A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Abstract

Abstract:The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L 1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05 • . In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds

“…We will apply state-of-the-art computer vision methods for evaluating cloud nowcasting using our CloudCast image dataset, which has recently seen considerable success in similar atmospheric nowcasting studies [22,31]. To match the resolution of most state-of-the-art video prediction methods [25,38], we crop and transform our dataset using a stereographic projection to cover Central Europe with a spatial resolution of 128x128.…”

“…Optical flow is one of the most important methods for global data assimilation in meteorology [28,29]. The method is analogous to advection equations in meteorology and has been used to extrapolate future radar images for precipitation nowcasting as early as 2004 [30], and more recently in 2018 for nowcasting the effective cloud albedo in satellite imagery [31]. In the latter, the authors implement the T V -L 1 optical flow method introduced by [32] to capture cloud motions on multiple scales spatially based on two subsequent raw satellite images.…”

“…We implement the optical flow algorithm T V -L 1 similar to the authors of [31], which can capture cloud motion on multiple spatial scales and is one of the most popular optical flow algorithms for meteorological purposes [31]. One of the underlying assumptions of optical flow is constant pixel intensity over time [31]. This is violated due to cloud formation and dissipation.…”

“…The T V -L 1 algorithm effectively has 11 parameters. The authors [31] optimized these by finding the lowest absolute bias among 21 different parameter settings calculated using a variant of the area-under-the-curve calculation over absolute bias as a function of forecast time [31]. Instead, we conduct a grid search over a hyperspace of 360 different combinations chosen relative to the default values and the optimal hyperparameters found in [31].…”

A Novel Approach for the Detection of Developing Thunderstorm Cells

“…Consequently, convection leads typically to serious errors in the AMV, see e.g. [11,12]. Further, smoothing and filtering by quality procedures hamper the accurate detection of the relevant meso-scale movement, see [11] for further details.…”