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](https://binder.pangeo.io/v2/gh/pangeo-data/climpred/main?urlpath=lab/tree/docs/source/examples/subseasonal/daily-subx-example.ipynb) or view it [on Github](https://github.com/pangeo-data/climpred/blob/main/docs/source/examples/subseasonal/daily-subx-example.ipynb)."
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"## Calculate skill of a MJO Index of SubX model GEOS_V2p1 as function of daily lead time "
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"In this example, we demonstrate: \n",
"\n",
"1. How to remotely access data from the Subseasonal Experiment (SubX) hindcast database and set it up to be used in `climpred`. \n",
"2. How to calculate the Anomaly Correlation Coefficient (ACC) using daily data with `climpred`\n",
"3. How to calculate and plot historical forecast skill of the real-time multivariate MJO (RMM) indices as function of lead time."
]
},
{
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"source": [
"### The Subseasonal Experiment (SubX)\n",
"\n",
"Further information on SubX is available from {cite:t}`Pegion2019` and the [SubX project website](http://cola.gmu.edu/subx/).\n",
"\n",
"The SubX public database is hosted on the International Research Institute for Climate and Society (IRI) data server [http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/](http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/).\n",
"\n",
"Since the SubX data server is accessed via this notebook, the time for the notebook to run may is several minutes and will vary depending on the speed that data can be downloaded. This is a large dataset, so please be patient. If you prefer to download SubX data locally, scripts are available from [https://github.com/kpegion/SubX](https://github.com/kpegion/SubX.)."
]
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"### Definitions\n",
"\n",
"**RMM**\n",
": Two indices (RMM1 and RMM2) are used to represent the MJO. Together they define the MJO based on 8 phases and can represent both the phase and amplitude of the MJO {cite:p}`Wheeler2004`. This example uses the observed RMM1 provided by Matthew Wheeler at the Center for Australian Weather and Climate Research. It is the version of the indices in which interannual variability has not been removed.\n",
"\n",
"**Skill of RMM**\n",
": Traditionally, the skill of the RMM is calculated as a bivariate correlation encompassing the skill of the two indices together {cite:p}`Rashid2011,Gottschalck2010`. Currently, `climpred` does not have the functionality to calculate the bivariate correlation, thus the anomaly correlation coefficient for RMM1 index is calculated here as a demonstration. The bivariate correlation metric will be added in a future version of `climpred`."
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"<xarray.Dataset>\n",
"Dimensions: (S: 510, M: 4, L: 45)\n",
"Coordinates:\n",
" * S (S) float32 1.424e+04 1.425e+04 1.426e+04 ... 2.044e+04 2.045e+04\n",
" * M (M) float32 1.0 2.0 3.0 4.0\n",
" * L (L) float32 0.5 1.5 2.5 3.5 4.5 5.5 ... 40.5 41.5 42.5 43.5 44.5\n",
"Data variables:\n",
" RMM1 (S, M, L) float32 -0.03237 0.1051 0.1946 ... -0.1499 0.08326 0.1389\n",
"Attributes:\n",
" Conventions: IRIDL<xarray.Dataset>\n",
"Dimensions: (lead: 45)\n",
"Coordinates:\n",
" * lead (lead) int64 0 1 2 3 4 5 6 7 8 9 ... 35 36 37 38 39 40 41 42 43 44\n",
" skill <U11 'initialized'\n",
"Data variables:\n",
" rmm1 (lead) float64 0.9782 0.9719 0.9632 0.9508 ... 0.304 0.2801 0.2616\n",
"Attributes:\n",
" Conventions: IRIDL\n",
" prediction_skill_software: climpred https://climpred.readthedocs.io/\n",
" skill_calculated_by_function: HindcastEnsemble.verify()\n",
" number_of_initializations: 510\n",
" number_of_members: 4\n",
" alignment: maximize\n",
" metric: pearson_r\n",
" comparison: e2o\n",
" dim: init\n",
" reference: []