Influence Of Cloud Cover Research Articles

Research on the influencing factors of the difference in warming rates between the east, west and central regions of North America during the 21st century

Temperature in North America (NA) is a significant focus of current climate change research. This study focuses on various climate variables and employs linear regression and correlation coefficient analysis to examine the factors influencing the differences in warming rates across the eastern, western, and central regions of NA in the 21st century, as well as the correlations between these factors and temperature. Results indicate that NA temperatures are rising with notable regional variations in warming rates. Under the influence of cloud cover, the impact of precipitation on warming rates is relatively minor, whereas radiation significantly affects warming rates. Additionally, sea level pressure influences wind patterns, thereby significantly impacting warming rates. This study highlights the complexity of regional climate dynamics in NA and provides deep insights into regional climate differences.

Reformulation of the Used Model to Estimate Soil Temperature

The Earth is permanently surrounded by cloud cover that, particularly, is an essential component in the planet’s energy balance. In turn, cloud cover intervenes in the main conditioning factor for soil temperature: solar radiation. In particular, the soil thermal amplitude should be dampened with the attenuation of solar radiation. However, the scientific community rarely analyzes this relationship, neglecting the model that is used to estimate the soil temperature. In this context, the present study seeks to reformulate the model by inserting a variable referent to cloud cover. Thus, to achieve this objective, a physical-mathematical review of the heat flow in the vertical profile of soil is performed. The reformulated model indicates the influence of cloud cover, intervening for both the soil’s heating (nighttime period) and cooling (daytime period). Finally, the reformulated model should be employed to estimate the soil thermal behavior (in particular, on “overcast sky” days).

Evaluation of ECMWF model to predict daily and monthly solar radiation over Indonesia region

Solar radiation forecast is a pivotal information needed in the operational activity of large-scale solar energy production. In this study, the reliability of SSRD (surface solar radiation downward) forecast from the 51 ensemble members in the ECMWF (European Centre for Medium Range Forecast) long-range forecast to predict daily and monthly radiation in 5 climatological stations in Indonesia is evaluated. The global horizontal irradiance (GHI) data from the solar radiation observation network from January 2018 – December 2020 are used in the quantitative evaluation of the SSRD forecast. Post-processing methods are applied to the model output, namely the bilinear interpolation method and the empirical quantile mapping to reduce consistent biases in the model output. The evaluation was carried out for different cloud covers based on the calculation of clearness index (k_t). The cloud condition affects the performance of the model, where the highest correlation value is achieved during sunny days (0.18 – 0.65) and the lowest correlation happens in overcast days (0.05 – 0.35). Models also tend to underestimate radiation when the sky is clear and overestimate it in cloudy days, based on negative MBE values during clear days (-0.47 kWh/m2 – -1.29 kWh/m2). The spatial averaging method did not necessarily improve the accuracy of the forecast, but the empirical quantile mapping method provides better accuracy, which is indicated by a values (mean error ratio) lower than 1 in most stations. Information about the influence of cloud cover on model performance can be used in future application of the model output and the bias correction process carried out in this study can be applied to reduce bias in the model.

Monitoring Solar Radiation UV Exposure in the Comoros.

As part of the UV-Indien project, a station for measuring ultraviolet radiation and the cloud fraction was installed in December 2019 in Moroni, the capital of the Comoros, situated on the west coast of the island of Ngazidja. A ground measurement campaign was also carried out on 12 January 2020 during the ascent of Mount Karthala, located in the center of the island of Ngazidja. In addition, satellite estimates (Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument) and model outputs (Copernicus Atmospheric Monitoring Service and Tropospheric Ultraviolet Model) were combined for this same region. On the one hand, these different measurements and estimates make it possible to quantify, evaluate, and monitor the health risk linked to exposure to ultraviolet radiation in this region, and, on the other, they help to understand how cloud cover influences the variability of UV-radiation on the ground. The measurements of the Ozone Monitoring Instrument onboard the EOS-AURA satellite, being the longest timeseries of ultraviolet measurements available in this region, make it possible to quantify the meteorological conditions in Moroni and to show that more than 80% of the ultraviolet indices are classified as high and that 60% of these are classified as extreme. The cloud cover measured in Moroni by an All Sky Camera was used to distinguish between the cases of UV index measurements taken under clear or cloudy sky conditions. The ground-based measurements thus made it possible to describe the variability of the diurnal cycle of the UV index and the influence of cloud cover on this parameter. They also permitted the satellite measurements and the results of the simulations to be validated. In clear sky conditions, a relative difference of between 6 and 11% was obtained between satellite or model estimates and ground measurements. The ultraviolet index measurement campaign on Mount Karthala showed maximum one-minute standard erythemal doses at 0.3 SED and very high daily cumulative erythemal doses at more than 80 SED. These very high levels are also observed throughout the year and all skin phototypes can exceed the daily erythemal dose threshold at more than 20 SED.

