downscaled ens. mean forecast

TRMM (obs)

( adapted from Hamill and Whitaker 2006)

Fcst 200507132. Analog technique

3. Forecast VerificationEvaluate the different analog techniques,

simple interpolation, and basic resampling downscaling

Verification conditioned on the forecast:◦ Mean errors◦ Reliability ◦ Predictability

Verification conditioned on the observation◦ Discrimination (ROC)

For lead times 1,5 and 10 daysat 0.25 and 1 degree spatial resolution, Daily and 5 day accumulation

Mean ErrorsMean Errors

0.25 degreeOhio Basin2002-2006TRMM as obs

Upper tercile: improved bias

Reliability of ens. spreadReliability of ens. spread


Improved reliability

PredictabilityPredictability


Status quo or no improvement

DiscriminationDiscrimination

ROC diagram0.25 degreeOhio Basin2002-2006TRMM as obs

False alarm rateP

rob.

of

dete

ctio

nO

r hit

rate

Spatial structureSpatial structure2005, Jul 13th

75th Percentilebasin daily acc., 2002-2006 TRMM

ConclusionsConclusionsThe analog technique with a moving spatial

window improves:

◦ reliability (considerably), mean errors (slightly)Status quo on:

◦ discrimination,predictabilityResults consistent at different spatial and

temporal scales ( not shown, 1 degree and 5 day acc.)

More realistic precipitation patterns.Spatial rank structure?

◦ An analog technique with no moving spatial window would ensure it. Issue with short observed dataset.

◦ Try the NWS EPP.

Climatologies of forecastsClimatologies of forecasts

Ohio Basin2002-2006





1 degreeOhio Basin2002-2006TRMM as obs



0.25 degree5 day acc.Ohio Basin2002-2006TRMM as obs


ReliabilityReliability


- Improved reliability- poor reliability for medium tercile- poor reliability lead time 10



- Improved reliability- No reliability for medium tercile- No reliability lead time 10


0.25 degree5 day accOhio Basin2002-2006TRMM as obs

- Improved reliability-No reliability for medium tercile- Some reliability day 6-10

SharpnessSharpness


Improved sharpnessfor lower tercile

SharpnessSharpness


Improved sharpnessfor lower tercile

SharpnessSharpness


No improvement















0.25 degree5 day acc.Ohio Basin2002-2006TRMM as obs

Documents

Verification of a downscaling approach for large area flood prediction over the Ohio River Basin