Prediction of Cloud Movement from Satellite Images Using Neural Networks

Abstract

Predicting cloud movement and dynamics is an important aspect in several areas, including prediction of solar energy generation. Knowing where a cloud will be or how it evolves over a given geographical area can help energy providers to better estimate their production levels. In this paper we propose a novel approach to predicting cloud movement based on satellite imagery. It combines techniques of generating motion vectors from sequential images with neural networks. First, the images are masked to isolate cloud pixels, then Farneback's version of the Optical Flow algorithm is used to detect motion from one image to the next and generate motion vector flow for each pair of images. After that, a feed forward back propagation neural network is trained with the vector data derived from the dataset imagery. Different parameters for the duration of the training, size of the input, and the neighborhood radius of one point in the scene are used. Promising results are presented and discussed to weight the potential of the proposed algorithm for forecasting cloud cover and cloud position in a scene.

A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum

“…The paper extends our previous work on the cloud dynamics forecast [11][12][13]. Specifically, in this article we introduce the edge-cloud continuum platform and the irradiance forecast method.…”

“…The module requires a series of sequential satellite images. It then proceeds by analyzing all sequential pairs using optical flow and generates a master flow from the resulting set of displacement fields [13]. The master flow is an average of the movement detected in the scene, so we consider it to be similar to a wind map of the scene as cloud motion is driven by windand clouds are the only moving object in the scene.…”

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

“…To date, several methods have been adopted to solve the cloud forecasting problem, mainly including numerical weather prediction (NWP) [30], [31] and optical flow method [31]- [34]. Kurzrock et al [30] reviewed the literature for short-term cloud forecasting using the NWP method and pointed that the cloud forecast performance in the first 12-24 h would be strongly influenced by the initial information.…”

MeghNA: Cloud Detection and Motion Prediction in INSAT Images

“…Shakya and Kumar in [2] study the movement of clouds using a modified fractional-order optical flow technique to generate two images between the time frame of captures for INSAT-3D Imager. Authors in [3] propose a neural network based approach for cloud motion prediction by selecting input points linearly and increasing the input features to every neuron reducing the error in prediction. This method can be further extended to generate the next image in the time-series.…”