Assessment of the impact of current weather forecasts in the task of space survey planning

The article is devoted to the actual task of planning the work of a group of different types of spacecraft for remote sensing of the Earth. An enlarged algorithm for solving the planning problem for different types of spacecraft is described. The result of the enlarged algorithm is sought in the form of a set of reference plans for groups of similar spacecraft, thinned out by removing some of the conflicting operations of resetting the sensing data. The characteristics of the developed plans largely depend on the methodology used to account for the impact of cloud cover. The possibility of implementing a methodology based on the use of files of current weather forecasts of hydrometeorological centers downloaded from the Internet in the form of a special application is investigated. The created application is being tested on the real data of the hydrometeorological center downloaded from the American server, which covers a large region, including the European part of Eurasia and part of Africa. An application that simulates the distribution of points within a region estimates the number of points covered by weak cloud cover (20% or less). Based on the results of the simulation, it was found that the proportion of points available for shooting lies in the range from about a quarter to a third. Based on the obtained quantitative estimates, it is concluded that taking into account the influence of cloud cover radically changes the reference plans calculated taking into account only illumination, and affects the structure of the enlarged planning algorithm.

Vegetation cover monitoring in tropical regions using SAR-C dual-polarization index: seasonal and spatial influences

ABSTRACT An approach to monitor vegetation without the influence of cloud cover and employing Synthetic Aperture Radar (SAR) data was developed by the DPSVI (Dual Polarization SAR Vegetation Index), based on Sentinel-1 mission data. However, DPSVI performance in areas of dense vegetation and variation in vegetation classes is unknown. Thus, this paper aimed to investigate the performance of DPSVI in the monitoring of vegetation in the Brazilian Atlantic Forest biome, as well as to propose modifications to improve its capacity to monitor the vegetation and investigate seasonal and spatial influences on the performance of the proposed index (DPSVIm). Three approaches were adopted: 1) use Sentinel-1 and Landsat 8 scenes obtained from four hydrological years (2015–2016 to 2018–2019), to compute monthly DPSVI, DPSVIm, NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) indices, to investigate seasonal influences; 2) perform topographic correction in Landsat 8 and Sentinel-1 data to verify the influence of relief on DPSVIm performance; and 3) confronting DPSVIm with an attribute of Atlantic Forest fragments: the above ground biomass (AGB). The scenes were processed on the Google Earth Engine platform. The DPSVIm was able to better distinguish the vegetation, compared to the DPSVI, mainly in forest planting areas and Atlantic Forest, which evidences the proposed improvements. The rainfall regime made the DPSVIm performance seasonal in comparison with the NDVI and EVI (Normalized Difference Vegetation Index and Enhanced Vegetation Index), presenting better agreements in the dry season and worse in the rainy season. There was a greater agreement between DPSVIm and EVI in the results to monitor dense vegetation since EVI does not saturate as NDVI does. Finally, the DPSVIm has a good fit to AGB, being able to distinguish different levels of AGB from 50 to 200 Mg ha, approximately. The study allowed improving the DPSVI making the DPSVIm adequate to quantify vegetation in the study area, even considering the effects of topography and rainfall regime. The latter environmental factor should be addressed in future studies, searching to mitigate this source of temporal uncertainty in DPSVIm.

