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Register a new userAn Extremely-low Cost Ground-Based Whole Sky Imager
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Ground-based Whole Sky Imagers (WSIs) are increasingly being used for various remote sensing applications. While the fundamental requirements of a WSI are to make it climate-proof with an ability to capture high resolution images, cost also plays a significant role for wider scale adoption. This paper proposes an extremely low-cost alternative to the existing WSIs. In the designed model, high resolution images are captured with auto adjusting shutter speeds based on the surrounding light intensity. Furthermore, a manual data backup option using a portable memory drive is implemented for remote locations with no internet access.
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Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of the effects of cloud formations, which can be useful in many applications. Some such imagers are available commercially, but their cost is relatively high, and their flexibility is limited. Therefore, we built a new daytime Whole Sky Imager (WSI) called Wide Angle High-Resolution Sky Imaging System. The strengths of our new design are its simplicity, low manufacturing cost and high resolution. Our imager captures the entire hemisphere in a single high-resolution picture via a digital camera using a fish-eye lens. The camera was modified to capture light across the visible as well as the near-infrared spectral ranges. This paper describes the design of the device as well as the geometric and radiometric calibration of the imaging system.
Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation. In this paper, we show how images taken by WSIs can be used to estimate solar radiation. Sky cameras are useful here because they provide additional information about cloud movement and coverage, which are otherwise not available from weather station data. Our setup includes ground-based weather stations at the same location as the imagers. We use their measurements to validate our methods.
Ground-based whole sky imagers are popular for monitoring cloud formations, which is necessary for various applications. We present two new Wide Angle High-Resolution Sky Imaging System (WAHRSIS) models, which were designed especially to withstand the hot and humid climate of Singapore. The first uses a fully sealed casing, whose interior temperature is regulated using a Peltier cooler. The second features a double roof design with ventilation grids on the sides, allowing the outside air to flow through the device. Measurements of temperature inside these two devices show their ability to operate in Singapore weather conditions. Unlike our original WAHRSIS model, neither uses a mechanical sun blocker to prevent the direct sunlight from reaching the camera; instead they rely on high-dynamic-range imaging (HDRI) techniques to reduce the glare from the sun.
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example, the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison to classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North-American continent, we see significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.
FIRST, the Fibered Imager foR a Single Telescope instrument, is an ultra-high angular resolution spectro-imager, able to deliver calibrated images and measurements beyond the telescope diffraction limit, a regime that is out of reach for conventional AO imaging. FIRST achieves sensitivity and accuracy by coupling the full telescope to an array of single mode fibers. Interferometric fringes are spectrally dispersed and imaged on an EMCCD. An 18-Fiber FIRST setup is currently installed on the Subaru Coronographic Extreme Adaptive Optics instrument at Subaru telescope. It is being exploited for binary star system study. In the late 2020 it will be upgraded with delay lines and an active LiNb03 photonic beam-combining chip allowing phase modulation to nanometer accuracy at MHz. On-sky results at Subaru Telescope have demonstrated that, thanks to the ExAO system stabilizing the visible light wavefront, FIRST can acquire long exposure and operate on significantly fainter sources than previously possible. A similar approach on a larger telescope would therefore offer unique scientific opportunities for galactic (stellar physics, close companions) and extragalactic observations at ultra-high angular resolution. We also discuss potential design variations for nulling and high contrast imaging.
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