Hi.
I just started to learn CIS following the documentation but seem to be having a lot of initial problems. I'll create separate post for each of them. I'm using it on JASMIN and my short-term goal is to compare UM outputs with AERONET AOD.
Following an example in section 7.1 of the documentation (Release 1.6.0 (Stable)), I tried this;
$ cis subset -v 340-440AngstromofDailyAvg:920801_170923_Abracos_Hill.lev20 t=[2002] -o 340-440Angstrom_Abracos_Hill_2002.dat
but this seems to fail with the following message;
2017-12-11 11:14:13,507 - INFO - Identified input file list: ['920801_170923_Abracos_Hill.lev20']
2017-12-11 11:14:13,560 - INFO - Retrieving data using product Aeronet...
An error occurred retrieving data using the product Aeronet. Check that this is the correct product plugin for your chosen data. Exception was TypeError: 'NoneType' object is not iterable.
2017-12-11 11:14:13,728 - ERROR - An error occurred retrieving data using the product Aeronet. Check that this is the correct product plugin for your chosen data. Exception was TypeError: 'NoneType' object is not iterable. - check cis.log for details
I also tried this in ipython;
dira='/group_workspaces/jasmin2/crescendo/Data/AERONET/'
fnma='920801_170923_Abracos_Hill.lev20'
cis.get_variables(dira+fnma, product=None, type=None)
This works and so I tried to read one of the variables;
vnma='AOT_551ofDailyAvg'
cis.read_data(dira+fnma, vnma, product='Aeronet')
but this also fails with basically the same messages as above;
ProductPluginException: An error occurred retrieving data using the product Aeronet. Check that this is the correct product plugin for your chosen data. Exception was TypeError: 'NoneType' object is not iterable.
The same thing happens with product=None or for other variables.
Maybe I have missed something very basic? Please could anyone help me? Thank you.
Best regards,
Masaru
Hi Masaru, sorry you've hit these problems!
I don't recognise that variable name from any of the Aeronet files I've seen. Could you send me an example file, or show me where you downloaded it from?
Many thanks
Hi duncanwp,
Thank you for your reply! I used a copy of data one of my colleagues downloaded. So this data is not a typical AERONET data? I chose this data and variable out of very many only because these are the similar ones (if not exactly the same) used in an example given in the documentation (p. 27). How can I send the file to you? Below I copied the header and the data for the first month.
Now I did the same thing with 920801_170923_Capo_Verde.lev20 ;
fnma='920801_170923_Capo_Verde.lev20'
vnma='AOT_551ofDailyAvg'
cis.read_data(dira+fnma, vnma, product='Aeronet')
This returned this;
WARNING:root:Identified 288 point(s) which were missing values for some or all coordinates - these points have been removed from the data.
Out[9]:
<cis 'UngriddedData' of Ungridded data: AOT_551ofDailyAvg / (1)
So the data has now been read in? But I still don't know how to use it...
summary(UngriddedData)
returns this;
NameError: name 'summary' is not defined
Masaru
=================================================================
Level 2.0. Quality Assured Data.<p>The following data are pre and post field calibrated, automatically cloud cleared and manually inspected.
Version 2 Direct Sun Algorithm
Location=Abracos_Hill,long=-62.358,lat=-10.760,elev=200,Nmeas=4,PI=Brent_Holben,Email=Brent.N.Holben@nasa.gov
AOD Level 2.0,Monthly Averages,UNITS can be found at,,, http://aeronet.gsfc.nasa.gov/data_menu.html
Month,AOT_1640 of Daily Avg,AOT_1020 of Daily Avg,AOT_870 of Daily Avg,AOT_675 of Daily Avg,AOT_667 of Daily Avg,AOT_555 of Daily Avg,AOT_55
1 of Daily Avg,AOT_532 of Daily Avg,AOT_531 of Daily Avg,AOT_500 of Daily Avg,AOT_490 of Daily Avg,AOT_443 of Daily Avg,AOT_440 of Daily Avg
