Forecast Improvements
NSSL Project 6 – Investigation of Synoptic and Mesoscale Meteorological Processes Associated with Hazardous Weather: Investigate Methods to Provide Improved Forecasts of Near Surface Conditions through the Use of Ensemble Forecasts
Funding Type: CIMMS Task II
Objectives
Evaluate the performance of the Bias-Corrected Ensemble (BCE) forecasting
system and the Binning Technique during the cool season; investigate
methods for further improvement of the performance of these two post-processing
techniques.
Accomplishments
A post-processing method initially developed to improve near surface forecasts
from a summertime multimodel short-range ensemble forecasting system
has been evaluated during the cool season of 2005- 06. The method, known
as the bias-corrected ensemble (BCE) approach, uses the past complete
12 days of model forecasts and surface observations to remove the mean
bias of near surface variables from each ensemble member for each station
location and forecast time. In addition, two other performance-based
weighted average BCE schemes, the Exponential Smoothing Method BCE and
Minimum Variance Estimate BCE were implemented and evaluated. Values
of root-mean-squared error from the 2-m temperature and dewpoint temperature
forecasts indicate that the BCE approach outperforms the routinely available
Global Forecast System (GFS) Model Output Statistics (MOS) forecasts
during the cool season by 9% and 8%, respectively. In contrast, the GFS
MOS provides more accurate forecasts of 10-m wind speed than any of the
BCE methods. The performance-weighted BCE schemes yield no significant
improvement in forecast accuracy for 2-m temperature and 2-m dewpoint
temperature when compared with the original BCE, although the weighted
BCE schemes are found to improve the forecast accuracy of the 10-m wind
speed. The probabilistic forecast guidance provided by the BCE system
is found to be more reliable than the raw ensemble forecasts. These results
parallel those obtained during the summers of 2002 to 2004 and indicate
that the BCE method is a promising and inexpensive statistical post-processing
scheme that could be used in all seasons.
The simple binning technique developed to produce reliable probabilistic quantitative precipitation forecasts (PQPFs) from a multimodel short-range ensemble forecasting system was evaluated during the cool season of 2005-06. The technique uses forecasts and observations of 3-h accumulated precipitation amounts from the past 12 days to adjust today’s 3-h quantitative precipitation forecasts from each ensemble member and for each 3-h forecast period. Results indicate that the PQPFs obtained from this simple binning technique are significantly more reliable than the raw (original) ensemble forecast probabilities. Brier skill scores and areas under the relative operating characteristic curve also reveal that this technique yields skillful probabilistic forecasts of rainfall amounts during the cool season. This holds true for accumulation periods of up to 48 h. The results obtained from this wintertime experiment parallel those obtained during the summer of 2004. In an attempt to reduce the effects of a small sample size on two-dimensional probability maps, the simple binning technique was modified by implementing 5- and 9- point smoothing schemes on the adjusted precipitation forecasts. Results indicate that the smoothed ensemble probabilities remain an improvement over the raw (original) ensemble forecast probabilities, although the smoothed probabilities are not as reliable as the unsmoothed adjusted probabilities. The skill of the PQPFs also is increased as the ensemble is expanded from 16 to 22 members during the period of study. These results highlight that simple post-processing techniques have the potential to provide greatly improved probabilistic guidance of rainfall events for all seasons of the year.
This project is ongoing.
Publications
Stensrud, D. J., and N. Yussouf, 2007: Reliable probabilistic quantitative
precipitation forecasts from a short-range ensemble forecasting system.
Wea. Forecasting, 22, 3-17.
Yussouf, N., and D. J. Stensrud, 2006: Prediction of near surface variables at independent locations from a bias-corrected ensemble forecasting system. Mon. Wea. Rev., 134, 3415-3424.
Yussouf, N., and D. J. Stensrud, 2007: Bias-corrected short-range ensemble forecasts of near surface variables during the 2005/06 Cool Season. Wea. Forecasting, accepted.
Yussouf, N., and D. J. Stensrud, 2007: Reliable probabilistic quantitative precipitation forecasts from a short-range ensemble forecasting system during the 2005-2006 cool season. Mon. Wea. Rev., submitted.
Stensrud, D. J., and N. Yussouf, 2006: Postprocessing multimodel ensemble data for improved short-range forecasting. 18th Conf. on Probability and Statistics in the Atmospheric Sciences, Atlanta, GA, Amer. Meteor. Soc., CD-ROM 5.4.
Values of MAE, rmse and bias (K) plotted as a function of forecast hour for the BCE system and the GFS MOS calculated over 158 days from 25 October 2005 through 31 March 2006. The values are averaged over 1374 NWS stations.