fun_cwt_J

Function: Total number of decomposition levels

The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition.

Ze Jiang

Wavelet-Based Quantile Mapping for Postprocessing Numerical
Weather Predictions

Fiona Johnson

fun_cwt_J function

<dl><dt>n</dt>
<dd>sample size.</dd>
<dt>dt</dt>
<dd>sampling resolution in the time domain.</dd>
<dt>dj</dt>
<dd>sampling resolution in the frequency domain.</dd></dl>

Arguments

Function: Total number of decomposition levels — fun_cwt_J

<dl>

<dt>n</dt>
<dd>sample size.</dd>


<dt>dt</dt>
<dd>sampling resolution in the time domain.</dd>


<dt>dj</dt>
<dd>sampling resolution in the frequency domain.</dd>

</dl>

fun_cwt_J: Function: Total number of decomposition levels

Description

Usage

Value

Arguments