For some Americans, this is a real weather phenomenon that risks the physical well-being of people and property. In fact, the frozen precipitation resulting from fast updrafts during strong thunder storms can lead to serious damage and harm. Each year, the U.S. sees approximately $1billion in property and crop damage. But how often does it happen? Where do these events normally happen?
As it turns out, the National Oceanic and Atmospheric Administration (NOAA) collects massive amounts of data on precipitation using radar stations across the country. Algorithms can be built on top of the data to track 'hail signatures' detecting when these events occur and the severity. This severe storm data is captured in NOAA's Severe Weather Data Inventory (SWDI) housed within the National Centers for Environmental Information (NCEI).
Between 2005 and 2010, NOAA has detected over 9 million hail signatures events. Often times, individual events on their own do not provide much context of prevailing conditions. In order to contextualize those events, scientists often process data into climatologies, or weather conditions averaged over a period of time. Climatologies can be presented in frequencies or probabilities over various time units (e.g. hourly, daily, monthly) and geographic units (e.g. 1 degree grid cell) for time horizons of 10 to 30 years. The practical applications are many, ranging from mitigating risk of encountering adverse storm events (e.g. when not to go to the beach) to anticipating the best time start growing certain crops. In short, climatologies help set probabilistic expectations.
The following two climatology graphs illustrate the natural rhythm of hail events over months and hours, respectively. A useful climatology typically focuses in on specific geographic areas, but for demonstration purposes, the data is processed on a national level. Each bar represents the proportion of all hail signatures detected in a given time unit over the 10 year period with clear peaks and troughs.
Where are the most heavily impacted areas? By reprocessing 9 million events down into 16,000
equally spaced grid points 0.25 degrees apart (~17 miles), it becomes easier to determine that the Midwest has a
far higher chance of experiencing a hail event on any given day.
When limiting the data to severe hail events (e.g. hail diameter > 3in),
the majority of these events occur in a smaller but significant area of the country.
Hail data is just the tip of the iceberg. NOAA weather data can be processed into climatologies and be applied to
determining when to plant a leafy vegetable to when to take a beach vacation.
To get you kick started, in this tutorial, we will go over the following critical steps in climate data processing in the R programming language:
To get started quickly, the code for this tutorial can be found at the following Github repo (https://github.com/CommerceDataService/tutorial_noaa_hail).
Start off by specifying the working directory as well as calling 7 libraries:
library(sqldf)
library(RColorBrewer)
library(leaflet)
library(googleVis)
library(rgdal)
The SWDI data covers a number of data series that use event detection algorithms on NEXRAD radar data. The main data series are as follows:
We'll use the SWDI API to extract all hail signatures between 2005 and 2015 in 30 day incremenets as the maximum number of days per API call is 31 days or 744 hours. The wrapper 'swdi_pull' will need three parameters:
swdi_pull <- function(start_date,end_date,series){
##translate the string into a date, and range
start <- as.Date(start_date,"%Y-%m-%d")
range <- as.numeric(as.Date(end_date,"%Y-%m-%d")-as.Date(start_date,"%Y-%m-%d"))
##Placeholder for the result
raw <- data.frame()
##Loop through Day 0 through the full range of days
for(i in seq(0,range,30)){
##Load in parameters, hit API
print(i)
period <- start + i
increment0 <- paste(format(period,"%Y"),format(period,"%m"),format(period,"%d"),sep="")
increment1 <- paste(format(period+30,"%Y"),format(period+30,"%m"),format(period+30,"%d"),sep="")
temp <- read.csv(paste("http://www.ncdc.noaa.gov/swdiws/csv/",series,"/",
increment0,":",increment1,sep=""))
##If the API kicks back a result
if(ncol(temp)!=1 && colnames(temp)[1]!="summary"){
raw <- rbind(raw,temp)
raw <- raw[!is.na(raw$LAT),]
}
}
##Clean up time steps -- remove data outside of specified period
raw$DATE<-as.Date(substr(raw$ZTIME,1,10),"%Y-%m-%d")
raw<-raw[raw$DATE<=as.Date(end_date,"%Y-%m-%d"),]
raw$HOUR<-substr(raw$ZTIME,12,13)
raw<-raw[,c("ZTIME","DATE","HOUR","WSR_ID","CELL_ID","PROB","SEVPROB","MAXSIZE","LAT","LON")]
return(raw)
}
Now, we can tap the API for data. We'll first specify the parameters.
