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<h1 class="title toc-ignore">APIs, time-series, and weather Data</h1>
<h3 class="subtitle"><em>Processing daily weather data from NOAA</em></h3>
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<h1>Reading</h1>
<ul>
<li>NULL</li>
</ul>
<div id="download" class="section level2">
<h2>Download</h2>
<table>
<thead>
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<th align="center"><a href="scripts/TK_09_nocomments.R"><i class='fas fa-code fa-2x' aria-hidden='true'></i><br> R Script</a></th>
<th align="center"><a href="scripts/TK_09.R"><i class='fa fa-file-code-o fa-2x'></i> <br> Commented R Script</a></th>
<th align="center"><a href="scripts/TK_09.Rmd"><i class='far fa-file-alt fa-2x'></i> <br> Rmd Script</a></th>
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<div id="climate-metrics" class="section level1">
<h1>Climate Metrics</h1>
<div id="climate-metrics-climdex" class="section level2">
<h2>Climate Metrics: ClimdEX</h2>
<p>Indices representing extreme aspects of climate derived from daily data:</p>
<p><img src="08_assets/climdex.png" alt="alt text" width="50%"></p>
<p>Climate Change Research Centre (CCRC) at University of New South Wales (UNSW) (<a href="http://www.climdex.org">climdex.org</a>).</p>
<div id="core-indices" class="section level3">
<h3>27 Core indices</h3>
<p>For example:</p>
<ul>
<li><strong>FD</strong> Number of frost days: Annual count of days when TN (daily minimum temperature) < 0C.</li>
<li><strong>SU</strong> Number of summer days: Annual count of days when TX (daily maximum temperature) > 25C.</li>
<li><strong>ID</strong> Number of icing days: Annual count of days when TX (daily maximum temperature) < 0C.</li>
<li><strong>TR</strong> Number of tropical nights: Annual count of days when TN (daily minimum temperature) > 20C.</li>
<li><strong>GSL</strong> Growing season length: Annual (1st Jan to 31st Dec in Northern Hemisphere (NH), 1st July to 30th June in Southern Hemisphere (SH)) count between first span of at least 6 days with daily mean temperature TG>5C and first span after July 1st (Jan 1st in SH) of 6 days with TG<5C.</li>
<li><strong>TXx</strong> Monthly maximum value of daily maximum temperature</li>
<li><strong>TN10p</strong> Percentage of days when TN < 10th percentile</li>
<li><strong>Rx5day</strong> Monthly maximum consecutive 5-day precipitation</li>
<li><strong>SDII</strong> Simple pricipitation intensity index</li>
</ul>
</div>
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<h1>Weather Data</h1>
<div id="climate-data-online" class="section level3">
<h3>Climate Data Online</h3>
<div class="figure">
<img src="08_assets/climatedataonline.png" alt="CDO" />
<p class="caption">CDO</p>
</div>
</div>
<div id="ghcn" class="section level3">
<h3>GHCN</h3>
<div class="figure">
<img src="08_assets/ghcn.png" alt="ghcn" />
<p class="caption">ghcn</p>
</div>
</div>
<div id="options-for-downloading-data" class="section level2">
<h2>Options for downloading data</h2>
<div id="feddata-package" class="section level3">
<h3><code>FedData</code> package</h3>
<ul>
<li>National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS)</li>
<li>National Hydrography Dataset (USGS)</li>
<li>Soil Survey Geographic (SSURGO) database</li>
<li>International Tree Ring Data Bank.</li>
<li><em>Global Historical Climatology Network</em> (GHCN)</li>
</ul>
</div>
<div id="noaa-api" class="section level3">
<h3>NOAA API</h3>
<div class="figure">
<img src="08_assets/noaa_api.png" alt="noaa api" />
<p class="caption">noaa api</p>
</div>
<p><a href="http://www.ncdc.noaa.gov/cdo-web/webservices/v2">National Climatic Data Center application programming interface (API)</a>.</p>
