Yesterday, the Baltimore Orioles and Chicago White Sox played a game at Camden
Yards in downtown Baltimore. The game was “closed to fans” due to the riots
that broke out in the city after the funeral for a man who died in police
custody. It’s the first time a Major League Baseball game has been played
without *any* fans in the stands, but unfortunately it’s not the first time
there have been riots in Baltimore.

After Martin Luther King, Jr. was murdered in April 1968, Baltimore rioted for six days, with local police, and more than eleven thousand National Guard, Army troops, and Marines brought in to restore order. According to wikipedia six people died, more than 700 were injured, 1,000 businesses were damaged and close to six thousand people were arrested.

At that time, the Orioles played in Memorial Stadium, about 4 miles north-northwest of where they play now. I don’t know much about that area of Baltimore, but I was curious to know whether the Orioles played any baseball games during those riots.

Retrosheet has one game, on April 10, 1968, with a reported attendance of 22,050. The Orioles defeated the Oakland Athletics by a score of 3–1. Thomas Phoebus got the win over future Hall of Famer Catfish Hunter. Other popular players in the game included Reggie Jackson, Sal Bando, Rick Mondy and Bert Campaneris for the A’s and Brooks Robinson, Frank Robinson, Davey Johnson, and Boog Powell for the Orioles.

The box score and play-by-play can be viewed here.

# Introduction

One of the best sources of weather data in the United States comes from the National Weather Service's Cooperative Observer Network (COOP), which is available from NCDC. It's daily data, collected by volunteers at more than 10,000 locations. We participate in this program at our house (station id DW1454 / GHCND:USC00503368), collecting daily minimum and maximum temperature, liquid precipitation, snowfall and snow depth. We also collect river heights for Goldstream Creek as part of the Alaska Pacific River Forecast Center (station GSCA2). Traditionally, daily temperature measurements were collecting using a minimum maximum thermometer, which meant that the only way to calculate average daily temperature was by averaging the minimum and maximum temperature. Even though COOP observers typically have an electronic instrument that could calculate average daily temperature from continuous observations, the daily minimum and maximum data is still what is reported.

In an earlier post we looked at methods used to calculate average daily temperature, and if there are any biases present in the way the National Weather Service calculates this using the average of the minimum and maximum daily temperature. We looked at five years of data collected at my house every five minutes, comparing the average of these temperatures against the average of the daily minimum and maximum. Here, we will be repeating this analysis using data from the Climate Reference Network stations in the United States.

The US Climate Reference Network is a collection of 132 weather stations that are properly sited, maintained, and include multiple redundant measures of temperature and precipitation. Data is available from http://www1.ncdc.noaa.gov/pub/data/uscrn/products/ and includes monthly, daily, and hourly statistics, and sub-hourly (5-minute) observations. We’ll be focusing on the sub-hourly data, since it closely matches the data collected at my weather station.

A similar analysis using daily and hourly CRN data appears here.

# Getting the raw data

I downloaded all the data using the following Unix commands:

```
$ wget http://www1.ncdc.noaa.gov/pub/data/uscrn/products/stations.tsv
$ wget -np -m http://www1.ncdc.noaa.gov/pub/data/uscrn/products/subhourly01/
$ find www1.ncdc.noaa.gov/ -type f -name 'CRN*.txt' -exec gzip {} \;
```

The code to insert all of this data into a database can be found
here.
Once inserted, I have a table named `crn_stations` that has the
station data, and one named `crn_subhourly` with the five minute
observation data.

# Methods

Once again, we’ll use R to read the data, process it, and produce plots.

## Libraries

Load the libraries we need:

```
library(dplyr)
library(lubridate)
library(ggplot2)
library(scales)
library(grid)
```

Connect to the database and load the data tables.

```
noaa_db <- src_postgres(dbname="noaa", host="mason")
crn_stations <- tbl(noaa_db, "crn_stations") %>%
collect()
crn_subhourly <- tbl(noaa_db, "crn_subhourly")
```

Remove observations without temperature data, group by station and date, calculate average daily temperature using the two methods, remove any daily data without a full set of data, and collect the results into an R data frame. This looks very similar to the code used to analyze the data from my weather station.

```
crn_daily <-
crn_subhourly %>%
filter(!is.na(air_temperature)) %>%
mutate(date=date(timestamp)) %>%
group_by(wbanno, date) %>%
summarize(t_mean=mean(air_temperature),
t_minmax_avg=(min(air_temperature)+
max(air_temperature))/2.0,
n=n()) %>%
filter(n==24*12) %>%
mutate(anomaly=t_minmax_avg-t_mean) %>%
select(wbanno, date, t_mean, t_minmax_avg, anomaly) %>%
collect()
```

The two types of daily average temperatures are calculated in this step:

```
summarize(t_mean=mean(air_temperature),
t_minmax_avg=(min(air_temperature)+
max(air_temperature))/2.0)
```

Where `t_mean` is the value calculated from all 288 five minute
observations, and `t_minmax_avg` is the value from the daily minimum
and maximum.

Now we join the observation data with the station data. This attaches station information such as the name and latitude of the station to each record.

```
crn_daily_stations <-
crn_daily %>%
inner_join(crn_stations, by="wbanno") %>%
select(wbanno, date, state, location, latitude, longitude,
t_mean, t_minmax_avg, anomaly)
```

Finally, save the data so we don’t have to do these steps again.

```
save(crn_daily_stations, file="crn_daily_averages.rdata")
```

# Results

Here are the overall results of the analysis.

```
summary(crn_daily_stations$anomaly)
```

## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -11.9000 -0.1028 0.4441 0.4641 1.0190 10.7900

The average anomaly across all stations and all dates is 0.44 degrees Celsius (0.79 degrees Farenheit). That’s a pretty significant error. Half the data is between −0.1 and 1.0°C (−0.23 and +1.8°F) and the full range is −11.9 to +10.8°C (−21.4 to +19.4°F).

