Clustered MCS dataset based on a global MCS dataset
Description
This is a clustered MCS dataset developed based on the global MCS dataset by Feng et al. (2023). This link includes the list of clustered events and the two scripts used to obtain the list of clustered events. The clustered MCSs are identified based on the distances between MCSs in time and space, and each cluster are required to have at least 3 MCSs that are close in time and space. Please see the method described in Hu et al. (2022) for more details, which uses the same method to identified clustered MCS but is applied to a United States MCS dataset.
The list of clustered MCSs corresponds to the MCS IDs in the global MCS dataset, so they need to be used simutaneously to get the complete information of clustered MCSs.
Files
Obtain_GlobalMCS_clusters_1.ipynb
Files
(2.6 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:484c208c6cfdb8fb1a54d80a67095be4
|
128.1 kB | Download |
|
md5:5220130029344383cb91486cfe403f38
|
120.5 kB | Download |
|
md5:df3169c17d3d933c3b403bb2ec360157
|
126.8 kB | Download |
|
md5:9b20dc4652d15a654dc0e65a81c862a2
|
130.0 kB | Download |
|
md5:d970d7d01ddaef7621079264bf8cefce
|
125.9 kB | Download |
|
md5:f554509253429686ea0d1e72902c55bb
|
123.9 kB | Download |
|
md5:67b342b8e6acb248bfdaaafdfe24d109
|
130.7 kB | Download |
|
md5:223cc78817ffb08ce6daede241320fe6
|
133.1 kB | Download |
|
md5:02248fa91810d8b9548da27b26006774
|
131.4 kB | Download |
|
md5:b89c165438ee0a6145ef4f564bec4bd2
|
132.1 kB | Download |
|
md5:ce8ea6f1ff1b54bc91a9831db0dd5726
|
130.1 kB | Download |
|
md5:d3ddebd9455e7df4c4f7a2a2f1aee3c7
|
126.9 kB | Download |
|
md5:9afaba3e7ec5728970f7f0ca486371ef
|
128.3 kB | Download |
|
md5:5d3d3d79fcc98174526fe00c71c0a9a6
|
125.1 kB | Download |
|
md5:0d7a6cbb2e76409c0dadca2bf8ed2265
|
123.3 kB | Download |
|
md5:23c43c7819db16a60bca07849fa8277c
|
126.2 kB | Download |
|
md5:17c5e13335022c89bf0e0bc5ce3c3da1
|
122.7 kB | Download |
|
md5:f08fcd5bf6912e4951dc1a947d15881c
|
126.8 kB | Download |
|
md5:93329a30198669f6c4e43d988d5ec215
|
121.0 kB | Download |
|
md5:0b46c8a755b64715a43694d937bf2bbc
|
117.5 kB | Download |
|
md5:98cdd811b037a35c95997a6a1735f268
|
7.2 kB | Preview Download |
|
md5:647d49d0e210bc22fee7d263369f2b0b
|
19.0 kB | Preview Download |
Additional details
References
- Feng, Z. (2023). PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data (v2). Retrieved from: https://doi.org/10.5281/zenodo.10023498
- Hu, H., Z. Feng, and L. R. Leung, 2022: Quantifying Flood Frequency Associated with Clustered Mesoscale Convective Systems in the United States. J. Hydrometeor., 23, 1685–1703, https://doi.org/10.1175/JHM-D-22-0038.1.