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Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions.
The biggest change is the introduction of a new unified timeline that combines both the precipitation predictions Dark Sky is best known for, and its weather forecast all on one page.
Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions.
Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD.
The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs).
Precipitation and soil moisture are more difficult to model than temperature due to the complex effects of topography, and vary more widely in precipitation predictions than for temperatures (IPCC 2007b).
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A hybrid WANN model is provided for the precipitation prediction of Ligvan Chai (Tabriz, Iran) watershed.
However, estimated values using these correlations can differ considerably, potentially having a negative effect on the reliability of wax precipitation prediction.
Ingsrisawang et al. (2008) compared several machine learning algorithms, such as decision trees (DT), neural networks (ANN), and SVMs for short-term precipitation prediction in Thailand.
The proposed asphaltene precipitation prediction model may be combined with existing asphaltene precipitation modeling softwares to speed up their performance, reduce the uncertainty and increase their prediction and modeling capabilities.
Analogue methods provide a statistical precipitation prediction based on synoptic predictors supplied by general circulation models or numerical weather prediction models.
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CEO of Professional Science Editing for Scientists @ prosciediting.com