| Type: | Package |
| Title: | Data Processing of SMN Hi-Res Weather Forecast from 'AWS' |
| Version: | 0.0.5 |
| Description: | Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services (https://registry.opendata.aws/smn-ar-wrf-dataset/) cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of 'WRF' forecast. |
| License: | GPL (≥ 3) |
| Depends: | R (≥ 4.1.0) |
| Imports: | aws.s3 (≥ 0.3.21), lubridate (≥ 1.9.3), terra (≥ 1.7-65), dplyr (≥ 1.1.4), ggplot2 (≥ 3.4.4), hydroGOF (≥ 0.5-4), stats (≥ 4.1.2), magrittr (≥ 2.0.3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-02-06 22:57:00 UTC; gonzalo |
| Author: | Gonzalo Diaz [cre, aut] |
| Maintainer: | Gonzalo Diaz <gonzalomartindiaz22@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-02-06 23:30:02 UTC |
Daily data to monthly
Description
Data transformation from daily to monthly scale
Usage
daily2monthly(data = data)
Arguments
data |
matrix with daily data from mg.evaluation output function |
Value
Data frame with monthly data
Daily data is obtained from hourly data
Description
....
Usage
daily.data.fields(raster.list, aggregate)
Arguments
raster.list |
Spat Raster variable with several times for a unique variable (T2 or HR2 or ...) |
aggregate |
Type of aggregation (sum, mean, min, max) |
Value
Spat Raster with daily information
Evaporation data (observation and model)
Description
Data of evaporation from in-situ observation and several soil model outputs
Usage
data(eva)
Format
An object of class "data.frame".
- Dates
1st column with dates
- evapo_obs
2nd column with evaporation observation
- OUT_PREC
Precipitation
- OUT_EVAP
Evaporation
- OUT_RUNOFF
Runoff
- OUT_BASEFLOW
Baseflow
- OUT_SOIL_MOIST_lyr_1
Soil moisture from 1st layer
- OUT_EVAP_CANOP
Evaporation from canopy
- OUT_SURF_TEMP
Surface temperature
References
Diaz et al. (2024) AAGG 2024 Not yet published
Examples
data(eva)
Temporal series of closest location
Description
Location of nearest point to lon/lat and temporal serie of location
Usage
find.nearest.point(data.spat.raster = data.spat.raster, lon = lon, lat = lat)
Arguments
data.spat.raster |
Spat Raster of WRF SMN (only one or several) |
lon |
Longitude location of nearest point to find |
lat |
Latitude location of nearest point to find |
Value
a vector with the nearest location (lon/lat) and time serie of that location
List of available files for downloading
Description
Character string with the filenames of WRF SMN located in AWS Bucket
Usage
get.wrf.files(year = year, month = month, day = day, cycle = cycle, time = time)
Arguments
year |
character or numeric of year |
month |
character or numeric of month |
day |
character or numeric of day |
cycle |
cycle of forecast, "00", "06", "12" or "18" |
time |
selection of datasets, "01H", "24H" or "10M" |
Value
string of the list of elements in the Bucket
Calculation of ITH index
Description
ITH index calculation is made from gridded observational or model data. If the data is needed in lat/lon projection is better to use first the load.by.variable function to change projection
The index is calculated as:
ITH = 1.8 * T(ºC) + 32 - (((0.55 - (0.55 * RelHum(\%))) / 100) * ((1.8 * T(ºC)) - 26))
where T(ªC) is the temperature in celsius degrees and RelHum(%) is the relative humidity in percentage
Usage
ith(raster.list = raster.list)
Arguments
raster.list |
Spat Raster variable with several variables and times or only one Spat Raster field |
Value
Spat Raster with ITH calculation for each time
Load and projection of data
Description
Open of netcdf files of WRF SMN from AWS and optional projection
Usage
load.by.variable(nc.filenames, variable, transform, method)
Arguments
nc.filenames |
netcdf files |
variable |
name of variable from https://odp-aws-smn.github.io/documentation_wrf_det/Formato_de_datos/ as character |
transform |
TRUE to project data to longlat datum=WGS84 |
method |
if transform is set TRUE define projection method taken from project function of terra package |
Value
Spat Raster with the chosen variable (optional: with the projection changed)
Evaluation of regression
Description
Evaluation of the linear multiple regression obtained from the multiple.guidance function
Usage
mg.evaluation(
input.data = input.data,
predictand = predictand,
predictors = predictors,
var.model = var.model,
lmodel = lmodel
)
Arguments
input.data |
Data frame with first column as a "POSIXct" class and one or more columns with data values. The predictand and predictors variables should be located in these columns |
predictand |
Character with column name of the predictand variable |
predictors |
Character array with one or more elements of the predictors chosen by the user |
var.model |
Character with column name of the modeled predicting variable |
lmodel |
Element of class lm obtained from multiple.guidance output function |
Value
List of two elements. First element is a matrix with the columns of observed data, modeled data and corrected data. Second element is a data frame of the statistical results of the modeled and corrected data versus observed data
Multiple lineal regression of data
Description
Definition of linear multiple regression adjustment based on predictor variables that fit a predicting variable
Usage
multiple.guidance(
input.data = input.data,
predictand = predictand,
predictors = predictors
)
Arguments
input.data |
Data frame with first column as a "POSIXct" class and one or more columns with data values. The predictand and predictors variables should be located in these columns |
predictand |
Character with column name of the predictand variable |
predictors |
Character array with one or more elements of the predictors chosen by the user |
Value
an element of class lm
Plot of data
Description
Plot of observed, modeled and corrected guidance series
Usage
ploting(data = data)
Arguments
data |
Data frame from daily2monthly output function or any other temporal series |
Value
ggplot element
Download of wrf files
Description
Download of WRF SMN data from AWS
Usage
wrf.download(wrf.name = wrf.name)
Arguments
wrf.name |
list of names to download from get.wrf.files. e.g.: "DATA/WRF/DET/2024/01/01/18/WRFDETAR_24H_20240101_18_000.nc" |
Value
downloaded netcdf files