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1. A simple cloud-filling approach for remote sensing water cover assessments

   https://doi.org/10.5194/hess-25-2373-2021  

   Mullen, Connor; Penny, Gopal; Müller, Marc F. (January 2021, Hydrology and Earth System Sciences)

   null (Ed.)

   Abstract. The empirical attribution of hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions, as one cannot go back in time to retrospectively collect the necessary data. Although global remote sensing data can alleviate this challenge, most satellite missions are too recent to capture changes that happened long ago enough to provide sufficient observations for adequate statistical inference. In that context, the 4 decades of continuous global high-resolution monitoring enabled by the Landsat missions are an unrivaled source of information. However, constructing a time series of land cover observation across Landsat missions remains a significant challenge because cloud masking and inconsistent image quality complicate the automatized interpretation of optical imagery. Focusing on the monitoring of lake water extent, we present an automatized gap-filling approach to infer the class (wet or dry) of pixels masked by clouds or sensing errors. The classification outcome of unmasked pixels is compiled across images taken on different dates to estimate the inundation frequency of each pixel, based on the assumption that different pixels are masked at different times. The inundation frequency is then used to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a variety of global lakes with substantial long term or seasonal fluctuations, the approach successfully captured water extent variations obtained from in situ gauges (where applicable), or from other Landsat missions during overlapping time periods. Although sensitive to classification errors in the input imagery, the gap-filling algorithm is straightforward to implement on Google's Earth Engine platform and stands as a scalable approach to reliably monitor, and ultimately attribute, historical changes in water bodies. 

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2. Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

   https://doi.org/10.3390/atmos12030395  

   Park, Seongha; Kim, Yongho; Ferrier, Nicola J.; Collis, Scott M.; Sankaran, Rajesh; Beckman, Pete H. (March 2021, Atmosphere)

   Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities. 

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3. Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

   https://doi.org/10.3390/rs10081226  

   E. Nyland, Kelsey; E. Gunn, Grant; I. Shiklomanov, Nikolay; N. Engstrom, Ryan; A. Streletskiy, Dmitry (August 2018, Remote Sensing)

   Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region. 

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4. Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms

   Comden, Joshua; Yao, Sijie; Chen, Niangjun; Xing, Haipeng; Liu, Zhenhua (January 2019, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   Due to mainstream adoption of cloud computing and its rapidly increasing usage of energy, the efficient management of cloud computing resources has become an important issue. A key challenge in managing the resources lies in the volatility of their demand. While there have been a wide variety of online algorithms (e.g. Receding Horizon Control, Online Balanced Descent) designed, it is hard for cloud operators to pick the right algorithm. In particular, these algorithms vary greatly on their usage of predictions and performance guarantees. This paper aims at studying an automatic algorithm selection scheme in real time. To do this, we empirically study the prediction errors from real-world cloud computing traces. Results show that prediction errors are distinct from different prediction algorithms, across virtual machines, and over the time horizon. Based on these observations, we propose a simple prediction error model and prove upper bounds on the dynamic regret of several online algorithms. We then apply the empirical and theoretical results to create a simple online meta-algorithm that chooses the best algorithm on the fly. Numerical simulations demonstrate that the performance of the designed policy is close to that of the best algorithm in hindsight. 

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5. Regional flow conditions associated with stratocumulus cloud-eroding boundaries over the southeast Atlantic

   https://doi.org/10.1175/MWR-D-20-0250.1  

   Tomkins, Laura M.; Mechem, David B.; Yuter, Sandra E.; Rhodes, Spencer R. (March 2021, Monthly Weather Review)

   null (Ed.)

   Abstract Large, abrupt clearing events have been documented in the marine stratocumulus cloud deck over the subtropical Southeast Atlantic Ocean. In these events, clouds are rapidly eroded along a line hundreds–to–thousands of kilometers in length that generally moves westward away from the coast. Because marine stratocumulus clouds exert a strong cooling effect on the planet, any phenomenon that acts to erode large areas of low clouds may be climatically important. Previous satellite-based research suggests that the cloud-eroding boundaries may be caused by westward-propagating atmospheric gravity waves rather than simple advection of the cloud. The behavior of the coastal offshore flow, which is proposed as a fundamental physical mechanism associated with the clearing events, is explored using the Weather Research and Forecasting model. Results are presented from several week-long simulations in the month of May when cloud-eroding boundaries exhibit maximum frequency. Two simulations cover periods containing multiple cloud-eroding boundaries (active periods), and two other simulations cover periods without any cloud-eroding boundaries (null periods). Passive tracers and an analysis of mass flux are used to assess the character of the diurnal west-African coastal circulation. Results indicate that the active periods containing cloud-eroding boundaries regularly experience stronger and deeper nocturnal offshore flow from the continent above the marine boundary layer, compared to the null periods. Additionally, we find that the boundary layer height is higher in the null periods than in the active periods, suggesting that the active periods are associated with areas of thinner clouds that may be more susceptible to cloud erosion. 

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