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Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations
Journal of Applied Remote Sensing ( IF 1.7 ) Pub Date : 2021-07-01 , DOI: 10.1117/1.jrs.15.038501
Allyson Rugg 1 , Julie Haggerty 1 , Daniel Adriaansen 1 , William L. Smith 2
Affiliation  

The lack of shortwave (SW, visible, and near-infrared) geostationary satellite data at night results in degradation of many weather forecasts and real-time diagnostic products. We present a method to extrapolate SW GOES-16 advanced baseline imager data through night using nighttime longwave (LW, infrared) observations and the relationships between LW and SW data observed during the previous day. The method is not a forecast since it requires LW nighttime observations but can provide continuity through day, night, and satellite terminator hours. To provide performance statistics, the algorithm is applied during the day so the SW extrapolations can be compared to observations. Typical mean absolute errors (MAEs) range from 1.0% to 12.7% reflectance depending on the SW channel. These MAEs can be predicted using a diagnostic metric called 0-h MAE which quantifies the quality of the algorithm’s input data. In addition to quantitative error statistics, three case studies are presented, including an animation of extrapolated imagery from dusk through dawn. Considerations for future improvements include use of convolutional neural networks and/or object-based extrapolations where mesoscale features are extrapolated individually.

中文翻译:

使用长波观测将云的短波地球静止卫星图像外推到夜间

夜间短波(SW、可见光和近红外)地球静止卫星数据的缺乏导致许多天气预报和实时诊断产品的质量下降。我们提出了一种方法,使用夜间长波(LW,红外)观测以及前一天观测到的 LW 和 SW 数据之间的关系,在夜间外推 SW GOES-16 高级基线成像仪数据。该方法不是预报,因为它需要 LW 夜间观测,但可以提供白天、黑夜和卫星终止时间的连续性。为了提供性能统计数据,该算法在白天应用,因此可以将 SW 外推与观测值进行比较。典型的平均绝对误差 (MAE) 范围从 1.0% 到 12.7% 反射率,具体取决于 SW 通道。可以使用称为 0-h MAE 的诊断指标来预测这些 MAE,该指标量化算法输入数据的质量。除了定量误差统计外,还介绍了三个案例研究,包括从黄昏到黎明的外推图像动画。未来改进的考虑包括使用卷积神经网络和/或基于对象的外推,其中中尺度特征是单独外推的。
更新日期:2021-07-08
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