Mathematical Sciences: Statistics in Atmospheric Sciences

数学科学：大气科学统计学

• 批准号: 9524770
• 负责人: Peter Guttorp
• 金额: $ 39.95万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 1999-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9524770&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Statistics; Atmospheric

9524770 Guttorp The problem of subgrid scale variability in general circulation models, and the generally poor quality of the precipitation part of such models, indicate the need for precipitation models based on large-scale atmospheric processes. A realistic stochastic model of precipitation can be based on meteorologically homogeneous weather states, each driving a simple stochastic model of precipitation. In order to determine appropriate weather states, a variant of canonical correlation analysis, appropriate for dependent data, is developed. Statistical theory allowing a space-time decomposition of atmospheric fields with attendant standard error assessment is developed. When applied to the leading canonical variates, such a method can isolate spatial and temporal scales of interest. A slightly different approach uses a hidden Markov model, driven by atmospheric data in a less explicit form than expressed above. This model is fairly accurate on relatively small spatial scales, and for temporally homogeneous parts of the year. This hidden Markov model approach is extended to a seasonal model, and its performance compared with the weather state approach, as well as with general meteorological forecast models. %%% The general circulation models used to assess climate change have relatively poor performance when it comes to precipitation. Furthermore, in order to assess the effect on regional hydrology of climate changes, models with a finer resolution than the circulation models are needed, since the hydrologic processes usually operate on a much smaller scale than the large-scale atmospheric processes that dominate the global climate models. Rather than developing precise deterministic models of rainfall, the researchers build a partly probability-based class of models, and compare it with meteorological forecast models and applied to both weather data and circulation model outputs. ***

小行星9524770 次网格尺度变化的问题，在一般的环流模式，和这种模式的降水部分的质量普遍较差，表明需要降水模型的基础上大规模的大气过程。 一个现实的降水随机模型可以基于 在气象上同质的天气状态，每个驱动一个简单的 降水随机模型为了确定合适的天气 状态，典型相关分析的变体，适用于依赖 数据，发展。 允许时空分解的统计理论 的大气场与伴随的标准误差评估。 当应用于前导典型变量时，这种方法可以隔离空间 和感兴趣的时间尺度。一种稍微不同的方法使用隐藏的 马尔可夫模型，由大气数据驱动，其形式不如所表达的那样明确 以上该模型在相对较小的空间尺度上相当准确，并且适用于一年中时间均匀的部分。 这种隐马尔可夫模型的方法扩展到一个季节模型，其性能相比，天气状态的方法，以及与一般的气象预报模型。 用于评估气候变化的大气环流模式在降水方面的表现相对较差。此外，为了评估气候变化对区域水文的影响，需要比环流模型分辨率更高的模型，因为水文过程通常比主导全球气候模型的大尺度大气过程在小得多的尺度上起作用。研究人员没有开发精确的降雨确定性模型，而是建立了一个部分基于概率的模型，并将其与气象预报模型进行比较，并应用于天气数据和环流模型输出。 ***