</div>
<div id="rnoaa-package" class="section level3">
<h3><code>rNOAA</code> package</h3>
<p>Handles downloading data directly from NOAA APIv2.</p>
<ul>
<li><code>buoy_*</code> NOAA Buoy data from the National Buoy Data Center</li>
<li><code>ghcnd_*</code> GHCND daily data from NOAA</li>
<li><code>isd_*</code> ISD/ISH data from NOAA</li>
<li><code>homr_*</code> Historical Observing Metadata Repository</li>
<li><code>ncdc_*</code> NOAA National Climatic Data Center (NCDC)</li>
<li><code>seaice</code> Sea ice</li>
<li><code>storm_</code> Storms (IBTrACS)</li>
<li><code>swdi</code> Severe Weather Data Inventory (SWDI)</li>
<li><code>tornadoes</code> From the NOAA Storm Prediction Center</li>
</ul>
<hr />
</div>
<div id="libraries" class="section level3">
<h3>Libraries</h3>
<pre class="r"><code>library(raster)
library(sp)
library(rgdal)
library(ggplot2)
library(ggmap)
library(dplyr)
library(tidyr)
library(maps)
library(scales)
# New Packages
library(rnoaa)
library(climdex.pcic)
library(zoo)
library(reshape2)
library(broom)</code></pre>
</div>
<div id="station-locations" class="section level3">
<h3>Station locations</h3>
<p>Download the GHCN station inventory with <code>ghcnd_stations()</code>.</p>
<pre class="r"><code>datadir="data"
st = ghcnd_stations()
## Optionally, save it to disk
# write.csv(st,file.path(datadir,"st.csv"))
## If internet fails, load the file from disk using:
# st=read.csv(file.path(datadir,"st.csv"))</code></pre>
</div>
<div id="ghcnd-variables" class="section level3">
<h3>GHCND Variables</h3>
<p>5 core values:</p>
<ul>
<li><strong>PRCP</strong> Precipitation (tenths of mm)</li>
<li><strong>SNOW</strong> Snowfall (mm)</li>
<li><strong>SNWD</strong> Snow depth (mm)</li>
<li><strong>TMAX</strong> Maximum temperature</li>
<li><strong>TMIN</strong> Minimum temperature</li>
</ul>
<p>And ~50 others! For example:</p>
<ul>
<li><strong>ACMC</strong> Average cloudiness midnight to midnight from 30-second ceilometer</li>
<li><strong>AWND</strong> Average daily wind speed</li>
<li><strong>FMTM</strong> Time of fastest mile or fastest 1-minute wind</li>
<li><strong>MDSF</strong> Multiday snowfall total</li>
</ul>
</div>
<div id="filter-to-temperature-and-precipitation" class="section level3">
<h3><code>filter()</code> to temperature and precipitation</h3>
<pre class="r"><code>st=dplyr::filter(st,element%in%c("TMAX","TMIN","PRCP"))</code></pre>
</div>
<div id="map-ghcnd-stations" class="section level3">
<h3>Map GHCND stations</h3>
<p>First, get a global country polygon</p>
<pre class="r"><code>worldmap=map_data("world")</code></pre>
<p>Plot all stations:</p>
<pre class="r"><code>ggplot(data=st,aes(y=latitude,x=longitude)) +
facet_grid(element~.)+
annotation_map(map=worldmap,size=.1,fill="grey",colour="black")+
geom_point(size=.1,col="red")+
coord_equal()</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-6-1.png" /><!-- --></p>
<p>It’s hard to see all the points, let’s bin them…</p>
<pre class="r"><code>ggplot(st,aes(y=latitude,x=longitude)) +
annotation_map(map=worldmap,size=.1,fill="grey",colour="black")+
facet_grid(element~.)+
stat_bin2d(bins=100)+
scale_fill_distiller(palette="YlOrRd",trans="log",direction=-1,
breaks = c(1,10,100,1000))+
coord_equal()</code></pre>
<img src="TK_09_files/figure-html/unnamed-chunk-7-1.png" /><!-- -->
<div class="well">
<h2 id="your-turn">Your turn</h2>
<p>Produce a binned map (like above) with the following modifications:</p>
<ul>
<li>include only stations with a data record that starts before 1950 and ends after 2000 (keeping only complete records during that time).</li>
