Skip to main content

Ionospheric Bubble Probability

Project description

The ionospheric bubble probability statistical model is a Swarm L2 product, named IBP_CLI. The output of the Ionospheric Bubble Probability (IBP) product is an index, that depends on the day of year or the month of the year, geographic longitude, local time and solar flux index.

The output floating point index ranges 0-1 and characterizes the percentage probability of low latitude bubble occurence at the specified time, location and solar flux.

This empirical IBP model has been derived from magnetic observations obtained by the CHAMP and Swarm missions. The model is representative for the altitude range 350 - 500 km and low geographic latitudes of +/- 45 degree.

Documentation

Detailed documentation can be found at: https://ibp-model.readthedocs.io

Quick Start

Installation

Using pip:

$ pip install ibpmodel

Dependencies:

  • numpy

  • pandas

  • matplotlib

  • scipy

  • cdflib

Usage

The return value of the function ibpmodel.calculateIBPindex() is of type pandas.DataFrame.

>>> import ibpmodel as ibp
>>> ibp.calculateIBPindex(day_month=15,           # Day of Year or Month
              longitude=0,                        # Longitude in degree
              local_time=20.9,                    # Local time in hours
              f107=150)                           # F10.7 cm Solar Flux index
   Doy  Month  Lon    LT  F10.7     IBP
0   15      1    0  20.9    150  0.3547
 >>> ibp.calculateIBPindex(day_month=['Jan','Feb','Mar'], local_time=22)
      Doy  Month  Lon  LT  F10.7     IBP
 0     15      1 -180  22    150  0.0739
 1     15      1 -175  22    150  0.0722
 2     15      1 -170  22    150  0.0717
 3     15      1 -165  22    150  0.0728
 4     15      1 -160  22    150  0.0771
 ..   ...    ...  ...  ..    ...     ...
 211   74      3  155  22    150  0.2061
 212   74      3  160  22    150  0.2025
 213   74      3  165  22    150  0.1994
 214   74      3  170  22    150  0.1967
 215   74      3  175  22    150  0.1943

[216 rows x 6 columns]
>>> ibp.plotIBPindex(doy=349)
>>>
Contour plot of the IBP index for the given day

The IBP model reproduces the high occurrence probability of EPDs ranging between 50-90% over the South American (75-25°W) sector and low occurrence probability over the Pacific sector during the period around December solstice.

>>> ibp.plotButterflyData(f107=150)
>>>
Contour plot of result from function ButterflyData()

The monthly global occurrence rate of EPDs from the IBP model, is derived for a fixed value of F10.7=150 s.f.u for all integer longitudes at a resolution of 5° at the middle of each month and averaged between 19 and 1 LT. The seasonal and longitudinal variations of the EPD occurrence rates are particularly well-characterized by the IBP model as compared to its climatology with highest rates seen around the equinoxes and winter solstice in the America-Atlantic-Africa region and lowest rates during November-February in the Pacific sector and during May-July in the America-Atlantic and Indian sectors.

References

Stolle, C., Siddiqui, T. A., Schreiter, L., Das, S. K., Rusch, I., Rother, M., & Doornbos, E. (2024). An empirical model of the occurrence rate of low latitude post‐sunset plasma irregularities derived from CHAMP and Swarm magnetic observations. Space Weather, 22, e2023SW003809. https://doi.org/10.1029/2023SW003809

Lucas Schreiter, Anwendungsorientierte Modellierung der Auftretenswahrscheinlichkeit und relativen Häufigkeit von äquatorialen Plasmabubbles, Master’s thesis, Institute of Mathematics, University of Potsdam, 2016. (in German only.)

Information for developers

Setup environment

$ git clone https://igit.iap-kborn.de/ibp/ibp-model.git
$ cd ibp-model
$ pip install -r requirements-dev.txt
$ pip install -e .

Test of package using doctest

$ python src/ibpmodel/ibpcalc.py

No error should occur.

Test run of the documentation

$ cd docs
$ make clean && make html

The docs/build/html/ directory contains the html files. Open index.html in browser. The results of the code examples on the usage page are generated automatically. Therefore the ibpmodel package must be installed (pip install -e .).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ibpmodel-1.2.2.tar.gz (4.8 MB view hashes)

Uploaded Source

Built Distribution

ibpmodel-1.2.2-py3-none-any.whl (178.0 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page