libraries used

# data input
library(readr)
library(readxl)

# data organization
library(tidyr)
library(dplyr)
library(stringi)
library(magrittr)
library(lubridate)
library(zoo)

# website organization
library(htmltools)
library(htmlwidgets)

# plotting
library(ggformula)
library(gridExtra)
library(RColorBrewer)

Introduction

Description

Motivated by empirical observations from myself and other runners, as well as a Reddit post:
“What is up with this summer?”

“Maybe it’s recency bias, maybe it’s short term memory but I’ve lived here for 30 years and I can’t remember a summer this freaking humid. Every day feels like high 80s with 70-80% humidity making outside just feel muggy and nasty. I know Wisconsin has hot summers but I remember a dry heat breezes etc not this swamp. I legit feel like iam in the south half the time with how i step straight into humidity the minute my door opens. Has anyone else noticed this?”

Goals:

Examine daily (min, max, average) wet-bulb temperature across last ~50 years.

Data Setup

Source

Data are pulled from the NOAA’s Local Climatological Data, from January 1985 - July 2024. Measured at the weather station at Chippewa Valley Regional Airport.

Read in the data

Climate_1985_1994 <- read.csv("ChipAirport1985to1994.csv")
Climate_1995_2004 <- read.csv("ChipAirport1995to2004.csv")
Climate_2005_2014 <- read.csv("ChipAirport2005to2014.csv")
Climate_2015_2024 <- read.csv("ChipAirport2015to2024.csv")

Combine the data

LocalClimate_40years = rbind(Climate_1985_1994,
                             Climate_1995_2004,
                             Climate_2005_2014,
                             Climate_2015_2024)
dim(LocalClimate_40years)

## [1] 470772    124

nhours <- dim(LocalClimate_40years)[1]

Data Cleaning

Data Review and Spot-Checks

Inital Plots:

head(select(LocalClimate_40years, 
            DATE, DailyAverageWetBulbTemperature, HourlyWetBulbTemperature))

##                  DATE DailyAverageWetBulbTemperature HourlyWetBulbTemperature
## 1 1985-01-01T00:00:00                             NA                       12
## 2 1985-01-01T01:00:00                             NA                       11
## 3 1985-01-01T02:00:00                             NA                       11
## 4 1985-01-01T03:00:00                             NA                       11
## 5 1985-01-01T04:00:00                             NA                       11
## 6 1985-01-01T05:00:00                             NA                       11

plot( LocalClimate_40years$HourlyWetBulbTemperature , type="l")

## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

plot( LocalClimate_40years$HourlyRelativeHumidity , type="l")

## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion

There is a lot of missing wet bulb temp information. Let’s try a direct calculation:

Td_C = 20; RH = 50
Td_C*atan(0.151977*sqrt(RH+8.313659) ) + atan(Td_C + RH) - atan(RH - 1.676331) + 0.00391838*(RH^(3/2))*atan(0.023101*RH) - 4.686035

## [1] 13.69934

LocalClimate_40years <- mutate(LocalClimate_40years, Td_F = as.numeric(HourlyDryBulbTemperature),
                               Td_C = round((Td_F-32)*(5/9),2),
                               RH = as.numeric(HourlyRelativeHumidity),
                               Tw_C = round(Td_C*atan(0.151977*sqrt(RH+8.313659) ) + atan(Td_C + RH) - atan(RH - 1.676331) + 0.00391838*(RH^(3/2))*atan(0.023101*RH) - 4.686035,2),
                               Tw_F = (9/5)*Tw_C + 32, 
                               T_dewp_F = as.numeric(HourlyDewPointTemperature),
                               T_dewp_C = round((T_dewp_F-32)*(5/9),2))

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `Td_F = as.numeric(HourlyDryBulbTemperature)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

gf_point(Tw_F ~ as.numeric(HourlyWetBulbTemperature), data=LocalClimate_40years)

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## Warning: Removed 117571 rows containing missing values or values outside the scale range
## (`geom_point()`).

colSums(is.na(LocalClimate_40years[,c(125:131)]))

##     Td_F     Td_C       RH     Tw_C     Tw_F T_dewp_F T_dewp_C 
##    53945    53945    54048    54101    54101    54086    54086

And voila, we have managed to regain about half the missing data via direct computation!

Format and Organize Dates

splitdate = strsplit(LocalClimate_40years$DATE, "T")
matrixdate = matrix(unlist(splitdate), ncol = 2, byrow = TRUE)

LocalClimate_40years <- mutate(LocalClimate_40years,
                               DateUsable=as.Date(format(as.Date(matrixdate[,1]), "%Y-%m-%d")),
                               DateFormatted=format(as.Date(matrixdate[,1]), format = "%B %d %Y"),
                               Time=hms(matrixdate[,2])
)

LocalClimate_40years$DateUsable[1:10]

##  [1] "1985-01-01" "1985-01-01" "1985-01-01" "1985-01-01" "1985-01-01"
##  [6] "1985-01-01" "1985-01-01" "1985-01-01" "1985-01-01" "1985-01-01"

LocalClimate_40years$DateFormatted[1:10]

##  [1] "January 01 1985" "January 01 1985" "January 01 1985" "January 01 1985"
##  [5] "January 01 1985" "January 01 1985" "January 01 1985" "January 01 1985"
##  [9] "January 01 1985" "January 01 1985"

Summarize Variables by Day

LocalHumidity_40years <- select(LocalClimate_40years,125:134)
length(unique(LocalHumidity_40years$DateUsable))

## [1] 14455

names(LocalHumidity_40years)