##Set Parameters
start_date = "2005-01-01"
end_date = "2014-12-31"
range <- as.Date(end_date,"%Y-%m-%d")-as.Date(start_date,"%Y-%m-%d")
series = "nx3hail"
fraction = 0.25
And using those parameters, we can now draw down the data. This will take a while – about 10 million records.
raw <- swdi_pull(start_date,end_date,series)
As the SWDI data is point-level data that will be processed into equal-interval grid points, we will want to add spatial context to the data by spatially joining points to county boundary files. The US Census Bureau provides boundary shapefiles through their website (http://www2.census.gov/geo/tiger/GENZ2014/shp/cb_2014_us_county_20m.zip). To efficiently load it in, we'll write a simple function to download, unzip and load a shapefile.
shape_direct <- function(url, shp) {
temp = tempfile()
download.file(url, temp) ##download the URL taret to the temp file
unzip(temp,exdir=getwd()) ##unzip that file
return(readOGR(paste(shp,".shp",sep=""),shp))
}
To run the shape_direct function, we just need the url and the shapefile name.
shp <- shape_direct(url="http://www2.census.gov/geo/tiger/GENZ2014/shp/cb_2014_us_county_20m.zip",
shp= "cb_2014_us_county_20m")
In addition, we're going to pull in a reference table that links Federal Information Processing System (FIPS) codes that contain numeric identifiers for states. This'll be useful for clearly indicating in plain language which counties are in a given state.
fips <- read.delim("http://www2.census.gov/geo/docs/reference/state.txt",sep="|")
fips <- fips[,c("STATE","STUSAB")] ##Keep the FIPS code and state abbreviation
fips$STATE <- as.numeric(fips$STATE) ##Convert FIPS code to numeric
fips$STUSAB <- as.character(fips$STUSAB) ##Convert state name to character
At this point, we have all the data (hail signatures, county shapefiles, FIPs codes).
paste("Number of records in Hail data: ",nrow(raw),sep="")
paste("Number of counties in shapefile: ",nrow(as.data.frame(shp)))
Now, we can begin the process down the data. The first issue is to convert hail signatures from events to regularly spaced grid points at the daily level. As two or more radar stations may detect the same hail event at the same time, this basic gridding process will help to reduce double counting as well as allow for calculating a climatology.
To start, we’ll set a bounding box of the continental US.
#Cut down bounding box
raw <- raw[raw$LON<(-50) & raw$LON>(-140) & raw$LAT > 25,]
Also, the hail event coordinates will be rounded to the nearest fraction as specified in the starting parameters. In this case, the fraction is 1/4 of a degree or about 17.25 miles latitudinally. Using SQL, we will group hail events by date, lat, lon, and hail size. Then based on the hail size, we’ll produce dummy variables are produced for each 2+ inch and 3+ inch thresholds. For context, the diameter of a baseball is approximately 2.9 inches.
##Round coordinates
raw$LON <- round(raw$LON/fraction)*fraction
raw$LAT <- round(raw$LAT/fraction)*fraction
##De-duplicate by day, latitude and longitude
deduped_day <- sqldf("SELECT DATE, LON, LAT, MAXSIZE
FROM raw
GROUP BY DATE, LON, LAT, MAXSIZE")
##Dummy variable (and 3+ in)
deduped_day$lvl_3in<-0
deduped_day$lvl_3in[deduped_day$MAXSIZE>3]<-1
Based on the de-duplicated daily, gridded data, we’ll now group by once again by lat and lon coordinates. This time, we’ll count the number of records per gridpoint (cnt = any hail event) as well as sum the dummy variables for 3+ inch (cnt_3) events. These count variables are then normalized by the number of days specified in the API call (range).
