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A range of different statistical downscaling models was calibrated using both observed and general c...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamic...
Global climate change is a major area of concern to public and climate researchers. It impacts flood...
Statistical downscaling methods seek to model the relationship between large scale atmospheric circu...
A new open source neural network temporal downscaling model is described and tested using CRU-NCEP r...
The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climat...
A flexible framework of multi-model of three statistical downscaling approaches was established in w...
AbstractA flexible framework of multi-model of three statistical downscaling approaches was establis...
This research presented a holistic approach for downscaling of precipitation in both space and time ...
This paper describes a novel technique for downscaling daily rainfall which uses a combination of a ...
In light of the success of superresolution (SR) applications in computer vision, recent studies have...
AbstractA new open source neural network temporal downscaling model is described and tested using CR...
This research is focused on the development of statistical downscaling model using neural network te...
Weather forecasts at high spatio-temporal resolution are of great relevance for industry and society...
Precipitation is an important meteorological indicator that has a direct and significant impact on e...
A range of different statistical downscaling models was calibrated using both observed and general c...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamic...
Global climate change is a major area of concern to public and climate researchers. It impacts flood...
Statistical downscaling methods seek to model the relationship between large scale atmospheric circu...
A new open source neural network temporal downscaling model is described and tested using CRU-NCEP r...
The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climat...
A flexible framework of multi-model of three statistical downscaling approaches was established in w...
AbstractA flexible framework of multi-model of three statistical downscaling approaches was establis...
This research presented a holistic approach for downscaling of precipitation in both space and time ...
This paper describes a novel technique for downscaling daily rainfall which uses a combination of a ...
In light of the success of superresolution (SR) applications in computer vision, recent studies have...
AbstractA new open source neural network temporal downscaling model is described and tested using CR...
This research is focused on the development of statistical downscaling model using neural network te...
Weather forecasts at high spatio-temporal resolution are of great relevance for industry and society...
Precipitation is an important meteorological indicator that has a direct and significant impact on e...
A range of different statistical downscaling models was calibrated using both observed and general c...
Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamic...
Global climate change is a major area of concern to public and climate researchers. It impacts flood...