DEEP LEARNING-BASED RECONSTRUCTION OF SPATIOTEMPORALLY FUSED SATELLITE IMAGES FOR SMART AGRICULTURE APPLICATIONS IN A HETEROGENEOUS AGRICULTURAL REGION

Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.

Influence of cloud cover on the relationship between the sky view factor and nocturnal urban heat islands

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 79:127-137 (2019) - DOI: https://doi.org/10.3354/cr01584 Influence of cloud cover on the relationship between the sky view factor and nocturnal urban heat islands Guixin Zhang1, Guangxing Zhang2, Chuxuan Zhou3, Shanyou Zhu2,* 1School of Geography Science and 2School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, PR China 3National Meteorological Center, China Meteorological Administration, Beijing 100081, PR China *Corresponding author: zsyzgx@163.com ABSTRACT: Cloud amounts, their spatial distribution, and the sky view factor (SVF) all have effects on the nocturnal upwelling longwave radiation and result in different degrees of urban-rural radiation cooling, which are widely investigated in studies of the urban heat island phenomenon. Currently, most research studies discuss the influence of synoptic conditions and urban geometric morphology on urban heat islands. Here we attempted to determine the influence of cloud cover on the relationship between the SVF and nocturnal urban heat islands by calculating the SVF considering the cloud amount and its distribution. The Adelaide central urban area of South Australia was selected as the study area, and we used urban 3-dimensional building data with a spatial resolution of 1 m and field measurement meteorological data, as well as ceilometer-observed cloud data. We sequentially analyzed the influence of cloud amount on the instantaneous nocturnal urban heat island intensity (UHII), SVF, and SVF-UHII relationship. The SVF-UHII feature space was then constructed to explain the nocturnal urban heat island. The results reveal that there are high negative correlations between the total cloud amount and UHII, while the correlations change with site location. The cloud amount has an effect on the defined SVF. SVFs at all sites gradually increase with the cloud amount, and the SVF differences between various sites decrease with increasing cloud amount. The SVF-UHII feature space can be preliminarily used to explain and simulate the UHII at some sites under different cloud cover conditions, and the mean relative error of the simulated UHII for the example data was 20%. KEY WORDS: Cloud cover · Sky view factor · Urban heat island · Feature space Full text in pdf format PreviousNextCite this article as: Zhang G, Zhang G, Zhou C, Zhu S (2019) Influence of cloud cover on the relationship between the sky view factor and nocturnal urban heat islands. Clim Res 79:127-137. https://doi.org/10.3354/cr01584 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 79, No. 2. Online publication date: December 05, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China

Abstract The trade-off between the temporal and spatial resolutions, and/or the influence of cloud cover, makes it difficult to obtain continuous fine-scale satellite data for surface urban heat island (SUHI) analysis. To relieve these difficulties, this study employs multi-temporal and multi-sensor fusion methods for a long-term and fine-scale summer SUHI analysis of the city of Wuhan in China. By integrating several series of satellite images, we generated 26-year (1988 to 2013) high spatial resolution (Landsat-like) summer land surface temperature (LST) data. This series of data was then used for a qualitative and quantitative analysis of the SUHI patterns, evolution characteristics, and mechanisms. This study not only provides a generalized research framework for the long-term and fine-scale analysis of the SUHI effect, but also reveals several findings about the heat distribution and SUHI characteristics in Wuhan. Firstly, our results show that the high temperature and sub-high temperature areas were continuously concentrated from rural to urban areas, but the high temperature area within the old city zones showed an obvious decreasing tendency. Secondly, a more important finding is that the SUHI intensity first increased and then decreased over the 26 years. The maximum temperature difference between the city zone and the rural area was in 2003 (7.19 K for the old city zone, and 4.65 K for the area within the third ring road). Finally, we confirm that the relationships between heat distribution and land cover (especially vegetation and impervious surfaces) were interannually stable, and that the influences of industry, businesses, and residential districts on the SUHI effect were in descending order in Wuhan.