<li>include only <code>tmax</code></li>
</ul>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo1">
Show Solution
</button>
<div id="demo1" class="collapse">
<pre class="r"><code>ggplot(filter(st,
first_year<=1950 &
last_year>=2000 &
element=="TMAX"),
aes(y=latitude,x=longitude)) +
annotation_map(map=worldmap,size=.1,fill="grey",colour="black")+
stat_bin2d(bins=75)+
scale_fill_distiller(palette="YlOrRd",trans="log",direction=-1,
breaks = c(1,10,50))+
coord_equal()</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-8-1.png" /><!-- --></p>
</div>
</div>
</div>
</div>
<div id="download-daily-data-from-ghcn" class="section level2">
<h2>Download daily data from GHCN</h2>
<p><code>ghcnd()</code> will download a <code>.dly</code> file for a particular station. But how to choose?</p>
<div id="geocode-in-ggmap-package-useful-for-geocoding-place-names" class="section level3">
<h3><code>geocode</code> in ggmap package useful for geocoding place names</h3>
<p>Geocodes a location (find latitude and longitude) using either (1) the Data Science Toolkit (<a href="http://www.datasciencetoolkit.org/about" class="uri">http://www.datasciencetoolkit.org/about</a>) or (2) Google Maps.</p>
<pre class="r"><code>geocode("University at Buffalo, NY")</code></pre>
<pre><code>## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=University%20at%20Buffalo,%20NY&sensor=false</code></pre>
<pre><code>## Warning: geocode failed with status OVER_QUERY_LIMIT, location =
## "University at Buffalo, NY"</code></pre>
<pre><code>## lon lat
## 1 NA NA</code></pre>
<p>However, you have to be careful:</p>
<pre class="r"><code>geocode("My Grandma's house")</code></pre>
<pre><code>## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=My%20Grandma's%20house&sensor=false</code></pre>
<pre><code>## Warning: geocode failed with status OVER_QUERY_LIMIT, location = "My
## Grandma's house"</code></pre>
<pre><code>## lon lat
## 1 NA NA</code></pre>
<p>But this is pretty safe for well known places.</p>
<pre class="r"><code>coords=as.matrix(geocode("Buffalo, NY"))</code></pre>
<pre><code>## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buffalo,%20NY&sensor=false</code></pre>
<pre><code>## Warning: geocode failed with status OVER_QUERY_LIMIT, location = "Buffalo,
## NY"</code></pre>
<pre class="r"><code>coords</code></pre>
<pre><code>## lon lat
## [1,] NA NA</code></pre>
<p>Now use that location to spatially filter stations with a rectangular box.</p>
<pre class="r"><code>dplyr::filter(st,
grepl("BUFFALO",name)&
between(latitude,coords[2]-1,coords[2]+1) &
between(longitude,coords[1]-1,coords[1]+1)&
element=="TMAX")</code></pre>
<pre><code>## # A tibble: 0 x 11
## # ... with 11 variables: id <chr>, latitude <dbl>, longitude <dbl>,
## # elevation <dbl>, state <chr>, name <chr>, gsn_flag <chr>,
## # wmo_id <chr>, element <chr>, first_year <int>, last_year <int></code></pre>
<p>You could also spatially filter using <code>over()</code> in sp package…</p>
<p>With the station ID, we can now download daily data from NOAA.</p>
<pre class="r"><code>d=meteo_tidy_ghcnd("USW00014733",
var = c("TMAX","TMIN","PRCP"),
keep_flags=T)
head(d)</code></pre>
<pre><code>## # A tibble: 6 x 14
## id date mflag_prcp mflag_tmax mflag_tmin prcp qflag_prcp
## <chr> <date> <chr> <chr> <chr> <dbl> <chr>
## 1 USW0… 1938-05-01 T " " " " 0 " "
## 2 USW0… 1938-05-02 T " " " " 0 " "
## 3 USW0… 1938-05-03 " " " " " " 25 " "
## 4 USW0… 1938-05-04 " " " " " " 112 " "
## 5 USW0… 1938-05-05 T " " " " 0 " "
## 6 USW0… 1938-05-06 " " " " " " 64 " "
## # ... with 7 more variables: qflag_tmax <chr>, qflag_tmin <chr>,
## # sflag_prcp <chr>, sflag_tmax <chr>, sflag_tmin <chr>, tmax <dbl>,
## # tmin <dbl></code></pre>
<p>See <a href="http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf">CDO Daily Description</a> and raw <a href="http://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt">GHCND metadata</a> for more details. If you want to download multiple stations at once, check out <code>meteo_pull_monitors()</code></p>
