Advanced methods for assimilating satellite data in numerical weather prediction

资料同化是利用最新的观测资料初始化计算机模式预报的过程，已被证明是现代天气预报准确性的基础。每天大约有107个观测数据被气象局吸收。这些观测来自各种平台上的无数不同仪器;包括气象气球，飞机和船只。然而，卫星上仪器的观测已被证明对制作准确的预报具有最大的影响。这是由于其覆盖范围广，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显示出系统误差，例如由于校准不良、不利的环境影响或将观测与模型变量相关联的辐射传输方程中的误差。数据中的系统误差违反了数据同化的核心理论，因此，为了使卫星数据有用，必须首先对它们进行偏差校正。目前用于进行偏差校正的方法依赖于这样的假设，即同化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，而且正在成为卫星数据使用的一个限制因素。该项目将开发新的数学技术，用于执行偏差校正，能够区分和校正观测和模型中的偏差。

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.