Track dataset of Indian monsoon low-pressure systems in Subseasonal-to-Seasonal prediction models, ERA-Interim and MERRA-2 reanalysis datasets

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 ([e-mail]).

本数据集收录1999-2010年5-10月共同再预报时段内，次季节到季节（Subseasonal-to-Seasonal, S2S）预测计划的11个模式的所有集合成员中识别出的印度季风低压系统（Indian monsoon low-pressure systems, LPSs）的轨迹与强度信息；同时包含1999-2010年6-9月间，ERA-Interim与MERRA-2再分析资料中识别出的季风低压系统轨迹细节。

所有S2S模式的时间分辨率为逐日（协调世界时0000时），而ERA-Interim与MERRA-2的时间分辨率分别为每6小时和每3小时。本数据集采用基于识别并关联850百帕（hPa）相对涡度极大值轨迹点的特征追踪算法（Hunt等，2016；2018）来追踪季风低压系统，并通过温压过滤算法剔除非季风低压系统（如热低压）。本数据集所用S2S模式的完整说明，以及追踪与追踪后处理流程，详见论文：https://doi.org/10.1175/WAF-D-20-0081.1

数据集文件：

1. 次季节到季节（S2S）模式数据集

bom_lps：收录澳大利亚气象局（Bureau of Meteorology）模式全部集合成员所识别出的季风低压系统轨迹信息
cma_lps：收录中国气象局（China Meteorological Administration）模式全部集合成员所识别出的季风低压系统轨迹信息
cnrm_lps：收录法国气象局/法国国家气象研究中心（Météo France/Centre National de Recherche Meteorologiques）模式全部集合成员所识别出的季风低压系统轨迹信息
eccc_lps：收录加拿大环境与气候变化部（Environment and Climate Change Canada）模式全部集合成员所识别出的季风低压系统轨迹信息
ecmwf_lps：收录欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts）模式全部集合成员所识别出的季风低压系统轨迹信息
hmcr_lps：收录俄罗斯水文气象中心（Hydrometeorological Centre of Russia）模式全部集合成员所识别出的季风低压系统轨迹信息
isac-cnr_lps：收录意大利国家研究理事会大气科学与气候研究所（Institute of Atmospheric Sciences and Climate of the National Research Council）模式全部集合成员所识别出的季风低压系统轨迹信息
jma_lps：收录日本气象厅（Japan Meteorological Agency）模式全部集合成员所识别出的季风低压系统轨迹信息
kma_lps：收录韩国气象局（Korea Meteorological Administration）模式全部集合成员所识别出的季风低压系统轨迹信息
ncep_lps：收录美国国家环境预报中心（National Centers for Environmental Prediction）模式全部集合成员所识别出的季风低压系统轨迹信息
ukmo_lps：收录英国气象局（UK Met Office）模式全部集合成员所识别出的季风低压系统轨迹信息

数据字段说明：

candidate_id：每个季风低压系统的随机标识编号
hindcast：识别出该季风低压系统的再预报文件的再预报日期
lat：对应时间步长下季风低压系统的纬度
lon：对应时间步长下季风低压系统的经度
lead：预报提前量，计算方式为该季风低压系统的有效日期与其所属再预报文件的再预报日期之差
time：表征季风低压系统存在时刻的时间戳
vort：对应时间步长下季风低压系统中心的850百帕相对涡度
member：识别出该季风低压系统的集合成员编号，控制试验以0表示

2. 再分析资料数据集

era-interim_lps：收录ERA-Interim再分析资料中识别出的季风低压系统轨迹细节
merra-2_lps：收录MERRA-2再分析资料中识别出的季风低压系统轨迹细节

数据字段说明：

time：表征季风低压系统存在时刻的时间戳
lon：对应时间步长下季风低压系统的经度
lat：对应时间步长下季风低压系统的纬度
candidate_id：每个季风低压系统的随机标识编号
vort：对应时间步长下季风低压系统中心的850百帕相对涡度

如需进一步咨询，请联系Akshay Deoras（邮箱：[e-mail]）。