sun, 14-feb-2016, 10:02

Since the middle of 2010 we’ve been monitoring the level of Goldstream Creek for the National Weather Service by measuring the distance from the top of our bridge to the surface of the water or ice. In 2012 the Creek flooded and washed the bridge downstream. We eventually raised the bridge logs back up onto the banks and resumed our measurements.

This winter the Creek had been relatively quiet, with the level hovering around eight feet below the bridge. But last Friday, we awoke to more than four feet of water over the ice, and since then it's continued to rise. This morning’s reading had the ice only 3.17 feet below the surface of the bridge.

//media.swingleydev.com/img/blog/2016/02/bridge_1024.jpg

Overflow within a few feet of the bridge

Water also entered the far side of the slough, and is making it’s way around the loop, melting the snow covering the old surface. Even as the main channel stops rising and freezes, water moves closer to the dog yard from the slough.

//media.swingleydev.com/img/blog/2016/02/slough_1024.jpg

Water entering the slough

One of my longer commutes to work involves riding east on the Goldstream Valley trails, crossing the Creek by Ballaine Road, then riding back toward the house on the north side of the Creek. From there, I can cross Goldstream Creek again where the trail at the end of Miller Hill Road and the Miller Hill Extension trail meet, and ride the trails the rest of the way to work. That crossing is also covered with several feet of water and ice.

//media.swingleydev.com/img/blog/2016/02/mh_crossing_1024.jpg

Trail crossing at the end of Miller Hill

Yesterday one of my neighbors sent email with the subject line, “Are we doomed?,” so I took a look at the heigh data from past years. The plot below shows the height of the Creek, as measured from the surface of the bridge (click on the plot to view or download a PDF, R code used to generate the plot appears at the bottom of this post).

The orange region is the region where the Creek is flowing; between my reporting of 0% ice in spring and 100% ice-covered in fall. The data gap in July 2014 was due to the flood washing the bridge downstream. Because the bridge isn’t in the same location, the height measurements before and after the flood aren’t completely comparable, but I don’t have the data for the difference in elevation between the old and new bridge locations, so this is the best we’ve got.

//media.swingleydev.com/img/blog/2016/02/creek_heights_2010-2016_by_year.svgz

Creek heights by year

The light blue line across all the plots shows the current height of the Creek (3.17 feet) for all years of data. 2012 is probably the closest year to our current situation where the Creek rose to around five feet below the bridge in early January. But really nothing is completely comparable to the situation we’re in right now. Breakup won’t come for another two or three months, and in most years, the Creek rises several feet between February and breakup.

Time will tell, of course, but here’s why I’m not too worried about it. There’s another bridge crossing several miles downstream, and last Friday there was no water on the surface, and the Creek was easily ten feet below the banks. That means that there is a lot of space within the banks of the Creek downstream that can absorb the melting water as breakup happens. I also think that there is a lot of liquid water trapped beneath the ice on the surface in our neighborhood and that water is likely to slowly drain out downstream, leaving a lot of empty space below the surface ice that can accommodate further overflow as the winter progresses. In past years of walking on the Creek I’ve come across huge areas where the top layer of ice dropped as much as six feet when the water underneath drained away. I’m hoping that this happens here, with a lot of the subsurface water draining downstream.

The Creek is always reminding us of how little we really understand what’s going on and how even a small amount of flowing water can become a huge force when that water accumulates more rapidly than the Creek can handle it. Never a dull moment!

Code

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

wxcoder <- read_csv("data/wxcoder.csv", na=c("-9999"))
feb_2016_incomplete <- read_csv("data/2016_02_incomplete.csv",
                                na=c("-9999"))

wxcoder <- rbind(wxcoder, feb_2016_incomplete)

wxcoder <- wxcoder %>%
   transmute(dte=as.Date(ymd(DATE)), tmin_f=TN, tmax_f=TX, tobs_f=TA,
             tavg_f=(tmin_f+tmax_f)/2.0,
             prcp_in=ifelse(PP=='T', 0.005, as.numeric(PP)),
             snow_in=ifelse(SF=='T', 0.05, as.numeric(SF)),
             snwd_in=SD, below_bridge_ft=HG,
             ice_cover_pct=IC)

creek <- wxcoder %>% filter(dte>as.Date(ymd("2010-05-27")))

creek_w_year <- creek %>%
   mutate(year=year(dte),
         doy=yday(dte))

ice_free_date <- creek_w_year %>%
   group_by(year) %>%
   filter(ice_cover_pct==0) %>%
   summarize(ice_free_dte=min(dte), ice_free_doy=min(doy))

ice_covered_date <- creek_w_year %>%
   group_by(year) %>%
   filter(ice_cover_pct==100, doy>182) %>%
   summarize(ice_covered_dte=min(dte), ice_covered_doy=min(doy))

flowing_creek_dates <- ice_free_date %>%
   inner_join(ice_covered_date, by="year") %>%
   mutate(ymin=Inf, ymax=-Inf)

latest_obs <- creek_w_year %>%
   mutate(rank=rank(desc(dte))) %>%
   filter(rank==1)

