English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Visible (VIS) imagery of satellites has various important applications inmeteorology, including monitoring Tropical Cyclones (TCs). However, it isunavailable at night because of the lack of sunlight. This study presents aConditional Generative Adversarial Networks (CGAN) model that generates highlyaccurate nighttime visible reflectance using infrared (IR) bands and sunlightdirection parameters as input. The model was trained and validated using targetarea observations of the Advanced Himawari Imager (AHI) in the daytime. Thisstudy also presents the first nighttime model validation using the Day/NightBand (DNB) of the Visible/Infrared Imager Radiometer Suite (VIIRS). The daytimestatistical results of the Structural Similarity Index Measure (SSIM), PeakSignal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), CorrelationCoefficient (CC), and Bias are 0.885, 28.3, 0.0428, 0.984, and -0.0016respectively, completely surpassing the model performance of previous studies.The nighttime statistical results of SSIM, PSNR, RMSE, and CC are 0.821, 24.4,0.0643, and 0.969 respectively, which are slightly negatively impacted by theparallax between satellites. We performed full-disk model validation whichproves our model could also be readily applied in the tropical ocean withoutTCs in the northern hemisphere. This model contributes to the nighttimemonitoring of meteorological phenomena by providing accurate AI-generatedvisible imagery with adjustable virtual sunlight directions.

Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks