A Python Package to Calculate the OLR-Based Index of the Madden-
                        Julian-Oscillation (OMI) in Climate Science and Weather Forecasting

Hoffmann, Christoph G.; Kiladis, George N.; Gehne, Maria; Savigny, Christian von

doi:10.5334/jors.331

Cited by 11 publications

(26 citation statements)

References 15 publications

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“…A time series of the two PCs of the original OMI and the PCs projected onto the rotated EOFs are shown in Figure 4. The time series is plotted for 2011 to compare with Hoffmann et al (2021), and the PCs are normalized using the full 1979-2012 time period. In general, the PCs from the rotated index preserve the basic structure of the original OMI, with deviations in spring and fall months where the oscillatory behavior of the original EOFs is most pronounced.…”

Section: Resultsmentioning

confidence: 99%

“…A Python library with the rotation post-processing procedure is currently publicly available. It can be used as an additional component to the OMI package released by Hoffmann et al (2021) or added to calculations of other EOF-based MJO indices.…”

Section: Discussionmentioning

confidence: 99%

“…Implementation of the original OMI index was performed using the Python package published in Hoffmann et al. (2021). The rotation method derived in this paper acts as an additional post‐processing step to the same package.…”

Section: Methodsmentioning

confidence: 99%

“…To compare EOFs directly to the original paper, EOFs are derived from the period 1979-2012, but the procedure can be repeated with differing time periods. Implementation of the original OMI index was performed using the Python package published in Hoffmann et al (2021). The rotation method derived in this paper acts as an additional post-processing step to the same package.…”

Section: Datamentioning

confidence: 99%

“…Interpolated daily OLR data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html. The open source mjoindices Python package used to calculate the original OMI was published by Hoffmann et al (2021). Python code for the projection and rotation of the EOFs is currently available at https://doi.org/10.5281/zenodo.6870694 as an addition to the mjoindices package published by Hoffmann et al (2021).…”

Section: Conflict Of Interestmentioning

confidence: 99%

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A Rotation Procedure to Improve Seasonally Varying Empirical Orthogonal Function Bases for MJO Indices

Weidman

Kleiner

Kuang

2022

Geophysical Research Letters

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The Madden-Julian oscillation (MJO) is characterized by eastward propagating convection anomalies in the Indian and Pacific tropical oceans with a period of 30-60 days (Madden & Julian, 1971, 1972. In addition to being a dominant intraseasonal variability in the tropics, the state of the MJO has also been connected to weather at higher latitudes (e.g., Arcodia et al., 2020;Henderson et al., 2016), among many other aspects of global atmospheric circulation. Reviews on the significance of the MJO appear in Jiang et al. (2020) andZhang (2005).The influence of the MJO on the global climate system is dependent on the spatial pattern and amplitude of the MJO signal, which are often quantified using an index generated by empirical orthogonal functions (EOFs). The phase of the MJO, based on these indices, is used to predict or otherwise characterize the influence of the MJO on various atmospheric phenomena (J. Wang et al., 2020). One frequently used index is the real-time multivariate (RMM) index (M. C. Wheeler & Hendon, 2004). The RMM is based on the zonal structure of OLR and zonal wind at 200 and 850 hPa, and can be used for real-time monitoring of the MJO. However, Straub (2013) showed that the RMM underrepresents convection compared to zonal wind, causing the RMM to miss some MJO-like convective signals. In addition, the lack of meridional structure confounds the MJO signal with equatorial Kelvin waves (Roundy et al., 2009).An OLR-based MJO index (OMI) was developed to counteract some of these issues, since it incorporates the zonal and meridional structure of OLR into a pair of propagating EOFs over the course of the year (Kiladis et al., 2014) (hereafter K14). Incorporation of meridional structure helps separate the MJO signal from Kelvin waves, and using solely OLR more directly tracks the convective signal. Since OLR can be measured directly by satellites, the OMI provides a reliable long-term record of tropical convective patterns (S. Wang, 2019).Due to these benefits, the OMI is a widespread index used for MJO analyses. However, there remain a few small but important issues with the original OMI. Since the EOFs of the OMI represent propagating waves of the MJO, the leading EOF pair are of a similar magnitude (North et al., 1982), resulting in a somewhat arbitrary choice when assigning the EOFs as EOF1 or EOF2. In the original OMI calculation, the leading pair of eigenvalues

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Conflict Of Interestmentioning

confidence: 99%

See 3 more Smart Citations

A Rotation Procedure to Improve Seasonally Varying Empirical Orthogonal Function Bases for MJO Indices

Weidman

Kleiner

Kuang

2022

Geophysical Research Letters

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Time‐lagged correlations of pre‐monsoon precipitation in the Indochina Peninsula confirmed in a large ensemble simulation dataset

Chhin

Siev

Yoden

2021

Intl Journal of Climatology

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Statistically significant time‐lagged relationship of pre‐monsoon precipitation in Indochina Peninsula (ICP) with large‐scale feature over the Pacific and Indian Oceans, which was recently found in observational datasets by the authors, is investigated with a large‐ensemble (100 ensembles) simulation database called “Database for Policy Decision‐Making for Future Climate Change” (d4PDF). Two different strategies were taken to perform Empirical Orthogonal Function (EOF) analysis on the large‐ensemble dataset, namely statistics of the analysis results for each ensemble member separately and analysis on the 6,000‐year data (100 times 60 years). d4PDF can reproduce well the climatological characteristics of the observed pre‐monsoon precipitation. Significant time‐lagged correlations of several climate indices related to sea surface temperature (SST) with the pre‐monsoon precipitation in ICP as obtained in the observation are confirmed in d4PDF for both strategies of EOF analyses on each ensemble member and on 6,000‐year data. The climate simulation in d4PDF can capture the lag‐to‐lag variations of the time‐lagged correlation patterns very well, though it is weaker. The time‐lagged regressions of SST and zonal wind at 850 hPa upon the first principal component (PC1) time‐series of the EOF analysis of the pre‐monsoon precipitation over ICP are investigated in low latitudes over the Pacific and Indian Oceans. The observed time‐lagged regression features are also confirmed in d4PDF dataset; the significant regression areas expand larger and significant time lags become longer than the observation, especially in the case of 6,000‐year data, due to enough number of samples. Cluster analysis on the regression maps shows that the obtained groups with larger numbers of ensemble members are closer to the observation than the other groups. The randomness of time‐lagged regression feature among the 100 ensemble members is not directly related to the randomness of the SST perturbation introduced in the lower boundary condition for the ensemble simulation of d4PDF.

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A Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally‐Varying Modal Structures

et al. 2022

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To first order, intraseasonal convection follows the sun, peaking immediately southward of the equator during boreal winter, and northward of the equator during boreal summer (Zhang & Dong, 2004).Since their discovery, significant effort has been devoted to formally identifying the ISOs in a variety of observational data sets based on eigenvector analysis. While many indices have been developed to represent ISOs, most

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A Python Package to Calculate the OLR-Based Index of the Madden- Julian-Oscillation (OMI) in Climate Science and Weather Forecasting

Cited by 11 publications

References 15 publications

A Rotation Procedure to Improve Seasonally Varying Empirical Orthogonal Function Bases for MJO Indices

A Rotation Procedure to Improve Seasonally Varying Empirical Orthogonal Function Bases for MJO Indices

Time‐lagged correlations of pre‐monsoon precipitation in the Indochina Peninsula confirmed in a large ensemble simulation dataset

A Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally‐Varying Modal Structures

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