Improved modelling and analysis of variable resolution IR and MW satellite observations using convective scale numerical weather prediction

使用对流尺度数值天气预报改进可变分辨率红外和微波卫星观测的建模和分析

• 批准号: NE/G011281/1
• 金额: $ 8.4万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FG011281%2F1
• 关键词: Improved; modelling; analysis; variable; resolution

The accurate and timely forecasting of extreme rainfall events is of the utmost importance to both life and property. Satellite observations from multi-spectral, multi-sensor observations provide information that aid the forecasting of such events. To advance the accuracy of these forecasts at the necessary temporal and spatial resolutions, further research is necessary to address the optimal use of such information. Satellite observations exist at a range of temporal and spatial resolutions, and different wavelengths sense different depths within weather systems. These effects lead to different errors in modelling the observations arising from, for example, heterogeneous field of views, and representativeness leading both to correlated and uncorrelated components of error. It is not well understood how these errors propagate into the analysis using variational data assimilation. The UK Meteorological Office operates a number of numerical weather prediction models, of which the 1.5km convective scale model provides very high spatial and temporal resolution short-range forecasts of weather systems. However, the satellite observations assimilated into the high-resolution model have generally much coarser resolutions (both spatially and temporally). The impact of this scale mis-match will assessed quantitatively through the comparison with the results from the Meteorological Office's North Atlantic and European model (12km resolution). A number of UK weather events will be selected for this study covering both high-profile events (such as the Morpeth floods of September 2008) and more representative weather situations. The output of the NWP simulations will be used to simulate the expected satellite observations through radiative transfer models, which in turn will be compared with the actual satellite observations. The overall aim of this project is to investigate the scale-induced errors associated with assimilation of satellite radiances into numerical weather prediction models. The objectives are: 1. To characterise the errors in radiative transfer modelling arising from the computationally affordable treatment of fine scale structure in clouds and precipitation; 2. To improve our understanding of the information availability in different channels with varying resolutions; 3. To assess the different assumptions within the 1D analysis scheme for a range of meteorological conditions; and, 4. To evaluate the effects of beam-filling inherent in low-resolution satellite observations. The project will be based in the School of Geography, Earth and Environmental Sciences at the University of Birmingham, with supervision provided by Dr Chris Kidd and Dr Xiaoming Cai. The Satellite Radiance Assimilation Group of the UK Meteorological Office in Exeter will provide the necessary 'industry' support through the expertise of Stephen English. Both project partners will be supported by members of their research teams to provide the necessary research and training environment. Dr Chris Kidd will provide expertise in satellite observations and retievals supported by Dr Xiaoming Cai with expertise in atmospheric modelling. Dr Stephen English will provide expertise in microwave radiative transfer modelling, data assimilation and applications of infrared and microwave sounding data. Both organisations have complementary experience in satellite meteorology and data assimilation that can be exploited in the proposed studentship. The proposed research is important to both parties in that it addresses advancing the effective use of cloud and precipitation information from satellites observations in high resolution NWP. On-going work on the assimilation radar observations is essentially restricted to land-areas, consequently the proposed research will complement this by advancing our ability to assimilate available information over ocean areas.

准确、及时地预报极端降雨事件对生命和财产至关重要。多光谱、多传感器观测的卫星观测提供了有助于预测这类事件的信息。为了在必要的时间和空间分辨率上提高这些预测的准确性，有必要进一步研究如何最佳利用这些信息。卫星观测具有一系列的时间和空间分辨率，不同的波长在天气系统中感知不同的深度。这些影响导致不同的错误，在模拟所产生的意见，例如，异构领域的意见，和代表性导致相关和不相关的组件的错误。目前还不清楚这些误差是如何传播到变分资料同化分析中的。英国气象局运作多个数值天气预报模式，其中1.5公里对流尺度模式提供极高的空间和时间分辨率的天气系统短期预报。然而，同化到高分辨率模式中的卫星观测通常具有更粗糙的分辨率（空间和时间）。通过与气象局的北大西洋和欧洲模式（12公里分辨率）的结果进行比较，将定量评估这种尺度不匹配的影响。本研究将选择一些英国天气事件，包括备受瞩目的事件（如2008年9月的莫尔佩斯洪水）和更具代表性的天气情况。NWP模拟的输出将用于通过辐射传输模型模拟预期的卫星观测，然后将其与实际卫星观测进行比较。该项目的总体目标是研究与卫星辐射同化到数值天气预报模式中有关的尺度引起的误差。目标是：1.消除由于云和降水中的细尺度结构的计算负担得起的处理而引起的辐射传输模式中的误差; 2。提高我们对不同分辨率的不同渠道信息可用性的理解; 3.评估一系列气象条件下一维分析方案中的不同假设;以及4.评价低分辨率卫星观测中固有的波束填充效应。该项目将设在伯明翰大学地理、地球和环境科学学院，由Chris Kidd博士和Xiaoming Cai博士监督。位于埃克塞特的英国气象局的卫星辐射同化小组将通过Stephen English的专业知识提供必要的“行业”支持。两个项目合作伙伴将得到其研究小组成员的支持，以提供必要的研究和培训环境。克里斯·基德博士将提供卫星观测方面的专业知识，蔡晓明博士将提供大气建模方面的专业知识。Stephen English博士将提供微波辐射传输建模、数据同化以及红外和微波探测数据应用方面的专业知识。这两个组织在卫星气象学和数据同化方面拥有互补的经验，可以在拟议的助学金中利用。拟议的研究对双方都很重要，因为它解决了在高分辨率数值预报中有效利用卫星观测的云和降水信息的问题。正在进行的同化雷达观测工作基本上限于陆地地区，因此，拟议的研究将通过提高我们同化海洋地区现有信息的能力来补充这一点。