precip

Station details<ul>
<li>Name: Philadelphia International Airport</li>
<li>Network ID: COOP:366889</li>
<li>Latitude/Longitude:	39.87327°, -75.22678°</li>
<li>Elevation: 3m</li>
</ul>

datasets

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

precip function

A data.table with 79633 rows and 2 variables:<dl>
<dt>date</dt>
<dd>POSIXct format date/time</dd><dt>value</dt>
<dd>precipitation totals</dd>
</dl>

Format

Hourly station precipitation data — precip

A data.table with 79633 rows and 2 variables:<dl>
<dt>date</dt>
<dd>POSIXct format date/time</dd>

<dt>value</dt>
<dd>precipitation totals</dd>


</dl>

precip: Hourly station precipitation data

Description

Usage

Arguments

Format

Details