Define the class for multiple sites

Description

Define the class for multiple sites

Slots

num

total number of weather station

records

A pointer vector to weather records of each site

result

A pointer for all results name and type.

Define the class of WeaAna

Description

Define the class of WeaAna

Slots

name

Name of weather station

number

Station number of weather station

latitude

Latitude of weather station

longitude

Latitude of weather station

tav

Annual average ambient temperature

amp

Annual amplitude in mean monthly temperature

marker

The extra marker for this site

year

A vector of year of weather station

day

A vector of day of weather station

radn

A vector of radiation of weather station

maxt

A vector of maximum temperature of weather station

mint

A vector of minimum temperature of weather station

evap

A vector of evaporation of weather station

rain

A vector of rainfall of weather station

vp

A vector of pressure atmosphere of weather station

code

The 6 digit code indicates the source of the 6 data columns

extra

A list of variables need to store

res

All statistics results store in this slot

figures

A list to store all plotted figures.

file.path

The file path for this site.

data.format

The data format for this site.

load.later

Whether are records loaded laterly.

Getter to access the weather data at a specific position.

Description

Getter to access the weather data at a specific position.

Usage

## S4 method for signature 'WeaAna'
x[i, j, drop]

Arguments

Value

A WeaAnaSite object at the position i.

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
records[1]
records[1:2]
records[2:2]

Add relative humidity to APSIM weather data

Description

This function adds relative humidity data to APSIM weather records based on a standard weather file.

Usage

add_rh(apsim, standard, output = NULL, overwrite = FALSE)

Arguments

Value

A data frame containing the new weather records with relative humidity added.

Change weather records

Description

Change weather records

Change weather records

Usage

changeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
changeWeatherRecords(object, ...)

Arguments

Value

A new WeaAna object with updated records

Summarise the climate variable by growth stages

Description

Summarise the climate variable by growth stages

Usage

climate_by_stages(
  climates,
  sowing,
  emergence,
  heading = NULL,
  flowering = NULL,
  maturity,
  latitude
)

Arguments

Details

Define of growth stages

• S0: From start of year to emergence

• S1: From emergence to flowering time - 300Cd

• S2: From flowering time - 300Cd to flowering time + 100Cd

• S3: From flowering time + 100 Cd to flowering time + 600Cd

• S4: From flowering + 600Cd to maturity

Climate variables

• stage: defination of stages

• n: Number of days in each stage

• avgt: average temperature (C)

• sum.tt: total thermal time (Cd) with base temperature 0C

• avg.mint: average minimum temperature

• avg.maxt: average maximum temperature

• sum.rain: total rainfall

• avg.evap: average evapration

• avg.radn average radiation

• hot.days: number of hot days (daily maximum temperature is more than 30C)

• very.hot.days: number of very hot days (daily maximum temperature is more than 35C)

• frost.days: number of frost days (daily minimum temperature is less than 0C)

• hot.sum: total thermal time above 30C of hot days (daily maximum temperature is more than 30C)

• very.hot.sum: total thermal time above 35C of very hot days (daily maximum temperature is more than 35C)

• frost.sum: total thermal time below 0C of frost days (daily minimum temperature is less than 0C)

• vpd: vapour-pressure deficit

• te: transpiration efficiency

• bio.radn: bio.radn

• bio.water: bio.water

• bio.tt: bio.tt

• ptq: photothermal quotient

• avt.diffuse.radn: average diffuse radiation

Value

a data.frame for summarised climate variable by stages. See details for more information.

Examples

if (FALSE) {
   sowing <- rep(as.Date("1981-05-01"), 10)
   emergence <- rep(10, 10)
   heading <- NULL
   flowering <- runif(10) * 20 + 50
   maturity <- runif(10) * 20 + 100
   latitude <- -27
   res <- climate_by_stages(climates = climates, 
                            sowing = sowing,
                            emergence = emergence, 
                            heading = heading,
                            flowering = flowering,
                            maturity = maturity,
                            latitude = latitude)
}

Convert a data frame to weaana class

Description

Convert a data frame to weaana class

Usage

convert2Records(infor, records)

Arguments

Value

A new WeaAna object

create WeaAna class

Description

create WeaAna class

Usage

createWeaAna(mets)

Arguments

Value

A new WeaAna class

The time elapsed in hours between the specified sun angle from 90 degree in am and pm. +ve above the horizon, -ve below the horizon.

