Collaborative Research: Generation of Improved Land-surface Data and an Assessment of its Impact on Mesoscale Predictions

合作研究：改进的地表数据的生成及其对中尺度预测的影响评估

• 批准号: 0243720
• 负责人: Lance Leslie
• 金额: $ 27.1万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0243720&HistoricalAwards=false
• 关键词: Collaborative; Research; Generation; Improved; Land

A major problem area for improved numerical modeling of near surface variables is the sensitivity of model predictions to the accuracy of key land surface parameters. In particular, the forecasts of near surface quantities such as 2 m temperatures and relative humidity and 10 m winds are influenced strongly by the state of the land surface, as is precipitation. The ability to predict these parameters is important to a wide variety of human activities, ranging from planning outdoor and weekend activities to transportation routing and energy conservation. Vegetation characteristics such as fractional vegetation coverage (FVEG) and leaf area index (LAI) arguably are the most important land surface parameters that need to be defined accurately. However, present practices for defining these parameters are overly simplistic and typically are based only on climatology. Yet vegetation responds to daily variations in rainfall and is far from static. Recent studies by the PIs demonstrated potentially large impacts from specifying both FVEG and LAI, at high spatial and temporal resolution, in a state-of-the-art coupled atmosphere-land surface modeling system.This research is a unique collaboration of expertise between three institutions in the key areas of mesoscale atmospheric modeling, land surface modeling and the generation of real-time, high resolution, satellite-derived land surface parameters. The intellectual merit of this work consist of: 1) an evaluation and improvement of various land surface models from the routine daily predictions of near surface variables, 2) the first four-dimensional assimilation system of both standard observational network data and raw flux data from the Oklahoma Mesonet, and 3) the development of an automated knowledge-based system based upon daily polar-orbiting satellite data to provide daily updates of land surface variables. The focus region for this study is the Great Plains, especially during the growing season of 2003 (March to October).The broader impacts of this research are eventual improvements to short-range predictions of near surface variables such as temperature and moisture, thereby affecting power load, air quality, and convective weather forecasting, all of which have significant economic implications.

改进近地表变量数值模拟的一个主要问题是模式预测对关键地表参数精度的敏感性。特别是，对近地表数量的预报，如2米的温度和相对湿度以及10米的风，受到陆地表面状况的强烈影响，降水也是如此。预测这些参数的能力对各种各样的人类活动都很重要，从规划户外和周末活动到运输路线和节能。植被覆盖度（FVEG）和叶面积指数（LAI）等植被特征可以说是最重要的地表参数，需要准确定义。然而，目前确定这些参数的做法过于简单，通常只以气候学为基础。然而，植被对每日降雨量的变化作出反应，远非静止不变。pi最近的研究表明，在最先进的大气-陆地表面耦合模拟系统中，以高时空分辨率指定FVEG和LAI可能会产生巨大影响。这项研究是三个机构在中尺度大气模拟、地表模拟和实时、高分辨率、卫星衍生地表参数生成等关键领域的独特专业知识合作。这项工作的智力价值包括：1)从近地表变量的日常预测中评估和改进各种地表模型；2)标准观测网络数据和俄克拉荷马Mesonet原始通量数据的第一个四维同化系统；3)基于极地轨道卫星数据的自动化知识系统的开发，以提供地表变量的日常更新。本研究的重点区域是大平原，特别是在2003年的生长季节（3 - 10月）。这项研究的更广泛影响是最终改进近地表变量（如温度和湿度）的短期预测，从而影响电力负荷、空气质量和对流天气预报，所有这些都具有重大的经济意义。