Dear Robin,
yes, ARTS should be well suited for this. There even already is a python
classroom exercise with a setup for computing and displaying optical
depth. (On github, in package atmtools/arts-lectures, directory
exercises/04-rtcalc .) What you may need some advice on is which
absorption models to actually use (ARTS offers a lot of choices, I
don’t remember if the ones in the exercise are the best for a real
application).
ARTS is free to use, the best reward for us is involvement in scientific
publications. So, depending on how much support you will need, we would
expect the person(s) that helped you to be included in the first
publication on this.
Best wishes,
Stefan
On 15 Apr 2020, at 13:05, Robin van der Schalie wrote:
Good afternoon,
My name is Robin van der Schalie and I am currently the person in
charge of
running soil moisture retrievals based on passive microwave
observations within the ESA Climate Change Initiative (
https://www.esa-soilmoisture-cci.org/).
In our never ending search for ways to further improve our soil
moisture
retrieval algorithm, which is based on the Land Parameter Retrieval
Model,
I would like to get a better handle on the atmospheric effects that
alter
the AMSR2 (and other historical mission) brightness temperatures from
ground level. In essence, having more realistic Atmospheric Optical
Depth
values. This would be for multiple frequencies, i.e. L-band (1.4 GHz),
C-band (6.9 GHz), X-band (10.7 GHz), Ku-band (18 GHz), K-band (23 GHz)
and
Ka-band (37 GHz). For this I am already preparing a database from
reanalysis (ERA5) on the water vapor, atmospheric pressure, and air
temperature as input for the calculation.
From going through the ARTS documentation it seems to me that this
goal
would be achievable using your package (especially the Typhon as we
work
with python). Is that a correct assumption? And if so, could you maybe
provide me with some guidance on how to get started on this?
Hope to hear from you soon,
Robin van der Schalie
------------------------------------------------------------
--------------------------
*dr. Robin van der Schalie* // Senior Remote Sensing Scientist
VanderSat // Satellite observed water data. Globally. Daily.
Wilhelminastraat 43a, 2011 VK, Haarlem, The Netherlands
*T* +31 23 3690093 *M* +31 6 81631591 *W* www.vandersat.com
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