Description

The time elapsed in hours between the specified sun angle from 90 degree in am and pm. +ve above the horizon, -ve below the horizon.

Usage

dayLength(doy, lat, angle = -6)

Arguments

Value

day length in hours

Calculate the diurnal variation in air temperature with Parton and Logan, 1981

Description

Calculate the diurnal variation in air temperature. Parton WJ, Logan JA (1981) A model for diurnal variation in soil and air temperature. Agricultural Meteorology, 23, 205?216. Codes copied from APSIM Utilities.cpp

Usage

diurnalT(maxt, mint, doy, hour, latitude, A = 1.5, B = 4, C = 1)

Arguments

Value

A vector with diurnal air temperature

Examples

diurnalT(maxt = 20, mint = 10, doy  = 1, 
   hour = seq(from = 1, to = 23.99, by = 0.1), 
   latitude = -10, A = 1.5, B = 4, C = 1)

Calculate Dew Point Temperature from Minimum Temperature and Relative Humidity

Description

This function estimates the dew point temperature (C) using the Magnus-Tetens approximation, based on the minimum air temperature and relative humidity at that time.

Usage

dp_mint(mint, minrh)

Arguments

Details

The dew point temperature is calculated using the following equations:

\gamma = \frac{a \cdot T}{b + T} + \ln\left(\frac{RH}{100}\right)

T_{dew} = \frac{b \cdot \gamma}{a - \gamma}

where a = 17.62 and b = 243.12.

Value

Numeric. Dew point temperature in degrees Celsius.

Examples

dp_mint(10, 80)  # Expected output: around 6.71

Get all weather records by year range

Description

Get all weather records by year range

Get all weather records by year range

Usage

getWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
getWeatherRecords(object, yrange = NULL, vars = "all", ...)

Arguments

Value

A data frame with all weather records

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
getWeatherRecords( records, yrange = c( 2008, 2009 ) )
getWeatherRecords( records, yrange = c( 2008, 2009 ), length = 10 )

Return a y value from a linear interpolation function

Description

Return a y value from a linear interpolation function

Usage

interpolationFunction(x, y, values, split = "\\s+")

Arguments

Value

The interpolated values

Calculate the moving values

Description

Calculate the moving values

Usage

mov(x, k = 10, shift = "centre", fun = "mean")

Arguments

Value

The moving value of vector x at moving windows k. A NULL will be returned for any unsupported fun

Use Calculate the moving average. For compatibility only.

Description

Note that for n = odd, can average at central period. If n = even, must average at end of period and then shift values

Usage

mov.avg(x, k = 10, shift = "centre")

Arguments

Value

The moving average of vector x at moving windows n

Calculate the moving maximum. For compatibility only.

Description

Calculate the moving maximum. For compatibility only.

Usage

mov.max(x, k, shift = "centre")

Arguments

Value

The moving maximum of vector x at moving windows k

Calculate the moving minimum. For compatibility only.

Description

Calculate the moving minimum. For compatibility only.

Usage

mov.min(x, k, shift = "centre")

Arguments

Value

The moving minimum of vector x at moving windows k

Calculate the moving sum. For compatibility only.

Description

Calculate the moving sum. For compatibility only.

Usage

mov.sum(x, k, shift = "centre")

Arguments

Value

The moving sum of vector x at moving windows k

Read weather records from a file list and/or a folder list

Description

Read weather records from a file list and/or a folder list

Usage

readWeatherRecords(
  dataFiles = NULL,
  dataFolders = NULL,
  dataFormat = "APSIM",
  dataWeather = NULL,
  load.later = FALSE,
  ...
)

Arguments

Value

A WeaAna class which contains all weather data.

Demo weather records

Description

Demo weather records

Usage

records

Format

An object of class WeaAna of length 1.

Define the class for statistics results

Description

Define the class for statistics results

Slots

name

Name of result

type

Type of result

Show basic information of class WeaAna

Description

Show the name, number, latitude, longitude of all weather stations.

Usage

## S4 method for signature 'WeaAna'
show(object)

Arguments

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
show( records )
records

Get site information

Description

Get site information

Get site information

Get site information

Usage

siteInfor(object, ...)

