Most costly events
Synopsis
In this note we show that tornado, thunderstorm wind and heat are the most harmful with respect to population health in the United State. We also show that flood, hurricane and tornado cause most property damage while drought and flood cause most crop damage. We use U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur. It also estimates of any fatalities, injuries, and property damage.
Data Processing
In this section we present code we used for data processing. We are also going to use local cache, so we need to create a file for this.
Load required libraries, set directories and file hash
First we load packages needed and set cashier.
require(knitr)## Loading required package: knitrrequire(data.table)## Loading required package: data.tablerequire(ggplot2)## Loading required package: ggplot2require(xtable)## Loading required package: xtablerequire(filehash)## Loading required package: filehash
## filehash: Simple key-value database (2.2-2 2013-12-16)if (!file.exists('data')) {dir.create('data')}
dbPath <- 'data/DB'
if (!file.exists(dbPath)) {dbCreate(dbPath)}
db <- dbInit(dbPath)Download and read data
Now we download data and chache data.table created. Note that we save cache localy, so analysis start from the downloaded raw data file.
download.storm.dt <- function() {
fileURI <- 'https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2'
fileBzPath <- 'data/repdata-data-StormData.csv.bz2'
if (!file.exists(fileBzPath)) {
download.file(fileURI, destfile=fileBzPath, method="curl")
}
storm.df <- read.csv(bzfile(fileBzPath))
as.data.table(storm.df)
}
get.or.cache <- function(fun, svar) {
if (dbExists(db, svar)) {
#We check if svar is cached. If not we exectute function and cache
#its value.
#print("Reading data.table form cache...")
dbFetch(db, svar)
} else {
var <- fun()
dbInsert(db, svar, var)
var
}
}
storm.dt <- get.or.cache(download.storm.dt, "storm.dt")Cleaning data
Now, we preper new data.table with selected column, that we use for analysis.
clean.dt <- storm.dt[,.(FATALITIES, INJURIES, PROPDMG, PROPDMGEXP,
CROPDMG, CROPDMGEXP)]
clean.dt[,Evtype:=tolower(storm.dt[,EVTYPE])]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowWe clean some names in the evtype column. For example tstm wind is clearly thunderstorm wind, raining is the same as rain etc. We follow events in table 2.1.1
https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
therefore we unified entries like hurricane, wildfire. Probably there are more problems there, but later we will show that they are insignificant in the analysis we do.
clean.dt[,Evtype:=gsub('winds','wind',Evtype)]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('tstm wind|thunderstorm wind', Evtype),Evtype:='thunderstorm wind']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('heavy rain', Evtype),Evtype:='heavy rain']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('flash flood', Evtype),Evtype:='flash flood']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl("river flood|river flooding|^flooding", Evtype),Evtype:='flood']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl("coastal flood", Evtype),Evtype:='coastal flood']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('hurricane', Evtype),Evtype:='hurricane']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('storm surge', Evtype),Evtype:='storm surge/tide']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowclean.dt[grepl('wildfire', Evtype),Evtype:='wildfire']## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype
