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%matplotlib inline
This demo requires that you download, from Brightspace, the following files and place them in your working directory:
From last week, do you remember the "Mussel" model?
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import model_Mussel_IbarraEtal2014 as MusselModel
days, dt, par, InitCond = MusselModel.load_defaults()
output = MusselModel.run_model(days,dt,InitCond,par)
MusselModel.plot_model(output)
The Objectives of this DEMO are:
The following snipet of code downloads the monthly-averaged Sea Surface Temperature calculated from the POES, AVHRR and GAC Satellites... and make a plot of the downloaded data.
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import read_satellites as rs
year = 2015
month = 8
minlat = 38
maxlat = 48
minlon = -67
maxlon = -50
isub = 0.5
# This one line of code does the whole thing (Download + Plot)
lon, lat, sst = rs.read_frame(year,month,minlat,maxlat,minlon,maxlon,isub)
There! Average SST for August 2016. You not only get the plot, but you also get the actual data. Check out sst
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sst
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Lets do some playing around...
Lets plot the average temperature in February 2015
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month = 2
lon, lat, sst = rs.read_frame(year,month,minlat,maxlat,minlon,maxlon,isub)
You can see that the water is colder in February (compared to the plot of August above), but also there are missing data because of more cloud cover in February.
Lets query a larger region:
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minlon = -70
maxlon = -45
lon, lat, sst = rs.read_frame(year,month,minlat,maxlat,minlon,maxlon,isub)
Noe lets use the rs.read_timeSeries function, which download all the monthly average for ONE single spot over a specified period. If no period is specified, the default is between 2010-01-01 and 2015-05-31
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import read_satellites as rs
lat = 43
lon = -62
sst = rs.read_timeSeries(lat,lon)
As you can see, you get the time-series of Sea Surface Temperature from the queried lat/lon. You get the plot plus the data. Note that there are some "empty spot" where there where clouds.
Now lets import a modified "Mussel model" that accepts sst as an input, and uses it to force the model (before Temperature was constant throughout the entire model run).
Lets see how the New "Mussel model" with forcing works:
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import model_Mussel_IbarraEtal2014_SSTforcing as MusselModel_SST
days, dt, par, InitCond = MusselModel_SST.load_defaults()
days, sst = MusselModel_SST.read_satellite_sst(dt,lat=43,lon=-62)
output = MusselModel_SST.run_model(days,dt,InitCond,par,sst)
MusselModel_SST.plot_model(output)
Just to compare, lets run the "old" Mussel model:
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import model_Mussel_IbarraEtal2014 as MusselModel
days, dt, par, InitCond = MusselModel.load_defaults()
output = MusselModel.run_model(days,dt,InitCond,par)
MusselModel.plot_model(output)
The difference is substantial, growth slows down during the cold parts of the year!
If you compare model_Mussel_IbarraEtal2014.py against model_Mussel_IbarraEtal2014_SSTforcing.py... There are only 3 minor changes:
(1) Indicate sst as input
Line 65 BEFORE: def run_model(days,dt,InitCond,par):
Line 65 AFTER: def run_model(days,dt,InitCond,par,sst):
(2) Initialize Temp with sst
Line 107 BEFORE: Temp = InitCond['Temp']
Line 107 AFTER: Temp = sst
(3) Make Temp as function of time: Temp[t]
Line 122 and 122 BEFORE:
L_Temp[t] = min(max(0.,1.-np.exp(-par['KTempL']*(Temp-par['TempL']))), \
max(0.,1.+((1.-np.exp(par['KTempH']*Temp))/(np.exp(par['KTempH']*par['TempH'])-1.))))Line 122 and 122 AFTER:
L_Temp[t] = min(max(0.,1.-np.exp(-par['KTempL']*(Temp[t]-par['TempL']))), \
max(0.,1.+((1.-np.exp(par['KTempH']*Temp[t]))/(np.exp(par['KTempH']*par['TempH'])-1.))))