</div>
<div id="quality-control-mflag" class="section level3">
<h3>Quality Control: MFLAG</h3>
<p>Measurement Flag/Attribute</p>
<ul>
<li><strong>Blank</strong> no measurement information applicable</li>
<li><strong>B</strong> precipitation total formed from two twelve-hour totals</li>
<li><strong>H</strong> represents highest or lowest hourly temperature (TMAX or TMIN) or average of hourly values (TAVG)</li>
<li><strong>K</strong> converted from knots</li>
<li>…</li>
</ul>
<p>See <a href="http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf">CDO Description</a></p>
</div>
<div id="quality-control-qflag" class="section level3">
<h3>Quality Control: QFLAG</h3>
<ul>
<li><strong>Blank</strong> did not fail any quality assurance check</li>
<li><strong>D</strong> failed duplicate check</li>
<li><strong>G</strong> failed gap check</li>
<li><strong>K</strong> failed streak/frequent-value check</li>
<li><strong>N</strong> failed naught check</li>
<li><strong>O</strong> failed climatological outlier check</li>
<li><strong>S</strong> failed spatial consistency check</li>
<li><strong>T</strong> failed temporal consistency check</li>
<li><strong>W</strong> temperature too warm for snow</li>
<li>…</li>
</ul>
<p>See <a href="http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf">CDO Description</a></p>
</div>
<div id="quality-control-sflag" class="section level3">
<h3>Quality Control: SFLAG</h3>
<p>Indicates the source of the data…</p>
</div>
</div>
<div id="summarize-qc-flags" class="section level2">
<h2>Summarize QC flags</h2>
<p>Summarize the QC flags. How many of which type are there? Should we be more conservative?</p>
<pre class="r"><code>table(d$qflag_tmax) </code></pre>
<pre><code>##
## G I
## 29027 2 10</code></pre>
<pre class="r"><code>table(d$qflag_tmin) </code></pre>
<pre><code>##
## G I S
## 29026 2 7 4</code></pre>
<pre class="r"><code>table(d$qflag_prcp) </code></pre>
<pre><code>##
## G
## 29038 1</code></pre>
<ul>
<li><strong>T</strong> failed temporal consistency check</li>
</ul>
<div id="filter-with-qc-data-and-change-units" class="section level4">
<h4>Filter with QC data and change units</h4>
<pre class="r"><code>d_filtered=d%>%
mutate(tmax=ifelse(qflag_tmax!=" "|tmax==-9999,NA,tmax/10))%>% # convert to degrees C
mutate(tmin=ifelse(qflag_tmin!=" "|tmin==-9999,NA,tmin/10))%>% # convert to degrees C
mutate(prcp=ifelse(qflag_tmin!=" "|prcp==-9999,NA,prcp))%>% # convert to degrees C
arrange(date)</code></pre>
<p>Plot temperatures</p>
<pre class="r"><code>ggplot(d_filtered,
aes(y=tmax,x=date))+
geom_line(col="red")</code></pre>
<pre><code>## Warning: Removed 11 rows containing missing values (geom_path).</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-16-1.png" /><!-- --></p>
<p>Limit to a few years and plot the daily range and average temperatures.</p>
<pre class="r"><code>d_filtered_recent=filter(d_filtered,date>as.Date("2013-01-01"))
ggplot(d_filtered_recent,
aes(ymax=tmax,ymin=tmin,x=date))+
geom_ribbon(col="grey",fill="grey")+
geom_line(aes(y=(tmax+tmin)/2),col="red")</code></pre>
<pre><code>## Warning: Removed 11 rows containing missing values (geom_path).</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-17-1.png" /><!-- --></p>
</div>
<div id="zoo-package-for-rolling-functions" class="section level3">
