Computing synthetic satellite images from weather prediction data

Bjorn Olsson; Anders Ynnerman; Reiner Lenz

doi:10.1117/12.526829

Abstract

Satellite images are important sources of information for meteorologists to predict rapid weather changes, for example storms, now and in the near-future (Nowcasting). It is not possible to use traditional numerical weather forecasts for this purpose since these are computed with a time-lag of several hours. This means that the most recent weather changes are not taken into account. This paper presents a method to compute synthetic satellite images from simulated forecast files. The cloud information in numerical forecast data sets is of much more interest if it can be visualized with a well-known representation like the satellite image. The proposed method uses artificial neural network technology to construct a model which is trained with data from numerical forecasts and classified satellite data captured at the same points in time. The cloud cover parameters in the forecast data set are tied to the cloud classification in the satellite image using a point-to-point representation. The results show that this is a useful method to compute synthetic satellite images. The level of detail in the resulting images is lower than in a real satellite image, but detailed enough to provide information about the principal features of the cloud cover.

Citation Download Citation

Bjorn Olsson, Bjorn Olsson, Anders Ynnerman, Anders Ynnerman, Reiner Lenz, Reiner Lenz, } "Computing synthetic satellite images from weather prediction data", Proc. SPIE 5295, Visualization and Data Analysis 2004, (4 June 2004); doi: 10.1117/12.526829; https://doi.org/10.1117/12.526829

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