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in situ data

The stations

These stations were the closest measurements available to the main mooring site (CP).

Monitoring stations:

  • Dauphin Island (DI)
  • Orange Beach (ORB)
  • FOCAL/CP (CP)
  • Perdido Pass (PP)

Hydrographic data were available at all four stations, whereas meteorological data were only available at DI and ORB.

What is 'hydrographic' vs 'meteorological' data?

NOAA National Data Buoy Center

Standard meteorological data from two NOAA National Data Buoy Center (NDBC):

  • DPIA1 (DI) on Dauphin Island, Alabama.
  • 42012 offshore of Orange Beach, Alabama (ORB),

The meteorological data included

  • air temperature
  • relative humidity/dewpoint temperature (ORB only)
  • wind speed and direction.

Video demo:

Google search for noaa national data buoy center.

The paper gives station ID, so we can search for that. First is DPIA1 (what is DPHA1?**)

To do a parameter search, e.g., find all records when air temperature is > 30, do:

Can't subset based on day, so we will get all historical data for 2020.

Click Method 2 to see what is there. It will show up in the browser.

Then, under Method 1, right click and copy the link address: https://www.ndbc.noaa.gov/data/historical/stdmet/dpia1h2020.txt.gz

To get the code in the notebook:

!curl -O https://www.ndbc.noaa.gov/data/historical/stdmet/dpia1h2020.txt.gz

Okay, DPIA has meterological and DPHA has hydrographic.

Also,

On 02/28/2023, Dauphin Island was disestablished. See the notice below for more details.

For Ocean data, like those used in the MAS labs, for 2020:

Use curl, since mac doesn't have wget. Get the file, unzip it. Define column names, read in the file and rename the columns.

!curl -O https://www.ndbc.noaa.gov/data/historical/ocean/dpha1o2020.txt.gz
!gunzip dpha1o2020.txt.gz
!ls
vars = ['YY','MM','DD','hh','mm','depth','temp','cond','salt','o2percent','o2ppm','clcon','turb','ph','eh']
#verbose=True: indicate number of NA values placed in non-numeric columns
df =  pd.read_table("dpha1o2020.txt",skiprows=2,header=None,names=vars,sep='\s+',verbose=True)
df.head()