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

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 prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

Compare different downscaling techniques

  • Applicable at a global scale

  • For precipitation forecast

  • Improve or conserve the skill


Outline
Outline prediction over the Ohio River Basin

  • 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 prediction over the Ohio River Basin

  • 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 prediction over the Ohio River Basin

( 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 prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

  • 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 prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Reliability of ens spread
Reliability of ens. spread prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Improved reliability


Predictability
Predictability prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Discrimination
Discrimination prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

2005, Jul 13th

75th Percentile

basin daily acc., 2002-2006 TRMM


Conclusions
Conclusions prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

Ohio Basin

2002-2006


Mean errors1
Mean Errors prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Mean errors2
Mean Errors prediction over the Ohio River Basin

1 degree

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Mean errors3
Mean Errors prediction over the Ohio River Basin

0.25 degree

5 day acc.

Ohio Basin

2002-2006

TRMM as obs

Upper tercile: improved bias


Reliability
Reliability prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

- Improved reliability

- poor reliability for medium tercile

- poor reliability lead time 10


Reliability1
Reliability prediction over the Ohio River Basin

1 degree

Ohio Basin

2002-2006

TRMM as obs

- Improved reliability

- No reliability for medium tercile

- No reliability lead time 10


Reliability2
Reliability prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Improved sharpness

for lower tercile


Sharpness1
Sharpness prediction over the Ohio River Basin

1 degree

Ohio Basin

2002-2006

TRMM as obs

Improved sharpness

for lower tercile


Sharpness2
Sharpness prediction over the Ohio River Basin

0.25 degree

5 day acc

Ohio Basin

2002-2006

TRMM as obs

No improvement


Predictability1
Predictability prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Predictability2
Predictability prediction over the Ohio River Basin

1 degree

Ohio Basin

2002-2006

TRMM as obs

Status quo or no improvement


Predictability3
Predictability prediction over the Ohio River Basin

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 prediction over the Ohio River Basin

0.25 degree

Ohio Basin

2002-2006

TRMM as obs


Reliability of ens spread2
Reliability of ens. spread prediction over the Ohio River Basin

1 degree

Ohio Basin

2002-2006

TRMM as obs


Reliability of ens spread3
Reliability of ens. spread prediction over the Ohio River Basin

0.25 degree

5 day acc.

Ohio Basin

2002-2006

TRMM as obs