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Eli Jacks
and
S. Trivikrama Rao

Abstract

In this study, the performance of the Model Output Statistics (MOS) objective temperature forecasting for Albany, NY, during the period 1975–81 is examined by using various statistical technique. Both paired and unpaired statistical analysis procedures are used to evaluate the performance of the MOS model. Extreme value analysis is also utilized to examine the performance of MOS in predicting the higher observed temperatures. A relatively new statistical technique, called the “bootstrap” method, is used to evaluate the model performance in simulating the extreme values. The results suggest that the MOS model has a warm bias in simulating the minimum temperatures and a cold bias in simulating the maxiimum temperatures. Finally, casts comprising the best and worst forecasts made by MOS are compared with surface weather maps to develop a picture of the synoptic situations that may be conducive to the production of errors in the MOS model predictions.

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Mary C. Erickson
,
J. Brent Bower
,
Valery J. Dagostaro
,
J. Paul Dallavalle
,
Eli Jacks
,
John S. Jensenius Jr.
, and
James C. Su

Abstract

In the spring of 1990, the National Meteorological Center (NMC) tested several modifications to the Regional Analysis and Forecast System (RAFS). In order to compare the proposed version of the RAFS to the current operational RAFS, NMC ran both systems in parallel for a 4-week period. Simultaneously, the Techniques Development Laboratory (TDL) applied the operational RAFS-based Model Output Statistics (MOS) equations to output from both the operational and proposed (parallel) versions of the Nested Grid Model (NGM) to generate two sets of MOS forecasts. Our goal was to determine the impact of RAFS modifications on the NGM MOS forecasts. At the end of the 4-week test period, we verified both the operational and parallel NGM MOS forecasts. Virtually no differences in accuracy or skill existed between the operational and parallel MOS forecasts of max/min temperature, probability of precipitation, and surface wind. The skill of the cloud amount forecasts, however, deteriorated significantly. The NGM 300-mb relative humidity field changed markedly as a result of the RAFS modifications, and this change affected the cloud forecasts. Since the cloud cover forecasts were the only NGM MOS products adversely impacted by the new parallel RAFS, we rederived the cloud equations without the 300-mb relative humidity. These equations were implemented operationally in September 1990. When the new RAFS is implemented, we expect that the impact on the current NGM MOS guidance will be minimal.

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Eli Jacks
,
J. Brent Bower
,
Valery J. Dagostaro
,
J. Paul Dallavalle
,
Mary C. Erickson
, and
James C. Su

Abstract

In this paper, we describe the development and use of new nested grid model (NGM)-based model output statistics (MOS) guidance that has been available since 26 July 1989 for 204 stations in the contiguous United States. The new guidance, which replaced the NGM-based perfect prog package that had been operational since May 1987, consists of forecasts of max/min temperature, probability of precipitation, cloud amount, and surface wind. Guidance for all four elements is available for projections of 1 and 2 days from 0000 and 1200 UTC. The limited-area fine-mesh model (LFM)-based MOS guidance package is still available and was not affected by this change. Verification on independent data shows that NGM-based MOS and LFM-based MOS temperature forecasts are about equally accurate and that both sets of MOS guidance are clearly superior to the NGM-based perfect prog guidance. For the probability of precipitation, the NGM-based MOS guidance is consistently more skillful than the perfect prog guidance, and usually more skillful than the LFM-based MOS guidance. For cloud amount, the NGM-based MOS forecasts are more skillful than either the LFM-based MOS or the NGM-based perfect prog. Finally, the NGM-based MOS and perfect prog wind forecasts are about equally skillful, and both sets are superior to the LFM-based MOS guidance.

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