# Plots

Let’s look at some plots.

## Raw data by latitude

To start, we’ll look at all the anomalies by station latitude. The plot only shows one percent of the actual anomalies because plotting 512,460 points would take a long time and the general pattern is clear from the reduced data set.

```
set.seed(43)
p <- ggplot(data=crn_daily_stations %>% sample_frac(0.01),
aes(x=latitude, y=anomaly)) +
geom_point(position="jitter", alpha="0.2") +
geom_smooth(method="lm", se=FALSE) +
theme_bw() +
scale_x_continuous(name="Station latitude", breaks=pretty_breaks(n=10)) +
scale_y_continuous(name="Temperature anomaly (degrees C)",
breaks=pretty_breaks(n=10))
print(p)
```

The clouds of points show the differences between the min/max daily average and the actual daily average temperature, where numbers above zero represent cases where the min/max calculation overestimates daily average temperature. The blue line is the fit of a linear model relating latitude with temperature anomaly. We can see that the anomaly is always positive, averaging around half a degree at lower latitudes and drops somewhat as we proceed northward. You also get a sense from the actual data of how variable the anomaly is, and at what latitudes most of the stations are found.

Here are the regression results:

```
summary(lm(anomaly ~ latitude, data=crn_daily_stations))
```

## ## Call: ## lm(formula = anomaly ~ latitude, data = crn_daily_stations) ## ## Residuals: ## Min 1Q Median 3Q Max ## -12.3738 -0.5625 -0.0199 0.5499 10.3485 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.7403021 0.0070381 105.19 <2e-16 *** ## latitude -0.0071276 0.0001783 -39.98 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.9632 on 512458 degrees of freedom ## Multiple R-squared: 0.00311, Adjusted R-squared: 0.003108 ## F-statistic: 1599 on 1 and 512458 DF, p-value: < 2.2e-16

The overall model and coefficients are highly significant, and show a slight decrease in the positive anomaly as we move farther north. Perhaps this is part of the reason why the analysis of my station (at a latitude of 64.89) showed an average anomaly close to zero (−0.07°C / −0.13°F).

## Anomalies by month and latitude

One of the results of our earlier analysis was a seasonal pattern in the anomalies at our station. Since we also know there is a latitudinal pattern, in the data, let’s combine the two, plotting anomaly by month, and faceting by latitude.

Station latitude are binned into groups for plotting, and the plots themselves show the range that cover half of all anomalies for that latitude category × month. Including the full range of anomalies in each group tends to obscure the overall pattern, and the plot of the raw data didn’t show an obvious skew to the rarer anomalies.

Here’s how we set up the data frames for the plot.

```
crn_daily_by_month <-
crn_daily_stations %>%
mutate(month=month(date),
lat_bin=factor(ifelse(latitude<30, '<30',
ifelse(latitude>60, '>60',
paste(floor(latitude/10)*10,
(floor(latitude/10)+1)*10,
sep='-'))),
levels=c('<30', '30-40', '40-50',
'50-60', '>60')))
summary_stats <- function(l) {
s <- summary(l)
data.frame(min=s['Min.'],
first=s['1st Qu.'],
median=s['Median'],
mean=s['Mean'],
third=s['3rd Qu.'],
max=s['Max.'])
}
crn_by_month_lat_bin <-
crn_daily_by_month %>%
group_by(month, lat_bin) %>%
do(summary_stats(.$anomaly)) %>%
ungroup()
station_years <-
crn_daily_by_month %>%
mutate(year=year(date)) %>%
group_by(wbanno, lat_bin) %>%
summarize() %>%
group_by(lat_bin) %>%
summarize(station_years=n())
```

And the plot itself. At the end, we’re using a function called
`facet_adjust`, which adds x-axis tick labels to the facet on the
right that wouldn't ordinarily have them. The code comes from this
stack overflow
post.

```
p <- ggplot(data=crn_by_month_lat_bin,
aes(x=month, ymin=first, ymax=third, y=mean)) +
geom_hline(yintercept=0, alpha=0.2) +
geom_hline(data=crn_by_month_lat_bin %>%
group_by(lat_bin) %>%
summarize(mean=mean(mean)),
aes(yintercept=mean), colour="darkorange", alpha=0.5) +
geom_pointrange() +
facet_wrap(~ lat_bin, ncol=3) +
geom_text(data=station_years, size=4,
aes(x=2.25, y=-0.5, ymin=0, ymax=0,
label=paste('n =', station_years))) +
scale_y_continuous(name="Range including 50% of temperature anomalies") +
scale_x_discrete(breaks=1:12,
labels=c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')) +
theme_bw() +
theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1.25),
axis.title.x=element_blank())
facet_adjust(p)
```

Each plot shows the range of anomalies from the first to the third quartile (50% of the observed anomalies) by month, with the dot near the middle of the line at the mean anomaly. The orange horizontal line shows the overall mean anomaly for that latitude category, and the count at the bottom of the plot indicates the number of “station years” for that latitude category.

It’s clear that there are seasonal patterns in the differences between the mean daily temperature and the min/max estimate. But each plot looks so different from the next that it’s not clear if the patterns we are seeing in each latitude category are real or artificial. It is also problematic that three of our latitude categories have very little data compared with the other two. It may be worth performing this analysis in a few years when the lower and higher latitude stations have a bit more data.

# Conclusion

This analysis shows that there is a clear bias in using the average of minimum and maximum daily temperature to estimate average daily temperature. Across all of the CRN stations, the min/max estimator overestimates daily average temperature by almost a half a degree Celsius (0.8°F).