<h3>Zoo package for rolling functions</h3>
<p>Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations)</p>
<ul>
<li><code>rollmean()</code>: Rolling mean</li>
<li><code>rollsum()</code>: Rolling sum</li>
<li><code>rollapply()</code>: Custom functions</li>
</ul>
<p>Use rollmean to calculate a rolling 60-day average.</p>
<ul>
<li><code>align</code> whether the index of the result should be left- or right-aligned or centered</li>
</ul>
<pre class="r"><code>d_rollmean = d_filtered_recent %>%
arrange(date) %>%
mutate(tmax.60 = rollmean(x = tmax, 60, align = "center", fill = NA),
tmax.b60 = rollmean(x = tmax, 60, align = "right", fill = NA))</code></pre>
<pre class="r"><code>d_rollmean%>%
ggplot(aes(ymax=tmax,ymin=tmin,x=date))+
geom_ribbon(fill="grey")+
geom_line(aes(y=(tmin+tmax)/2),col=grey(0.4),size=.5)+
geom_line(aes(y=tmax.60),col="red")+
geom_line(aes(y=tmax.b60),col="darkred")</code></pre>
<pre><code>## Warning: Removed 11 rows containing missing values (geom_path).</code></pre>
<pre><code>## Warning: Removed 70 rows containing missing values (geom_path).
## Warning: Removed 70 rows containing missing values (geom_path).</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-19-1.png" /><!-- --></p>
<div class="well">
<h2 id="your-turn-1">Your Turn</h2>
<p>Plot a 30-day rolling “right” aligned sum of precipitation.</p>
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo2">
Show Solution
</button>
<div id="demo2" class="collapse">
<pre class="r"><code>tp=d_filtered_recent %>%
arrange(date) %>%
mutate(prcp.30 = rollsum(x = prcp, 30, align = "right", fill = NA))
ggplot(tp,aes(y=prcp,x=date))+
geom_line(aes(y=prcp.30),col="black")+
geom_line(col="red") </code></pre>
<pre><code>## Warning: Removed 40 rows containing missing values (geom_path).</code></pre>
<pre><code>## Warning: Removed 11 rows containing missing values (geom_path).</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-20-1.png" /><!-- --></p>
</div>
</div>
</div>
</div>
</div>
<div id="time-series-analysis" class="section level1">
<h1>Time Series analysis</h1>
<p>Most timeseries functions use the time series class (<code>ts</code>)</p>
<pre class="r"><code>tmin.ts=ts(d_filtered_recent$tmin,frequency = 365)</code></pre>
<div id="temporal-autocorrelation" class="section level2">
<h2>Temporal autocorrelation</h2>
<p>Values are highly correlated!</p>
<pre class="r"><code>ggplot(d_filtered_recent,aes(y=tmin,x=lag(tmin)))+
geom_point()+
geom_abline(intercept=0, slope=1)</code></pre>
<pre><code>## Warning: Removed 12 rows containing missing values (geom_point).</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-22-1.png" /><!-- --></p>
<div id="autocorrelation-functions" class="section level3">
<h3>Autocorrelation functions</h3>
<ul>
<li>autocorrelation <span class="math inline"><em>x</em></span> vs. <span class="math inline"><em>x</em><sub><em>t</em> − 1</sub></span> (lag=1)</li>
<li>partial autocorrelation. <span class="math inline"><em>x</em></span> vs. <span class="math inline"><em>x</em><sub><em>n</em></sub></span> <em>after</em> controlling for correlations <span class="math inline"> ∈ <em>t</em> − 1 : <em>n</em></span></li>
</ul>
<div id="autocorrelation" class="section level4">
<h4>Autocorrelation</h4>
<pre class="r"><code>acf(tmin.ts,lag.max = 365*3,na.action = na.exclude )</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-23-1.png" /><!-- --></p>
</div>
<div id="partial-autocorrelation" class="section level4">
<h4>Partial Autocorrelation</h4>
<pre class="r"><code>pacf(tmin.ts,lag.max = 365*3,na.action = na.exclude )</code></pre>
