MJOcast provides you with essential tools to effortlessly compute the Madden-Julian Oscillation (MJO) index, with a specific focus on the respected Wheeler and Hendon Real-Time Multivariate MJO Index (RMM).

mjoindices is a Python package relevant for atmospheric scientists. It provides functionality to compute an index of the Madden-Julian-Oscillation (MJO), particularly the OLR-based MJO index (OMI).

MJO-related analysis: collect MJO-active days, identify MJO phase, and get phase composites. Prepare MJO heating and mean state for the LBM simulation. See more details in Methods of Zhou et al. (2020)

Welcome to the official documentation for the MJOforecaster toolbox - your comprehensive python package for generating MJO hindcast forecasts based on Wheeler and Hendon 2004 Real-time Multivariate MJO (RMM) methodology.

The Madden-Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty of the MJO prediction directly.

The Wheeler and Hendon Real-time Multivariate MJO (RMM) index is a widely used diagnostic tool for tracking and quantifying the Madden-Julian Oscillation (MJO), a significant tropical atmospheric phenomenon characterized by its eastward-propagating convective and atmospheric circulation anomalies.

MJOcast provides you with tools to compute the Madden-Julian Oscillation (MJO) index, with a specific focus on the respected Wheeler and Hendon Real-Time Multivariate MJO Index (RMM). Additionally, it simplifies the generation of compiled netcdf variables and facilitates accurate forecasts of the MJO, and provides essential skill metrics.

OMI: OLR-based MJO index.

Compute the MJO RMM index (Wheeler & Hendon, 2004) with a combined EOF analysis of OLR and winds in the equatorial area

在接下來的範例中，會示範如何將冬季 (DJF) 8個相位的MJO做降雨的合成分析以及統計檢定。 This function uses the Student’s t-test to test the null hypothesis that the sample means are from the same population (i.e. H 0 : ave1=ave2). Rejection of the null hypothesis (i.e. acceptance of the alternative hypothesis) indicates that the sample means are from two different populations.