,AOT_412 of Daily Avg,AOT_380 of Daily Avg,AOT_340 of Daily Avg,Water(cm) [935nm] of Daily Avg,440-870 Angstrom of Daily Avg,380-500 Angstro
m of Daily Avg,440-675 Angstrom of Daily Avg,500-870 Angstrom of Daily Avg,340-440 Angstrom of Daily Avg,440-675 Angstrom (Polar) of Daily A
vg,AOT_1640 of Weighted Avg,AOT_1020 of Weighted Avg,AOT_870 of Weighted Avg,AOT_675 of Weighted Avg,AOT_667 of Weighted Avg,AOT_555 of Weig
hted Avg,AOT_551 of Weighted Avg,AOT_532 of Weighted Avg,AOT_531 of Weighted Avg,AOT_500 of Weighted Avg,AOT_490 of Weighted Avg,AOT_443 of
Weighted Avg,AOT_440 of Weighted Avg,AOT_412 of Weighted Avg,AOT_380 of Weighted Avg,AOT_340 of Weighted Avg,Water(cm) [935nm] of Weighted A
vg,440-870 Angstrom of Weighted Avg,380-500 Angstrom of Weighted Avg,440-675 Angstrom of Weighted Avg,500-870 Angstrom of Weighted Avg,340-4
40 Angstrom of Weighted Avg,440-675 Angstrom (Polar) of Weighted Avg,N Days[AOT_1640],N Days[AOT_1020],N Days[AOT_870],N Days[AOT_675],N Day
s[AOT_667],N Days[AOT_555],N Days[AOT_551],N Days[AOT_532],N Days[AOT_531],N Days[AOT_500],N Days[AOT_490],N Days[AOT_443],N Days[AOT_440],N
Days[AOT_412],N Days[AOT_380],N Days[AOT_340],N Days[Water(cm)],N Days[440-870 Angstrom],N Days[380-500 Angstrom],N Days[440-675 Angstrom],
N Days[500-870 Angstrom],N Days[340-440 Angstrom],N Days[440-675 Angstrom],N Obs[AOT_1640],N Obs[AOT_1020],N Obs[AOT_870],N Obs[AOT_675],N O
bs[AOT_667],N Obs[AOT_555],N Obs[AOT_551],N Obs[AOT_532],N Obs[AOT_531],N Obs[AOT_500],N Obs[AOT_490],N Obs[AOT_443],N Obs[AOT_440],N Obs[AO
T_412],N Obs[AOT_380],N Obs[AOT_340],N Obs[Water(cm)],N Obs[440-870 Angstrom],N Obs[380-500 Angstrom],N Obs[440-675 Angstrom],N Obs[500-870
Angstrom],N Obs[340-440 Angstrom],N Obs[440-675 Angstrom],N Months[AOT_1640],N Months[AOT_1020],N Months[AOT_870],N Months[AOT_675],N Months
[AOT_667],N Months[AOT_555],N Months[AOT_551],N Months[AOT_532],N Months[AOT_531],N Months[AOT_500],N Months[AOT_490],N Months[AOT_443],N Mo
nths[AOT_440],N Months[AOT_412],N Months[AOT_380],N Months[AOT_340],N Months[Water(cm)],N Months[440-870 Angstrom],N Months[380-500 Angstrom
],N Months[440-675 Angstrom],N Months[500-870 Angstrom],N Months[340-440 Angstrom],N Months[440-675 Angstrom]
1999-JAN,N/A,0.080880,0.088997,0.102903,N/A,N/A,N/A,N/A,N/A,0.132603,N/A,N/A,0.123092,N/A,0.147944,0.156590,4.779772,0.699176,0.419965,0.576
477,0.944812,1.096868,N/A,N/A,0.083359,0.091037,0.104866,N/A,N/A,N/A,N/A,N/A,0.134281,N/A,N/A,0.125186,N/A,0.149655,0.158552,4.746080,0.7123
06,0.435251,0.599359,0.946153,1.092255,N/A,N/A,5,5,5,N/A,N/A,N/A,N/A,N/A,5,N/A,N/A,5,N/A,5,5,5,5,5,5,5,5,N/A,N/A,27,27,27,N/A,N/A,N/A,N/A,N/
A,27,N/A,N/A,27,N/A,27,27,27,27,27,27,27,27,N/A,N/A,1,1,1,N/A,N/A,N/A,N/A,N/A,1,N/A,N/A,1,N/A,1,1,1,1,1,1,1,1,N/A
Hi duncanwp,
I now uploaded the data on Google Drive. Here is the link;
https://drive.google.com/file/d/1Cyh1VxsjobLtsKX3FxADsxsVS1AxVHCQ/view?u...
Thanks,
Masaru
Apologies, we never really use the monthly mean data so this problem hasn't come up before. We can try and add support in future versions, but in the meantime you can use the point data available here: https://aeronet.gsfc.nasa.gov/cgi-bin/webtool_aod_v3
This should then work straight-away with the 'Aeronet' plugin.
You're nearly there when reading the dataset in Python. You just need to assign it to a variable and then print that:
aod=cis.read_data(dira+fnma, vnma, product='Aeronet')
print(aod)
Unfortunately the Python interface isn't as well documented as I'd like, for now this set of notes should give you a good start though: https://nbviewer.jupyter.org/github/duncanwp/python_for_climate_scientis...
Hope that helps!