Correlation Between Corn Progress Stages and Fractal Dimension From MODIS-NDVI Time Series

In this letter, the relationship between corn crop progress stages and fractal dimension derived from remote sensing data has been revealed. Due to the effects of soil background and farming practices on emerged and harvested stages of the corn crop, the roughness of their corresponding normalized difference vegetation index (NDVI) images significantly changes. Therefore, image fractals, which normally indicate such roughness, are proposed to detect corn progress stages. There are four steps in the proposed procedure: 1) NDVI maximum composite process is conducted to eliminate the influence of cloud cover or missing data; 2) Cropland Data Layer (CDL) is used to remove noncorn pixels; 3) fractal dimension is calculated to estimate the spatial roughness of NDVI image; and 4) curve fitting is used to detect peaks that infer the certain progress stages. It is worth mentioning that a dimensionality-reduction-based differential box-counting algorithm is developed to estimate the fractal dimension of NDVI image in an irregular region of interest. The algorithm is applicable for masked NDVI image. Experiments based on Moderate Resolution Imaging Spectroradiometer NDVI time series and CDL of the State of Iowa are conducted. Comparison of the results with corn crop statistics from the National Agricultural Statistics Service indicates that the proposed method is able to detect corn emerged and harvested stages with success.

Cave cricket exit counts: environmental influences and duration of surveys

Cave cricket abundance is used as an indicator of integrity of cave ecosystems. One means of monitoring cave cricket abundance is counting crickets as they emerge from cave entrances for two hours after sunset. The influence of cloud cover, relative humidity, and surface temperature on counts is unknown and there might be few cave crickets that emerge during the first hour of the survey. Using mixed effects models, I assessed the influence of these environmental variables on exit counts and estimated when cave crickets emerged within the two-hour survey period. Exit-count surveys were conducted in eleven caves over four years in central Texas, and caves were surveyed up to four times a year across the four calendar seasons. Cloud cover, relative humidity, and temperature influenced counts, but the greatest influence was from temperature. Peaks in cave cricket counts occurred 80 to 90 minutes after the start of a survey and declined thereafter. Cave cricket exit count surveys should record surface temperature, cloud cover, and relative humidity at the start of surveys so that counts can be adjusted for these environmental influences. Also, surveys can be shortened to 1 or 1.5 hours in length.

The influence of cloud cover on the radiation budget, physical properties, and microwave scattering coefficient (σ°) of first-year and multiyear sea ice

Over sea ice, the shift from predominantly clear conditions in winter to persistent stratus cloud cover in late spring and early summer coincides with a dramatic increase in the net radiation balance at the sea ice surface. Transformation of the boundary layer climate and the geophysical properties of the snow covered sea ice result from this cloud-induced rise in surface energy balance. In this paper, in situ data from the Seasonal Sea Ice Monitoring the Modeling Site (SIMMS'93) experiment are used to examine the extent to which changes in surface radiation, sea ice physical and electrical properties, and microwave scattering are a function of clear versus cloudy conditions over first-year and multiyear sea ice types. The authors use the surface observations to drive a simple forward scattering model used to diagnose the potential mechanisms responsible for the observed changes in microwave scattering by the Earth Resources Satellite-1 (ERS-1). Their results indicate that under cloudy conditions the surface temperature increases because of the surplus in the longwave net radiative energy. This causes an increase in the surface temperature and formation of a temperature wave down through the snow volume into the ice surface. This increases the dielectric constant of the snow basal layer and ice surface over first-year sea ice because of the thin snow cover (relative to the thicker multiyear ice snow cover) and because of the temperature effect on brine volume. The observed increase in scattering is limited to very smooth first-year sea ice types. Models used to diagnose the principal scattering mechanisms suggest that the cloud cover effects the scattering either by creating a dielectrically rough basal layer in the snow volume near the ice surface or by increasing the scattering contribution from snow grains within the basal layer volume. Under either scenario the increase in scattering is small (/spl sim/5 dB) and is only detectable if the target presents a scattering magnitude less than about -20 dB.