## 1: tornado
## 2: tornado
## 3: tornado
## 4: tornado
## 5: tornado
## ---
## 902293: high wind
## 902294: high wind
## 902295: high wind
## 902296: blizzard
## 902297: heavy snowResults
Events most harmful with respect to population health
Fatalities
events <- unique(tolower(clean.dt[,Evtype]))
strom.col.ordered <- function(cname) {
sco <- clean.dt[,sum(get(cname)),by=Evtype]
sco[order(sco$V1, decreasing=T)]
}
fatalities<-strom.col.ordered("FATALITIES")
cnum <- dim(fatalities)[1]
fatalities.40 <- xtable(fatalities[1:40])
print(fatalities.40, type="html")| Evtype | V1 | |
|---|---|---|
| 1 | tornado | 5633.00 |
| 2 | excessive heat | 1903.00 |
| 3 | flash flood | 1035.00 |
| 4 | heat | 937.00 |
| 5 | lightning | 816.00 |
| 6 | thunderstorm wind | 753.00 |
| 7 | flood | 481.00 |
| 8 | rip current | 368.00 |
| 9 | high wind | 283.00 |
| 10 | avalanche | 224.00 |
| 11 | winter storm | 206.00 |
| 12 | rip currents | 204.00 |
| 13 | heat wave | 172.00 |
| 14 | extreme cold | 162.00 |
| 15 | hurricane | 135.00 |
| 16 | heavy snow | 127.00 |
| 17 | extreme cold/wind chill | 125.00 |
| 18 | strong wind | 111.00 |
| 19 | high surf | 104.00 |
| 20 | blizzard | 101.00 |
| 21 | heavy rain | 99.00 |
| 22 | extreme heat | 96.00 |
| 23 | cold/wind chill | 95.00 |
| 24 | ice storm | 89.00 |
| 25 | wildfire | 75.00 |
| 26 | fog | 62.00 |
| 27 | tropical storm | 58.00 |
| 28 | heavy surf/high surf | 42.00 |
| 29 | cold | 38.00 |
| 30 | landslide | 38.00 |
| 31 | winter weather | 33.00 |
| 32 | tsunami | 33.00 |
| 33 | unseasonably warm and dry | 29.00 |
| 34 | urban/sml stream fld | 28.00 |
| 35 | winter weather/mix | 28.00 |
| 36 | wind | 24.00 |
| 37 | storm surge/tide | 24.00 |
| 38 | dust storm | 22.00 |
| 39 | dense fog | 18.00 |
| 40 | record/excessive heat | 17.00 |
There are total of 15145 fatalities and only 317 fatalities not included in Top 40.
Figure 1. Top 5 events that produce most fatalities.
ggplot(fatalities[1:5], aes(x=Evtype, y=V1))+geom_bar(stat = "identity")
Injuries
injuries<-strom.col.ordered("INJURIES")
cnum <- dim(injuries)[1]
injuries.40 <- xtable(injuries[1:40])
print(injuries.40, type="html")| Evtype | V1 | |
|---|---|---|
| 1 | tornado | 91346.00 |
| 2 | thunderstorm wind | 9493.00 |
| 3 | flood | 6794.00 |
| 4 | excessive heat | 6525.00 |
| 5 | lightning | 5230.00 |
| 6 | heat | 2100.00 |
| 7 | ice storm | 1975.00 |
| 8 | flash flood | 1802.00 |
| 9 | high wind | 1439.00 |
| 10 | hail | 1361.00 |
| 11 | hurricane | 1328.00 |
| 12 | winter storm | 1321.00 |
| 13 | heavy snow | 1021.00 |
| 14 | wildfire | 911.00 |
| 15 | blizzard | 805.00 |
| 16 | fog | 734.00 |
| 17 | wild/forest fire | 545.00 |
| 18 | dust storm | 440.00 |
| 19 | winter weather | 398.00 |
| 20 | heat wave | 379.00 |
| 21 | dense fog | 342.00 |
| 22 | tropical storm | 340.00 |
| 23 | strong wind | 301.00 |
| 24 | rip currents | 297.00 |
| 25 | heavy rain | 255.00 |
| 26 | rip current | 232.00 |
| 27 | extreme cold | 231.00 |
| 28 | glaze | 216.00 |
| 29 | avalanche | 170.00 |
| 30 | high surf | 156.00 |
| 31 | extreme heat | 155.00 |
| 32 | wild fires | 150.00 |
| 33 | ice | 137.00 |
| 34 | tsunami | 129.00 |
| 35 | wind | 87.00 |
| 36 | urban/sml stream fld | 79.00 |
| 37 | wintry mix | 77.00 |
| 38 | winter weather/mix | 72.00 |
| 39 | winter weather mix | 68.00 |
| 40 | landslide | 52.00 |
There are total of 140528 injuries and only 1035 injuries not included in Top 40.
