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Landsat has provided over five-decade Earth’s observations including cloud cover (CC). However, scene-wide and/or pixel-level research on CC and image acquisition probability (IAP) still lags much behind. Scene-based analysis is often reported with arbitrary thresholds, while mechanism of cloud probability (CP) dynamics remains poorly investigated. Understanding Landsat cloud climatology is critical for better utilizing historical archives especially in the tropics. Here, South and Southeast Asia (SSEA) was selected to investigate the potential of a coarse-and-fine thresholding (CAFT) method in determining appropriate threshold of CC with all available Landsat-4/5/7/8 Thematic Mapper (TM), Enhanced TM Plus (ETM+), and Operational Land Imager (OLI) scenes (a total of 691,919) during 1984–2022. Then, OLI-based pixel CP was further examined, followed by correlation analysis between CC and climatic variables (i.e., temperature and precipitation). Main conclusions include: (1) the CAFT method holds promising potential in determining the precise CC threshold (24%) in SSEA. (2) Landsat IAP (nearly 40%) and CP (about 36%) show significant monthly, regional, and sensor variations across SSEA. (3) ETM+ performs the best with the highest IAP (>45%), while OLI tends to have larger IAPs in persistently cloudy area. (4) Significant correlation between CP and precipitation or temperature indicates that the roles of climate variables on CC differ geographically in South Asia and Southeast Asia. This study contributes to developing a universal and simple method to determine optimal thresholds for global CC research and understanding the interaction between CC and climate change.

Solar energy is abundant, and technological advances have made solar energy systems more affordable than ever before. Using photovoltaic (PV) systems could significantly reduce our reliance on fossil fuels, and facilitate sustainable energy uses. Solar power utilities, such as self-compacting disposal bins could be used to enhance waste management processes. This is particularly important in Canada, where $3.3 billion was spent on waste management systems in 2016. In this study, solar irradiance and climatic conditions at eight locations on a University campus in Regina, Saskatchewan, are studied. Results suggest that solar utilities with automatically adjusting PV receivers could increase energy capture between 18.7 – 27.5%. Temporally, solar irradiance was similar in June and July, but lower in August. Statistical analysis found that some locations tended to be more susceptible to shadow effects. The results highlight the importance of spatial allocations of these small smart disposal bin systems. Regression analysis found that temperature was the most significant factor when relating climate to solar irradiance. The use of smart disposal bins fits well with the University's 2020–2025 Strategic Plan of reduction in ecological footprint. [ABSTRACT FROM AUTHOR]

Despite the general efforts of meteorological services to provide aeronautical observations at all ranges of airports automatically, for some meteorological variables, especially for the ones that highly rely on complex human perception (e.g., prevailing visibility and cloud coverage), reliable, fully automated observations cannot be ensured. This paper introduces novel possibilities to observe prevailing visibility and cloud coverage/height by means of a camera-based observation system that does not necessarily replace, but effectively and synergically amends the standard observations. We present human (and not automated) observations from a remote center that allows for an observer to report meteorological conditions remotely, only using images from cameras installed at the airport. The basic concept of the remote observer was developed within a previous SESAR project. The focus of our methods is set (1) on the quality of information with the occurrence of reduced visibility and enhanced cloud cover in inhomogeneous weather situations and (2) on a comparison of our approaches with those from local human observers. We conclude that for a correct estimation of the prevailing visibility, cloud coverage and cloud types, the automated sensors alone are inadequate; however, the camera-aided remote human approach to observations seems to be a promising supplement to eliminate the sensors' deficiencies, in terms of the quality (e.g., high quality camera records; no more point measurements), objectivity (e.g., database of archived weather situations) and efficiency (e.g., no need to have an observer physically present at the airport). The possibility to provide observations remotely seems to be advantageous in the COVID-19 and post-COVID-19 era when the society must adapt to different levels of quarantine conditions, affecting and/or disabling standard work and travelling regimes. [ABSTRACT FROM AUTHOR]

