AirPressureData
WindSpeedData
TimeSeries
TimeSeriesResample
Differences
Correlation
DateListPlot
UnitConvert

Geography
Data Science

Pressure Reading Drops Due to Hurricane Sandy

Compute the correlation between the air pressure and wind speed changes during a hurricane

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Use

AirPressureData

to examine pressure reading drops due to Hurricane Sandy at Long Island MacArthur Airport:

In[1]:=

pressure=AirPressureData["KISP",{DateObject[{2012,10,25}],DateObject[{2012,10,31}],All}]

Out[1]=

TimeSeries

Time: 25 Oct 2012 GMT -5 to 30 Oct 2012 GMT -5
Data points: 168



Display the data over time:

In[2]:=

plot1=DateListPlotpressure,



Out[2]=

Find the lowest pressure measured during the observation time and express it in various units:

In[3]:=

Map[UnitConvert[Min[pressure],#]&,{"Milibars","mmHg","inHG"}]

Out[3]=



968.507

mbar

726.44

mmHg

28.6

inHg



Uniformly resample the data and use

Differences

to investigate the pressure changes:

In[4]:=

resampled=TimeSeriesResample[pressure,Quantity[3,"Hours"]];

In[5]:=

changes=Differences[resampled];

Visualize the change in pressure data:

In[6]:=

DateListPlotchanges,

...



Out[6]=

Find the maximum registered drop and spike of the pressure:

In[7]:=

Map[#[changes]&,{Min,Max}]

Out[7]=



-0.3

inHg

0.3

inHg



Use

WindSpeedData

to obtain the increase in wind speeds:

In[8]:=

windspeed=WindSpeedData["KISP",{DateObject[{2012,10,25}],DateObject[{2012,10,31}],All}];

Compare the drop in air pressure with the increase in wind speeds:

In[9]:=

GraphicsColumnplot1,DateListPlotwindspeed,



Out[9]=

Compute the correlation between the two datasets:

In[10]:=

Correlation[pressure["Values"],windspeed["Values"]]

Out[10]=

-0.9387

Related Symbols

AirPressureData
WindSpeedData
TimeSeries
TimeSeriesResample
Differences
Correlation
DateListPlot
UnitConvert

Publisher Information

Contributed by: Wolfram Staff

Wolfram Language Example Repository

Pressure Reading Drops Due to Hurricane Sandy

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Publisher Information