current_height_df <- data.frame(
      year=c(2011, 2012, 2013, 2014, 2015, 2016),
      below_bridge_ft=latest_obs$below_bridge_ft)

q <- ggplot(data=creek_w_year %>% filter(year>2010),
            aes(x=doy, y=below_bridge_ft)) +
   theme_bw() +
   geom_rect(data=flowing_creek_dates %>% filter(year>2010),
             aes(xmin=ice_free_doy, xmax=ice_covered_doy, ymin=ymin, ymax=ymax),
             fill="darkorange", alpha=0.4,
             inherit.aes=FALSE) +
   # geom_point(size=0.5) +
   geom_line() +
   geom_hline(data=current_height_df,
              aes(yintercept=below_bridge_ft),
              colour="darkcyan", alpha=0.4) +
   scale_x_continuous(name="",
                      breaks=c(1,32,60,91,
                               121,152,182,213,
                               244,274,305,335,
                               365),
                      labels=c("Jan", "Feb", "Mar", "Apr",
                               "May", "Jun", "Jul", "Aug",
                               "Sep", "Oct", "Nov", "Dec",
                               "Jan")) +
   scale_y_reverse(name="Creek height, feet below bridge",
                   breaks=pretty_breaks(n=5)) +
   facet_wrap(~ year, ncol=1)

width <- 16
height <- 16
rescale <- 0.75
pdf("creek_heights_2010-2016_by_year.pdf",
    width=width*rescale, height=height*rescale)
print(q)
dev.off()
svg("creek_heights_2010-2016_by_year.svg",
    width=width*rescale, height=height*rescale)
print(q)
dev.off()
sat, 25-apr-2015, 10:21

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)
//media.swingleydev.com/img/blog/2015/04/crn_minmax_anomaly_scatterplot.svg

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)
//media.swingleydev.com/img/blog/2015/04/crn_minmax_anomalies_by_month_lat.svg

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.

tags: R  temperature  weather  climate  CRN  COOP  ggplot 
sun, 22-feb-2015, 11:33

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:

List of winter rain events, Fairbanks Airport
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)
Winter rain events by month
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.

tags: R  weather  winter  rain  dplyr  climate 
sun, 08-feb-2015, 14:13

Whenever we’re in the middle of a cold snap, as we are right now, I’m tempted to see how the current snap compares to those in the past. The one we’re in right now isn’t all that bad: sixteen days in a row where the minimum temperature is colder than −20°F. In some years, such a threshold wouldn’t even qualify as the definition of a “cold snap,” but right now, it feels like one.

Getting the length of consecutive things in a database isn’t simple. What we’ll do is get a list of all the days where the minimum daily temperature was warmer than −20°F. Then go through each record and count the number of days between the current row and the next one. Most of these will be one, but when the number of days is greater than one, that means there’s one or more observations in between the “warm” days where the minimum temperature was colder than −20°F (or there was missing data).

For example, given this set of dates and temperatures from earlier this year:

date tmin_f
2015‑01‑02 −15
2015‑01‑03 −20
2015‑01‑04 −26
2015‑01‑05 −30
2015‑01‑06 −30
2015‑01‑07 −26
2015‑01‑08 −17

Once we select for rows where the temperature is above −20°F we get this:

date tmin_f
2015‑01‑02 −15
2015‑01‑08 −17

Now we can grab the start and end of the period (January 2nd + one day and January 8th - one day) and get the length of the cold snap. You can see why missing data would be a problem, since it would create a gap that isn’t necessarily due to cold temperatures.

I couldn't figure out how to get the time periods and check them for validity all in one step, so I wrote a simple function that counts the days with valid data between two dates, then used this function in the real query. Only periods with non-null data on each day during the cold snap were included.

CREATE FUNCTION valid_n(date, date)
RETURNS bigint AS
  'SELECT count(*)
   FROM ghcnd_pivot
   WHERE station_name = ''FAIRBANKS INTL AP''
      AND dte BETWEEN $1 AND $2
      AND tmin_c IS NOT NULL'
LANGUAGE SQL
RETURNS NULL ON NULL INPUT;

Here we go:

SELECT rank() OVER (ORDER BY days DESC) AS rank,
       start, "end", days FROM (
   SELECT start + interval '1 day' AS start,
         "end" - interval '1 day' AS end,
         interv - 1 AS days,
         valid_n(date(start + interval '1 day'),
                  date("end" - interval '1 day')) as valid_n
   FROM (
      SELECT dte AS start,
            lead(dte) OVER (ORDER BY dte) AS end,
            lead(dte) OVER (ORDER BY dte) - dte AS interv
      FROM (
         SELECT dte
         FROM ghcnd_pivot
         WHERE station_name = 'FAIRBANKS INTL AP'
            AND tmin_c > f_to_c(-20)
      ) AS foo
   ) AS bar
   WHERE interv >= 17
) AS f
WHERE days = valid_n
ORDER BY days DESC;

And the top 10:

Top ten longest cold snaps (−20°F or colder minimum temp)
rank start end days
1 1917‑11‑26 1918‑01‑01 37
2 1909‑01‑13 1909‑02‑12 31
3 1948‑11‑17 1948‑12‑13 27
4 1925‑01‑16 1925‑02‑10 26
4 1947‑01‑12 1947‑02‑06 26
4 1943‑01‑02 1943‑01‑27 26
4 1968‑12‑26 1969‑01‑20 26
4 1979‑02‑01 1979‑02‑26 26
9 1980‑12‑06 1980‑12‑30 25
9 1930‑01‑28 1930‑02‑21 25

There have been seven cold snaps that lasted 16 days (including the one we’re currently in), tied for 45th place.

