total cloud cover
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2021 ◽  
Vol 2114 (1) ◽  
pp. 012070
Author(s):  
OsamaT. Al-Taai ◽  
Shiemaa A. Hashim ◽  
Wedyan G. Nassif ◽  
Zainab M. Abbood

Abstract Clouds greatly affect the elements of climate, energy balance, and solar radiation, which has increased the interest of many researchers in trying to find the best relationships and formulas that link these variables. In this work, the data of the European Center for Medium-Range Weather Forecasts (ECMWF) was relied on. The research aims to find the overlap between Cloud Cover (Low, Middle, High, and Total), (LCC, MCC, HCC, and TCC) respectively, with Total Solar Radiation (TSR) of Baghdad city, for the period (1981-2013), the work was carried out with the monthly and annual data of the (Low, Middle, High, and Total) cloud cover and the total solar radiation falling on a horizontal surface. And by using the correlation coefficient Spearman rho test (rs) to find the strength of the relationship between total solar radiation and cloud cover, it was found that there is an inverse relationship between the total solar radiation falling on a horizontal surface and the (Low, Middle, High, and Total) cloud cover.


MAUSAM ◽  
2021 ◽  
Vol 61 (4) ◽  
pp. 455-468
Author(s):  
A. K. JASWAL

Based upon 172 well distributed surface meteorological stations over India, annual and seasonal trends in total cloud cover and associated climatic variables diurnal temperature range and rainy days are investigated for 1961-2007. The data analysis indicates a general decrease in total cloud cover over most parts of India during winter, summer and monsoon. On monthly scale, statistically significant decrease in total cloud cover has occurred during April (3% per decade), June to September (2% per decade) and December (5% per decade). Seasonally, the declining trends in total cloud cover are significant for summer and monsoon (2% per decade). Spatial analysis of trends suggests coherent decrease in total cloud cover over central India (all seasons) and south peninsula (except post monsoon).   All India averaged monthly, annual and seasonal trends in diurnal temperature range and rainy days are mixed and weak. Spatially, trends in diurnal temperature range are decreasing over north and increasing over south peninsula while trends in rainy days are decreasing over large number of stations during winter and monsoon and increasing in summer and post monsoon seasons. However, the sizes of the same trend regions show considerable variability between seasons. Monsoon season total cloud cover and Nino3.4 sea surface temperature anomalies are significantly negatively correlated over all regions of the country except northeast indicating a strong relationship between them.


Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1367
Author(s):  
Jie Zhao ◽  
Tiejian Li ◽  
Kaifang Shi ◽  
Zhen Qiao ◽  
Zhongye Xia

In order to verify the accuracy of precipitable water vapor (PWV) in remote sensing and reanalysis datasets under different climatic conditions and ensure the reliability of analysis results, the performances of ERA-5 reanalysis PWV data and the Atmospheric Infrared Sounder (AIRS) remotely-sensed PWV data were tested in the northern Qinghai-Tibet Plateau by using weather balloon radiosonde data from meteorological stations from 2002 to 2016. The coincidence degree of total cloud cover was also verified, and then the PWV data precision with different levels of cloud cover was analyzed. The results show that: (1) Both ERA-5 and AIRS data underestimate PWV in the studied high plateau region, and higher altitude leads to greater deviation. (2) Compared with AIRS data, ERA-5 data have better consistency with radiosonde data in PWV and total cloud cover. (3) For the long-term trend of PWV, the ERA-5 data are the opposite to the radiosonde data with a clear sky, but both datasets showed a significant increasing trend in cloudy skies. It can be concluded that in high altitude areas, the ERA-5 data can be used for general analysis, but are not well qualified to reflect the changing trend of PWV under climate change.


2021 ◽  
Vol 11 (18) ◽  
pp. 8533
Author(s):  
Jaehoon Cha ◽  
Moon Keun Kim ◽  
Sanghyuk Lee ◽  
Kyeong Soo Kim

This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.


2021 ◽  
Author(s):  
Theresa Mieslinger ◽  
Bjorn Stevens ◽  
Tobias Kölling ◽  
Manfred Brath ◽  
Martin Wirth ◽  
...  

Abstract. We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models as well as Large Eddy Simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds contribute significantly to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well and especially how they change in a future climate we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a clear-sky perspective. Using well-known radiative transfer relations we retrieve the clear-sky contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the clear-sky part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud masking algorithms and those undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by a factor of 2. Lidar measurements on board the HALO aircraft support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the clear-sky signal can cause an underestimation of the cloud radiative effect of up to −32 %. We further discuss possible artificial correlations in aersol-cloud cover interaction studies that might arise from undetected optically thin clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestiamtion of cloud brightness in models is even higher than assumed so far.


2021 ◽  
Vol 21 (1) ◽  
pp. 17-25
Author(s):  
Edward Z. Łaszyca

Abstract This paper contains a description of nephological conditions in the Bydgoszcz area based on data sourced from the Bydgoszcz-Airport weather station for 1971–2010. In the analysed forty-year period from 1971 to 2010 the average annual total cloud cover in Bydgoszcz – measured on a scale of 0–8 – was 5.5; for the warm season (April – September) it was 5.1, and for the cold season (October – March) 5.8. This corresponds to, respectively, 69, 64 and 72% coverage of the sky by cloud. Cloud cover was largest from November to February (6.1–5.8) and smallest in August (4.7). In 1971–2010, the average mean cloud cover value (scale 0–8) decreased from 5.6 in 1971–1990 to 5.4 in the multi-annual period 1991–2010 (by 0.05 per 10 years).


