@ -3,9 +3,9 @@ The code in this repository accompanies an article about the influence of the Ma
* The **Madden–Julian Oscillation (MJO)** is the most dominant component of the climate variability in the tropics on the timescale of tens of days.
* 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.
* 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
### Download the large data 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`.
If you want to run the code on a local computer, you need to download all files from from authors' server [https://eee.ipfran.ru/files/mjo](https://eee.ipfran.ru/files/mjo) and put them to the `data` folder.
### Get the code and prepare to launch
### 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`:
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`:
@ -24,13 +24,13 @@ In order to run the code, you will need to have the following python packages:
### About the attached data
### 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.
* `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.
* `OLR_41year_NOAA.npy` This is a `numpy` array with the shape `(14976, 180, 360)`, containing daily averaged (14976 days) Outgoing Longwave Radiation flux values from NOAA PSL dataset [olr.day.mean.nc](https://downloads.psl.noaa.gov/Datasets/olrcdr/olr.day.mean.nc) 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-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.
* `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).
* `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).
* `sunspot_number_data.csv` contains information about the total sunspot number for every day of the years 1818--2022. You can download this file [here](https://www.sidc.be/silso/datafiles).
### About the scripts
### About the scripts
* `map_of_contributions` plots the anomalies in grid cell contributions to the IP during different MJO phases.
* `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.
* `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, the two components of the RMM index, OLR and solar activity.
* `eof_analysis` represents a more elaborate analysis of contributions to the IP using the concept of empirical orthogonal functions (EOFs) and principal components (PCs).
* `eof_analysis` represents a more elaborate analysis of contributions to the IP using the concept of empirical orthogonal functions (EOFs) and principal components (PCs).
