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- Author or Editor: P. S. Brown Jr. x
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Abstract
Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.
Abstract
Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.
Abstract
It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50 min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.
Abstract
It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50 min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.