Ecological Niche Modelling (ENM) portrays the relationship between the actual geographical distribution of a species and the environmental factors that induced this distribution. Yet most models study species over the wider range of their distribution; thus, they are rarely appropriate for forest management and forest restoration on the local scale. This study aims to understand the major environmental factors affecting the distribution of 27 species, through limiting ENM at national level (Lebanon). MaxENT software was used for modelling. Area under the curve (AUC) values showed a very good robustness of the models. Minimal biogeographic and climatic parameters such as elevation, distance from the sea, annual mean precipitation, the average minimum temperature of the coldest month, the average maximum temperature of the warmest month, and Emberger Quotient were sufficient to obtain robust modelling results. Cloud coverage during summer was identified as a novelty factor explaining species distribution at the edge of their range. Composite soil and topography predictors such as Potential Direct Incident Radiation (PDIR) and the Integrated Moisture Index (IMI) were reduced to simple factors such as aspect, slope and available water content, whose contribution was conditioned to higher data resolution. The high number of presence points enabled us to study the range of species distribution gathering them according to their ecological characteristics. The generated reforestation suitability maps and the likelihood of occurrence of each species were achieved to define priority species for conservation and forest management. This information could be useful for decision-makers and foresters.

There are many areas with cloud coverage in remote sensing images frequently, and they will shelter the ground objects under clouds. When images with clouds are used in mosaicking, cloud coverage areas may still exist in mosaics, which constrain the application of mosaics. Therefore, a simple and fast image mosaicking method based iteratively minimizing cloud coverage areas is proposed in this letter. In the proposed method, both cloud-free images and images with clouds are used to generate a mosaic with minimal cloud coverage areas. After the generation of initial effective mosaic polygons (EMPs) for images, a quadtree iterative procedure is presented to optimize each EMP by excluding the residual cloud coverage areas in each EMP. The procedure fully takes into account the spatial distribution of cloud coverage areas, and each area with cloud coverage is minimized by as few images as possible. The experimental results show that the mosaic generated by the proposed method can achieve a better radiometric continuity and higher efficiency than the pixel-based mosaic method. Besides, the proposed method can make the full use of the cloud-free areas in images, which can improve the utilization of images and the currency of mosaics. [ABSTRACT FROM AUTHOR]

Despite the general efforts of meteorological services to provide aeronautical observations at all ranges of airports automatically, for some meteorological variables, especially for the ones that highly rely on complex human perception (e.g., prevailing visibility and cloud coverage), reliable, fully automated observations cannot be ensured. This paper introduces novel possibilities to observe prevailing visibility and cloud coverage/height by means of a camera-based observation system that does not necessarily replace, but effectively and synergically amends the standard observations. We present human (and not automated) observations from a remote center that allows for an observer to report meteorological conditions remotely, only using images from cameras installed at the airport. The basic concept of the remote observer was developed within a previous SESAR project. The focus of our methods is set (1) on the quality of information with the occurrence of reduced visibility and enhanced cloud cover in inhomogeneous weather situations and (2) on a comparison of our approaches with those from local human observers. We conclude that for a correct estimation of the prevailing visibility, cloud coverage and cloud types, the automated sensors alone are inadequate; however, the camera-aided remote human approach to observations seems to be a promising supplement to eliminate the sensors’ deficiencies, in terms of the quality (e.g., high quality camera records; no more point measurements), objectivity (e.g., database of archived weather situations) and efficiency (e.g., no need to have an observer physically present at the airport). The possibility to provide observations remotely seems to be advantageous in the COVID-19 and post-COVID-19 era when the society must adapt to different levels of quarantine conditions, affecting and/or disabling standard work and travelling regimes.

Summary: The northwest region in China located at arid and semiarid areas, atmospheric precipitation converted by the cloud is an important part of water resources, and if we fully utilize cloud for cloud‐water conversion to alleviate the scarcity of water, thereby it is particularly important to analyze the macroscopic characteristics and the changing trends of clouds in the northwest region. In this paper, the 2016 Level 1 data of Fengyun Satellite has been calibrated, corrected, and processed, using the improved multi‐spectral thresholding method to calculate cloud coverage and cloud classification data. The optical thickness inversion uses the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) radiation transmission mode to establish a radiation look‐up table with optical thickness as a function variable under different conditions of observation geometry, underlying surface type, and atmospheric environment. The results show that the coverage of clouds in the northwest region accounts for about 45%, and the cloud coverage changes with the seasons. The classification of clouds mainly consists of high clouds, low clouds, and cumulonimbus. The optical thickness of clouds is largely distributed between 10 and 25. [ABSTRACT FROM AUTHOR]

