filter_temperatures

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.

utility

The phenology of plants (i.e. the timing of their annual life
phases) depends on climatic cues. For temperate trees and many other plants,
spring phases, such as leaf emergence and flowering, have been found to result
from the effects of both cool (chilling) conditions and heat. Fruit tree
scientists (pomologists) have developed some metrics to quantify chilling
and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>).
'chillR' contains functions for processing temperature records into
chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
(Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
considered the most promising, but they are difficult to calculate. This package
makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
relating phenological dates (e.g. bloom dates) to either mean temperatures or
mean chill and heat accumulation rates, based on long-term weather and phenology
records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>).
As of version 0.65, it also includes functions for generating weather
scenarios with a weather generator, for conducting climate change analyses
for temperature-based climatic metrics and for plotting results from such
analyses. Since version 0.70, 'chillR' contains a function for interpolating
hourly temperature records.

Eike Luedeling

chillR

Statistical Methods for Phenology Analysis in Temperate Fruit
Trees

Lars Caspersen

Eduardo Fernandez

filter_temperatures function

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

Arguments

Author

Quality filter for temperature records — filter_temperatures

<dl>

<dt>temp_file</dt>
<dd>file containing temperature data. Should have columns
c("Year","Month","Day","Temp" - and "Hour" for hourly data).</dd>


<dt>remove_value</dt>
<dd>numeric value indicating 'no data'.</dd>


<dt>running_mean_filter</dt>
<dd>deviation from a running mean over all temperature
data that identifies a value as an erroneous outlier.</dd>


<dt>running_mean_length</dt>
<dd>number of records to be included in a running mean.</dd>


<dt>min_extreme</dt>
<dd>lowest plausible temperature on the record. All lower ones are removed.</dd>


<dt>max_extreme</dt>
<dd>highest plausible temperature on the record. All higher ones are removed.</dd>


<dt>max_missing_in_window</dt>
<dd>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.</dd>


<dt>missing_window_size</dt>
<dd>size of the window used for checking for missing values.</dd>

</dl>

filter_temperatures: Quality filter for temperature records

Description

Usage

Value

Arguments

Author

Examples