1. DOWNSCALING GLOBAL MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR FLOOD PREDICTION
Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier
Civil and Environmental Engineering 3rd HEPEX workshop
University of Washington, Seattle June 27-29, Stresa, Italy
1. Abstract
2. The medium range global prediction scheme
3. Bias Correction
We are developing a prototype system for medium range (up to two week lead) flood prediction in large rivers, which is intended for global implementation. The procedure draws from the experimental North American Land Data Assimilation System (NLDAS) and the University of Washington West-wide Seasonal Hydrologic Forecast System for streamflow prediction. Our vision is to rely heavily on weather prediction model and satellite remote sensing, which will reduces the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient, but where a global hydrologic forecast capability arguably would have the greatest value.
This poster focuses on downscaling of global two week lead precipitation (and other surface variable) forecasts, that would be used to drive a macroscale hydrology model. Two key processing steps are required to transform ensemble weather forecasts to have appropriate statistical probabilities and are at the appropriate spatial resolution (in our case, one-half degree, considerably higher resolution than the global weather forecast model) to force the hydrology model. The first processing step is a bias correction, for which we use a probability mapping (quantile-quantile) method. This method is applied to weather forecast model forecast ensembles (as a surrogate, we use reforecasts from the NCEP global model produced by Tom Hamill and Jeffrey Whitaker at the NOAA Earth System Research Laboratory) in order to provide probabilistic consistency with a spin up meteorological dataset (in real-time, we expect to use weather model analysis fields in lieu of gridded surface observations; in this work, ERA-40 serves as a surrogate for weather forecast analysis fields). Although the procedures we outline are appropriate for application to all hydrologic model forcings (including, e.g., solar and longwave energy fluxes, surface humidity, wind, and temperature), the challenges are greatest for precipitation, to which hydrologic forecasts are most sensitive, and therefore our focus here is on precipitation. We verify that the bias correction applied to the precipitation forecasts either improves or at least maintains the original NCEP reforecast skill. Verification results are shown for the Mississippi, Danube and Zambeze River basins for
Bias correction uses the Quantile-Quantile method with respect to ERA-40 climatology, that is, for the same percentile, it maps from the quantile of one cumulative distribution function (CDF) to the other:
from GFS reforecast , daily CDF for the 15 ensembles, for each lead time, based on time of the year
to ERA-40 (Obs) , daily CDF , based on time of the year
Notes: 1) Extreme values use fitted (rather than empirical) distributions; 2) quantile mapping includes correction for intermittency
Several years back
Medium range forecasts
NCEP Reforecasts (Hamill et al. 2006) 15 ensemble members – 15 day forecast – 2.5 degree (fixed GFS version of 1998)
Daily ERA-40,
surrogate for near real time analysis fields
Forecast Verification
Bias correction at 2.5 degree, with respect to ERA-40 (Ensures consistency between spinup and the reforecasts)
Downscaling sequence of the precipitation forecasts
Mississippi Danube Zambeze
Forecast Verification
Downscaling to 0.5 degree
Downscaling from 2.5 to 0.5 degree
using the Schaake Shuffle ( Clark et al. 2004) with higher spatial resolution satellite GPCP 1dd (Huffman et al. 2001) and TRMM 3B42 precipitations
Atmospheric inputs
VIC Hydrology Model
Hydrologic
model spin up
(0.5 degree global simulation)
Hydrologic forecast simulation
INITIAL STATE
(0.5 degree global simulation: stream flow, soil moisture, SWE, runoff )
Several years back
Nowcasts
Medium range forecasts ( up to 2 weeks)
4. Precipitation forecast verification over the period
MISSISSIPPI
DANUBE
ZAMBEZE
4.1 Bias and RMSE : mean errors
Statistics for different thresholds:
>=0mm for all days
>=1mm for all days with observed precipitation larger than or equal 1mm
>=10mm for all days with observed precipitation larger than or equal 10mm
Improved mean daily bias for all events and for short lead times
Improved RMSE
No improvement for large events
4.1 Bias and RMSE
4.2 Rank histograms : ensemble reliability
This is the frequency that the observed value has a certain rank with respect to the ensemble members ( rank 1 is for the highest value). Reliability is reached when the histogram looks uniform.
Improved reliability for all events through intermittency correction
No improvement for precipitation events
4.2 Rank Histograms
8386 events
>= 0mm
8386 events
>= 0mm
8386 events
>= 0mm
4.3 Continuous Rank Probability Score:
Predictability
The CRPS is a probabilistic weighted average error. The smaller the CRPS the better
Improved predictability in general
No improvement for the Zambeze basin
2596 events
>= 1mm
1378 events
>= 1mm
3212 events
>= 1mm
4.3 Continuous Rank Probability Scores
344 events
>= 10mm
88 events
>= 10mm
488 events
>= 10mm
5. Conclusions
1/ Impact of bias correction on forecast verification:
Improved RMSE
Improved intermittency (rank histograms)
No improvement in ensemble reliability, especially with longer lead times (rank histograms)
Improved predictability (CRPS)
2/ Would a subsequent step of forecast calibration improve the reliability?