Package 'aws.wrfsmn'

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

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