Development of Novel Convection Nowcasting Techniques for Southeast Asia

东南亚新型对流临近预报技术的开发

• 批准号: 2604259
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2604259
• 关键词: Development; Novel; Convection; Nowcasting; Techniques

Background and RationaleThe Maritime Continent region of Southeast Asia contains the countries of Indonesia, Malaysia, Philippines and Papua New Guinea. It is a region of complex topography and a global hotspot for intense rainfall. Storms such as the squall line featured in Figure 1 are common across the region and are regularly the cause of landslides and flooding.The skill of numerical weather forecasts on the 1-5 day timescale remains low, often with little skill beyond that provided by a daily persistence forecast (i.e. the weather tomorrow will be the same as today). "Nowcasting" uses detailed descriptions of the current weather (from satellite observations for example) and extrapolates up to 6 hours into the future to produce a short range weather forecast. In the Maritime Continent region, tropical convective systems can have lifetimes of several hours up to more than a day, which means there is an opportunity to make better use of the nowcasting techniques to aid weather forecasting.Project details and objectivesThe simplest way to nowcast with geostationary satellite observations is to use brightness temperature at the ~11 um infrared channel to identify cold cloud tops. More complex systems use information from multiple channels; e.g. estimating convective rain rate from a combination of brightness temperature at both the infrared and water vapour channels with visible reflectivity during the day.A number of countries in Southeast Asia, such as Indonesia and Malaysia, operate a precipitation radar network and the Met Office has been routinely producing high resolution (convection-permitting) deterministic and ensemble forecasts over the Maritime Continent since 2017.Optical flow and machine learning approaches will be utilised to forward propagate the observations in time and to integrate the satellite and radar observations with numerical model forecasts. The integration of the radar and model forecasts is expected to lead to significant improvements in nowcasting of convective initiation, as it is expected to enable the identification of convergence lines using the convection-permitting model and visible channel satellite observations during the day. The integrated nowcasting system would be designed to optionally include lightning observations, which have been shown to significantly improve satellite-based nowcasts. Lightning sensors are currently subject to feasibility studies for upcoming Himawari satellites, and global lightning datasets are available for selected periods through the World Wide Lightning Location Network dataset.Objectives:Develop a blended nowcasting system that combines high quality Himawari satellite data (Figure 2) with ground-based radar and high resolution model forecastsProduce quantitative rainfall nowcasts and short-term forecasts over Southeast Asia for a test periodCompare the skill of the new forecasts with existing weather forecasts and persistence forecastsFigure 2 Left: Artist rendition of the Himawari-8 Satellite. Image credit: MELCO. Right: Satellite images of clouds over Indonesia and Malaysia from the Himawari-8 Satellite. Top: full-colour image. Bottom: infrared image, where very cold cloud tops that suggest heavy rainfall are coloured pink.TrainingYou will be based at the University of Leeds and be supervised by Meteorology and Nowcasting experts from University of Leeds, University of Edinburgh and the Met Office, Exeter. You will gain strong expertise in using a mix of techniques in remote sensing, dynamical meteorology, weather forecasting and data analysis to develop and test a new weather forecasting tool.Project outputsThe project will develop a step-change in the quality of short-term precipitation forecasts (up to 6-12 hours lead time), which will improve the ability of Southeast

背景和理由东南亚的海洋大陆地区包括印度尼西亚、马来西亚、菲律宾和巴布亚新几内亚。它是一个地形复杂的地区，也是全球强降雨的热点。像图1所示的暴风在整个地区都很常见，并且经常是山体滑坡和洪水的原因。1-5天时间尺度的数值天气预报的技能仍然很低，通常除了每日持续性预报所提供的技能之外，几乎没有什么技能(即明天的天气将与今天相同)。Nowcast使用对当前天气的详细描述(例如，来自卫星观测)，并将最多6小时外推到未来，以产生短期天气预报。在海洋大陆地区，热带对流系统的寿命可以达到几个小时到一天以上，这意味着有机会更好地利用近景预报技术来辅助天气预报。项目细节和目标利用地球静止卫星观测进行近景预报的最简单方法是使用~11微米红外通道的亮度温度来识别冷云顶。更复杂的系统使用来自多个通道的信息；例如，根据白天具有可见光反射率的红外和水汽通道的亮度温度估计对流雨率。东南亚一些国家，如印度尼西亚和马来西亚，运行着降水雷达网，自2017年以来，气象局一直在海洋大陆上空制作高分辨率(允许对流)确定性和集合预报。将利用光流和机器学习方法及时向前传播观测，并将卫星和雷达观测与数值模式预报结合起来。雷达预报和模式预报的结合预计将大大改进对流启动的即场预报，因为预计这将使人们能够利用白天的对流允许模式和可见通道卫星观测来确定汇聚线。综合短时预报系统将被设计为可选地包括闪电观测，这已被证明显著改善了基于卫星的短时预报。闪电传感器目前正在对即将推出的Himawa卫星进行可行性研究，通过世界范围闪电位置网络数据集可以获得某些时期的全球闪电数据集。目标：开发一个将高质量Himawa卫星数据(图2)与地面雷达和高分辨率模式预报结合在一起的混合短时预报系统制作测试期间东南亚地区的定量降雨短时预报和短期预报。图片来源：新濠国际。右图：喜马威8号卫星拍摄的印度尼西亚和马来西亚上空的云层图像。上图：全彩图像。下图：红外图像，非常冷的云层顶部涂上了粉红色，预示着暴雨。培训你将驻扎在利兹大学，由来自利兹大学、爱丁堡大学和埃克塞特气象局的气象学和预报专家监督。你将在使用遥感、动力气象学、天气预报和数据分析等技术组合开发和测试新的天气预报工具方面获得强大的专业知识。项目成果该项目将在短期降水预报的质量方面产生阶段性变化(最长可达6-12小时)，这将提高东南地区的能力