Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach [PDF]
Hydrology and Earth System Sciences, 2013 Satellite remote sensing can be used to investigate spatially distributed hydrological states for use in modeling, assessment, and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images.
V. López-Burgos, H. V. Gupta, M. Clark
doaj +2 more sources
Data‐Driven Equation Discovery of a Cloud Cover Parameterization [PDF]
Journal of Advances in Modeling Earth Systems, 2023 A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning‐based parameterizations using output from global storm‐resolving models.
Arthur Grundner +3 more
doaj +2 more sources
Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets
Remote Sensing, 2021 Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide.
Ankur Srivastava +4 more
doaj +2 more sources
The impact of mineral dust on cloud formation during the Saharan dust event in April 2014 over Europe [PDF]
Atmospheric Chemistry and Physics, 2018 A regional modeling study on the impact of desert dust on cloud formation is presented for a major Saharan dust outbreak over Europe from 2 to 5 April 2014.
M. Weger +19 more
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Crop NDVI Monitoring Based on Sentinel 1
Remote Sensing, 2019 Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops ...
Roberto Filgueiras +4 more
doaj +2 more sources
Deep Learning Based Cloud Cover Parameterization for ICON [PDF]
Journal of Advances in Modeling Earth Systems, 2021 A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm‐resolving model (SRM) simulations.
Arthur Grundner +5 more
semanticscholar +1 more source
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling [PDF]
European Conference on Computer Vision, 2022 4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-
Zhongang Cai +14 more
semanticscholar +1 more source
Cloud Cover Nowcasting with Deep Learning [PDF]
International Conference on Image Processing Theory Tools and Applications, 2020 Nowcasting is a field of meteorology which aims at forecasting weather on a short term of up to a few hours. In the meteorology landscape, this field is rather specific as it requires particular techniques, such as data extrapolation, where conventional ...
Léa Berthomier +2 more
semanticscholar +1 more source
Analysis Effect of Cloud Cover, Wind Speed, and Water Temperature to BOD and DO Concentration Using QUAL2Kw Model (Case Study In Winongo River, Yogyakarta) [PDF]
MATEC Web of Conferences, 2019 Winongo river is one of the rivers in the province of D.I. Yogyakarta that included in the category of contaminated. the research aims to learn on the effect variation of cloud cover, wind speed, and water temperature on BOD and DO concentrate in order ...
Marlina Nelly, Melyta Dirja
doaj +1 more source
Total cloud cover from satellite observations and climate models [PDF]
Atmospheric Research, 2010 Abstract. Global and zonal monthly means of cloud cover fraction for total cloudiness (CF) from the ISCCP D2 dataset are compared to same quantity produced by the 20th century simulations of 21 climate models from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset archived by the ...
P. Probst +4 more
openaire +3 more sources