Precipitation from convective storms has a significant impact on the global, regional and local hydrological cycle. Our understanding of how convective storms form, grow, and dissipate remains a scientific challenge that HAP scientists are attacking by means of field programs and high resolution modeling through the STEP and Water System programs. Short term forecasting (0-8 hours) of thunderstorms is a particular focus for the applied research on convective storms. The AutoNowcaster system is a RAL developed decision support system focused on 0-2 hour nowcasts of thunderstorms, including growth, dissipation, and especially initiation.
The system is currently being transitioned to the National Weather Service for operational use at Weather Forecast Offices. The system was used operationally by the Chinese Weather Bureau during the 2008 summer Olympics.
The research and development of the Convective Weather Program are geared towards very short-term forecasting of high-impact weather. The objective is to enhance existing and facilitate new capabilities of monitoring (i.e., analysis), nowcasting (< 2 h), and forecasting (> 2 h) weather-related conditions that pose a hazard or threat to human safety, transportation on land, water, and in the air, and to infrastructure.
The program strives to advance a basic understanding of dynamic, thermodynamic, and microphysical processes related to severe weather, including the initiation of storms and their subsequent evolution, by focusing on observations, data assimilation, numerical modeling, forecasting, and diagnostic evaluation. Integration of short-term weather prediction with user applications is a high priority.
High-resolution, short-term forecasts of thunderstorms provide critical information for a wide range of users, including the aviation community, ground transportation, urban emergency and water resources management groups, recreation facilities, construction industries, and the military, that assists them to safely and efficiently deploy resources. However, achieving reliable and accurate convective weather forecasts remains a scientific challenge due to uncertainty in grasping initial conditions, shortcomings in model physics and computational capabilities, and limitations of our understanding of how nature works. The forecast skill of observation-driven expert systems decreases rapidly with increasing lead-time, while numerical weather prediction models exhibit a limited forecast ability within the first few hours after initialization primarily due to spin-up problems. Furthermore, forecast methodology and display systems have to be tuned to the needs of different users—for example, a line of severe convective storms predicted in the wrong place may be perceived as a bad forecast from a water resources manager (e.g., dam operator), yet the same forecast might be quite good for an en-route air traffic control manager.
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Convective Weather - Aviation