A model for the estimation of cloud cover from satellite data

2015 ◽  
Author(s):  
N. Pratummasoot ◽  
S. Buntoung ◽  
S. Janjai
Keyword(s):  
Satellite Data ◽  
Cloud Cover

scholarly journals Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

2018 ◽  
Vol 11 (5) ◽  
pp. 2949-2965 ◽  
Author(s):  
Dunya Alraddawi ◽  
Alain Sarkissian ◽  
Philippe Keckhut ◽  
Olivier Bock ◽  
Stefan Noël ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Satellite Data ◽  
Cloud Cover ◽  
Surface Albedo ◽  
Hydrological Cycle ◽  
Radiation Budget ◽  
The Arctic ◽  
Gnss Measurements ◽  
Gnss Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001–2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data

1991 ◽  
Vol 11 (3) ◽  
pp. 51-54 ◽  
Author(s):  
L.L. Stowe ◽  
E.P. McClain ◽  
R. Carey ◽  
P. Pellegrino ◽  
G.G. Gutman ◽  
...  
Keyword(s):  
Satellite Data ◽  
Cloud Cover ◽  
Global Distribution ◽  
Noaa Avhrr

Arctic Cloud Changes from Surface and Satellite Observations

2010 ◽  
Vol 23 (15) ◽  
pp. 4233-4242 ◽  
Author(s):  
Ryan Eastman ◽  
Stephen G. Warren
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Cloud Cover ◽  
The Arctic ◽  
Interannual Variations ◽  
Infrared Observation ◽  
Cloud Data ◽  
Surface Data ◽  
Avhrr Data ◽  
Very High

Abstract Visual cloud reports from land and ocean regions of the Arctic are analyzed for total cloud cover. Trends and interannual variations in surface cloud data are compared to those obtained from Advanced Very High Resolution Radiometer (AVHRR) and Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) satellite data. Over the Arctic as a whole, trends and interannual variations show little agreement with those from satellite data. The interannual variations from AVHRR are larger in the dark seasons than in the sunlit seasons (6% in winter, 2% in summer); however, in the surface observations, the interannual variations for all seasons are only 1%–2%. A large negative trend for winter found in the AVHRR data is not seen in the surface data. At smaller geographic scales, time series of surface- and satellite-observed cloud cover show some agreement except over sea ice during winter. During the winter months, time series of satellite-observed clouds in numerous grid boxes show variations that are strangely coherent throughout the entire Arctic.

scholarly journals Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

2018 ◽  
Vol 11 (7) ◽  
pp. 4153-4170
Author(s):  
Fanny Jeanneret ◽  
Giovanni Martucci ◽  
Simon Pinnock ◽  
Alexis Berne
Keyword(s):  
Satellite Data ◽  
Cloud Cover ◽  
Swiss Alps ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Cloud Detection ◽  
Tree Model ◽  
Validation Data ◽  
Cloud Data ◽  
Mountainous Regions

Abstract. The validation of long-term cloud data sets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s. A trustworthy reference cannot be found easily at every location and every time. Mountainous regions present a particular problem since ground-based measurements are sparse. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite data sets over elevated areas. In winter for ground levels above 1000 m (a.s.l.) in Switzerland, the cloud occurrence of the newly released cloud property data sets of the ESA Climate Change Initiative Cloud_cci Project (Advanced Very High Resolution Radiometer afternoon series (AVHRR-PM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua series) is 132 to 217 % that of surface synoptic (SYNOP) observations, corresponding to a rate of false cloud detections between 24 and 54 %. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties. In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite data sets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to provide independent validation of the cloud cover over 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The validation data are then co-located with the satellite observations, and a decision tree model is trained to automatically detect the overestimations in the satellite cloud masks. Cross-validated results show that 62±13 % of these overestimations can be identified by the model, reducing the systematic error in the satellite data sets from 14.4±15.5 % to 4.3±2.8 %. The amount of errors is lower, and, importantly, their distribution is more homogeneous as well. These corrections happen at the cost of a global increase of 7±2 % of missed clouds. Using this model, it is possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci AVHRR-PM and MODIS-Aqua products.

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 Aerosol-cloud interaction inferred from MODIS satellite data and global aerosol models

2006 ◽  
Vol 6 (5) ◽  
pp. 9351-9388 ◽  
Author(s):  
G. Myhre ◽  
F. Stordal ◽  
M. Johnsrud ◽  
Y. J. Kaufman ◽  
D. Rosenfeld ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Optical Depth ◽  
Water Uptake ◽  
Satellite Data ◽  
Cloud Cover ◽  
Hygroscopic Growth ◽  
Modis Data ◽  
Aerosol Cloud ◽  
Cloud Parameters ◽  
Ångström Exponent

Abstract. We have used the Modis satellite data and two global aerosol models to investigate relationships between aerosol optical depth (AOD) and cloud parameters that may be affected by the aerosol concentration. The relationships that are studied are mainly between AOD on the one hand and cloud cover, cloud liquid water path, and water vapour on the other. Additionally, cloud droplet effective radius, cloud optical depth, cloud top pressure and aerosol Ångström exponent, have been analysed in a few cases. In the Modis data we found as in earlier studies an enhancement in the cloud cover with increasing AOD. We find it likely that most of the strong increase in cloud cover with AOD, at least for AOD<0.2, is a result of aerosol-cloud interactions and prolonged cloud lifetime. Meteorology seems not to be a cause for the increase in cloud cover with AOD in this range. When water uptake of the aerosols is not taken into account in the models the modelled cloud cover actually decreases with AOD. Part of the relationship found in the Modis data for AOD>0.2 can be explained by larger water uptake close to clouds since relative humidity is higher in regions with higher cloud cover. The efficiency of the hygroscopic growth depends on aerosol type, hygroscopic nature of the aerosol, the relative humidity, and to some extent the cloud screening. By analysing the Ångström exponent we find that the hygroscopic growth of the aerosol is not likely to be a main contributor to the cloud cover increase with AOD. Since the largest increase in cloud cover with AOD is for low AOD (~0.2) and thus also for low cloud cover, cloud contamination is not likely to play a large role. However, interpretation of the complex relationships between AOD and cloud parameters should be made with great care and further work is clearly needed.

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