seasonalAR

Generates a seasonal Gaussian AR process over a vector of dates, one season
at a time, using precomputed ACS-derived AR coefficients.

internal

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

Kevin Shook

CoSMoS

Complete Stochastic Modelling Solution

Simon Michael Papalexiou

Francesco Serinaldi

Filip Strnad

Yannis Markonis

seasonalAR function

<dl><dt>x</dt>
<dd>POSIXct vector of dates for which to generate the process</dd>
<dt>ACS</dt>
<dd>named list of ACS vectors (one per season, starting at lag 0)</dd>
<dt>season</dt>
<dd>character; name of a date-component function (default
<code>"month"</code>)</dd></dl>

Arguments

Seasonal AR model — seasonalAR

<dl>

<dt>x</dt>
<dd>POSIXct vector of dates for which to generate the process</dd>


<dt>ACS</dt>
<dd>named list of ACS vectors (one per season, starting at lag 0)</dd>


<dt>season</dt>
<dd>character; name of a date-component function (default
<code>"month"</code>)</dd>

</dl>

seasonalAR: Seasonal AR model

Description

Usage

Value

Arguments

See Also