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The determination of objective temperature forecasts using proxy stations.

Article Excerpt
ABSTRACT: Temperature forecasts are needed for many client applications, particularly in the agricultural and utility industries. Objective minimum and maximum temperature forecasts using the model output statistics (MOS) approach are provided by the National Weather Service (Meteorological Development Lab) for at least 600 stations nationwide and up to 20 stations in New Jersey and nearby border areas. However, many locations that require temperature forecasts have no objective temperature forecasts pro vided for them. The local forecaster or user must decide how to apply available forecast data from surrounding stations that have guidance, to his or her location of interest. In this study a procedure for accomplishing this is proposed and then tested for New Brunswick, NJ. The essence of the procedure is to compute a sample forecast error for forecasts made via MOS for candidate proxy stations. The verification is done by comparing the forecasts to observations at New Brunswick. This is done separately for each season (fall and winter in this pilot study), and different meteorological conditions such as variations in cloud coverage and wind speed. These variables are known to significantly affect temperature. From the results the user has important information in deciding what proxy station to use for temperature forecast guidance given the season, time of day, and meteorological conditions. For example, the results from this study indicate that the station that is farthest from New Brunswick among those employed, supplied the best temperature forecast guidance for fall season minimum temperatures. This illustrates that the user should not blindly employ guidance from the closest station to his/her location of interest.

KEY WORDS: temperature forecasting, New Brunswick temperatures

INTRODUCTION

The National Weather Service issues objective temperature forecasts for future 12-hour periods (00-12 UTC and 12-00 UTC) twice per day using a statistical approach called Model Output Statistics (MOS) (Carter et. al., 1989). Temperature is but one element included in the MOS messages, which is disseminated on the WEB (URL: tgsv5.nws.gov/tdl). There is a separate set of forecasts for up to 600 stations, with the number varying depending on which numerical model serves to supply predictor variable values to the regression equations. For short-range (i.e., 6 to 72 hour projection) forecasts, the Nested Grid Model (NGM) (Dallavalle et. al., 1992) and Aviation Model (AVN) (Dallavalle et. al., 2000) supply input to a separate set of objective temperature forecast equations. New stations are added to the list periodically. For the state of New Jersey there are 6 (NGMMOS) to 14 (AVN-MOS) stations that have a set of MOS predictions produced. Near the border with New York and Pennsylvania there are an additional 5 to 6 stations with supplied MOS guidance. This means that there are a large number of locations, including many mid-size cities, for which there are no MOS forecasts issued. The question is then raised: How to effectively use available MOS forecast guidance to make forecasts at stations lacking specific guidance? A strongly motivated user could derive a set of equations for a city of interest using much of the same techniques that are employed to produce the equations now used operationally at nearby locations. This would be difficult. A more viable possibility is that the user could simply pick the closest station to use for forecast guidance based on some criteria such as closest distance to the point of interest or using the station with the perceived best match of microclimate characteristics. Also, the user could somehow combine forecast information from two or more nearby locations in order to produce the desired result. There is much research available that indicates that temperatures can vary significantl y over short distances especially under certain meteorological conditions at night. Therefore, simply using the forecast guidance from the spatially closest station to make a forecast might not be optimal. Typically forecasters...

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