Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data
Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data
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DOI：
10.5194/hess-15-771-2011
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发表时间：
2010-09
影响因子：
6.3
通讯作者：
N. Ghilain;A. Arboleda;F. Gellens-Meulenberghs
中科院分区：
文献类型：
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作者：
N. Ghilain;A. Arboleda;F. Gellens-Meulenberghs
抽象的。我们提出了一个蒸散（ET）模型的框架下，EUMETSAT“卫星应用设施”（SAF）的陆面分析（LSA）。该模型是一个简化的土壤-植被-大气传输方案，将遥感数据和大气模型输出结合起来作为输入。基于遥感的输入是LSA-SAF产品：反照率（AL）、下行地表短波通量（DSSF）和下行地表长波通量（DSLF）。它们具有MSG SEVIRI仪器的空间分辨率。覆盖整个MSG视野的ET地图每30分钟从模型中产生一次，几乎是实时的，适用于所有天气条件。本文介绍了所采用的方法和一组验证结果。模型质量的评估有两种方式。首先，ET结果与地面观测（从CarboEurope和国家气象局）进行比较，不同的土地覆盖类型，在一个完整的植被周期在北方半球在2007年。验证表明，该模型能够再现温带气候区从周日到年度时间尺度的ET时间演变：平均偏差小于0.02 mm h-1，均方根误差在0.06和0.10之间mm h-1。然后，ET模式输出的欧洲中期天气预报中心（ECMWF）和全球陆地数据同化系统（GLDAS）进行了比较。从这个比较中，注意到中午左右的高度空间相关性在80%到90%之间。然而，也观察到一些差异，这是由于使用了不同的输入变量和参数化。
Abstract. We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.