We want to understand whether cloud seeding has the potential to increase rainfall in Qatar. This project will use radiosonde data to identify periods where the atmosphere contains sufficient clouds that could be targeted for cloud seeding. You will use a cloud parcel model intiialised with aerosol particles and hygroscopic cloud seeding nuclei to assess whether the hygroscopic nuclei has the potential to increase surface rainfall.
The code repository for the Bin Microphysics Model is available at https://github.com/UoM-maul1609/bin-microphysics-model. This model is written in Fortran and has some associated Python scripts for analysis and plotting.
Prof. Paul Connolly will start up the VM, give you your username, SSH key (which is a file) and the IP address of the server (VM). To login you need to open CMD (on windows), terminal if on a mac or Chromebook. You would then type:
ssh -i id_virtual_students.key -X <username>@<IP-address>
The first time you log in you may need to answer 'yes' to a question about connecting.
Once logged in you will be interacting the VM in the same way as you would a linux server through the terminal. To download the bin microphysics model type:
git clone https://github.com/UoM-maul1609/bin-microphysics-model
This should download the code to the folder bin-microphysics-model
You need to be in the bin-microphysics-model folder to compile the code. Type
cd bin-microphysics-model
in the terminal / cmd window to enter the folder.
We need to then tell the computer to build the machine code executable that the computer CPU understands. This can be done by typing:
make NETCDFLIB=-L/usr/lib/x86_64-linux-gnu/ NETCDFMOD=/usr/include/ FFLAGS='-O2 -w -o' FFLAGS2='-O2 -w -o' FFLAGSOMP='-O2 -w -o'
The make command tells the computer to use a file called Makefile which is in the bin-microphysics-model folder you downloaded. In this file are rules for compiling the code. There are some additional variables that we pass into the Makefile. These are NETCDFLIB, NETCDFMOD, FFLAGS, FFLAGS2 and FFLAGSOMP, which overrides the values they are set to in the Makefile. They need to be altered for our environment.
You will need radiosonde data, which gives the thermodynamic conditions of the atmosphere close to Qatar. The script download_data_wyoming.py will automatically download data from the Wyoming database at https://weather.uwyo.edu/upperair/sounding.html. You could download each radiosonde file individually, but that would be very time consuming. The script will download a years worth of data for both 0 and 12 local time. Run the script as follows
python3 download_data_wyoming.py
It will take a while to complete, but will download the data into a folder called SondeData in your home folder. You might want to check this folder is empty before you start.
Once downloaded we can read in all of the radiosonde data using the supplied script readSondeData.py. In order to look at the data interactively you will want to run this script from within ipython3. If you just pass the script to the python3 interpreter it will read the files and the exit, so all information will be lost. So type the following:
ipython3
Once iPython has started you can run the readSondeData.py script as follows:
run -i readSondeData.py
This script will read all of the radiosonde files at ~/SondeData into a python list called dats. You can find how many files there are by typing
len(dats)
It probably wont be 2 x number of days in a year because there will be some missing files, which happens when there is a problem with the radiosonde on a particular day. Do not worry about this.
We generally extract one of these radiosonde ascent files to look at in more detail. Before we do that lets look at some general metrics for the data we have just read in.
When we read in the radiosondes using readSondeData.py it puts the following variables into memory within iPython (you can type who or whos to see them)
time
tlcl
plcl
cape
cin
pw
These are: (1) time; (2) temperature of the lifting condensation level; (3) pressure of the lifting condensation level; (4) Convective Available Potential Energy; (5) Convective Inhibition; (6) Precipitable Water. You can then do a plot of CAPE for example as follows
import matplotlib.pyplot as plt
plt.ion()
plt.plot(time,cape,'.-')
plt.xlabel('time')
plt.ylabel('CAPE')
plt.savefig(dataPath.joinpath('cape.png'))
This is just one example, but you could plot other things in a similar way. The data I have downloaded shows that some values of CAPE are 0. These cases are not suitable for analysis. If you would like to download this file you can log in with SSH and then type the following, which will download the file to your local machine.
get SondeData/cape.png
Figure 1. Plot of the CAPE for the radio sonde data we have downloaded.
From the ipython prompt, after running
run -i readSondeData.py
you will have the radiosonde data in memory. Type
whos
to see all data in memory. There should be several variables, but the main ones are data1 and dats. data1 is the last radiosonde file read in and dats is all of them in a list.
To create a tephigram you can just type
run -i plotTephigram.py
This will write a file to the location /tmp/<username>/tephigram.png
Figure 2. Tephigram of the last radiosonde read in.The radiosonde suggests conditions are right for a shallow convective cloud between 10 degrees C and 2 degrees C
If you would like to create a different tephigram, the procedure is to set the data1 variable equal to another radiosonde dataset in the dats list, e.g.
data1=dats[33]
and then run the plotTephigram.py script again.
We require the background aerosol data. This paper Farahat et al. 2016 has an aerosol size distribution measured over Saudi Arabia in Figure 6a. The data have been digitised and put into the script backgroundAerosol.py. This data is
The result is shown in Figure 3 along with lognormal fit parameters that the model can use.
Figure 3. Background aerosol assumptions and lognormal fit parameters. Blue is the data, and orange is the lognormal fit
The bin-microphysics model should have already been downloaded and compiled in the home folder / top level folder. Within the bin-microphysics-model folder there is a subfolder
python/cloud_seeding/
which contains the file namelist.default. This file already has the background aerosol distribution in it and is set-up so that it can be run in batch mode, which is the easiest way the run the model for this project.
Note, for this analysis you should exit from ipython3. You can either start up a new ssh login, or just type exit if you are already in ipython3.
You should also be in the folder projects-and-teaching/cloud-seeding and you should have downloaded and compiled the bin-microphysics-model in the same folder that projects-and-teaching is in. The script cloud-seeding/batch_runs.py is able to run the bin-microphysics-model many times. At the end of the script it imports the radiosonde data, and then selects all cases where the cape is >100. , then it calls the function batchRuns to run every case. There is a flag called seedFlag which is set to False by default. If you set it to true it will add the seed aerosol to the parcel model too.
The seed aerosol are taken from the paper by Tessendorf et al. 2020. You can run the batch_runs.py script as follows
python3 batch_runs.py
You would may to run twice. Once with seedFlag=False and once with seedFlag=True (i.e. run the default case, then edit the file and run again).
This will take a while, if you want to leave it running in the background you can use nohup, e.g.
nohup python3 batch_runs.py &
This will save model output files in /tmp/<username>/.
An example of how to look at precipitation is shown in the script cloud_seeding_analysis.py. This script loops through each run and reads in the precipitation for both without and with cloud seeding. It then generates a plot. note you may need to export the plot using
plt.savefig('<filename>.png')
and then download it. It is just an example and you may want to perform other analyses.
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Farahat, A., El-Askary, H., and Dogan, A. U., 2016: Aerosols size distribution characteristics and role of precipitation during dust storm formation over Saudi Arabia. Aerosol Air Qual. Res., 16, 2523-2534, doi:10.4209/aaqr.2015.11.0656.
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Tessendorf, S. A., Chen, S., Weeks, C., Bruintjes, R., Rasmussen, R. M., & Xue, L. (2021). The influence of hygroscopic flare seeding on drop size distribution over Southeast Queensland. Journal of Geophysical Research: Atmospheres, 126, e2020JD033771. https://doi.org/10.1029/2020JD033771