We also found that this error is larger at lower latitudes, and that there are seasonal patterns to the anomalies, although the seasonal patterns don’t seem to have clear transitions moving from lower to higher latitudes.

The current length of the CRN record is quite short, especially for the sub-hourly data used here, so the patterns may not be representative of the true situation.

# Abstract

The following is a document-style version of a presentation I gave at work a
couple weeks ago. It's a little less useful for a general audience because you
don't have access to the same database I have, but I figured it might be useful
for someone who is looking at using `dplyr` or in manipulating the GHCND data
from NCDC.

# Introduction

Today we’re going to briefly take a look at the GHCND climate database
and a couple new R packages (`dplyr` and `tidyr`) that make data
import and manipulation a lot easier than using the standard library.

For further reading, consult the vignettes for `dplyr` and `tidyr`,
and download the cheat sheet:

# GHCND database

The GHCND
database
contains daily observation data from locations around the world. The
README linked above describes the data set and the way the data is
formatted. I have written scripts that process the station data and the
yearly download files and insert it into a PostgreSQL database
(`noaa`).

The script for inserting a yearly file (downloaded from http://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/) is here: ghcn-daily-by_year_process.py

# “Tidy” data

Without going into too much detail on the subject (read Hadley Wickham’s paper) for more information, but the basic idea is that it is much easier to analyze data when it is in a particular, “tidy”, form. A Tidy dataset has a single table for each type of real world object or type of data, and each table has one column per variable measured and one row per observation.

For example, here’s a tidy table representing daily weather observations with station × date as rows and the various variables as columns.

Station | Date | tmin_c | tmax_c | prcp | snow | ... |
---|---|---|---|---|---|---|

PAFA | 2014-01-01 | 12 | 24 | 0.00 | 0.0 | ... |

PAFA | 2014-01-01 | 8 | 11 | 0.02 | 0.2 | ... |

... | ... | ... | ... | ... | ... | ... |

Getting raw data into this format is what we’ll look at today.

# R libraries & data import

First, let’s load the libraries we’ll need:

```
library(dplyr) # data import
library(tidyr) # column / row manipulation
library(knitr) # tabular export
library(ggplot2) # plotting
library(scales) # “pretty” scaling
library(lubridate) # date / time manipulations
```

`dplyr` and `tidyr` are the data import and manipulation libraries
we will use, `knitr` is used to produce tabular data in report-quality
forms, `ggplot2` and `scales` are plotting libraries, and
`lubridate` is a library that makes date and time manipulation easier.

Also note the warnings about how several R functions have been “masked”
when we imported `dplyr`. This just means we'll be getting the
`dplyr` versions instead of those we might be used to. In cases where
we need both, you can preface the function with it's package:
`base::filter` would us the normal `filter` function instead of the
one from `dplyr`.

Next, connect to the database and the three tables we will need:

```
noaa_db <- src_postgres(host="mason",
dbname="noaa")
ghcnd_obs <- tbl(noaa_db, "ghcnd_obs")
ghcnd_vars <- tbl(noaa_db, "ghcnd_variables")
```

The first statement connects us to the database and the next two create table links to the observation table and the variables table.

Here’s what those two tables look like:

```
glimpse(ghcnd_obs)
```

## Observations: 29404870 ## Variables: ## $ station_id (chr) "USW00027502", "USW00027502", "USW00027502", "USW0... ## $ dte (date) 2011-05-01, 2011-05-01, 2011-05-01, 2011-05-01, 2... ## $ variable (chr) "AWND", "FMTM", "PRCP", "SNOW", "SNWD", "TMAX", "T... ## $ raw_value (dbl) 32, 631, 0, 0, 229, -100, -156, 90, 90, 54, 67, 1,... ## $ meas_flag (chr) "", "", "T", "T", "", "", "", "", "", "", "", "", ... ## $ qual_flag (chr) "", "", "", "", "", "", "", "", "", "", "", "", ""... ## $ source_flag (chr) "X", "X", "X", "X", "X", "X", "X", "X", "X", "X", ... ## $ time_of_obs (int) NA, NA, 0, NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, N...

```
glimpse(ghcnd_vars)
```

## Observations: 82 ## Variables: ## $ variable (chr) "AWND", "EVAP", "MDEV", "MDPR", "MNPN", "MXPN",... ## $ description (chr) "Average daily wind speed (tenths of meters per... ## $ raw_multiplier (dbl) 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0....

Each row in the observation table rows contain the `station_id`, date,
a variable code, the raw value for that variable, and a series of flags
indicating data quality, source, and special measurements such as the
“trace” value used for precipitation under the minimum measurable value.

Each row in the variables table contains a variable code, description and the multiplier used to convert the raw value from the observation table into an actual value.

This is an example of completely “normalized” data, and it’s stored this way because not all weather stations record all possible variables, and rather than having a single row for each station × date with a whole bunch of empty columns for those variables not measured, each row contains the station × data × variable data.

We are also missing information about the stations, so let’s load that data:

```
fai_stations <-
tbl(noaa_db, "ghcnd_stations") %>%
filter(station_name %in% c("FAIRBANKS INTL AP",
"UNIVERSITY EXP STN",
"COLLEGE OBSY"))
glimpse(fai_stations)
```

## Observations: 3 ## Variables: ## $ station_id (chr) "USC00502107", "USW00026411", "USC00509641" ## $ station_name (chr) "COLLEGE OBSY", "FAIRBANKS INTL AP", "UNIVERSITY ... ## $ latitude (dbl) 64.86030, 64.80389, 64.85690 ## $ longitude (dbl) -147.8484, -147.8761, -147.8610 ## $ elevation (dbl) 181.9656, 131.6736, 144.7800 ## $ coverage (dbl) 0.96, 1.00, 0.98 ## $ start_date (date) 1948-05-16, 1904-09-04, 1904-09-01 ## $ end_date (date) 2015-04-03, 2015-04-02, 2015-03-13 ## $ variables (chr) "TMIN TOBS WT11 SNWD SNOW WT04 WT14 TMAX WT05 DAP... ## $ the_geom (chr) "0101000020E6100000A5BDC117267B62C0EC2FBB270F3750...

