Diurnal Cycles of Clouds and How They Affect Polar-Orbiting Satellite Data

2008 ◽  
Vol 21 (16) ◽  
pp. 3989-3996 ◽  
Author(s):  
Donald Wylie
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Cloud Cover ◽  
Time Trend ◽  
Summer Season ◽  
High Cloud ◽  
Diurnal Cycles ◽  
The Earth ◽  
Polar Orbiting Satellite ◽  
Infrared Data

Abstract Diurnal cycles of clouds were investigated using the NOAA series of polar-orbiting satellites. These satellites provided four observations per day for a continuous 11-yr period from 1986 to 1997. The High Resolution Infrared Radiation Sounder (HIRS) multispectral infrared data were used from the time trend analysis of Wylie et al. The previous study restricted its discussion to only the polar orbiters making observations at 0200 and 1400 LT because gaps in coverage occurred in the 0800 and 2000 LT coverage. This study shows diurnal cycles in cloud cover over 10% in amplitude in many regions, which is very similar to other studies that used geostationary satellite data. The use of only one of the polar-orbiting satellites by Wylie et al. caused biases up to 5% in small regions but in general they were small (e.g., ≤2% for most of the earth). The only consistently large bias was in high cloud cover over land in North America, Europe, and Asia north of 35°N latitude in the summer season where the 0200 and 1400 LT average high cloud frequency was 2%–5% more than the daily average. This occurred only in the summer season, not in the winter.

scholarly journals Responses of Tropical and Subtropical High-Cloud Statistics to Global Warming

2014 ◽  
Vol 27 (20) ◽  
pp. 7753-7768 ◽  
Author(s):  
A. T. Noda ◽  
M. Satoh ◽  
Y. Yamada ◽  
C. Kodama ◽  
T. Seiki
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
High Resolution ◽  
Radiative Forcing ◽  
Cloud Cover ◽  
High Cloud ◽  
Cloud Radiative Forcing ◽  
Almost All ◽  
High Clouds ◽  
Horizontal Grid

Abstract Data from global high-resolution, nonhydrostatic simulations, covering a 1-yr period and with horizontal grid sizes of 7 and 14 km, were analyzed to evaluate the response of high cloud to global warming. The results indicate that, in a warmer atmosphere, high-cloud cover increases robustly and associated longwave (LW) cloud radiative forcing (CRF) increases on average. To develop a better understanding of high-cloud responses to climate change, the geographical distribution of high-cloud size obtained from the model was analyzed and compared with observations. In warmer atmospheres, the contribution per cloud to CRF decreases for both the LW and shortwave (SW) components. However, because of significant increases in the numbers of high clouds in almost all cloud size categories, the magnitude of both LW and SW CRF increases in the simulations. In particular, the contribution from an increase in the number of smaller clouds has more effect on the CRF change. It was also found that the ice and liquid water paths decrease in smaller clouds and that particularly the former contributes to reduced LW CRF per high cloud.

scholarly journals THE DETERMINATION OF CLOUD COVER PARAMETERS USING SATELLITE DATA PROCESSING SYSTEMS

2020 ◽  
pp. 124-134
Author(s):  
Elena Viktorovna Volkova ◽  
◽  
Anzhelika Andreevna Kostornaya ◽  
Ruslana Aleksandrovna Amikishieva ◽  
◽  
...  
Keyword(s):  
Satellite Data ◽  
Cloud Cover ◽  
Western Siberia ◽  
Cloud Parameters ◽  
Cloud Characteristics ◽  
Avhrr Data ◽  
Polar Orbiting Satellite ◽  
Cloud Mask

The paper discusses the results of comparing cloud cover properties determined by using polar orbiting satellite data (AVHRR/NOAA and MSU-MR/Meteor-M No. 2) for the European territory of Russia and Western Siberia. The cloud characteristics were computed by two threshold techniques: Complex Threshold Technique (CTT) (developed at the European Centre of the State Research Center ‗Planeta‘) and Cloud Cover Detection Technique (CCDT) (developed at the Siberian Centre of ‗Planeta‘). Pixel-by-pixel comparison was performed for very close in time satellite observations, and it showed that in spite of technical similarity of the two radiometers and little difference between both techniques used for the classifications, the results were not the same. The quality of the MSU-MR classification is significantly worse than that of the two AVHRR classifications. In fact, the MSU-MR derivation of cloud parameters fails in optically thin cirrus and altocumulus clouds, thus underestimating the cloud top height for multilayered clouds. As a result, the cloud top is found to be lower, warmer and less iced in comparison with both AVHRR estimates, regardless of the region and other conditions; on the contrary, the cloud top of low and middle clouds appears to be colder, higher and more iced according to MSU-MR data. The MSU-MR cloud mask is strongly underestimated at night during the cold period of the year. The CTT and CCDT‘s cloud top height, temperature and water phase retrieved by AVHRR data are quite close for both regions.

Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube

2015 ◽  
Vol 9 (1) ◽  
pp. 106-111 ◽  
Author(s):  
Adam Lewis ◽  
Leo Lymburner ◽  
Matthew B. J. Purss ◽  
Brendan Brooke ◽  
Ben Evans ◽  
...  
Keyword(s):  
High Resolution ◽  
Environmental Change ◽  
Satellite Data ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
The Earth ◽  
Earth Observation Data

Generation of DEM and Ortho-image from High Resolution Stereo Satellite Data for a selected part of Southeast Bangladesh

2011 ◽  
Vol 4 (1) ◽  
pp. 500-502
Author(s):  
Md. Fazlul Haque ◽  
◽  
Md. Mostafizur Rahman Akhand ◽  
Dr. Dewan Abdul Quadir
Keyword(s):  
High Resolution ◽  
Satellite Data

A high-resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

2013 ◽  
Vol 20 (4) ◽  
pp. 1191-1210 ◽  
Author(s):  
Jonas Jägermeyr ◽  
Dieter Gerten ◽  
Wolfgang Lucht ◽  
Patrick Hostert ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Ecosystem Respiration

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

<p>The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth’s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using  satellites.  At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST  using the grey body equation :<br>             R<sub>lup</sub> = εσ T<sub>s</sub><sup>4</sup> + (1 − ε) R<sub> ldw         </sub>(1)<br>where R<sub>lup</sub> is the upwelling longwave radiation, R<sub>ldw</sub> is the downwelling longwave radiation, ε is the surface emissivity, <em>T<sub>s</sub>  </em>is the surface temperature and σ  is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:<br>               R<sub>lup</sub> = εσ T<sub>s</sub><sup>4 </sup>                       (2)<br>Despite  widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct ε needed for in-situ LST retrievals using tower-based measurements.<br>The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST  obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.</p>

Machine Learning based Extraction of Electrical Substations from High Resolution Satellite Data: Outcome of the ICETCI 2021 Challenge

2021 ◽  
Author(s):  
Sreenivasan G ◽  
Anju Bajpai ◽  
Prakasa Rao D S ◽  
Girish Kumar T P ◽  
Ashish Shrivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data
Sign in / Sign up

Export Citation Format

Share Document