<p><img src="TK_09_files/figure-html/unnamed-chunk-24-1.png" /><!-- --></p>
</div>
</div>
</div>
</div>
<div id="checking-for-significant-trends" class="section level1">
<h1>Checking for significant trends</h1>
<div id="compute-temporal-aggregation-indices" class="section level2">
<h2>Compute temporal aggregation indices</h2>
<div id="group-by-month-season-year-and-decade." class="section level3">
<h3>Group by month, season, year, and decade.</h3>
<p>How to convert years into ‘decades’?</p>
<pre class="r"><code>1938</code></pre>
<pre><code>## [1] 1938</code></pre>
<pre class="r"><code>round(1938,-1)</code></pre>
<pre><code>## [1] 1940</code></pre>
<pre class="r"><code>floor(1938/10)*10</code></pre>
<pre><code>## [1] 1930</code></pre>
<p>Calculate seasonal and decadal mean temperatures.</p>
<pre class="r"><code>d_filtered2=d_filtered%>%
mutate(month=as.numeric(format(date,"%m")),
year=as.numeric(format(date,"%Y")),
season=ifelse(month%in%c(12,1,2),"Winter",
ifelse(month%in%c(3,4,5),"Spring",
ifelse(month%in%c(6,7,8),"Summer",
ifelse(month%in%c(9,10,11),"Fall",NA)))),
dec=(floor(as.numeric(format(date,"%Y"))/10)*10))
knitr::kable(head(d_filtered2))</code></pre>
<table>
<thead>
<tr class="header">
<th align="left">id</th>
<th align="left">date</th>
<th align="left">mflag_prcp</th>
<th align="left">mflag_tmax</th>
<th align="left">mflag_tmin</th>
<th align="right">prcp</th>
<th align="left">qflag_prcp</th>
<th align="left">qflag_tmax</th>
<th align="left">qflag_tmin</th>
<th align="left">sflag_prcp</th>
<th align="left">sflag_tmax</th>
<th align="left">sflag_tmin</th>
<th align="right">tmax</th>
<th align="right">tmin</th>
<th align="right">month</th>
<th align="right">year</th>
<th align="left">season</th>
<th align="right">dec</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">USW00014733</td>
<td align="left">1938-05-01</td>
<td align="left">T</td>
<td align="left"></td>
<td align="left"></td>
<td align="right">0</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">14.4</td>
<td align="right">3.9</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
<tr class="even">
<td align="left">USW00014733</td>
<td align="left">1938-05-02</td>
<td align="left">T</td>
<td align="left"></td>
<td align="left"></td>
<td align="right">0</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">21.1</td>
<td align="right">8.3</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
<tr class="odd">
<td align="left">USW00014733</td>
<td align="left">1938-05-03</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right">25</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">16.7</td>
<td align="right">7.2</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
<tr class="even">
<td align="left">USW00014733</td>
<td align="left">1938-05-04</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right">112</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">20.6</td>
<td align="right">9.4</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
<tr class="odd">
<td align="left">USW00014733</td>
<td align="left">1938-05-05</td>
<td align="left">T</td>
<td align="left"></td>
<td align="left"></td>
<td align="right">0</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">31.1</td>
<td align="right">10.6</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
<tr class="even">
<td align="left">USW00014733</td>
<td align="left">1938-05-06</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right">64</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="right">19.4</td>
<td align="right">7.8</td>
<td align="right">5</td>
<td align="right">1938</td>
<td align="left">Spring</td>
<td align="right">1930</td>
</tr>
</tbody>
</table>