The first part is the same as before, loading the `ghcnd_stations`
table, but we are filtering that data down to just the Fairbanks area
stations with long term records. To do this, we use the pipe operator
`%>%` which takes the data from the left side and passes it to the
function on the right side, the `filter` function in this case.

`filter` requires one or more conditional statements with variable
names on the left side and the condition on the right. Multiple
conditions can be separated by commas if you want all the conditions to
be required (AND) or separated by a logic operator (`&` for AND, `|`
for OR). For example: `filter(latitude > 70, longitude < -140)`.

When used on database tables, `filter` can also use conditionals that
are built into the database which are passed directly as part of a
`WHERE` clause. In our code above, we’re using the `%in%` operator
here to select the stations from a list.

Now we have the `station_id`s we need to get just the data we want
from the observation table and combine it with the other tables.

# Combining data

Here’s how we do it:

```
fai_raw <-
ghcnd_obs %>%
inner_join(fai_stations, by="station_id") %>%
inner_join(ghcnd_vars, by="variable") %>%
mutate(value=raw_value*raw_multiplier) %>%
filter(qual_flag=='') %>%
select(station_name, dte, variable, value) %>%
collect()
glimpse(fai_raw)
```

In order, here’s what we’re doing:

- Assign the result to
`fai_raw` - Join the observation table with the filtered station data, using
`station_id`as the variable to combine against. Because this is an “inner” join, we only get results where`station_id`matches in both the observation and the filtered station data. At this point we only have observation data from our long-term Fairbanks stations. - Join the variable table with the Fairbanks area observation data,
using
`variable`to link the tables. - Add a new variable called
`value`which is calculated by multiplying`raw_value`(coming from the observation table) by`raw_multiplier`(coming from the variable table). - Remove rows where the quality flag is not an empty space.
- Select only the station name, date, variable and actual value columns from the data. Before we did this, each row would contain every column from all three tables, and most of that information is not necessary.
- Finally, we “collect” the results.
`dplyr`doesn’t actually perform the full SQL until it absolutely has to. Instead it’s retrieving a small subset so that we can test our operations quickly. When we are happy with the results, we use`collect()`to grab the full data.

# De-normalize it

The data is still in a format that makes it difficult to analyze, with each row in the result containing a single station × date × variable observation. A tidy version of this data requires each variable be a column in the table, each row being a single date at each station.

To “pivot” the data, we use the `spread` function, and we'll also
calculate a new variable and reduce the number of columns in the result.

```
fai_pivot <-
fai_raw %>%
spread(variable, value) %>%
mutate(TAVG=(TMIN+TMAX)/2.0) %>%
select(station_name, dte, TAVG, TMIN, TMAX, TOBS, PRCP, SNOW, SNWD,
WSF1, WDF1, WSF2, WDF2, WSF5, WDF5, WSFG, WDFG, TSUN)
head(fai_pivot)
```

## Source: local data frame [6 x 18] ## ## station_name dte TAVG TMIN TMAX TOBS PRCP SNOW SNWD WSF1 WDF1 ## 1 COLLEGE OBSY 1948-05-16 11.70 5.6 17.8 16.1 NA NA NA NA NA ## 2 COLLEGE OBSY 1948-05-17 15.55 12.2 18.9 17.8 NA NA NA NA NA ## 3 COLLEGE OBSY 1948-05-18 14.40 9.4 19.4 16.1 NA NA NA NA NA ## 4 COLLEGE OBSY 1948-05-19 14.15 9.4 18.9 12.2 NA NA NA NA NA ## 5 COLLEGE OBSY 1948-05-20 10.25 6.1 14.4 14.4 NA NA NA NA NA ## 6 COLLEGE OBSY 1948-05-21 9.75 1.7 17.8 17.8 NA NA NA NA NA ## Variables not shown: WSF2 (dbl), WDF2 (dbl), WSF5 (dbl), WDF5 (dbl), WSFG ## (dbl), WDFG (dbl), TSUN (dbl)

`spread` takes two parameters, the variable we want to spread across
the columns, and the variable we want to use as the data value for each
row × column intersection.

# Examples

Now that we've got the data in a format we can work with, let's look at a few examples.

## Find the coldest temperatures by winter year

First, let’s find the coldest winter temperatures from each station, by winter year. “Winter year” is just a way of grouping winters into a single value. Instead of the 2014–2015 winter, it’s the 2014 winter year. We get this by subtracting 92 days (the days in January, February, March) from the date, then pulling off the year.

