Adaptation of Short-Term Cloud Forecasting to Global Scale and Evaluation of its Contribution to Earth Observing Satellite Planning

Abstract

A short-term cloud forecasting method based on optical flows is used in order to increase the number of cloud free images acquired at the end of an Earth observation satellites planning process. Following a first validation of a satellite-based cloud forecasting method [Roussel et al. 2018] for cloud fraction forecasting, it is adapted from local to global scale using parameters optimized for several climate types. This is done using a new dataset built on the basis of two nowcasting satellite application facilities products derived from meteosat second generation images, which allowed us to create a large number of test samples 0associated to six different climate types and regularly distributed throughout a whole year. Then, two test cases are performed in order to estimate the gain in terms of cloud-free acquired images proportion between the satellite-based cloud forecasting method and a more classical numerical weather prediction model, i.e., the high resolution configuration of the integrated forecasting system developed by ECMWF and Meteo-France. The first does not consider the satellites trajectory and assumes that all samples can be acquired in a single pass. The second one is realistic and imply a mission planning simulator. In both cases, the satellite-based cloud forecasting method leads to much better cloud-free images rates.

Keywords

• Cloud forecasting
• optical flow
• remote sensing
• satellite planning