## S4 method for signature 'WeaAna'
siteInfor(object, load.now = FALSE)

## S4 method for signature 'WeaAnaSite'
siteInfor(object, load.now = FALSE)

Arguments

Value

Site information in the WeaAna object

Site information in the WeaAnaSite object

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
siteInfor( records )
siteInfor( records, load.now = TRUE )

Calculate the sphere distance

Description

Calculate the sphere distance

Usage

sphericalDistance(lat1, lon1, lat2, lon2)

Arguments

Value

Distance in km

Calculate thermal time using cardinal temperatures

Description

Calculate thermal time using cardinal temperatures

Usage

thermalTime(weather, x_temp, y_temp, method = NULL)

Arguments

Value

A data.frame with three columns: year, day and thermalTime.

Examples

met_file <- system.file("extdata/WeatherRecordsDemo1.met", package = "weaana")
records <- readWeatherRecords(met_file)
x_temp <- c(0, 26, 34)
y_temp <- c(0, 26, 0)
res <- thermalTime(records, x_temp, y_temp)
head(res)
res <- thermalTime(records, x_temp, y_temp, method = "3hr")
head(res)

Calculate thermal time using cardinal temperatures

Description

Calculate thermal time using cardinal temperatures

Usage

thermalTimeDaily(mint, maxt, x_temp, y_temp, method = NULL)

Arguments

Value

The thermal time.

Examples

mint <- c(0, 10)
maxt <- c(30, 40)
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeDaily(mint, maxt, x_temp, y_temp)
thermalTimeDaily(mint, maxt, x_temp, y_temp, method = '3hr')

Calculate thermal time using the hourly temperature (non daily temperature)

Description

Calculate thermal time using the hourly temperature (non daily temperature)

Usage

thermalTimeHourly(timestamp, temperature, x_temp, y_temp)

Arguments

Value

A data frame with daily thermal time

Examples

met_file <- system.file("extdata/WeatherHourly.csv", package = "weaana")
hourly <- read.csv(met_file, as.is = TRUE) 

hourly$timestamp <- as.POSIXct(hourly$timestamp, format = "%Y-%m-%dT%H:%M:%SZ")
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeHourly(hourly$timestamp, hourly$temperature, x_temp, y_temp)

Hourly Air Temperature Interpolation

Description

Computes hourly estimates of air temperature (Ta) from daily Tmin, Tmax, and day length using the method of Goudriaan (1994). The function assumes a sinusoidal temperature variation during daylight hours and an exponential decay after sunset.

Usage

tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)

Arguments

Details

- **Daytime interpolation**: During sunlight hours, Ta is estimated using a sinusoidal curve fitted between Tmin and Tmax. - **Nighttime interpolation**: After sunset, Ta follows an exponential decline from the sunset temperature to the next morning's Tmin. - **Sunrise and Sunset Calculation**: - Sunrise hour: 'hsrise = 12 - d/2' - Sunset hour: 'hsset = 12 + d/2' - Tmax occurs at hour = 13.5.

If the Controlled Environment module is used for weather data, the function takes hourly values from that instead of computing them.

Value

A numeric vector of length 24 containing the estimated hourly air temperatures (°C).

References

Goudriaan, J. (1994). Advanced Methods for Calculating Radiation Models. Netherlands Journal of Agricultural Science, 42(4), 315-323.

Examples

tmin <- 10
tmax <- 20
tmax_b <- 21
tmin_a <- 11
lat <- -27
doy <- 10
hourly_temps <- tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)

Significantly t-test with auto-correlation for time serial data

Description

Method is presented by Santer et al. 2000

Usage

ttest_ts(y, slope = NULL)

Arguments

Value

p values of t-test

Calculate weather variables through function or a string formula.

Description

There are two modes to use wcal, function mode if FUN is not null, and string formula mode if FUN is NULL.

Usage

wcal(object, ...)

## S4 method for signature 'WeaAna'
wcal(object, FUN = NULL, ..., var.args = NULL, var.name = NULL)

Arguments

Examples

library(weaana)
data( "records" ) 
# Daily mean temperature
wcal( records, avgt2 = "( maxt + mint ) / 2" )
# Moving average temperature
wcal( records, FUN = mov.avg, var.args = "avgt", k = 5, shift = "begin", var.name = "mov.avg" )

Write weather records into file

Description

Write weather records into file

Write weather records into file

Usage

writeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
writeWeatherRecords(object, file, cols = NULL)

Arguments

Value

No return values