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

9871807 Stensrud题目：利用卫星获取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。需要有能力以更高的空间和时间分辨率清查和绘制土地覆盖情况和监测地表变化。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进行一项合作研究，以改善中尺度数值预报模式对地表热量和水分通量的模拟。该项目将侧重于目前运作中的数值预报模式不能很好地预报的天气事件，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）评价通过混合甚高分辨率卫星数据改进每日粗分辨率（1公里）卫星数据的能力; (2)通过比较模拟通量与俄克拉荷马州上空可用的独特通量观测值，评估在中尺度模式模拟中使用详细的陆面参数化方案的改进;（3）通过比较有和没有详细的陆面覆盖数据的中尺度模式模拟，记录在模拟美国上空发生的几次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报系统（ARPS），用于业务NWP工作。从这项研究中获得的知识将提供必要的信息，以改善极端天气事件的预测，特别是极端高温和低温的预测，这是一个重大的经济问题。预计改进的温度预报将对保险公司、电力公司、农业和运输业有很大用处。

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.