Verification of a downscaling approach for large area flood prediction over the ohio river basin
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Verification of a downscaling approach for large area flood prediction over the Ohio River Basin. N. Voisin, J.C. Schaake and D.P. Lettenmaier University of Washington, Seattle, WA AMS Annual Meeting, Phoenix AZ 11-15 Jan 2009. Objective.

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

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Verification of a downscaling approach for large area flood prediction over the ohio river basin

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

N. Voisin, J.C. Schaake and D.P. Lettenmaier

University of Washington, Seattle, WA

AMS Annual Meeting, Phoenix AZ 11-15 Jan 2009


Objective

Objective

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


Objective1

Objective

BCSD = Bias correction and statistical downscaling

Forecast schematic

Several years back

Medium range forecasts (2 weeks)

ECMWF EPS

50 ensemble members 2002-2008

Daily ERA-40

surrogate for near real time analysis fields

1979-2002

Daily

ECMWF Analysis

2002-2008

BCSD to 0.25 degree

BCSD with forecast calibration, 0.25 degree

Atmospheric inputs

VIC Hydrology

Model

Hydrologic model spinup 0.25 degree

Hydrologic fcst

(stream flow, soil moist., SWE,

runoff )

Initial State

Flow fcst calibration


Objective2

Objective

Compare different downscaling techniques

  • Applicable at a global scale

  • For precipitation forecast

  • Improve or conserve the skill


Outline

Outline

  • Existing downscaling methods

  • Analog technique and various variations of it

  • Forecast Verification at different spatial and temporal scales:

    • Mean errors

    • Predictability, reliability

    • Spatial rank structure


1 downscaling techniques

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

2. Analog technique

( adapted from Hamill and Whitaker 2006)

Retrosp. FCST dataset, +/- 45 days around day n

1 degree resolution

Corresp.

Observation (TRMM)

0.25 degree resolution

FCST D DAY

OBS D DAY

Downscaled

FCST

day n

0.25 degree

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST D DAY

OBS D DAY

FCST

n

+/- 45 days

Year-1

OBS

n

+/- 45 days

Year-1

FCST day n

1 degree

5 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

5 degree


2 analog technique1

2. Analog technique

Spatial domain for the analog

  • Choose 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


Verification of a downscaling approach for large area flood prediction over the ohio river basin

2. Analog technique

Ens. Mean Fcst, 20050713

Fcst 20050713

4 closest analogs in the retrospective forecast dataset

Corresponding 0.25 degree TRMM for the analogs,

Downscaled ensemble forecastmembers

Downscaled ens. mean forecast

TRMM (obs)

( adapted from Hamill and Whitaker 2006)


3 forecast verification

3. Forecast Verification

  • Evaluate 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 days

      at 0.25 and 1 degree spatial resolution,

      Daily and 5 day accumulation


Mean errors

Mean Errors

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Reliability of ens spread

Reliability of ens. spread

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Improved reliability


Predictability

Predictability

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Discrimination

Discrimination

ROC diagram

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Prob. of detection

Or hit rate

False alarm rate


Spatial structure

Spatial structure

2005, Jul 13th

75th Percentile

basin daily acc., 2002-2006 TRMM


Conclusions

Conclusions

The analog technique with a moving spatial window

  • improves:

    • reliability (considerably), mean errors (slightly)

  • Status quo on:

    • discrimination,predictability

  • Results 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 forecasts

Climatologies of forecasts

Ohio Basin

2002-2006


Mean errors1

Mean Errors

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Mean errors2

Mean Errors

1 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Mean errors3

Mean Errors

0.25 degree

5 day acc.

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Reliability

Reliability

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

- Improved reliability

- poor reliability for medium tercile

- poor reliability lead time 10


Reliability1

Reliability

1 degree

Ohio Basin

2002-2006

TRMM as obs

- Improved reliability

- No reliability for medium tercile

- No reliability lead time 10


Reliability2

Reliability

0.25 degree

5 day acc

Ohio Basin

2002-2006

TRMM as obs

  • - Improved reliability

  • No reliability for medium tercile

  • - Some reliability day 6-10


Sharpness

Sharpness

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Improved sharpness

for lower tercile


Sharpness1

Sharpness

1 degree

Ohio Basin

2002-2006

TRMM as obs

Improved sharpness

for lower tercile


Sharpness2

Sharpness

0.25 degree

5 day acc

Ohio Basin

2002-2006

TRMM as obs

No improvement


Predictability1

Predictability

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Predictability2

Predictability

1 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Predictability3

Predictability

0.25 degree

5 day acc

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Reliability of ens spread1

Reliability of ens. spread

0.25 degree

Ohio Basin

2002-2006

TRMM as obs


Reliability of ens spread2

Reliability of ens. spread

1 degree

Ohio Basin

2002-2006

TRMM as obs


Reliability of ens spread3

Reliability of ens. spread

0.25 degree

5 day acc.

Ohio Basin

2002-2006

TRMM as obs


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