Here’s the code.

```
fai_winter_year_minimum <-
fai_pivot %>%
mutate(winter_year=year(dte - days(92))) %>%
filter(winter_year < 2014) %>%
group_by(station_name, winter_year) %>%
select(station_name, winter_year, TMIN) %>%
summarize(tmin=min(TMIN*9/5+32, na.rm=TRUE), n=n()) %>%
filter(n>350) %>%
select(station_name, winter_year, tmin) %>%
spread(station_name, tmin)
last_twenty <-
fai_winter_year_minimum %>%
filter(winter_year > 1993)
last_twenty
```

## Source: local data frame [20 x 4] ## ## winter_year COLLEGE OBSY FAIRBANKS INTL AP UNIVERSITY EXP STN ## 1 1994 -43.96 -47.92 -47.92 ## 2 1995 -45.04 -45.04 -47.92 ## 3 1996 -50.98 -50.98 -54.04 ## 4 1997 -43.96 -47.92 -47.92 ## 5 1998 -52.06 -54.94 -54.04 ## 6 1999 -50.08 -52.96 -50.98 ## 7 2000 -27.94 -36.04 -27.04 ## 8 2001 -40.00 -43.06 -36.04 ## 9 2002 -34.96 -38.92 -34.06 ## 10 2003 -45.94 -45.94 NA ## 11 2004 NA -47.02 -49.00 ## 12 2005 -47.92 -50.98 -49.00 ## 13 2006 NA -43.96 -41.98 ## 14 2007 -38.92 -47.92 -45.94 ## 15 2008 -47.02 -47.02 -49.00 ## 16 2009 -32.98 -41.08 -41.08 ## 17 2010 -36.94 -43.96 -38.02 ## 18 2011 -47.92 -50.98 -52.06 ## 19 2012 -43.96 -47.92 -45.04 ## 20 2013 -36.94 -40.90 NA

See if you can follow the code above. The pipe operator makes is easy to see each operation performed along the way.

There are a couple new functions here, `group_by` and `summarize`.
`group_by` indicates at what level we want to group the data, and
`summarize` uses those groupings to perform summary calculations using
aggregate functions. We group by station and winter year, then we use
the minimum and n functions to get the minimum temperature and number of
days in each year where temperature data was available. You can see we
are using `n` to remove winter years where more than two weeks of data
are missing.

Also notice that we’re using `spread` again in order to make a single
column for each station containing the minimum temperature data.

Here’s how we can write out the table data as a restructuredText document, which can be converted into many document formats (PDF, ODF, HTML, etc.):

```
sink("last_twenty.rst")
print(kable(last_twenty, format="rst"))
sink()
```

winter_year | COLLEGE OBSY | FAIRBANKS INTL AP | UNIVERSITY EXP STN |
---|---|---|---|

1994 | -43.96 | -47.92 | -47.92 |

1995 | -45.04 | -45.04 | -47.92 |

1996 | -50.98 | -50.98 | -54.04 |

1997 | -43.96 | -47.92 | -47.92 |

1998 | -52.06 | -54.94 | -54.04 |

1999 | -50.08 | -52.96 | -50.98 |

2000 | -27.94 | -36.04 | -27.04 |

2001 | -40.00 | -43.06 | -36.04 |

2002 | -34.96 | -38.92 | -34.06 |

2003 | -45.94 | -45.94 | NA |

2004 | NA | -47.02 | -49.00 |

2005 | -47.92 | -50.98 | -49.00 |

2006 | NA | -43.96 | -41.98 |

2007 | -38.92 | -47.92 | -45.94 |

2008 | -47.02 | -47.02 | -49.00 |

2009 | -32.98 | -41.08 | -41.08 |

2010 | -36.94 | -43.96 | -38.02 |

2011 | -47.92 | -50.98 | -52.06 |

2012 | -43.96 | -47.92 | -45.04 |

2013 | -36.94 | -40.90 | NA |

## Plotting

Finally, let’s plot the minimum temperatures for all three stations.

```
q <-
fai_winter_year_minimum %>%
gather(station_name, tmin, -winter_year) %>%
arrange(winter_year) %>%
ggplot(aes(x=winter_year, y=tmin, colour=station_name)) +
geom_point(size=1.5, position=position_jitter(w=0.5,h=0.0)) +
geom_smooth(method="lm", se=FALSE) +
scale_x_continuous(name="Winter Year", breaks=pretty_breaks(n=20)) +
scale_y_continuous(name="Minimum temperature (degrees F)", breaks=pretty_breaks(n=10)) +
scale_color_manual(name="Station",
labels=c("College Observatory",
"Fairbanks Airport",
"University Exp. Station"),
values=c("darkorange", "blue", "darkcyan")) +
theme_bw() +
# theme(legend.position = c(0.150, 0.850)) +
theme(axis.text.x = element_text(angle=45, hjust=1))
print(q)
```

To plot the data, we need the data in a slightly different format with each row containing winter year, station name and the minimum temperature. We’re plotting minimum temperature against winter year, coloring the points and trendlines using the station name. That means all three of those variables need to be on the same row.

To do that we use `gather`. The first parameter is the name of
variable the columns will be moved into (the station names, which are
currently columns, will become values in a row named `station_name`).
The second is the name of the column that stores the observations
(`tmin`) and the parameters after that are the list of columns to
gather together. In our case, rather than specifying the names of the
columns, we're specifying the inverse: all the columns *except*
`winter_year`.

The result of the `gather` looks like this:

```
fai_winter_year_minimum %>%
gather(station_name, tmin, -winter_year)
```

## Source: local data frame [321 x 3] ## ## winter_year station_name tmin ## 1 1905 COLLEGE OBSY NA ## 2 1907 COLLEGE OBSY NA ## 3 1908 COLLEGE OBSY NA ## 4 1909 COLLEGE OBSY NA ## 5 1910 COLLEGE OBSY NA ## 6 1911 COLLEGE OBSY NA ## 7 1912 COLLEGE OBSY NA ## 8 1913 COLLEGE OBSY NA ## 9 1915 COLLEGE OBSY NA ## 10 1916 COLLEGE OBSY NA ## .. ... ... ...

