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On the Use of Mesoscale and Cloud-Scale Models in Operational Forecasting

Harold E. BrooksNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Charles A. Doswell IIINOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Robert A. MaddoxNOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

In the near future, the technological capability will be available to use mesoscale and cloud-scale numerical models for forecasting convective weather in operational meteorology. We address some of the issues concerning effective utilization of this capability. The challenges that must be overcome are formidable. We argue that explicit prediction on the cloud scale, even if these challenges can be met, does not obviate the need for human interpretation of the forecasts. In the case that humans remain directly involved in the forecasting process, another set of issues is concerned with the constraints imposed by human involvement. As an alternative to direct explicit prediction of convective events by computers, we propose that mesoscale models be used to produce initial conditions for cloud-scale models. Cloud-scale models then can be run in a Monte Carlo–like mode, in order to provide an estimate of the probable types of convective weather for a forecast period. In our proposal, human forecasters fill the critical role as an interface between various stages of the forecasting and warning process. In particular, they are essential in providing input to the numerical models from the observational data and in interpreting the model output. This interpretative step is important both in helping the forecaster anticipate and interpret new observations and in providing information to the public.

Abstract

In the near future, the technological capability will be available to use mesoscale and cloud-scale numerical models for forecasting convective weather in operational meteorology. We address some of the issues concerning effective utilization of this capability. The challenges that must be overcome are formidable. We argue that explicit prediction on the cloud scale, even if these challenges can be met, does not obviate the need for human interpretation of the forecasts. In the case that humans remain directly involved in the forecasting process, another set of issues is concerned with the constraints imposed by human involvement. As an alternative to direct explicit prediction of convective events by computers, we propose that mesoscale models be used to produce initial conditions for cloud-scale models. Cloud-scale models then can be run in a Monte Carlo–like mode, in order to provide an estimate of the probable types of convective weather for a forecast period. In our proposal, human forecasters fill the critical role as an interface between various stages of the forecasting and warning process. In particular, they are essential in providing input to the numerical models from the observational data and in interpreting the model output. This interpretative step is important both in helping the forecaster anticipate and interpret new observations and in providing information to the public.

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