chillR (version 0.75)

patch_daily_temperatures: Patch gaps in daily weather records

Description

This function is deprecated. Better use patch_daily_temps!

Usage

patch_daily_temperatures(
  weather,
  patch_weather,
  vars = c("Tmin", "Tmax"),
  max_mean_bias = NA,
  max_stdev_bias = NA
)

Value

list of two elements: weather (the patched weather record, with additional columns specifying the data source for each value) and statistics (containing data.frames for each element of patch_weather that indicate the mean bias, the number of values that were filled from this source and the number of missing records that remained after exhausting this auxiliary data source.)

Arguments

weather

chillR-compatible weather record to be patched

patch_weather

list of chillR-compatible weather records to be used for patching holes in weather. They are used sequentially, until all have been used or until there are no holes left.

vars

vector of column names to be considered in patching. Defaults to c("Tmin","Tmax"), the most common variables in chillR applications.

max_mean_bias

maximum mean bias of auxiliary data compared to the original dataset (applied to all variables in vars). If this threshold is exceeded, the respective variable from that particular dataset will not be used. Defaults to NA, meaning no records are excluded.

max_stdev_bias

maximum standard deviation of the bias in the auxiliary data compared to the original dataset (applied to all variables in vars). If this threshold is exceeded, the respective variable from that particular dataset will not be used. Defaults to NA, meaning no records are excluded.

Author

Eike Luedeling

Details

This function uses auxiliary data sources to fill gaps in daily weather data. It can accommodate multiple sources of auxiliary information, which are used in the user-specified sequence. There have to be some overlapping records for this to work, because without bias correction, this procedure could produce erroneous records. Bias correction is done by computing the mean difference between main and auxiliary data for each variable and adjusting for it in filling the gaps. You can specify a maximum mean bias and a maximum standard deviation of the bias to exclude unsuitable records that aren't similar enough to the original data.

Examples

gap_weather<-KA_weather[1:100,]
gap_weather[c(3,4,7:15,20,22:25,27:28,35:45,55,67,70:75,80:88,95:97),"Tmin"]<-NA
gap_weather[c(10:25,30,36:44,50,57,65,70:80,86,91:94),"Tmax"]<-NA
p1<-KA_weather[65:95,]
p1$Tmin<-p1$Tmin-2
p2<-KA_weather[c(15:40,60:80),]
p2$Tmax<-p2$Tmax+3
p3<-KA_weather[12:35,]
p3$Tmax<-p3$Tmax-2
p4<-KA_weather
p4$Tmax<-p4$Tmax+0.5
patch_weather<-list(stat1=p1,st2=p2,home=p3,last=p4)

patched<-patch_daily_temperatures(gap_weather,patch_weather,max_mean_bias=1)