A NEURAL-NETWORK AS A NONLINEAR TRANSFER-FUNCTION MODEL FOR RETRIEVING SURFACE WIND SPEEDS FROM THE SPECIAL SENSOR MICROWAVE IMAGER
A NEURAL-NETWORK AS A NONLINEAR TRANSFER-FUNCTION MODEL FOR RETRIEVING SURFACE WIND SPEEDS FROM THE SPECIAL SENSOR MICROWAVE IMAGER
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DOI：
10.1029/95jc00857
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发表时间：
1995-06-15
影响因子：
3.6
通讯作者：
GEMMILL, WH
中科院分区：
文献类型：
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作者：
KRASNOPOLSKY, VM;BREAKER, LC;GEMMILL, WH
建立了一种单一的、扩展范围的神经网络（SER NN）来模拟特殊传感器微波成像仪（SSM/I）地面风速反演的传递函数。将该算法应用于以往SSM/I风速检索研究中使用的数据集，与浮标观测结果相比，偏差为0.05 m/s，均方根差为1.65 m/s。SER神经网络在晴朗（低湿度）和多云（高湿度/小雨）条件下的精度等于针对每种情况分别训练的神经网络的精度。结合SER神经网络，提出了一种基于云液态水这一单一物理可解释参数的水分反演准则。利用该检索准则，(1)估算了云液态水的湿度检索阈值为0.5 kg/m(2),(2)以前雨旗拒绝的数据可以恢复40%。当使用该检索标准训练SER神经网络时，获得的偏差为0.03 mis，均方根值为1.58 m/s，只有2%的数据被拒绝。此外，通过在85 GHz下加入SSM/I亮度温度，可以略微提高多云条件下的检索精度（类似于10%）。最后，结合目前的应用，讨论了神经网络算法的局限性。
A single, extended-range neural network (SER NN) has been developed to model the transfer function for special sensor microwave imager (SSM/I) surface wind speed retrievals. Applied to data sets used in previous SSM/I wind speed retrieval studies, this algorithm yields a bias of 0.05 m/s and an rms difference of 1.65 m/s, compared to buoy observations. The accuracy of the SER NN for clear (low moisture) and cloudy (higher moisture/light rain) conditions equals the accuracy of NNs trained separately for each of these cases. A new moisture retrieval criterion based on a single, physically interpretable parameter, cloud liquid water, is proposed in conjunction with the SER NN. Using this retrieval criterion, (1) a moisture retrieval threshold for cloud liquid water of 0.5 kg/m(2) was estimated, and (2) 40% of the data rejected by previous rain flags could be recovered. When the SER NN was trained using this retrieval criterion, a bias of 0.03 mis and an rms value of 1.58 m/s were obtained and only 2% of the data were rejected. Also, a slight improvement in retrieval accuracy for cloudy conditions was achieved (similar to 10%) by including SSM/I brightness temperatures at 85 GHz. Finally, the limitations of NN algorithms are discussed in light of the present application.