### ggplot2

The plot is produced using `ggplot2`. A full introduction would be a
seminar by itself, but the basics of our plot can be summarized as
follows.

```
ggplot(aes(x=winter_year, y=tmin, colour=station_name)) +
```

`aes` defines variables and grouping.

```
geom_point(size=1.5, position=position_jitter(w=0.5,h=0.0)) +
geom_smooth(method="lm", se=FALSE) +
```

`geom_point` draws points, `geom_smooth` draws fitted lines.

```
scale_x_continuous(name="Winter Year", breaks=pretty_breaks(n=20)) +
scale_y_continuous(name="Minimum temperature (degrees F)",
breaks=pretty_breaks(n=10)) +
scale_color_manual(name="Station",
labels=c("College Observatory", "Fairbanks Airport",
"University Exp. Station"),
values=c("darkorange", "blue", "darkcyan")) +
```

Scale functions define how the data is scaled into a plot and controls labelling.

```
theme_bw() +
theme(axis.text.x = element_text(angle=45, hjust=1))
```

Theme functions controls the style.

For more information:

## Linear regression, winter year and minimum temperature

Finally let’s look at the significance of those regression lines:

```
summary(lm(data=fai_winter_year_minimum, `COLLEGE OBSY` ~ winter_year))
```

## ## Call: ## lm(formula = `COLLEGE OBSY` ~ winter_year, data = fai_winter_year_minimum) ## ## Residuals: ## Min 1Q Median 3Q Max ## -19.0748 -5.8204 0.1907 3.8042 17.1599 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -275.01062 105.20884 -2.614 0.0114 * ## winter_year 0.11635 0.05311 2.191 0.0325 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 7.599 on 58 degrees of freedom ## (47 observations deleted due to missingness) ## Multiple R-squared: 0.07643, Adjusted R-squared: 0.06051 ## F-statistic: 4.8 on 1 and 58 DF, p-value: 0.03249

```
summary(lm(data=fai_winter_year_minimum, `FAIRBANKS INTL AP` ~ winter_year))
```

## ## Call: ## lm(formula = `FAIRBANKS INTL AP` ~ winter_year, data = fai_winter_year_minimum) ## ## Residuals: ## Min 1Q Median 3Q Max ## -15.529 -4.605 -1.025 4.007 19.764 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -171.19553 43.55177 -3.931 0.000153 *** ## winter_year 0.06250 0.02221 2.813 0.005861 ** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 7.037 on 104 degrees of freedom ## (1 observation deleted due to missingness) ## Multiple R-squared: 0.07073, Adjusted R-squared: 0.06179 ## F-statistic: 7.916 on 1 and 104 DF, p-value: 0.005861

```
summary(lm(data=fai_winter_year_minimum, `UNIVERSITY EXP STN` ~ winter_year))
```

## ## Call: ## lm(formula = `UNIVERSITY EXP STN` ~ winter_year, data = fai_winter_year_minimum) ## ## Residuals: ## Min 1Q Median 3Q Max ## -15.579 -5.818 -1.283 6.029 19.977 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -158.41837 51.03809 -3.104 0.00248 ** ## winter_year 0.05638 0.02605 2.164 0.03283 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 8.119 on 100 degrees of freedom ## (5 observations deleted due to missingness) ## Multiple R-squared: 0.04474, Adjusted R-squared: 0.03519 ## F-statistic: 4.684 on 1 and 100 DF, p-value: 0.03283

Essentially, all the models show a significant increase in minimum temperature over time, but none of them explain very much of the variation in minimum temperature.

# RMarkdown

This presentation was produced with the RMarkdown package. Allows you to mix text and R code, which is then run through R to produce documents in Word, PDF, HTML, and presentation formats.

# Introduction

Last week I gave a presentation at work about the National Climate Data
Center’s
GHCND
climate database and methods to import and manipulate the data using the
`dplyr` and `tidyr` R packages (a report-style version of it is here). Along the way, I used this function
to calculate the average daily temperature from the minimum and maximum
daily temperatures:

```
mutate(TAVG=(TMIN+TMAX)/2.0))
```

One of the people in the audience asked why the Weather Service would calculate average daily temperature this way, rather than by averaging the continuous or hourly temperatures at each station. The answer is that many, perhaps most, of the official stations in the GHCND data set are COOP stations which only report minimum and maximum temperature, and the original instrument provided to COOP observers was likely a mercury minimum / maximum thermometer. Now that these instruments are digital, they could conceivably calculate average temperature internally, and observers could report minimum, maximum and average as calculated from the device. But that’s not how it’s done.

In this analysis, I look at the difference between calculating average daily temperature using the mean of all daily temperature observations, and using the average of the minimum and maximum reported temperature each day. I’ll use five years of data collected at our house using our Arduino-based weather station.

# Methods

Our weather station records temperature every few seconds, averages this data every five minutes and stores these five minute observations in a database. For our analysis, I’ll group the data by day and calculate the average daily temperature using the mean of all the five minute observations, and using the average of the minimum and maximum daily temperature. I’ll use R to perform the analysis.