Tropical cyclones generate a large and wide cold wake along their trajectories, which conditions the subsequent evolution of the tropical cyclone themselves. The cold wakes persist for weeks, impacting both the upper ocean, the air‐sea fluxes, and the atmosphere. The study by Z. Ma et al. (2020, https://doi.org/10.1029/2020GL088873) for the first time analyzes a composite of remotely sensed data sets to show that cold wakes modify surface winds and reduce cloud coverage and rainfall. These results contribute to shedding light on the mechanisms at the origin of the air‐sea feedbacks, which can differ at different latitudes depending on the stability of the marine atmospheric boundary layer. The work stimulates further research to assess whether the cloud cover anomalies induced by tropical cyclones significantly modify the radiative budget of the Earth. Plain Language Summary: Strong winds in tropical cyclones stir the upper ocean and generate a cold wake at the sea surface. The reduced sea surface temperature can limit further intensification of the cyclone itself. In the wake, temperatures can be up to 10°C colder than in normal conditions and the anomalies last for weeks. Their effects on the ocean have been extensively investigated but not those on the atmosphere. Z. Ma et al. (2020, https://doi.org/10.1029/2020GL088873) use a combination of satellite derived data and demonstrate that winds, clouds, and rainfall are largely modified by the cold wake. This stimulates further research to understand whether the effects are just local or whether they also modify the seasonal mean basinwide insolation at the surface. Key Points: Tropical cyclones leave cold wakes that affect winds and air‐sea fluxes for weeksCloud cover and rainfall are reduced in the wakeOcean small‐scale structures generate spatial variability in the atmosphere [ABSTRACT FROM AUTHOR]

In the explosively increased number of applications of earth observation satellites (EOSs), scheduling is a significative issue to satisfy more requests and obtain a high observation efficiency. This article investigates the scheduling of multiple EOSs under the impact of clouds. First, we formulate the presences of clouds as stochastic events, and propose an expectation model. Afterwards, for the first time, a branch-and-price algorithm based on Dantzig–Wolfe decomposition is devised to solve the model optimally and efficiently. In order to obtain initial columns for column generation, a dynamic programming algorithm and a heuristic algorithm are suggested, respectively. Furthermore, we discuss the impact of clouds in the case of joint probabilities, and establish a sample average approximation (SAA) model accordingly. With respect to the expectation model, numerical experiment results demonstrate the relative dominance of the proposed branch-and-price algorithm in terms of solution time compared to CPLEX. In addition, the solution of the proposed SAA model is proven to be more robust than that of the expectation model. [ABSTRACT FROM AUTHOR]

This contribution is focused on MEO satellite communication systems operating at optical wavelengths, and especially on the optimum dimensioning of their optical ground station (OGS) network (OGSN). The objective of the optimization is to minimize the size of the network, while still ensuring the target link availabilities due to cloud blockage. Two new optimization algorithms for this optimum selection of OGSs are proposed. The algorithms take into account the monthly variability of cloud coverage, take advantage of locations in different hemispheres, and select OGSs, which are within the visibility area of the satellite for longer time. The methodology for calculating the single and joint cloud free line of sight considering the integrated liquid water content statistics, the time dependent elevation angles and the MEO satellite movement are presented. Representative numerical results are presented considering either a single MEO satellite or a full MEO constellation. [ABSTRACT FROM AUTHOR]

Earth observation satellites (EOSs) are specially equipped with remote sensing instruments to acquire images. In practical EOS scheduling, the uncertainty of cloud coverage is inevitable. We are the first to address robust EOS scheduling under uncertainty due to cloud coverage where the objective function aims to maximize the entire observation profit. We provide a robust formulation of the scheduling problem on the basis of a budgeted uncertainty set, while preserving the formulation linearity. A column-generation-based heuristic is also developed, in which the scheduling decisions in each satellite orbit are represented as columns. Eventually, a high-quality feasible solution is obtained using the generated columns. Extensive simulations are conducted on the basis of one of China's EOS constellations. The results indicate that the average optimality gap is less than 5%, which validates the performance of the proposed heuristic. [ABSTRACT FROM AUTHOR]