##Daily gridded frequencies
singles <- sqldf("SELECT LON, LAT,COUNT(DATE) cnt, SUM(lvl_3in) cnt_3
FROM deduped_day
GROUP BY LON, LAT")
##Normalize
for(i in 3:ncol(singles)){
singles[,i]<-singles[,i]/as.numeric(range)
}
Similar to gridded processing, hourly and monthly climatologies can be processed using simple group by statements. Note that climatologies are calculating based on day-grid cells (e.g. whether a hail signature was detected in a 0.25 degree grid cell in a given day) as opposed to the raw hail signatures.
##Process Into Weeks
deduped_day$DATE <- as.Date(as.character(deduped_day$DATE,"%Y-%m-%d"))
deduped_day$MONTH <- as.numeric(format(deduped_day$DATE,"%m"))
##Frequency: Hour
hourly <- sqldf("SELECT HOUR, COUNT(HOUR) count_all
FROM deduped_day
GROUP BY HOUR")
hourly$`All Hail` <- round(100*hourly$count_all/sum(hourly$count_all),2)
#Frequency: Month
monthly <- sqldf("SELECT MONTH, COUNT(DATE) count_all
FROM deduped_day
GROUP BY MONTH")
monthly$`All Hail` <- round(100*monthly$count_all/sum(monthly$count_all),2)
#Assign monthly string label to each month
monthly$mon <- "Jan"
monthly$mon[monthly$MONTH == 2] <- "Feb"
monthly$mon[monthly$MONTH == 3] <- "Mar"
monthly$mon[monthly$MONTH == 4] <- "Apr"
monthly$mon[monthly$MONTH == 5] <- "May"
monthly$mon[monthly$MONTH == 6] <- "Jun"
monthly$mon[monthly$MONTH == 7] <- "Jul"
monthly$mon[monthly$MONTH == 8] <- "Aug"
monthly$mon[monthly$MONTH == 9] <- "Sep"
monthly$mon[monthly$MONTH == 10] <- "Oct"
monthly$mon[monthly$MONTH == 11] <- "Nov"
monthly$mon[monthly$MONTH == 12] <- "Dec"
At this point, the data has been processed into a manageable form and visualizing the data is fairly straight. The bulk of the work going forward is focused on formatting and adding features to the visualizations. To do so, the data could be exported into JSON or CSVs to be ingested into visualization libraries such as D3.js, Google Charts, Dygraphs.js, and leaflet.js. In the R statistical programming language, there have been advancements in extending the functionality and interactivity such that web-based visualizations can be produced without leaving the platform. In this section, we’ll build climatology graphs using the googleVis library that leverages the Google Charts API as well as build climatology maps using leaflet.js.
GoogleVis functions largely like any other plotting library and accepts drames, sends them through to the Google Charts API, and kicks back a JavaScript-based visualization.
### Column chart
mon_clim <- gvisColumnChart(monthly,
xvar="mon",
yvar="All Hail",
options=list(vAxis.gridlines.count=1,
hAxis="{title:'Months'}", ##Title
vAxis="{gridlines:{ count:0}}", #Remove gridlines
series="[{color:'darkred',
targetAxisIndex: 0}]")) #Series color
hour_clim <- gvisColumnChart(hourly, xvar="HOUR", yvar=c("All Hail"),
options=list(vAxis.gridlines.count=1,
hAxis="{title:'Hours (24h)'}",
vAxis="{gridlines:{color:'red', count:0}}",
series="[{color:'darkred', targetAxisIndex: 0},
{color: 'red',targetAxisIndex:1}]"))
plot(mon_clim)
plot(hour_clim)
Knowing where risks occur is not sufficient. A good risk map contains context. To do this for grid points, we’ll spatially join the grid points to the closest county, that is see which county a grid point falls. It’s not a perfect match, but gives some basic context for the local area.
