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Advanced methods for assimilating satellite data in numerical weather prediction

数值天气预报中同化卫星数据的先进方法

基本信息

批准号：
2285064
负责人：
金额：
--
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2285064
关键词：
Advanced methods assimilating satellite data

项目摘要

Data assimilation, the process of initializing a computer model forecast using the latest observational data, has proven fundamental to the accuracy of modern day weather forecasting. Every day of the order of 107 observations are assimilated at the Met Office. These observations come from a myriad of different instruments onboard various platforms; including weather balloons, aircrafts and ships. However, observations from instruments onboard satellites have been shown to have the greatest impact on producing accurate forecasts. This is due to their extensive coverage, high sampling resolution and the information they provide about key model variables: temperature, humidity and winds. The problem is that satellite data often exhibit systematic errors, for example due to poor calibration, adverse environmental effects, or errors in the radiative transfer equations that relate the observations to the model variables. Systematic errors in the data violate the theory that is central to data assimilation and so for satellite data to be useful they must first be bias corrected. The methods currently in use for performing the bias correction rely on the assumption that the computer model assimilating the observational data itself is unbiased. Unfortunately this is rarely true and is becoming a limiting factor in the use of satellite data. This project will develop new mathematical techniques for performing bias correction that are able to distinguish and correct for biases in both the observations and model.

资料同化，即利用最新观测资料初始化计算机模式预报的过程，已被证明对现代天气预报的准确性至关重要。每天大约有107次的观测结果在英国气象局被同化。这些观测结果来自不同平台上的无数不同仪器；包括气象气球、飞机和船只。然而，卫星上的仪器所作的观测已被证明对作出准确的预报有最大的影响。这是由于它们覆盖范围广，采样分辨率高，以及它们提供的关于关键模型变量的信息：温度、湿度和风。问题是卫星数据经常出现系统误差，例如由于校准不良、不利的环境影响或将观测与模式变量联系起来的辐射传递方程中的误差。数据中的系统误差违反了数据同化的核心理论，因此要使卫星数据有用，就必须首先纠正偏差。目前用于进行偏差校正的方法依赖于这样一个假设，即吸收观测数据的计算机模型本身是无偏的。不幸的是，这种情况很少发生，并且正在成为使用卫星数据的一个限制因素。该项目将开发新的数学技术来执行偏差校正，能够区分和纠正观测和模型中的偏差。