Using Satellite-derived Landuse Data to Improve Meso- and Storm-scale Numerical Weather Prediction

使用卫星土地利用数据改进中观和风暴规模数值天气预报

• 批准号: 9871807
• 负责人: David Stensrud
• 金额: $ 67.46万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9871807&HistoricalAwards=false
• 关键词: Using; Satellite; derived; Landuse; Data

9871807 Stensrud Title:Using Satellite-Derived Landuse Data to Improve Meso- and Storm-scale Numerical Weather Prediction Improvements in numerical weather prediction (NWP) have been hindered by deficiencies in the availability and quality of land cover data. Capabilities to inventory and map land cover conditions and to monitor land surface changes at higher spatial and temporal resolution are needed. To address these issues, a collaborative study between researchers in Oklahoma and Nebraska will be carried out to improve the simulation of surface heat and moisture fluxes in mesoscale NWP models. This project will focus upon weather events that are not forecast well by present operational NWP models, such as extreme high and low temperature events, where the incorporation of improved land cover data is likely to have the greatest impact. Four specific objectives are defined: (1) evaluate the ability to improve upon the daily coarse-resolution (1 km) satellite data by blending in very high-resolution satellite data; (2) evaluate the improvements from using a detailed land surface parameterization scheme in mesoscale model simulations by comparing modeled fluxes to the unique flux observations available over Oklahoma; (3) document the improvements in simulating several extreme temperature events that have occurred over the United States by comparing mesoscale model simulations with and without the detailed land cover data; and (4) incorporate the high- resolution land cover into the Advanced Regional Prediction System (ARPS) for use in operational NWP efforts. The knowledge gained from this study will provide the information necessary to improve the forecasts of extreme weather events, particularly extreme high and low temperature forecasts, that are a significant economic concern. It is expected that improved forecasts of temperatures would be of great use to insurance companies, electric power utilities, agriculture, and the transportation industries.

利用卫星衍生的土地利用数据改进中尺度和风暴尺度数值天气预报，数值天气预报（NWP）的改进一直受到土地覆盖数据可用性和质量不足的阻碍。需要有能力清查和绘制土地覆盖状况，并以更高的空间和时间分辨率监测陆地表面变化。为了解决这些问题，俄克拉荷马州和内布拉斯加州的研究人员将开展一项合作研究，以改进中尺度NWP模式中地表热通量和水分通量的模拟。该项目将侧重于目前运行的NWP模式不能很好地预测的天气事件，例如极端高温和低温事件，在这些事件中，纳入改进的土地覆盖数据可能会产生最大的影响。确定了四个具体目标：(1)评估通过混合非常高分辨率卫星数据改善每日粗分辨率（1公里）卫星数据的能力；(2)通过将模拟通量与俄克拉何马州独特的通量观测值进行比较，评估在中尺度模式模拟中使用详细地表参数化方案的改进；(3)通过比较有和没有详细土地覆盖数据的中尺度模式模拟，记录了在模拟美国发生的几个极端温度事件方面的改进；(4)将高分辨率土地覆盖纳入高级区域预测系统（ARPS），用于实际的NWP工作。从这项研究中获得的知识将提供必要的信息，以改进极端天气事件的预报，特别是极端高温和极端低温的预报，这是一个重大的经济问题。预计，改进后的气温预报将对保险公司、电力公司、农业和运输业有很大的用处。