Dear Robin,
As Stefan wrote, we don't have much experience of the lowest frequencies you mentioned,
but in
https://www.atmos-meas-tech.net/12/6341/2019/
we used ARTS down to 7 GHz. We did not consider the atmospheric settings in detail, so I
can not say anything about the absolute accuracy. Anyhow, based on what I know, to get you
started I would say:
* Among gases, only N2, O2 and H2O should matter. Other gases can be ignored.
* For these frequencies you can avoid "line-by-line" and get full coverage by applying a
continua model for N2, and full absorption models for O2 and H20.
* I would recommend to include LWC (liquid water content), that at these frequencies can
be considered as purely absorbing.
* As the continua and absorption models are quite fast you can calculate absorption
"on-the-fly" (instead of using a pre-calculated lookup-table, that is a more complex setup).
* The basic settings to achieve the above are:
# Agenda for scalar gas absorption calculation
Copy(abs_xsec_agenda, abs_xsec_agenda__noCIA )
# on-the-fly absorption
Copy( propmat_clearsky_agenda, propmat_clearsky_agenda__OnTheFly )
# Definition of species
#
abs_speciesSet( species = [
"N2-SelfContStandardType",
"O2-PWR98",
"H2O-PWR98",
"liquidcloud-ELL07"
] )
# No transitions needed
#
abs_lines_per_speciesSetEmpty
* Note that you must include N2, O2 and H2O vmrs, in vmr_field, in the order used in
abs_species (values between 0 and 1). LWC shall be given in kg/m3.
* Rain will have a significant impact, but is more complex to include in the simulations.
In addition, ERA5 does not give you full information on rain, only rain of stratiform type
is reported (convective rain is treated differently in the model and is not reported). And
it could be questioned if the rain is placed correctly in place and time.
Bye,
Patrick
On 2020-04-16 17:17, Stefan Buehler wrote:
Dear Robin,
the best is to write to arts_users for now. It will depend on the nature of the issues you
run into who will get most involved on our side.
The example from the classroom exercise calculates only the pure line-by-line gas
absorption spectrum. Scientific issues here could be:
- Should additional absorbing gases be included?
- Which gas absorption continua should be added? (I am not so familiar with the lower end
of your frequency range, so somebody should look into this a bit.)
- Do you want to add absorption by cloud liquid water and/or scattering by rain? Perhaps
negligible, again I have no good intuition for these low frequencies.
But before looking into these refinements, let’s first see if you can get the pure
line-by-line calculation running.
Best wishes,
Stefan
On 16 Apr 2020, at 15:56, Robin van der Schalie wrote:
Dear Stefan,
Thank you for the quick response, much appreciated. I'll move forwards with
installing the package and try to play around with the suggested exercise.
To be sure of correctly applying ARTS for this specific problem, it would
be great if someone can support us on this matter.
I see a positive impact for both the temperature input used for the soil
moisture retrievals (based on the 37 GHz) and the soil moisture retrievals
from higher frequencies (e.g. SSMI 18GHz) within the climate data record.
If this would be successfully implemented within the current algorithm (the
Land Parameter Retrieval Model), I see no problem in involving you in the
publication(s) that follow and I expect it will have a good outreach
through the CCI SM project.
Let me know if you recommend someone that I could contact or if you prefer
to be involved yourself.
Kind regards,
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
------------------------------------------------------------
--------------------------
Op do 16 apr. 2020 om 10:30 schreef Stefan Buehler <
stefan.bueh...@uni-hamburg.de>:
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
------------------------------------------------------------
--------------------------
_______________________________________________
arts_users.mi mailing list
arts_users.mi@lists.uni-hamburg.de
https://mailman.rrz.uni-hamburg.de/mailman/listinfo/arts_users.mi
_______________________________________________
arts_users.mi mailing list
arts_users.mi@lists.uni-hamburg.de
https://mailman.rrz.uni-hamburg.de/mailman/listinfo/arts_users.mi
_______________________________________________
arts_users.mi mailing list
arts_users.mi@lists.uni-hamburg.de
https://mailman.rrz.uni-hamburg.de/mailman/listinfo/arts_users.mi
