While these studies helped to quantify the value of the mesoscale models in predicting specific meteorological phenomena, a manual subjective intervention was used to perform phenomenological verification, which can be quite expensive in terms of the required manpower resources. These studies demonstrated that objective point error statistics alone (i.e., measures-oriented approach) cannot adequately define a mesoscale model's utility, and that phenomenological verification is also required as part of the validation process. (2002) performed both an objective and subjective verification of RAMS during the 2000 Florida summer, including a validation of the model-predicted SB and daily thunderstorm initiation. Nutter and Manobianco (1999) and Manobianco and Nutter (1999) performed an objective point verification and subjective phenomenological verification, respectively, of the 29-km version of the National Centers for Environmental Prediction (NCEP) Meso Eta Model. Some recent studies have addressed the deficiencies in applying traditional objective verification statistics to high-resolution NWP model configurations and have presented alternative means for verifying phenomena in mesoscale models. In addition, the SB can significantly impact space operations because of the sharp wind shifts and thunderstorm development often associated with SB transition zones. The verification of the SB was chosen because this phenomenon occurs quite frequently in east-central Florida, particularly in the spring and summer months. These techniques were applied to evaluate model performance in forecasting the sea-breeze (SB) phenomenon over east-central Florida. 1992) mesoscale NWP model, currently used operationally on the Eastern Range at Cape Canaveral Air Force Station (CCAFS). during the project), the Applied Meteorology Unit (operated by ENSCO, Inc.), and the National Aeronautics and Space Administration Kennedy Space Center (KSC) was established in order to develop advanced techniques for objectively evaluating the performance of the Regional Atmospheric Modeling System (RAMS Pielke et al. As a result, objective phenomenological-based verification methodologies are required in order to determine the added value of high-resolution NWP models.Ī coordinated effort between personnel from Dynacs, Inc. In addition, subjective evaluation techniques can be very costly and time consuming. Traditional objective techniques that evaluate NWP model performance based on point error statistics and precipitation threat scores may not be positively correlated with the value of forecast information for certain users of mesoscale NWP guidance. An ongoing challenge in mesoscale NWP is to determine the ideal method for verifying the performance of high-resolution, detailed forecasts based on the application. Numerical weather prediction (NWP) models are in widespread operational use for regional and global forecast applications. The CEM algorithm details are presented and validated against independent meteorological assessments of the sea-breeze transition times and results from a previously published subjective evaluation. The CEM technique improves upon traditional objective verification techniques and previously used subjective verification methodologies because it is automated, accounts for both spatial and temporal variations, correctly identifies and verifies the sea-breeze transition times, and provides verification contour maps and simple statistical parameters for easy interpretation. The contour error map (CEM) technique identifies sea-breeze transition times in objectively analyzed grids of observed and forecast wind, verifies the forecast sea-breeze transition times against the observed times, and computes the mean post-sea-breeze wind direction and wind speed to compare the observed and forecast winds behind the sea-breeze front. This paper presents a new objective technique to verify predictions of the sea-breeze phenomenon over east-central Florida by the Regional Atmospheric Modeling System (RAMS) NWP model. As a result, objective event-based verification methodologies are required in order to determine the added value of high-resolution NWP models. Traditional objective techniques that evaluate NWP model performance based on point error statistics may not be positively correlated with the value of forecast information for certain applications of mesoscale NWP, and subjective evaluation techniques are often costly and time consuming. An ongoing challenge in mesoscale numerical weather prediction (NWP) is to determine the ideal method for verifying the performance of high-resolution, detailed forecasts.
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