This dataset contains tracks and intensities of Indian monsoon low-pressure systems (LPSs), as identified in all ensemble members of eleven models of the Subseasonal-to-Seasonal (S2S) prediction project during a common reforecast period of May–October 1999–2010. Track details of LPSs identified in the ERA-Interim and MERRA-2 reanalysis datasets during June–September 1999–2010. The temporal resolution of all S2S models is daily (0000 UTC), whereas that of ERA-Interim and MERRA-2 are six-hourly and three-hourly respectively. LPSs were tracked using a feature-tracking algorithm (Hunt et al., 2016; 2018), which is based on identifying and linking track points featuring 850 hPa relative vorticity maximum. Non-LPSs (e.g., heat lows) were eliminated from the dataset using a temperature-pressure filter. A full description of S2S models used in the dataset, and the tracking as well as post-tracking process is described in the paper: https://doi.org/10.1175/WAF-D-20-0081.1 Files: 1. S2S models bom_lps: contains track details of LPSs identified in all ensemble members of the Bureau of Meteorology model cma_lps: contains track details of LPSs identified in all ensemble members of the China Meteorological Administration model cnrm_lps: contains track details of LPSs identified in all ensemble members of the Météo France/Centre National de Recherche Meteorologiques model eccc_lps: contains track details of LPSs identified in all ensemble members of the Environment and Climate Change Canada model ecmwf_lps: contains track details of LPSs identified in all ensemble members of the European Centre for Medium-Range Weather Forecasts model hmcr_lps: contains track details of LPSs identified in all ensemble members of the Hydrometeorological Centre of Russia model isac-cnr_lps: contains track details of LPSs identified in all ensemble members of the Institute of Atmospheric Sciences and Climate of the National Research Council model jma_lps: contains track details of LPSs identified in all ensemble members of the Japan Meteorological Agency model kma_lps: contains track details of LPSs identified in all ensemble members of the Korea Meteorological Administration model ncep_lps: contains track details of LPSs identified in all ensemble members of the National Centers for Environmental Prediction model ukmo_lps: contains track details of LPSs identified in all ensemble members of the UK Met Office model Columns: candidate_id: a random identity number for each LPS hindcast: the reforecast date of a hindcast file from which an LPS was identified lat: the latitude of an LPS at a given time step lon: the longitude of an LPS at a given time step lead: the forecast lead time, calculated as the difference between the LPS date and reforecast date of the hindcast from which it was identified time: a time stamp showing when an LPS was present vort: the 850 hPa relative vorticity at the centre of an LPS at a given time step member: the ensemble member from which an LPS was identified; the control run is indicated by a zero (0) 2. Reanalysis datasets era-interim_lps: contains track details of LPSs identified in the ERA-Interim reanalysis dataset. merra-2_lps: contains track details of LPSs identified in the MERRA-2 reanalysis dataset. Columns: time: a time stamp showing when an LPS was present lon: the longitude of an LPS at a given time step lat: the latitude of an LPS at a given time step candidate_id: a random identity number for each LPS vort: the 850 hPa relative vorticity at the centre of an LPS at a given time step For further details, contact Akshay Deoras (deorasakshay@gmail.com).

{"references": ["Hunt, K.M., Turner, A.G., Inness, P.M., Parker, D.E. and Levine, R.C., 2016. On the structure and dynamics of Indian monsoon depressions. Monthly Weather Review, 144(9). https://doi.org/10.1175/MWR-D-15-0138.1", "Hunt, K.M., Turner, A.G. and Shaffrey, L.C., 2018. The evolution, seasonality and impacts of western disturbances. Quarterly Journal of the Royal Meteorological Society, 144(710). https://doi.org/10.1002/qj.3200", "Deoras, A., Hunt, K.M. and Turner, A.G., 2021. Comparison of the prediction of Indian monsoon low-pressure systems by Subseasonal-to-Seasonal prediction models. Weather and Forecasting. https://doi.org/10.1175/WAF-D-20-0081.1"]}