The results of studying a variability of internal-gravity-wave characteristics and their signatures based on satellite data are presented. Observations of the wave processes under study over the Russian Federation Pacific coast in the period from 2015 to 2017 are considered. High-periodic Himawari-8 satellite images and thematic products of their processing are used. The methods and algorithms for retrieving parameters of internal gravity waves and their cloud manifestations are described. Trends for various characteristics of wave processes under study are constructed. The typical variability of parameters of internal gravity waves and their signatures is revealed on the basis of the longest lasting episodes of their observation. The results of analysis of coefficients of correlation between the various characteristics of the wave processes under study are presented. Similar episodes of observing signatures of internal gravity waves for the time period under study are established. The processes occurring in the "atmosphere–underlying surface" system and their influence on the creation of favorable conditions for the generation and propagation of wave processes under study are discussed. [ABSTRACT FROM AUTHOR]

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges.

Highlights • We study the problem of agile satellite scheduling with real-time cloud coverage. • A hierarchical scheduling model is established. • A hierarchical scheduling algorithm based on the ant colony algorithm is proposed. • The influence of environmental changes is well-mitigated by the proposed method. • The proposed method can greatly reduce the CPU time and increase the total profit. Abstract The imaging processes of optical satellites can be easily affected by unexpected environmental changes, such as changes in cloud coverage. Given the difficulty to predict environmental uncertainties, traditional offline scheduling methods need a follow-up re-scheduling process that responds to real-time environmental information. This repetitive scheduling processes make the offline fine scheduling process a waste of computational resources. Additionally, the offline scheduling method is quite complex owing to its lack of a hierarchy mechanism. To solve these problems, we propose a hierarchical scheduling method for the real-time scheduling problem. This method divides the scheduling process into three steps: pre-assignment, rough scheduling, and fine scheduling. A hierarchical scheduling algorithm based on ant colony algorithm is proposed. Tests with 36 scenarios show that the calculation time is efficiently reduced with this new mechanism. With this consideration of the dynamic environment, the re-scheduling process becomes unnecessary, meaning the wasting of computational resources is avoided and the solution profit is improved. [ABSTRACT FROM AUTHOR]

Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013–2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China’s seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (Pbias) at 14.1% (±2.6%), 7.6% (±2.3%) and 12.2% (±4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly Pbias ranging from 6.5% (±7.4%) to 20% (±13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant “missing trends” effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.

Characterizing the atmosphere is one of the most complex studies one can undertake due to the non-linearity and phenomenological variability. Clouds are also among the most variable atmospheric constituents, changing their size and shape over a short period of time. There are several sectors in which the study of cloudiness is of vital importance. In the renewable field, the increasing development of solar technology and the emerging trend for constructing and operating solar plants across the earth’s surface requires very precise control systems that provide optimal energy production management. Similarly, airports are hubs where cloud coverage is required to provide high-precision periodic observations that inform airport operators about the state of the atmosphere. This work presents an autonomous cloud detection system, in real time, based on the digital image processing of a low-cost sky camera. An algorithm was developed to identify the clouds in the whole image using the relationships established between the channels of the RGB and Hue, Saturation, Value (HSV) color spaces. The system’s overall success rate is approximately 94% for all types of sky conditions; this is a novel development which makes it possible to identify clouds from a ground perspective without the use of radiometric parameters.

The use of solar photovoltaic (PV) systems as green renewable sources for electricity generation in modern power networks is steadily increasing. One of the problems with using PV units in developing countries and small companies is access to a simple model to assess the short-time output of a solar cell. For example, a robust model that is capable of analyzing cloud variations and ambient temperature (important factors impacting the PV output) to assess short-term PV output would be very helpful. This article proposes a new methodology to assess the impacts of these factors on the hourly output power of a PV system. Our results showed that the proposed model has the ability to assess PV output by using the hourly data of cloud and ambient temperature change. To validate the model, the output was compared to results of measurements from PV systems installed in Sanandaj and Rasht cities located in North-West (latitude 35.31 and longitude 47.00) and North (latitude 37.28 and longitude 49.58) Iran, respectively. Also, the standardized root-mean-square error method (nRMSE) was used to validate the high accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract With the observed rise in temperature, many researchers have tried to identify the causes of such climate change to help mitigate its effects. The objective of this study is to determine whether, under the same carbon dioxide (CO 2) concentrations, CO 2 with lower cloud coverage would raise the temperature at a greater rate than CO 2 with higher cloud coverage. The hypothesis was tested through big data analysis and modeling. The relationships between the temperature and the CO 2 emissions, the temperature and the cloud coverage, and the CO 2 emissions and the cloud coverage were identified using the Pearson's correlation test. The data analysis concluded that the relationship between the temperature and the CO 2 emission is positively proportional with a significant correlation. The relationship between the cloud coverage and the temperature and the relationship between the CO 2 emissions and the cloud coverage were determined to be negatively proportional with significant correlations. For modeling, the temperature increased more rapidly as cloud coverage shrank. The results supported the hypothesis that the cloud coverage mitigates warming effects created by CO 2 emissions. [ABSTRACT FROM AUTHOR]