Figure 2. Top 5 events that produce most injuries.
ggplot(injuries[1:5], aes(x=Evtype, y=V1))+geom_bar(stat = "identity")
Events that have the greatest economic consequences
Property damage
propexps <- clean.dt[,PROPDMGEXP]
tonum <- function(ch) {
ch <- as.character(ch)
if (ch=='h' || ch =='H') {
1
} else if (ch == 'K'||ch == 'k') {
3
} else if (ch=='m'|| ch=='M') {
6
}
else if (ch=='B') {
9
} else if (ch %in% c("", "-", "?", "+")) {
0
} else {
as.integer(ch)
}
}
propexps.new <- get.or.cache(function() {sapply(propexps, tonum)}, "propexps.new")
clean.dt[, PropExp:=propexps.new]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype PropExp
## 1: tornado 3
## 2: tornado 3
## 3: tornado 3
## 4: tornado 3
## 5: tornado 3
## ---
## 902293: high wind 3
## 902294: high wind 3
## 902295: high wind 3
## 902296: blizzard 3
## 902297: heavy snow 3clean.dt[, PropD:=PROPDMG*(10)^PropExp]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype PropExp PropD
## 1: tornado 3 25000
## 2: tornado 3 2500
## 3: tornado 3 25000
## 4: tornado 3 2500
## 5: tornado 3 2500
## ---
## 902293: high wind 3 0
## 902294: high wind 3 0
## 902295: high wind 3 0
## 902296: blizzard 3 0
## 902297: heavy snow 3 0prop.dmg<-strom.col.ordered("PropD")
cnum <- dim(prop.dmg)[1]
prop.40 <- xtable(prop.dmg[1:40])
print(prop.40, type="html")| Evtype | V1 | |
|---|---|---|
| 1 | flood | 150002295307.00 |
| 2 | hurricane | 84756180010.00 |
| 3 | tornado | 56947380676.50 |
| 4 | storm surge/tide | 47964724000.00 |
| 5 | flash flood | 17588780095.91 |
| 6 | hail | 15735267062.70 |
| 7 | thunderstorm wind | 11575229120.10 |
| 8 | tropical storm | 7703890550.00 |
| 9 | winter storm | 6688497251.00 |
| 10 | high wind | 5878370043.00 |
| 11 | wildfire | 4865614000.00 |
| 12 | ice storm | 3944927860.00 |
| 13 | heavy rain | 3248548142.00 |
| 14 | wild/forest fire | 3001829500.00 |
| 15 | severe thunderstorm | 1205360000.00 |
| 16 | drought | 1046106000.00 |
| 17 | heavy snow | 932759140.00 |
| 18 | lightning | 930379429.50 |
| 19 | blizzard | 659213950.00 |
| 20 | wild fires | 624100000.00 |
| 21 | typhoon | 600230000.00 |
| 22 | coastal flood | 417616060.00 |
| 23 | landslide | 324596000.00 |
| 24 | hailstorm | 241000000.00 |
| 25 | strong wind | 177674240.00 |
| 26 | tsunami | 144062000.00 |
| 27 | high wind/cold | 110500000.00 |
| 28 | major flood | 105000000.00 |
| 29 | high surf | 89955000.00 |
| 30 | extreme cold | 67737400.00 |
| 31 | winter storm high wind | 60000000.00 |
| 32 | urban/sml stream fld | 58309650.00 |
| 33 | record cold | 56000000.00 |
| 34 | waterspout/tornado | 51110000.00 |
| 35 | lake-effect snow | 40115000.00 |
| 36 | winter weather | 20866000.00 |
| 37 | urban flood | 18288000.00 |
| 38 | erosion/cstl flood | 16200000.00 |
| 39 | coastal flooding/erosion | 15000000.00 |
| 40 | snow | 14827550.00 |
There are total of 428224866338 USD of property damage and only 296327300 USD not included in Top 40.
