TY - JOUR

T1 - Ensemble mean storm-scale performance in the presence of nonlinearity

AU - Hollan, Michael A.

AU - Ancell, Brian C.

N1 - Funding Information:
The authors thank fellow faculty members and graduate students of the Atmospheric Science Group at Texas Tech University for their support, John Halley Gotway and Michael James from the University Corporation for Atmospheric Research, and the High Performance Computing Center staffat Texas Tech University for assisting with software and computing issues. The authors also wish to thank two anonymous reviewers who provided a number of comments and suggestions that improved the article. This work was supported by NOAA CSTAR Grant NWS NA11NWS4680001.
Publisher Copyright:
© 2015 American Meteorological Society.

PY - 2015

Y1 - 2015

N2 - The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12-and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within aWRFModel utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble ("mean") and a deterministic forecast initialized from a set of mean initial conditions ("control") are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory.

AB - The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12-and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within aWRFModel utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble ("mean") and a deterministic forecast initialized from a set of mean initial conditions ("control") are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory.

KW - Ensembles

KW - Forecast verification/skill

KW - Forecasting

KW - Forecasting techniques

KW - Mesoscale models

KW - Models and modeling

KW - Numerical weather prediction/forecasting

KW - Operational forecasting

UR - http://www.scopus.com/inward/record.url?scp=84957831201&partnerID=8YFLogxK

U2 - 10.1175/MWR-D-14-00417.1

DO - 10.1175/MWR-D-14-00417.1

M3 - Article

AN - SCOPUS:84957831201

VL - 143

SP - 5115

EP - 5133

JO - Monthly Weather Review

JF - Monthly Weather Review

SN - 0027-0644

IS - 12

ER -