filter_temperatures: Quality filter for temperature records

In chillR: Statistical Methods for Phenology Analysis in Temperate Fruit Trees

Quality filter for temperature records

Description

This function attempts to remove erroneous temperature readings. This is tricky because of the wide range of errors that can occur, so this isn't necessarily sufficient for problems of particular records.

Usage

filter_temperatures(
  temp_file,
  remove_value = NA,
  running_mean_filter = NA,
  running_mean_length = 3,
  min_extreme = NA,
  max_extreme = NA,
  max_missing_in_window = 1,
  missing_window_size = 9
)

Arguments

temp_file	file containing temperature data. Should have columns c("Year","Month","Day","Temp" - and "Hour" for hourly data).
remove_value	numeric value indicating 'no data'.
running_mean_filter	deviation from a running mean over all temperature data that identifies a value as an erroneous outlier.
running_mean_length	number of records to be included in a running mean.
min_extreme	lowest plausible temperature on the record. All lower ones are removed.
max_extreme	highest plausible temperature on the record. All higher ones are removed.
max_missing_in_window	maximum share of values (0..1) in a running window of size missing_window_size around each value that can be missing. If this is exceeded, the value is removed.
missing_window_size	size of the window used for checking for missing values.

Value

filtered temperature dataset, from which records identified as erroneous were removed.

Author(s)

Eike Luedeling

Examples

weather<-fix_weather(KA_weather[which(KA_weather$Year>2009),])

hourtemps<-stack_hourly_temps(weather, latitude=50.4)

filtered<-filter_temperatures(hourtemps$hourtemps,remove_value=-99,
  running_mean_filter=3)

chillR documentation built on Nov. 28, 2023, 1:09 a.m.