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Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations
- Source :
- Journal of Applied Remote Sensing. 15
- Publication Year :
- 2021
- Publisher :
- SPIE-Intl Soc Optical Eng, 2021.
-
Abstract
- 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.
- Subjects :
- 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
Subjects
Details
- ISSN :
- 19313195
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Journal of Applied Remote Sensing
- Accession number :
- edsair.doi...........5ee6dde7f341b26d6a9914b246d65716