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A Four-Dimensional Data Assimilation System for the Nested Grid Model

A Four-Dimensional Data Assimilation System for the Nested Grid Model
嵌套网格模型的四维数据同化系统
批准号:
8807128
负责人:
Mohan Ramamurthy
金额:
$16.21万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1988
资助国家:
美国
项目状态:
已结题
起止时间:
1988-10-15 至 1991-12-31

项目摘要

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中文摘要
翻译
大气观测技术的最新进展有望大大增加用于数值天气预报的渐近观测的数量和种类。这些观测的空间和时间分辨率将比目前的数据高得多。目前的作战数据同化系统不能充分利用这些高分辨率数据中的信息。因此,从这些新的和多样化的数据源中提取最大信息并产生数值模式所需的初始场的技术的发展对大气科学界提出了重大挑战。这种发展也将对未来研究数据集的质量产生重要影响。本研究的目的是为区域模型开发一种基于牛顿松弛连续同化或“轻推”技术的四维数据同化系统。同化包将为国家气象中心(NMC)的嵌套网格模型(NGM)开发。在他的研究过程中,首席研究员将检查模型内的调整过程,将通过轻推技术获得的分析结果与当前操作数据同化系统产生的分析结果进行比较。GALE(大西洋低气压起源实验)数据集代表了这一目的的一组极好的观测结果。该数据集包含来自观测系统的高空间和时间分辨率的观测数据,这些观测系统与将在不久的将来用于业务的观测系统类似。该项目是美国国家气象中心(NOAA)和美国国家科学基金会(NSF)联合支持的数值天气预报基础研究项目的一部分。
英文摘要
Recent advances in atmospheric observing technology promise to greatly increase in the number and variety of asynoptic observations that are available for numerical weather prediction. These observations will be available at both spatial and temporal resolutions that are much higher than present data. Current operational data assimilation systems cannot fully utilize the information in these high resolution data. Thus, the development of techniques to extract maximum information from these new and diverse data sources and to produce the initial fields required for numerical models presents a major challenge to the atmospheric sciences community. Such developments also will have an important impact on the quality of future research data sets. The goal of this research is to develop for a regional model a four-dimensional data assimilation system based on the Newtonian relaxation continuous assimilation or "nudging" technique. The assimilation package will be developed for National Meteorological Center's (NMC) Nested Grid Model (NGM). In the course of his research, the principal investigator will examine adjustment processes within the model, comparing analyses obtained via the nudging technique with those produced with the current operational data assimilation system. The GALE (Genesis of Atlantic Lows Experiment) data set represents an excellent set of observations for this purpose. The data set contains observations at high spatial and temporal resolution from observing systems similar to those that will be used operationally in the near future. This project is part of a joint program in basic research related to numerical weather prediction supported by NMC (NOAA) and NSF.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis