Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations
| Autor: | Julie Haggerty, Allyson Rugg, Daniel R. Adriaansen, William L. Smith |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Terminator (solar) 0211 other engineering and technologies Longwave Mesoscale meteorology Dusk 02 engineering and technology 01 natural sciences 13. Climate action Geostationary orbit Range (statistics) General Earth and Planetary Sciences Environmental science Satellite Shortwave 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Journal of Applied Remote Sensing. 15 |
| ISSN: | 1931-3195 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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