A statistical model for sea surface diurnal warming driven by numerical weather prediction fluxes and winds
A statistical model for sea surface diurnal warming driven by numerical weather prediction fluxes and winds
复制标题
由数值天气预报通量和风驱动的海面昼夜变暖统计模型
DOI：
10.5194/osd-7-1497-2010
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
P. Borgne
中科院分区：
文献类型：
--
作者：
M. Filipiak;C. Merchant;H. Kettle;P. Borgne
本文从数值天气预报模式出发，建立了一个海表温度日变化与海面净热通量和海面风速的统计模式。该模型是使用通量和风从欧洲中期天气预报中心（ECMWF）数值预报模式和SST从旋转增强可见光和红外成像仪（SEVIRI）。在该模式中，日变暖有一个线性依赖于净地面热通量集成以来（大约）黎明和反平方依赖于最大的地面风速在同一时期。模型系数的匹配，对于一个给定的综合热通量，最大风速和观测到的变暖的频率分布。昼夜冷却，它发生的地方，模拟成比例的综合热通量除以热容量的季节性混合层。该模型再现了SEVIRI观测到的SST日变化的统计数据（平均值、标准差和95百分位数），并再现了高级微波扫描辐射计（AMSR-E）观测到的平均变暖的地理格局。我们使用统计模型中的函数依赖性来测试两个昼夜变暖物理模型的行为，显示对比系统误差。
A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.
影响因子：
13.5
作者：
C. Merchant;Owen Embury;P. Borgne;B. Bellec
通讯作者：
C. Merchant;Owen Embury;P. Borgne;B. Bellec