##  [1] "Td_F"          "Td_C"          "RH"            "Tw_C"         
##  [5] "Tw_F"          "T_dewp_F"      "T_dewp_C"      "DateUsable"   
##  [9] "DateFormatted" "Time"

LocalHumidity_Daily <- group_by(LocalHumidity_40years, DateUsable) |>
  summarize(meanTd = mean(Td_F, na.rm=T), maxTd = max(Td_F, na.rm=T), minTd = min(Td_F, na.rm=T),
            meanTw = mean(Tw_F, na.rm=T), maxTw = max(Tw_F, na.rm=T), minTw = min(Tw_F, na.rm=T),
            meanRH = mean(RH, na.rm=T), maxRH = max(RH, na.rm=T), minRH = min(RH, na.rm=T)
            ) |>
  mutate(meanTd7day = round(rollmean(meanTd, 7, fill=NA,align="center"),1),
         maxTd7day = round(rollmean(maxTd, 7, fill=NA,align="center"),1),
         minTd7day = round(rollmean(minTd, 7, fill=NA,align="center"),1),
         meanTw7day = round(rollmean(meanTw, 7, fill=NA,align="center"),1),
         maxTw7day = round(rollmean(maxTw, 7, fill=NA,align="center"),1),
         minTw7day = round(rollmean(minTw, 7, fill=NA,align="center"),1),
         meanRH7day = round(rollmean(meanRH, 7, fill=NA,align="center"),1),
         maxRH7day = round(rollmean(maxRH, 7, fill=NA,align="center"),1),
         minRH7day = round(rollmean(minRH, 7, fill=NA,align="center"),1),
         meanTd28day = round(rollmean(meanTd, 28, fill=NA,align="center"),1),
         maxTd28day = round(rollmean(maxTd, 28, fill=NA,align="center"),1),
         minTd28day = round(rollmean(minTd, 28, fill=NA,align="center"),1),
         meanTw28day = round(rollmean(meanTw, 28, fill=NA,align="center"),1),
         maxTw28day = round(rollmean(maxTw, 28, fill=NA,align="center"),1),
         minTw28day = round(rollmean(minTw, 28, fill=NA,align="center"),1),
         meanRH28day = round(rollmean(meanRH, 28, fill=NA,align="center"),1),
         maxRH28day = round(rollmean(maxRH, 28, fill=NA,align="center"),1),
         minRH28day = round(rollmean(minRH, 28, fill=NA,align="center"),1),
         DateFormatted=format(DateUsable, format = "%B %d %Y")) |>
  arrange(DateUsable)

head(LocalHumidity_Daily)

## # A tibble: 6 × 29
##   DateUsable meanTd maxTd minTd meanTw maxTw  minTw meanRH maxRH minRH
##   <date>      <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
## 1 1985-01-01   9.71    15    -3   7.32 12.4   -4.68   74.4    88    56
## 2 1985-01-02  -7.04     4   -14  -8.19  1.81 -13.9    58.8    75    37
## 3 1985-01-03   3.71    25   -15   1.36 19.9  -15.8    65.4    83    51
## 4 1985-01-04  23.9     36    11  20.8  31.6    9.46   77.0    92    61
## 5 1985-01-05  28.9     35    23  26.2  31.3   20.9    81.4    96    70
## 6 1985-01-06  26.6     30    17  25.0  28.2   15.3    89.9    96    81
## # ℹ 19 more variables: meanTd7day <dbl>, maxTd7day <dbl>, minTd7day <dbl>,
## #   meanTw7day <dbl>, maxTw7day <dbl>, minTw7day <dbl>, meanRH7day <dbl>,
## #   maxRH7day <dbl>, minRH7day <dbl>, meanTd28day <dbl>, maxTd28day <dbl>,
## #   minTd28day <dbl>, meanTw28day <dbl>, maxTw28day <dbl>, minTw28day <dbl>,
## #   meanRH28day <dbl>, maxRH28day <dbl>, minRH28day <dbl>, DateFormatted <chr>

Plots of Daily Temperatures (in Fahrenheit) across Date

Daily

7-day Running Average

## Warning: Removed 18 rows containing missing values or values outside the scale range
## (`geom_line()`).

28-day Running Average

## Warning: Removed 81 rows containing missing values or values outside the scale range
## (`geom_line()`).

Plots of Daily Wet-Bulb Temperatures (in Fahrenheit) across Date

Daily

7-day Running Average

## Warning: Removed 18 rows containing missing values or values outside the scale range
## (`geom_line()`).

28-day Running Average

## Warning: Removed 81 rows containing missing values or values outside the scale range
## (`geom_line()`).

Plots of Daily Relative Humidity (in %) across Date

Daily

7-day Running Average

## Warning: Removed 18 rows containing missing values or values outside the scale range
## (`geom_line()`).

28-day Running Average

## Warning: Removed 81 rows containing missing values or values outside the scale range
## (`geom_line()`).

Historic Climate data, local to Eau Claire

Jessica Kraker

created and data pulled Jul 2024; code updated Sep 2024; refreshed on March 01, 2025

libraries used

Introduction

Description

Goals:

Data Setup

Source

Read in the data

Combine the data

Data Cleaning

Data Review and Spot-Checks

Format and Organize Dates

Summarize Variables by Day

Plots of Daily Temperatures (in Fahrenheit) across Date

Daily

7-day Running Average

28-day Running Average

Plots of Daily Wet-Bulb Temperatures (in Fahrenheit) across Date

Daily

7-day Running Average

28-day Running Average

Plots of Daily Relative Humidity (in %) across Date

Daily

7-day Running Average

28-day Running Average