The present study has used Meteosat infrared brightness temperature images to investigate the regional and interannual variability of Central African cloudiness. Spatial and temporal variability were investigated using half–hourly data from the Meteosat-7 during June–July–August (JJA) of 1998–2002. The full domain of study (1.5E–17E, 1N–15N) was divided into six regions and statistics in each region were derived. Analysis of the dependence of cloud fraction to the brightness temperature threshold is explored both over land and ocean. Three diurnal cycle regimes (continental, oceanic, and coastal) are depicted according to the amplitude and peak time. Over regions of relatively flat terrain, results indicate enhancement of deep convection in the afternoon followed by a gradual decrease in the night. The diurnal cycle of convection is characterised by afternoon and early evening (around 15:00–18:00 LST) maxima located mainly downwind of the major mountain chains, and a more rapid nighttime decay. In terms of the harmonic amplitude, the diurnal signal shows significant regional contrast with the strongest manifestation over the Adamaoua Plateau and the weakest near the South Cameroon Plateau. This remarkable spatial dependence is clear evidence of orographic and heterogeneous land-surface impacts on convective development. Oceanic region exhibits weak activity of convective cloudiness with a maximum at noon. It is suggested that daytime heating of the land surface and moist environment may play a role in determining the spatial distribution of cloud fraction. This study further demonstrates the importance of the Cameroon coastline concavity and coastal mountains in regulating regional frequencies of convection and their initialization. The strength of the diurnal cycle of convective activity depends on mountain height, mean flow, coastal geometry.

The capacity of installed photovoltaic cells (PVs) has been remarkably increased globally over past few decades. In addition, changing cloud cover (Oktas-scale) is an important component that influences on solar radiation hourly and plays a key role in electrical output power of PVs. However, it is necessary for electrical utilities to use a powerful model in order to forecast PV power systems on electrical networks. Thus, forecasting power output of PV systems increase the quality of modern power systems. This paper presents a comprehensive review on short term forecasting of solar PV power output in modern electrical networks. Additionally, a new model based on hourly measurements has been proposed in this research and was reported during the past 20 years in Sanandaj (Applied Research Center of Kurdistan Weather), located in west of Iran (14°.35′N, 46°.35′E). The results show that the possibility of changing hourly and PV output power estimate based on Oktas analysis have been calculated with minimum fault. [ABSTRACT FROM AUTHOR]

With the observed rise in temperature, many researchers have tried to identify the causes of such climate change to help mitigate its effects. The objective of this study is to determine whether, under the same carbon dioxide (CO 2 ) concentrations, CO 2 with lower cloud coverage would raise the temperature at a greater rate than CO 2 with higher cloud coverage. The hypothesis was tested through data analysis and modeling. The relationships between the temperature and the CO 2 emissions, the temperature and the cloud coverage, and the CO 2 emissions and the cloud coverage were identified using Pearson's correlation test. The data analysis concluded that the relationship between the temperature and the CO 2 emission is positively proportional with a significant correlation. The relationship between the cloud coverage and the temperature and the relationship between the CO 2 emissions and the cloud coverage were determined to be negatively proportional with significant correlations. For modeling, the temperature increased more rapidly as cloud coverage shrank. The results supported the hypothesis that the cloud coverage mitigates warming effects created by carbon dioxide emissions. Further research is anticipated to reduce the uncertainties in the data along with specification of cloud types. [ABSTRACT FROM AUTHOR]