Keep in mind that defining days where the daily minimum is −20°F or colder is a pretty generous definition of a cold snap. If we require the minimum temperatures be below −40° the lengths are considerably shorter:

Top ten longest cold snaps (−40° or colder minimum temp)
rank start end days
1 1964‑12‑25 1965‑01‑11 18
2 1973‑01‑12 1973‑01‑26 15
2 1961‑12‑16 1961‑12‑30 15
2 2008‑12‑28 2009‑01‑11 15
5 1950‑02‑04 1950‑02‑17 14
5 1989‑01‑18 1989‑01‑31 14
5 1979‑02‑03 1979‑02‑16 14
5 1947‑01‑23 1947‑02‑05 14
9 1909‑01‑14 1909‑01‑25 12
9 1942‑12‑15 1942‑12‑26 12
9 1932‑02‑18 1932‑02‑29 12
9 1935‑12‑02 1935‑12‑13 12
9 1951‑01‑14 1951‑01‑25 12

I think it’s also interesting that only three (marked with a grey background) of the top ten cold snaps defined at −20°F appear in those that have a −40° threshold.

sun, 25-jan-2015, 08:26

Following up on yesterday’s post about minimum temperatures, I was thinking that a cumulative measure of cold temperatures would probably be a better measure of how cold a winter is. We all remember the extremely cold days each winter when the propane gells or the car won’t start, but it’s the long periods of deep cold that really take their toll on buildings, equipment, and people in the Interior.

One way of measuring this is to find all the days in a winter year when the average temperature is below freezing and sum all the temperatures below freezing for that winter year. For example, if the temperature is 50°F, that’s not below freezing so it doesn’t count. If the temperature is −40°, that’s 72 freezing degrees (Fahrenheit). Do this for each day in a year and add up all the values.

Here’s the code to make the plot below (see my previous post for how we got fai_pivot).

fai_winter_year_freezing_degree_days <-
   fai_pivot %>%
      mutate(winter_year=year(dte - days(92)),
               fdd=ifelse(TAVG < 0, -1*TAVG*9/5, 0)) %>%
      filter(winter_year < 2014) %>%
      group_by(station_name, winter_year) %>%
      select(station_name, winter_year, fdd) %>%
      summarize(fdd=sum(fdd, na.rm=TRUE), n=n()) %>%
      filter(n>350) %>%
      select(station_name, winter_year, fdd) %>%
      spread(station_name, fdd)

fdd_gathered <-
   fai_winter_year_freezing_degree_days %>%
      gather(station_name, fdd, -winter_year) %>%
      arrange(winter_year)
q <-
   fdd_gathered %>%
      ggplot(aes(x=winter_year, y=fdd, colour=station_name)) +
            geom_point(size=1.5, position=position_jitter(w=0.5,h=0.0)) +
            geom_smooth(data=subset(fdd_gathered, winter_year<1975),
                        method="lm", se=FALSE) +
            geom_smooth(data=subset(fdd_gathered, winter_year>=1975),
                        method="lm", se=FALSE) +
            scale_x_continuous(name="Winter Year",
                               breaks=pretty_breaks(n=20)) +
            scale_y_continuous(name="Freezing degree days (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(0.875, 0.120)) +
            theme(axis.text.x = element_text(angle=45, hjust=1))

rescale <- 0.65
svg('freezing_degree_days.svg', height=10*rescale, width=16*rescale)
print(q)
dev.off()

And the plot.

//media.swingleydev.com/img/blog/2015/01/freezing_degree_days.svg

Cumulative freezing degree days by winter year

You’ll notice I’ve split the trend lines at 1975. When I ran the regressions for the entire period, none of them were statistically significant, but looking at the plot, it seems like something happens in 1975 where the cumulative freezing degree days suddenly drop. Since then, they've been increasing at a faster, and statistically significant rate.

This is odd, and it makes me wonder if I've made a mistake in the calculations because what this says is that, at least since 1975, the winters are getting colder as measured by the total number of degrees below freezing each winter. My previous post (and studies of climate in general) show that the climate is warming, not cooling.

One bias that's possible with cumulative calculations like this is that missing data becomes more important, but I looked at the same relationships when I only include years with at least 364 days of valid data (only one or two missing days) and the same pattern exists.

Curious. When combined, this analysis and yesterday's suggest that winters in Fairbanks are getting colder overall, but that the minimum temperature in any year is likely to be warmer than in the past.

tags: R  weather  dplyr  climate  tidyr 

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