2021 ◽  
Vol 21 (6) ◽  
pp. 4899-4913
Author(s):  
Xiang Zhong ◽  
Shaw Chen Liu ◽  
Run Liu ◽  
Xinlu Wang ◽  
Jiajia Mo ◽  
...  

Abstract. Satellite observations (International Satellite Cloud Climatology Project (ISCCP), 1983–2009) of linear trends in cloud cover are compared to those in global precipitation (Global Precipitation Climatology Project (GPCP) pentad V2.2, 1983–2009), to investigate possible cause(s) of the linear trends in both cloud cover and precipitation. The spatial distributions of the linear trends in total cloud cover and precipitation are both characterized primarily by a broadening of the major ascending zone of Hadley circulation. Our correlation studies suggest that global warming, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) can explain 67 %, 49 % and 38 %, respectively, of the spatial variabilities in the linear trends in cloud cover, but causality is harder to establish. Further analysis of the broadening of the major ascending zone of Hadley circulation shows that the trend in global temperature, rather than those in AMO and PDO, is the primary contributor to the observed linear trends in total cloud cover and precipitation in 1983–2009. The underlying mechanism driving this broadening is proposed to be the moisture–convection–latent-heat feedback cycle under global warming conditions. The global analysis is extended by investigating connections between clouds and precipitation in China, based on a large number of long-running, high-quality surface weather stations in 1957–2005. This reveals a quantitative matching relationship between the reduction in light precipitation and the reduction in total cloud cover. Furthermore, our study suggests that the reduction in cloud cover in China is primarily driven by global temperature; PDO plays a secondary role, while the contribution from AMO and Niño3.4 is insignificant, consistent with the global analysis.


2021 ◽  
Author(s):  
Raphaela Vogel ◽  
Sandrine Bony ◽  
Anna Lea Albright ◽  
Bjorn Stevens ◽  
Geet George ◽  
...  

<p>The trade-cumulus cloud feedback in climate models is mostly driven by changes in cloud-base cloudiness, which can largely be attributed to model differences in the strength of lower-tropospheric mixing. Using observations from the recent EUREC<sup>4</sup>A field campaign, we test the hypothesis that enhanced lower-tropospheric mixing dries the lower cloud layer and reduces near-base cloudiness. The convective mass flux at cloud base is used as a proxy for the strength of convective mixing and is estimated as the residual of the subcloud layer mass budget, which is derived from dropsondes intensively launched along a circle of ~200 km diameter. The cloud-base cloud fraction is measured with horizontally-pointing lidar and radar from an aircraft flying near cloud base within the circle area. Additional airborne, ground- and ship-based radar, lidar and in-situ measurements are used to estimate the total cloud cover, the surface fluxes and to validate the consistency of the approach.</p><p>Preliminary mass flux estimates have reasonable mean values of about 15 mm/s. 3- circle (i.e. 3h) averaged estimates range between 0-40 mm/s and reveal substantial day-to-day and daily variability. The day-to-day variability in the mass flux is mostly due to variability in the mesoscale vertical velocity, whereas the entrainment rate mostly explains variability on the daily timescale, consistent with previous large-eddy simulations. We find the mass flux to be positively correlated to both the cloud-base cloud fraction and the total cloud cover (R=0.55 and R~0.4, respectively). Other indicators of lower-tropospheric mixing due to convection and mesoscale circulations also suggest positive relationships between mixing and cloudiness. Implications of these analyses for testing the hypothesized mechanism of positive trade-cumulus cloud feedback will be discussed.</p>


2021 ◽  
Author(s):  
Reona Satoh ◽  
Noriyuki Nishi ◽  
Hitoshi Mukougawa

<p>We investigated the spatial structure of the intraseasonal variation (15-30 day) in cloud cover in the mid-latitudes during winter. We attempted to interpret the spatial pattern of clouds in the context of Rossby waves.</p><p> </p><p>We used the total cloud cover in H-series dataset (1984-2016) by the International Satellite Cloud Climatology Project (ISCCP) based on the satellite observations, and ERA-Interim re-analysis data (1980-2016) including high, medium, and low cloud covers defined by σ coordinate.</p><p> </p><p>We calculated correlation coefficients between the geopotential height at 300hPa (Z300) at a certain position and the cloud covers, meridional wind, and vertical velocity in the surrounding area. The positions of the maximum of high (0.45≧σ) and medium cloud cover (0.8≧σ>0.45) relative to Z300 are longitudinally constant for all longitudes except the region from east Asia to western part of the Pacific. The position of the maximum of the high cloud cover is located just west of the ridge and just east of the maximum positions of the upward motions of re-analysis vertical velocity and its adiabatic component. These results suggest that the adiabatic upward motion in the southerly wind region west of the ridge contributes to the generation of high cloud cover. In contrast, the position of the maximum of medium cloud cover is located just east of the trough. The position of the maximum of diabatic upward motion, which is consider to be due to condensation process is located near the maximum of medium cloud cover. These results suggest that Rossby waves modulate activity of short-period disturbances with precipitation. Apart from high and medium cloud covers, the position of the maximum of low cloud cover (σ>0.8) has large longitudinal dependency. While the position of the maximum is located at almost the same as that of medium cloud cover mainly over the continent, the position of the maximum is located just east of the ridge mainly over the ocean.</p><p> </p><p>The correlation coefficients between ISCCP total cloud cover and Z300 are statistically significant only over the continent, where the positions of the maximum of high, medium, and low cloud covers are all located east of the trough and west of the ridge.</p>


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