Anticorrelation behavior between total ozone column and UV-B radiation was observed for clear sky conditions during the year 2000 at the Southern Space Observatory OES/CRSPE/INPE – MCT (29.44°S, 53.82°W) located in the southern part of Brazil. To eliminate geometrical dependence of the solar angular position in relation to the instrument, UV-B radiation was measured at fixed solar zenith angles. The UV-B radiation (280-315 nm) was measured by an Eko Instruments model MS-210W radiometer, and total ozone column data was obtained by a Total Ozone Mapping Spectrometer on board of the NASA Earth Probe satellite. Correlation coefficients varied from R = -0.86 at 70° and 55°, to R = -0.97 at 40°. Ozone variations explained 74 to 94% of the UV-B variability. A 1% reduction in ozone resulted in an increase of about 1% in UV-B for the solar zenith angles studied.

In recent years, the accessibility of weather forecasts has reached a point that checking it up on a smart device, like a smartphone, only takes a few seconds. Despite easy accessibility, false predictions of weather forecasts are commonly experienced, which this situation has yet to be put right. In meteorology institutions where this situation arises from, there is a need for many personnel working both on the fields and in the institution to make weather predictions as accurate as possible. As in all human-based systems, human mistakes constantly occur in weather forecasting systems. If human factor was to be minimized in weather forecasting systems, likewise human fallibility would diminish. The most feasible way to deal with this problem is to take advantage of the deep learning techniques, the pinnacle of modern software technologies, which requires almost no human effort on the domain they work, once developed. The deep learning methods have the capability of classifying big image datasets with image processing. With this feature and using the cloud pictures taken from the ground, they can be classified as clear/cloudy and the weather cloudiness can be determined as a numerical ratio. This study aims to reduce human-induced meteorology errors as much as possible with the application of deep learning techniques. The dataset that was preferred contains cloud pictures taken from the ground which are classified as either clear or cloudy. In order to compare different deep learning architectures and their efficiency on this subject, four particular pretrained models were selected. Among the models based on MobileNet V2, VGG-16, ResNet-152 V2, DenseNet-201: VGG-16 came as the best in terms of accuracy with 91.4%. In the future, it can be foreseen that all weather forecasting systems will prefer making their predictions based on modern artificial technologies like deep learning. • CNN based classification of cloud pictures taken from ground produce finer results. • Transfer learning with freeze out fine-tuning increases quality of outputs. • Among MobileNet V2, VGG-16, ResNet-152 V2, DenseNet-201: VGG-16 came as the best. • Class predictions might indicate the cloudiness percent in the future. [ABSTRACT FROM AUTHOR]

Critical analysis of experimental surface ozone data and cloud coverage is reported over Kolkata during the period January 2011 to December 2011. Significant relationship between these two parameters is observed. Analysis shows that the trend of surface ozone concentration and cloud coverage follow opposite tendency. Some exceptional observations are also reported. Apart from this, seasonal variation of surface ozone with columnar ozone concentration, NO and temperature is also examined in relation with cloud cover to obtain a possible explanation of this type of variation. [ABSTRACT FROM AUTHOR]

The spatiotemporal characteristics of aerosol direct radiative forcing (RF) and the relative contributions from aerosol species, as well as the impacts of cloud coverage and relative humidity on aerosol direct RF were quantified in East Asia using a regional climate model. Generally, the total aerosol produces net RFs of −12.78 W m−2 at surface, 1.72 W m−2 at TOA (top-of-atmosphere), and atmospheric heating of 14.50 W m−2. It was found that dust, black carbon, and sulfate made dominant contributions to the total RF at surface and TOA, and all aerosol species induced atmospheric heating, whereas more than 96% of which was induced by dust and black carbon. The remarkably seasonally decreasing tendency of the total and the absorbing aerosol RFs was found from spring to winter at surface. Moreover, dust contributes relatively larger to the positive TOA RF and to the atmospheric heating in spring and summer, which were weakened and smaller than black carbon in other seasons. Sensitivity studies further demonstrated cloud strengthens the dust and black carbon direct RF and weakens the other species direct RF at TOA, while induces weak direct RFs of all aerosol species at surface. Particularly, cloud induced larger reduction in dust longwave RF than shortwave leads to remarkable enhanced net surface direct RF of dust, especially in JJA. The aerosol swelling effect induced by relative humidity strengthens aerosol direct RF at both TOA and surface. The percentage changes in aerosol RF and its seasonal amplitude by cloud are considered larger at TOA than surface, however, the effects of relative humidity distribute relatively uniform vertically. Meteorological factors impact on scattering aerosols direct RF is assumed larger than absorbing aerosols. The impacts of cloud on aerosol direct RF are compared to the relative humidity and are supposed to be more important at TOA and surface. • Contributions and seasonal trends of different aerosol direct RF are studied. • Different aerosol direct RFs respond obviously different to the presence of cloud. • Larger loss of dust LW RF than SW by cloud induces enhanced net RF at surface in JJA. • Water vapor strengthens each type of aerosol direct RF at both TOA and surface. • Aerosol direct RF is more sensitive to cloud conditions than relative humidity. [ABSTRACT FROM AUTHOR]

