数值天气预报的创新之路--从初值问题到反问题

AN INNOVATIVE ROAD TO NUMERICAL WEATHER PREDICTION——FROM INITIAL VALUE PROBLEM TO INVERSE PROBLEM

中文摘要:

      基于大气并非是一个确定论的系统，从信息论的视角考察了数值天气预报问题。认为表征初值和边值的数据可以视为输入信息（信息源），而数值模式则不过是一个信息变换机构，它把输入信息变换成预报结论而输出来，输出的预报结论则是未来天气状况的信息。于是预报的准确性受制于：一是输入信息所包含的输出信息的信息量，另一是信息在变换过程中丢失的信息量。从初值的形成过程揭示出了当前观测系统在一个时刻提供的数据没有包含初值所要求的全部信息，而缺失的部分或多或少的隐藏在过去的观测数据中。提为初值问题意味着只依据一个时刻的状态导致输入的信息量缺失，应考虑过去的历史数据以增加输入数据中所包含的预报量的信息。文中指出由于输出信息比输入还多的数值模式是不存在的，这样的改进带有根本性。进一步论证了数值模式的误差信息，也或多或少的隐藏在过去的历史数据中，为了充分使用过去的观测数据，本文建议改变问题的提法，不提成初值问题，提成反问题。资料同化本质上是反问题，其欠定性不应人为夸大。提成反问题的数值天气预报能充分应用过去的历史资料，将天气方法、统计方法、动力方法有机地结合在一起。对于这个反问题如何具体求解方面，在分析了业务和研究的区别，模式的普适性和针对性的统一的基础上，给出了反问题的具体解决途径。强调无需构建新模式（这是非常困难的工作），只需运行现成的模式，借助所关心的预报对象的历史数据来改造现成模式，因而是完全可行的。

英文摘要:

      Given the fact that atmosphere is by no means a determinate system，this paper considers the numerical weather forecast in the view of information theory．The initial value and boundary value can be regarded as input information(information sources and a numerical model is just a tool which exchanges information．Numerical models can transform input information into results of the prediction of future weather information．So the forecast accuracy is enslaved to two aspects：one is how much output information is contained in input information，the other is how much information will be lost in the process of information transformation．The process of generating initial value means that the observational data of a moment does not include all required information for a model's initial value，and the absent information is more or less hidden in the historical observations．Therefore，it is necessary to utilize historical data to increase predicted information which is included in the input data．This paper concludes that a numerical model which can generate more output information than input information，does not exist，and inreasing input information is of essential sense．Furthermore，this paper demonstrates that model errors are also more or less hidden in historical data．In order to make good use of the historical observational data，this paper suggests that the forecasting problem should be regarded as an inverse problem rather than an initial value problem．Data assimilation is essentially an inverse problem，and its under determination should not be artificially exaggerated．The numerical weather prediction，an inverse problem，can not only make full use of historical data，but also use synoptic methods，statistical methods and dynamical methods in combination．This inverse problem can be resolved in practice by a specific method which synthesizes distinction between operational analysis and research results，universality of models，as well as statistics of pertinence．Therefore，it is a feasible approach to use historical data to improve model predictions without constructing a new model，which by any means is a very difficult work．