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首页> 外文期刊>Journal of hydrometeorology >Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts
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Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

机译：GVF推导方法对诺亚地表模型模拟和WRF模型预测的影响

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Green vegetation fraction (GVF) plays a crucial role in the atmosphere-land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.
机译：绿色植被级分（GVF）在大气 - 陆地水和能源交换中起着至关重要的作用。它是NoAh陆地表面模型（LSM）中的基本参数之一，作为NOAA环境预测（NCEP）的国家中心的许多运营数字天气预报模型的土地分量。 NCEP模型中使用的卫星GVF产品源自来自先进的非常高分辨率辐射计（AVHRR）的归一化差异植被指数（NDVI）的简单线性转换，或者来自可见红外成像辐射计的增强型植被指数（EVI）套房（VIIRS）计划不久的将来。由于NDVI或EVI是植被覆盖的简单光谱指数，因此来自它们的GVF可能缺乏NOAH LSM所需的生物物理意义。此外，基于NDVI或EVI的GVF数据产品可以系统地偏离与光谱植被指数相关的饱和度问题的密集植被区域上。另一方面，GVF与叶面积指数（LAI）物理相关，因此可以从LAI数据产品中获得GVF是有益的。在本文中，数学分析了基于EVI和基于LAI的GVF推导方法，并且发现彼此显着不同。进一步评估了GVF差异对NoAh LSM模拟和天气研究和预测天气预报（WRF）模型的影响。结果表明，基于LAI的GVF在离线NOAH LSM和WRF模型中使用时占EVI的基于EVI的形式。

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