As an urban heat island (UHI) is closely associated with a wide variety of environmental issues, it is necessary to monitor its spatiotemporal variations in an accurate and timely manner. However, the accuracy of surface urban heat island intensity (SUHII) estimation is greatly challenged by three issues. Namely, less attention has been given to city-based global studies, △SUHII (SUHII difference) is induced when various methods are used to the determine background reference, and uncertainty arises in the methods for coping with missing land surface temperature (LST). Thus, this paper quantitatively evaluated these different methods and their impacts on spatiotemporal SUHII variations across 1112 global cities. There are two major findings: (1) the modified equal area-rural (MEA-R) method can overcome the limitations of the other methods. The fixed buffer (FB) method is limited by the difficulty of buffer selection and the inapplicability of a single fixed buffer for various cities, particularly in large-scale studies. The simplified urban-extent (SUE) method is challenged by cluster discrepancies and data obsolescence issues, and the modified equal area-suburban (MEA-S) method underestimates SUHII resulting from strong human activities in suburban areas. The daytime △SUHII induced by different methods for the reference definition reaches 0.62 K, accounting for 42% of the average SUHII. △SUHII is 0.34 K (23%) at night. △SUHII with evident spatiotemporal variations indicates that the different methods used to define the background reference introduce a considerable uncertainty to SUHII research. (2) After comparing methods for coping with missing LST, the first spatial and after temporal aggregation with threshold removal (FSAT-T) method is demonstrated to provided improved robustness to data missing over other methods. Daytime △SUHII is over 0.1 K in 62% of cities, and nighttime △SUHII is over 0.1 K in 45% of cities. Strong latitudinal and seasonal variations of △SUHII induced by the different methods used to cope with missing data introduce uncertainty and incomparability to SUHII research. Moreover, various image quality control (QC) methods and the effects from these methods on SUHII are discussed. The accuracy of QC methods changes with the error distribution of the image quality. Further investigations are required to determine the optimal QC method. The questions of whether different background definition affects the atmospheric UHI were also discussed. In this paper, a promising strategy for estimating city-based SUHII is proposed and a global-scale database based on the Google Earth Engine (GEE) is established to support further research on the variations and controls of SUHI. • MEA-R is an optimal background reference-defining method for large-scale SUHII. • FSAT-T can improve the accuracy of LST estimation that is limited by missing data. • Accuracy of different methods for quality control changes with error distribution. • Various SUHII calculation methods induce uncertainty to spatiotemporal patterns. [ABSTRACT FROM AUTHOR]

