Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras

Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers) are increasingly used nowadays because of their applications in a number of fields, including climate modeling, weather prediction, renewable energy generation, and satellite communications. Due to the wide variety of cloud types and lighting conditions in such images, accurate and robust segmentation of clouds is challenging. In this paper, we present a supervised segmentation framework for ground-based sky/cloud images based on a systematic analysis of different color spaces and components, using partial least squares (PLS) regression. Unlike other state-of-the-art methods, our proposed approach is entirely learning-based and does not require any manually-defined parameters. In addition, we release the Singapore Whole Sky IMaging SEGmentation Database (SWIMSEG), a large database of annotated sky/cloud images, to the research community

Machine Learning Techniques and Applications For Ground-based Image Analysis

Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole sky imagers can have high spatial and temporal resolution, which is an important pre-requisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, etc. Extracting valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine learning techniques have become available to aid with the image analysis. This article provides a detailed walk-through of recent developments in these techniques and their applications in ground-based imaging. We aim to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applications -- segmentation, classification, and denoising

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Gaussian decomposition of HI surveys. V. Search for very cold clouds

In the previous papers of this series, we have decomposed into Gaussian components all the HI 21-cm line profiles of the Leiden-Argentina-Bonn (LAB) database, and studied statistical distributions of the obtained Gaussians. Now we are interested in separation from the general database of the components the "clouds" of closely spaced similar Gaussians. In this paper we describe the new cloud-finding algorithm. To separate the clouds of similar Gaussians, we start with the single-link hierarchical clustering procedure in five-dimensional (longitude, latitude, velocity, Gaussian width and height) space, but make some modifications to accommodate it to the large number of components. We also use the requirement that each cloud may be represented at any observed sky position by only one Gaussian and take into account the similarity of global properties of the merging clouds. As a test, we apply the algorithm for finding the clouds of the narrowest HI 21-cm line components. Using the full sky search for cold clouds, we easily detect the coldest known HI clouds and demonstrate that actually they are a part of a long narrow ribbon of cold clouds. We model these clouds as a part of a planar gas ring, deduce their spatial placement, and discuss their relation to supernova shells in the solar neighborhood. We conclude that the proposed algorithm satisfactorily solves the posed task. We guess that the study of the narrowest HI 21-cm line components may be a useful tool for finding the structure of neutral gas in solar neighborhood.Comment: 11 pages, 6 figures, short version will be published in "Astron. Astrophys", the version with full-resolution figures at http://www.aai.ee/~urmas/ast/Kits.pd

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Near-infrared study of new embedded clusters in the Carina complex

We analyse the nature of a sample of stellar overdensities that we found projected on the Carina complex. This study is based on 2MASS photometry and involves the photometry decontamination of field stars, elaboration of intrinsic colour-magnitude diagrams J$\times$(J-Ks), colour-colour diagrams (J-H)$\times$(H-Ks) and radial density profiles, in order to determine the structure and the main astrophysical parameters of the best candidates. The verification of an overdensity as an embedded cluster requires a CMD consistent with a PMS content and MS stars, if any. From these results, we are able to verify if they are, in fact, embedded clusters. The results were, in general, rewarding: in a sample of 101 overdensities, the analysis provided 15 candidates, of which three were previously catalogued as clusters (CCCP-Cl$\,$16, Treasure Chest and FSR$\,$1555), and the 12 remaining are discoveries that provided significant results, with ages not above 4.5$\,$Myr and distances compatible with the studied complex. The resulting values for the differential reddening of most candidates were relatively high, confirming that these clusters are still (partially or fully) embedded in the surrounding gas and dust, as a rule within a shell. Histograms with the distribution of the masses, ages and distances were also produced, to give an overview of the results. We conclude that all the 12 newly found embedded clusters are related to the Carina complex.Comment: 10 pages, 14 figures, accepted for publication in MNRA

Distribution and characteristics of Infrared Dark Clouds using genetic forward modelling

Infrared Dark Clouds (IRDCs) are dark clouds seen in silhouette in mid-infrared surveys. They are thought to be the birthplace of massive stars, yet remarkably little information exists on the properties of the population as a whole (e.g. mass spectrum, spatial distribution). Genetic forward modelling is used along with the Two Micron All Sky Survey and the Besancon Galactic model to deduce the three dimensional distribution of interstellar extinction towards previously identified IRDC candidates. This derived dust distribution can then be used to determine the distance and mass of IRDCs, independently of kinematic models of the Milky Way. Along a line of sight that crosses an IRDC, the extinction is seen to rise sharply at the distance of the cloud. Assuming a dust to gas ratio, the total mass of the cloud can be estimated. The method has been successfully applied to 1259 IRDCs, including over 1000 for which no distance or mass estimate currently exists. The IRDCs are seen to lie preferentially along the spiral arms and in the molecular ring of the Milky Way, reinforcing the idea that they are the birthplace of massive stars. Also, their mass spectrum is seen to follow a power law with an index of -1.75 +/- 0.06, steeper than giant molecular clouds in the inner Galaxy, but comparable to clumps in GMCs. This slope suggests that the IRDCs detected using the present method are not gravitationally bound, but are rather the result of density fluctuations induced by turbulence.Comment: 15 pages, 9 figures, accepted for publication in Ap

Finding the Needles in the Haystacks: High-Fidelity Models of the Modern and Archean Solar System for Simulating Exoplanet Observations

We present two state-of-the-art models of the solar system, one corresponding to the present day and one to the Archean Eon 3.5 billion years ago. Each model contains spatial and spectral information for the star, the planets, and the interplanetary dust, extending to 50 AU from the sun and covering the wavelength range 0.3 to 2.5 micron. In addition, we created a spectral image cube representative of the astronomical backgrounds that will be seen behind deep observations of extrasolar planetary systems, including galaxies and Milky Way stars. These models are intended as inputs to high-fidelity simulations of direct observations of exoplanetary systems using telescopes equipped with high-contrast capability. They will help improve the realism of observation and instrument parameters that are required inputs to statistical observatory yield calculations, as well as guide development of post-processing algorithms for telescopes capable of directly imaging Earth-like planets.Comment: Accepted for publication in PAS

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

We use microwave retrievals of upper tropospheric humidity (UTH) to estimate the impact of clear-sky-only sampling by infrared instruments on the distribution, variability and trends in UTH. Our method isolates the impact of the clear-sky-only sampling, without convolving errors from other sources. On daily time scales IR-sampled UTH contains large data gaps in convectively active areas, with only about 20-30 % of the tropics (30 S­ 30 N) being sampled. This results in a dry bias of about -9 %RH in the area-weighted tropical daily UTH time series. On monthly scales, maximum clear-sky bias (CSB) is up to -30 %RH over convectively active areas. The magnitude of CSB shows significant correlations with UTH itself (-0.5) and also with the variability in UTH (-0.6). We also show that IR-sampled UTH time series have higher interannual variability and smaller trends compared to microwave sampling. We argue that a significant part of the smaller trend results from the contrasting influence of diurnal drift in the satellite measurements on the wet and dry regions of the tropics