## Libraries

Load the libraries we need:

```
library(dplyr)
library(lubridate)
library(ggplot2)
library(scales)
library(readr)
```

## Retrieve the data

Connect to the database and retrieve the data. We’re using `build_sql`
because the data table we’re interested in is a view (sort of like a
stored SQL query), not a table, and `dplyr::tbl` can’t currently read
from a view:

```
dw1454 <- src_postgres(dbname="goldstream_creek_wx",
user="readonly")
raw_data <- tbl(dw1454, build_sql("SELECT * FROM arduino_west"))
```

The raw data contains the timestamp for each five minute observation, and the temperature, in degrees Fahrenheit for that observation. The following series of functions aggregates the data to daily data and calculates the average daily temperature using the two methods.

```
daily_average <-
raw_data %>%
filter(obs_dt>'2009-12-31 23:59:59') %>%
mutate(date=date(obs_dt)) %>%
select(date, wtemp) %>%
group_by(date) %>%
summarize(mm_avg=(min(wtemp)+max(wtemp))/2.0,
h_avg=mean(wtemp), n=n()) %>%
filter(n==24*60/5) %>% # 5 minute obs
collect()
```

All these steps are joined together using the “pipe” or “then” operator
`%>%` as follows:

`daily_average <-`: assign the result of all the operations to`daily_average`.`raw_data %>%`: start with the data from our database query (all the temperature observations).`filter(obs_dt>'2009-12-31 23:59:59') %>%`: use data from 2010 and after.`mutate(date=date(obs_dt)) %>%`: calculate the data from the timestamp.`select(date, wtemp) %>%`: reduce the columns to our newly calculated`date`variable and the temperatures.`group_by(date) %>%`: group the data by date.`summarize(mm_avg=(min(wtemp)+max(wtemp))/2.0) %>%`: summarize the data grouped by date, calculate daily average from the average of the minimum and maximum temperature.`summarize(h_avg=mean(wtemp), n=n()) %>%`: calculate another daily average from the mean of the temperaures. Also calculate the number of observations on each date.`filter(n==24*60/5) %>%`: Only include dates where we have a complete set of five minute observations. We don’t want data with too few or too many observations because those would skew the averages.`collect()`: This function retrieves the data from the database. Without`collect()`, the query is run on the database server, producing a subset of the full results. This allows us to tweak the query until it’s exactly what we want without having to wait to retrieve everything at each iteration.

Now we’ve got a table with one row for each date in the database where we had exactly 288 observations on that date. Columns include the average temperature calculated using the two methods and the number of observations on each date.

Save the data so we don’t have to do these calculations again:

```
write_csv(daily_average, "daily_average.csv")
save(daily_average, file="daily_average.rdata", compress=TRUE)
```

## Calculate anomalies

How does the min/max method of calculating average daily temperature compare against the true mean of all observed temperatures in a day? We calculate the difference between the methods, the anomaly, as the mean temperature subtracted from the average of minimum and maximum. When this anomaly is positive, the min/max method is higher than the actual mean, and when it’s negative, it’s lower.

```
anomaly <-
daily_average %>%
mutate(month=month(date),
anomaly=mm_avg-h_avg) %>%
ungroup() %>%
arrange(date)
```

We also populate a column with the month of each date so we can look at the seasonality of the anomalies.

# Results

This is what the results look like:

```
summary(anomaly$anomaly)
```

## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -6.8600 -1.5110 -0.1711 -0.1341 1.0740 9.3570

The average anomaly is very close to zero (-0.13), and I suspect it would be even closer to zero as more data is included. Half the data is between -1.5 and 1.1 degrees and the full range is -6.86 to +9.36°F.

# Plots

Let’s take a look at some plots of the anomalies.

## Raw anomaly data

The first plot shows the raw anomaly data, with positive anomalies (min/max calculate average is higher than the mean daily average) colored red and negative anomalies in blue.

```
# All anomalies
q <- ggplot(data=anomaly,
aes(x=date, ymin=0, ymax=anomaly, colour=anomaly<0)) +
geom_linerange(alpha=0.5) +
theme_bw() +
scale_colour_manual(values=c("red", "blue"), guide=FALSE) +
scale_x_date(name="") +
scale_y_continuous(name="Difference between min/max and hourly aggregation")
print(q)
```

I don't see much in the way of trends in this data, but there are short periods where all the anomalies are in one direction or another. If there is a seasonal pattern, it's hard to see it when the data is presented this way.

## Monthly boxplots

To examine the seasonality of the anomalies, let’s look at some boxplots, grouped by the “month” variable we calculated when calculating the anomalies.

```
mean_anomaly <- mean(anomaly$anomaly)
# seasonal pattern of anomaly
q <- ggplot(data=anomaly,
aes(x=as.factor(month), y=anomaly)) +
geom_hline(data=NULL, aes(yintercept=mean_anomaly), colour="darkorange") +
geom_boxplot() +
scale_x_discrete(name="",
labels=c("Jan", "Feb", "Mar", "Apr",
"May", "Jun", "Jul", "Aug",
"Sep", "Oct", "Nov", "Dec")) +
scale_y_continuous(name="Difference between min/max and hourly aggregation") +
theme_bw()
print(q)
```

There does seem to be a slight seasonal pattern to the anomalies, with spring and summer daily average underestimated when using the min/max calculation (the actual daily average temperature is warmer than was calculated using minimum and maximum temperatures) and slightly overestimated in fall and late winter. The boxes in a boxplot show the range where half the observations fall, and in all months but April and May these ranges include zero, so there's a good chance that the pattern isn't statistically significant. The orange line under the boxplots show the overall average anomaly, close to zero.

## Cumulative frequency distribution

Finally, we plot the cumulative frequency distribution of the absolute value of the anomalies. These plots have the variable of interest on the x-axis and the cumulative frequency of all values to the left on the y-axis. It’s a good way of seeing how much of the data falls into certain ranges.

```
# distribution of anomalies
q <- ggplot(data=anomaly,
aes(x=abs(anomaly))) +
stat_ecdf() +
scale_x_discrete(name="Absolute value of anomaly (+/- degrees F)",
breaks=0:11,
labels=0:11,
expand=c(0, 0)) +
scale_y_continuous(name="Cumulative frequency",
labels=percent,
breaks=pretty_breaks(n=10),
limits=c(0,1)) +
annotate("rect", xmin=-1, xmax=1, ymin=0, ymax=0.4, alpha=0.1, fill="darkcyan") +
annotate("rect", xmin=-1, xmax=2, ymin=0, ymax=0.67, alpha=0.1, fill="darkcyan") +
annotate("rect", xmin=-1, xmax=3, ymin=0, ymax=0.85, alpha=0.1, fill="darkcyan") +
annotate("rect", xmin=-1, xmax=4, ymin=0, ymax=0.94, alpha=0.1, fill="darkcyan") +
annotate("rect", xmin=-1, xmax=5, ymin=0, ymax=0.975, alpha=0.1, fill="darkcyan") +
theme_bw()
print(q)
```