The present study has used Meteosat infrared brightness temperature images to investigate the regional and interannual variability of Central African cloudiness. Spatial and temporal variability were investigated using half-hourly data from the Meteosat-7 during June-July-August (JJA) of 1998-2002. The full domain of study (1.5°E-17°E, 1°N-15°N) was divided into six regions and statistics in each region were derived. Analysis of the dependence of cloud fraction to the brightness temperature threshold is explored both over land and ocean. Three diurnal cycle regimes (continental, oceanic, and coastal) are depicted according to the amplitude and peak time. Over regions of relatively flat terrain, results indicate enhancement of deep convection in the afternoon followed by a gradual decrease in the night. The diurnal cycle of convection is characterised by afternoon and early evening (around 15:00-18:00 LST) maxima located mainly downwind of the major mountain chains, and a more rapid nighttime decay. In terms of the harmonic amplitude, the diurnal signal shows significant regional contrast with the strongest manifestation over the Adamaoua Plateau and the weakest near the South Cameroon Plateau. This remarkable spatial dependence is clear evidence of orographic and heterogeneous land-surface impacts on convective development. Oceanic region exhibits weak activity of convective cloudiness with a maximum at noon. It is suggested that daytime heating of the land surface and moist environment may play a role in determining the spatial distribution of cloud fraction. This study further demonstrates the importance of the Cameroon coastline concavity and coastal mountains in regulating regional frequencies of convection and their initialization. The strength of the diurnal cycle of convective activity depends on mountain height, mean flow, coastal geometry. [ABSTRACT FROM AUTHOR]

MODIS snow cover products are appealing for hydrological applications because of their good accuracy and daily availability. Their main limitation, however, is cloud obscuration. In this study we evaluate simple mapping methods, termed temporal and spatial filters, that reduce cloud coverage by using information from neighboring non-cloud covered pixels in time or space, and by combining MODIS data from the Terra and Aqua satellites. The accuracy of the filter methods is evaluated over Austria, using daily snow depth observations at 754 climate stations and daily MODIS images in the period 2003-2005. The results indicate that the filtering techniques are remarkably efficient in cloud reduction, and the resulting snow maps are still in good agreement with the ground snow observations. There exists a clear, seasonally dependent, trade off between accuracy and cloud coverage for the various filtering methods. An average of 63% cloud coverage of the Aqua images is reduced to 52% for combined Aqua-Terra images, 46% for the spatial filter, 34% for the 1-day temporal filter and 4% for the 7-day temporal filter, and the corresponding overall accuracies are 95.5%, 94.9%, 94.2%, 94.4% and 92.1%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the availability of the electricity supply when the sources consist of a combination of solar energy and a small hydro installation. Instead of flow-of-river, a small reservoir is used. By not using the hydro energy during sunny periods, the natural flow-of-river fills up the reservoir for later use. A model for global solar radiation is proposed with an astronomical part (deterministic) and a meteorological part (stochastic). The meteorological part is based on a Markov model of the cloud coverage. The solar model does not require solar radiation measurements, just cloud observations. A case study has been performed for Timbuktu (16.75°N, 0.07°W) in which generation availability is simulated for four different cases: solar power only; solar power with storage; solar and hydro power and solar and hydro power with storage. To be able to use exclusively renewable energy sources, a combination of sources is needed to secure the reliability of the supply. Using solar cells in combination with a small reservoir is favorable. Even with a weak flow the affect of the flow on the day time reliability is minor, but great benefits can be found for reliability during low load hours (night time). [Copyright &y& Elsevier]

Abstract: A stochastic model for simulating global solar radiation on a horizontal surface has been developed for use in power systems reliability calculations. The importance of an appropriate model for global solar radiation has increased with the increased use of photovoltaic power generation. The global solar radiation shows not only regular yearly and daily variations but also a random behaviour. The yearly and daily variations can be described in a deterministic way while the random behaviour has a high correlation with the state of the atmosphere. The astronomic effects can easily be described mathematical with only some minor simplifications but the atmospheric effects are more complicated to describe. The transmittivity of solar radiation in the atmosphere depends on various factors, e.g. humidity, air pressure and cloud type. By using cloud observations as input for the simulations, the local meteorological conditions can be accounted for. The model is usable for any geographical location if cloud observations are available at the location or at locations with similar climatological conditions. This is especially useful for development countries where long-term solar radiation measurement can be hard to obtain. Cloud observations can be performed without any expensive equipment and have been a standard parameter for many years throughout the world. Standard observations are done according to the Oktas-scale. It is the interval between observations that sets the resolution of the simulation: the observations are normally only every hour or every third hour. The model can easily be combined with cloud coverage simulations, has been proposed, for a more general model. For some calculations higher resolution may be needed. This can be obtained by including a stochastic model for the short-term variations and simple model has been proposed. Errors and limitations of the model are estimated and discussed. [Copyright &y& Elsevier]

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges. [ABSTRACT FROM AUTHOR]