To start, we need to convert the gridded data into a shapefile and define the projection as WGS84 – one of the most common spatial projections for global data.
##Set up spatial
points<-singles
coordinates(points)=~LON+LAT
proj4string(points)=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")
shp <- spTransform(shp, CRS("+proj=longlat +ellps=WGS84 +datum=WGS84") )
##Spatial join -- really simple!
a<-over(points,shp)
##Join the results back to singles
singles <- cbind(singles,a[,c("STATEFP","NAME")])
singles$NAME <- as.character(singles$NAME)
#Merge state abbreviation via FIPS
singles$STATEFP <-as.numeric(singles$STATEFP)
singles <- merge(singles,fips,by.x="STATEFP",by.y="STATE",all.x=T, sort=F)
singles$loc <- paste(singles$NAME,", ",singles$STUSAB,sep="")
singles$loc[is.na(singles$NAME)]<-""
singles <- singles[!is.na(singles$STUSAB),]
For the popup, we’ll need to create a separate subset for 3+ inch events, vectorize the fields, and combine fields for the popup message using paste0. The message needs to be written in HTML as this will be directly rendered in browser.
#Subset data into separate frames (needed for layers in leaflet.js)
in3 <- singles[singles$cnt_3>0,]
#Vectorize data for inclusion for popup text
#All points
county_all <- singles$loc
x_all <- singles$LON
y_all <- singles$LAT
pr <- round(100*singles$cnt,3)
#Hail balls > 3in
county_3 <- in3$loc
x3 <- in3$LON
y3 <- in3$LAT
pr3 <- round(100*in3$cnt_3,3)
#Popup
content_all <- paste("<h3>",county_all,"</h3>Grid Point: ",x_all,", ",y_all,"<p>Prob of Any Hail <span style='color:red'><strong>: ",pr,"%</strong></span></p>")
content_3 <- paste0("<h3>",county_3,"</h3>Grid Point: ",x3,", ",y3,"<p>Prob of Hail (> 3in)<span style='color:red'><strong>: ",pr,"%</strong></span></p>")
For each series, we will need two color schemes that are sscaled to the range and variability in a data series. The color palette “Set1” used is pulled from the RColorBrewer package. The color scheme is high contrast and divergent, perfect for maps with varying levels of activity.
pal <- colorNumeric(
palette = "Set1",
domain = pr
)
pal2 <- colorNumeric(
palette = "Set1",
domain = pr3
)
We’ll initialize a leaflet.js map, setting the view centered on the mean coordinates of the continental US at zoom 4 (higher the value, closer the zoom), attributing the layer to CartoDB. With this basic code, we can build out maps nationally of all hail events and 3+ inch events and customize the styles.
leaflet(width="100%") %>%
setView(lat = mean(y_all), lng = mean(x_all),4) %>%
addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png',
attribution = "NOAA NCEI SWDI, US Census Bureau TIGER, Cartodb basemap") %>%
addCircleMarkers(data = singles, lat = ~ LAT, lng = ~ LON,radius=(pr/5),
fillOpacity = 0.8,stroke = FALSE,
color = ~pal(pr), popup = content_all) %>%
addLegend("bottomright", pal = pal, values = pr,
title = "Prob(Hail)",labFormat = labelFormat(suffix = "%")
)
leaflet(width="100%") %>%
setView(lat = mean(y_all), lng = mean(x_all),4) %>%
addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png',
attribution = "NOAA NCEI SWDI, US Census Bureau TIGER, Cartodb basemap") %>%
addCircleMarkers(data = in3, lat = ~ LAT, lng = ~ LON, radius=2*(pr3),
fillOpacity = 0.8,stroke = FALSE,
color = ~pal2(pr3), popup = content_all) %>%
addLegend("bottomright", pal = pal2, values = pr3,
title = "Prob(Hail diameter > 3in)",labFormat = labelFormat(suffix = "%")
)