The overlapping rectangles on the plot show what percentages of anomalies fall in certain ranges. Starting from the innermost and darkest rectangle, 40% of the temperatures calculated using minimum and maximum are within a degree of the actual temperature. Sixty-seven percent are within two degrees, 85% within three degrees, 94% are within four degrees, and more than 97% are within five degrees of the actual value. There's probably a way to get R to calculate these intersections along the curve for you, but I looked at the plot and manually added the annotations.

# Conclusion

We looked at more than five years of data from our weather station in the Goldstream Valley, comparing daily average temperature calculated from the mean of all five minute temperature observations and those calculated using the average minimum and maximum daily temperature, which is the method the National Weather Service uses for it’s daily data. The results show that the difference between these methods average to zero, which means that on an annual (or greater) basis, there doesn't appear to be any bias in the method.

Two thirds of all daily average temperatures are within two degrees of the actual daily average, and with a few exceptions, the error is always below five degrees.

There is some evidence that there’s a seasonal pattern to the error, however, with April and May daily averages particularly low. If those seasonal patterns are real, this would indicate an important bias in this method of calculating average daily temperature.

Last night we got a quarter of an inch of rain at our house, making roads “impassable” according to the Fairbanks Police Department, and turning the dog yard, deck, and driveway into an icy mess. There are videos floating around Facebook showing Fairbanks residents playing hockey in the street in front of their houses, and a reported seven vehicles off the road on Ballaine Hill.

Here’s a video of a group of Goldstream Valley musicians ice skating on Golstream Road: http://youtu.be/_afC7UF0NXk

Let’s check out the weather database and take a look at how often Fairbanks experiences this type of event, and when they usually happen. I’m going to skip the parts of the code showing how we get pivoted daily data from the database, but they’re in this post.

Starting with pivoted data we want to look for dates from November through March with more than a tenth of an inch of precipitation, snowfall less than two tenths of an inch and a daily high temperature above 20°F. Then we group by the winter year and month, and aggregate the rain events into a single event. These occurrences are rare enough that this aggregation shoudln’t combine events from different parts of the month.

Here’s the R code:

```
winter_rain <-
fai_pivot %>%
mutate(winter_year=year(dte - days(92)),
wdoy=yday(dte + days(61)),
month=month(dte),
SNOW=ifelse(is.na(SNOW), 0, SNOW),
TMAX=TMAX*9/5+32,
TAVG=TAVG*9/5+32,
TMIN=TMIN*9/5+32,
PRCP=PRCP/25.4,
SNOW=SNOW/25.4) %>%
filter(station_name == 'FAIRBANKS INTL AP',
winter_year < 2014,
month %in% c(11, 12, 1, 2, 3),
TMAX > 20,
PRCP > 0.1,
SNOW < 0.2) %>%
group_by(winter_year, month) %>%
summarize(date=min(dte), tmax=mean(TMAX),
prcp=sum(PRCP), days=n()) %>%
ungroup() %>%
mutate(month=month(date)) %>%
select(date, month, tmax, prcp, days) %>%
arrange(date)
```

And the results:

Date | Month | Max temp (°F) | Rain (inches) | Days |
---|---|---|---|---|

1921-03-07 | 3 | 44.06 | 0.338 | 1 |

1923-02-06 | 2 | 33.98 | 0.252 | 1 |

1926-01-12 | 1 | 35.96 | 0.142 | 1 |

1928-03-02 | 3 | 39.02 | 0.110 | 1 |

1931-01-19 | 1 | 33.08 | 0.130 | 1 |

1933-11-03 | 11 | 41.00 | 0.110 | 1 |

1935-11-02 | 11 | 38.30 | 0.752 | 3 |

1936-11-24 | 11 | 37.04 | 0.441 | 1 |

1937-01-10 | 1 | 32.96 | 1.362 | 3 |

1948-11-10 | 11 | 48.02 | 0.181 | 1 |

1963-01-19 | 1 | 35.06 | 0.441 | 1 |

1965-03-29 | 3 | 35.96 | 0.118 | 1 |

1979-11-11 | 11 | 35.96 | 0.201 | 1 |

2003-02-08 | 2 | 34.97 | 0.291 | 2 |

2003-11-02 | 11 | 34.97 | 0.268 | 2 |

2010-11-22 | 11 | 34.34 | 0.949 | 3 |

This year’s event doesn’t compare to 2010 when almost and inch of rain fell over the course of three days in November, but it does look like it comes at an unusual part of the year.

Here’s the counts and frequency of winter rainfall events by month:

```
by_month <-
winter_rain %>%
group_by(month) %>%
summarize(n=n()) %>%
mutate(freq=n/sum(n)*100)
```

Month | n | Freq |
---|---|---|

1 | 4 | 25.00 |

2 | 2 | 12.50 |

3 | 3 | 18.75 |

11 | 7 | 43.75 |

There haven’t been any rain events in December, which is a little surprising, but next to that, February rains are the least common.

I looked at this two years ago (Winter freezing rain) using slightly different criteria. At the bottom of that post I looked at the frequency of rain events over time and concluded that they seem to come in cycles, but that the three events in this decade was a bad sign. Now we can add another rain event to the total for the 2010s.