Figure 3. Top 5 events that produce most property damage.
ggplot(prop.dmg[1:5], aes(x=Evtype, y=V1))+geom_bar(stat = "identity")
Crop damage
cropexps <- clean.dt[,CROPDMGEXP]
#cropexps.unique <- sort(as.character(unique(cropexps)))
cropexps.new <- get.or.cache(function() {sapply(cropexps, tonum)}, "cropexps.new")
clean.dt[, CropExp:=cropexps.new]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype PropExp PropD CropExp
## 1: tornado 3 25000 0
## 2: tornado 3 2500 0
## 3: tornado 3 25000 0
## 4: tornado 3 2500 0
## 5: tornado 3 2500 0
## ---
## 902293: high wind 3 0 3
## 902294: high wind 3 0 3
## 902295: high wind 3 0 3
## 902296: blizzard 3 0 3
## 902297: heavy snow 3 0 3clean.dt[, CropD:=CROPDMG*(10)^CropExp]## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: 0 15 25.0 K 0
## 2: 0 0 2.5 K 0
## 3: 0 2 25.0 K 0
## 4: 0 2 2.5 K 0
## 5: 0 2 2.5 K 0
## ---
## 902293: 0 0 0.0 K 0 K
## 902294: 0 0 0.0 K 0 K
## 902295: 0 0 0.0 K 0 K
## 902296: 0 0 0.0 K 0 K
## 902297: 0 0 0.0 K 0 K
## Evtype PropExp PropD CropExp CropD
## 1: tornado 3 25000 0 0
## 2: tornado 3 2500 0 0
## 3: tornado 3 25000 0 0
## 4: tornado 3 2500 0 0
## 5: tornado 3 2500 0 0
## ---
## 902293: high wind 3 0 3 0
## 902294: high wind 3 0 3 0
## 902295: high wind 3 0 3 0
## 902296: blizzard 3 0 3 0
## 902297: heavy snow 3 0 3 0crop.dmg<-strom.col.ordered("CropD")
cnum <- dim(crop.dmg)[1]
crop.dmg41 <- sum(crop.dmg[41:cnum]$V1)
crop.dmg1 <- sum(crop.dmg$V1)crop.40 <- xtable(crop.dmg[1:40])
print(crop.40, type="html")| Evtype | V1 | |
|---|---|---|
| 1 | drought | 13972566000.00 |
| 2 | flood | 10728307950.00 |
| 3 | hurricane | 5515292800.00 |
| 4 | ice storm | 5022113500.00 |
| 5 | hail | 3025954473.00 |
| 6 | flash flood | 1532197150.00 |
| 7 | extreme cold | 1312973000.00 |
| 8 | thunderstorm wind | 1255947988.00 |
| 9 | frost/freeze | 1094186000.00 |
| 10 | heavy rain | 795762800.00 |
| 11 | high wind | 679291900.00 |
| 12 | tropical storm | 678346000.00 |
| 13 | excessive heat | 492402000.00 |
| 14 | freeze | 456725000.00 |
| 15 | tornado | 414953270.00 |
| 16 | heat | 401461500.00 |
| 17 | damaging freeze | 296230000.00 |
| 18 | wildfire | 295972800.00 |
| 19 | excessive wetness | 142000000.00 |
| 20 | heavy snow | 134653100.00 |
| 21 | flood/rain/wind | 112800000.00 |
| 22 | blizzard | 112060000.00 |
| 23 | wild/forest fire | 106796830.00 |
| 24 | strong wind | 69953500.00 |
| 25 | frost | 66000000.00 |
| 26 | cold and wet conditions | 66000000.00 |
| 27 | early frost | 42000000.00 |
| 28 | agricultural freeze | 28820000.00 |
| 29 | winter storm | 26944000.00 |
| 30 | unseasonably cold | 25042500.00 |
| 31 | small hail | 20793000.00 |
| 32 | landslide | 20017000.00 |
| 33 | severe thunderstorms | 17000000.00 |
| 34 | extreme windchill | 17000000.00 |
| 35 | tropical storm jerry | 16000000.00 |
| 36 | winter weather | 15000000.00 |
| 37 | hard freeze | 13100000.00 |
| 38 | lightning | 12092090.00 |
| 39 | unseasonal rain | 10000000.00 |
| 40 | urban/sml stream fld | 8488100.00 |
There are total of 49104192181 USD of crop damage and only 50947930 USD not included in Top 40.