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The point rainfall model presented extends previous work on event-based rainfall models and overcomes some of their shortcomings.
To provide a stochastic rainfall model with an accurate parameterization, frontal rain systems over Belgium Western Europee) are analyzed.
Thus, to delineate and examine the role of the Indian Ocean warming on the monsoon circulation and rainfall, model sensitivity experiments using an ocean-atmosphere coupled climate model were carried out.
In this daily rainfall model, extremes above a threshold are distributed exponentially, for each season and atmospheric circulation pattern.
This study combines a new synthetic rainfall model and two independently-validated crop models (APSIM and SARRA-H) to assess sorghum yield response to possible shifts in seasonal rainfall characteristics in West Africa.
The model is driven by rainfall inputs generated by a stochastic rainfall model that is able to capture rainfall variability at a wide range of time scales.
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Indeed, rainfall modeling shows that rising temperatures and more arid conditions over the last few decades are likely to increase in the 21st century.
The findings of this study have implications for raingauge network design, rainfall modeling, and conclusive evaluation of radar and satellite estimates of rainfall.
Large scale rainfall models are needed for collective risk estimation in flood insurance, infrastructure networks and water resource management applications.
Logistic-regression modelling of distributions and similarity in projections produced by temperature and rainfall models suggest that changes in both variables will be important.
Recently artificial neural network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall modeling as well as other fields of hydrology.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com