Upload readme, figures and scripts

.gitignore

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+.ipynb_checkpoints
+data/*

Readme.md

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+# The effect of the MJO on the GEC
+The code in this repository accompanies an article about the influence of the Madden–Julian Oscillation on the global electric circuit.
+* The **Madden–Julian Oscillation (MJO)** is the most dominant component of the climate variability in the tropics on the timescale of tens of days.
+* We investigate the effect of the MJO on the direct current **global electric circuit (GEC)**, using both numerical simulations and the results of electric field measurements.
+
+### Get big binary files
+
+If you want run code on local computer, you need download all files from [https://eee.ipfran.ru/files/mjo](https://eee.ipfran.ru/files/mjo) and put them to folder `data`.
+
+### Get the code and prepare to launch
+All the code is written in `Python 3.7` using `Jupyter notebook`. You can get the latest version of the code and data as an archive  [(click here to download a zip-archive)](https://git260.ipfran.ru/eee/mjo-gec-2022/archive/master.zip) or using `Git`:
+```bash
+git clone https://git260.ipfran.ru/eee/mjo-gec-2022.git
+```
+<!-- You can also download a pure Python version of the code without using the Jupyter notebook ([click here to download a zip-archive](https://git260.ipfran.ru/eee/mjo-gec-2022/archive/pure_python.zip)). With `Git 1.7.10` or later you can also clone a specific branch: -->
+<!-- ```bash
+git clone --single-branch --branch pure_python https://git260.ipfran.ru/eee/mjo-gec-2022.git
+```-->
+### Python requirements
+In order to run the code, you will need to have the following python packages:
+* `scipy >= 1.6.0`
+* `matplotlib >= 3.5.0`
+* `cartopy >= 0.18.0`
+
+### About the attached data
+* `rmm.txt`. Source of this text file you can [download directly](http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt) from the Australian Bureau of Meteorology. The columns of the file represent the date (from 1974 onwards), components of the Real-time Multivariate MJO index (RMM), MJO phase and amplitude.
+* `OLR_41year_NOAA.npy` This is a `numpy` array with the shape `(14976, 180, 360)`, containing daily averaged (14976 days) Outgoing Longwave Radiation values from NOAA dataset [olr.day.mean.nc](https://psl.noaa.gov/data/gridded/data.interp_OLR.html) for every cell of a 1°×1° latitude-longitude grid (180×360) starting with 1980-1-1 and ending with 2021-12-31.
+* `DAILY-ENSO34.npy`. This is a `numpy` array with the shape `(4992,)`, containing daily averaged sea surface temperature in the Niño 3.4 region. Here 4992 is the number of days when every third day in 1980–2020 is taken.
+* `DAILY-IP-MAP-V4.3.npy`. This is a `numpy` array with the shape `(4992, 180, 360)`, containing daily averaged (4992 days) contributions to the ionospheric potential (IP) for every cell of a 1°×1° latitude-longitude grid (180×360) for every third day. The gird cell contributions are calculated with the Weather Research and Forecasting model (WRF) version 4.3.
+* `vostok_hourly <...>.txt` text files contain two columns, one of which represents the date and time (column `Datetime`) and the other, hourly averaged potential gradient (PG) values on the basis of the measurements at the Russian Antarctic station Vostok (column `Field`, the units are V/m).
+* `sunspot_number_data.csv` contains information about total sunspot number for every day of years 1818--2022. You can download file [here](https://www.sidc.be/silso/datafiles).
+
+### About the scripts
+* `map_of_contributions` plots the anomalies in grid cell contributions to the IP during different MJO phases.
+* `variations_ip_pg_rmm_with_mjo_phase` plots variations of various parameters with the MJO cycle. In particular, we plot the IP, the fair-weather PG and the two components of the RMM index.
+* `eof_analysis` represents a more elaborate analysis of contributions to the IP using the concept of empirical orthogonal functions (EOFs) and principal components (PCs).