Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “Monsoon Low-Pressure Systems”
Tracks of Indian Summer monsoon Low-Pressure Systems from ERA5
<p>This repository contains the codes and dataset as explained below:</p> <p>1) This dataset contains downstream and in situ LPS tracks over the Bay of Bengal (BoB) classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p>2) This also contains Principal components (PCs) of Rossby filtered OLR. </p> <p>3) The code to compute Transfer Entropy between PC1 of Rossby filtered OLR over West Pacific region and MSLP anomaly over BoB. </p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Tracks of Indian Summer monsoon Low-Pressure Systems from ERA5
<p>This repository contains the codes and dataset as explained below:</p> <p>1) This dataset contains downstream and in situ LPS tracks over the Bay of Bengal (BoB) classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p>2) This also contains Principal components (PCs) of Rossby filtered OLR. </p> <p>3) The code to compute Transfer Entropy between PC1 of Rossby filtered OLR over West Pacific region and MSLP anomaly over BoB. </p> <p>4) Codes and data to perform Kolmogorov Smirnov (KS) test.</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Indian Summer monsoon Low-Pressure Systems Dataset from ERA5
<p>This dataset contains downstream and in situ LPS genesis dates and their location over the Bay of Bengal classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Monsoon low-pressure system (LPS) tracks in ERA5 over India (1979-2019) with added environmental variables
<p>Derived from the LPS v3.0 dataset (https://doi.org/10.5281/zenodo.7568990). Filtered to monsoon LPSs (majority of track lifetime between June and September), with genesis over the Bay of Bengal and making landfall over India. Temporal resolution also reduced from hourly to six-hourly. This dataset accompanies the paper "Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems" (DOI to follow).</p> <p>Aside from the core variables described in the main LPS dataset (linked above), this version includes a large number of environmental variables, listed below. All are computed from ERA5 unless otherwise stated, "<em>mean</em>" means that the variable is computed as an average within 400 km of the LPS centre, "<em>mcz</em>" means that the variable is computed as an average in the box [75-85°E, 18.5-27°N].<br> <em>mean_u200</em>: 200 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_u850</em>: 850 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_skt</em>: surface temperature (K)<br> <em>mean_land_frac</em>: fraction of area within 400 km that is over land<br> <em>mcz_tcwv</em>: mean total column water vapour over monsoon trough (kg m<sup>-2</sup>)<br> <em>vortex_depth</em>: mean_vort_500 x mean_vort_700/mean_vort_850<sup>2</sup><br> <em>over_land</em>: flag for LPS centre (Boolean)<br> <em>dvo850_dt</em>: rate of change of mean_vort_850 (10<sup>-5</sup> s<sup>-1</sup> day<sup>-1</sup>) <br> <em>acc_land_time</em>: accumulated time where over_land = True (hours)<br> <em>total_land_time</em>: final value of acc_land_time} for a given LPS (hours)<br> <em>qshear_850</em>: meridional shear of 850 hPa specific humidity over India (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850</em>: meridional shear of 850 hPa zonal wind over India (m s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_cape</em>: CAPE (J kg<sup>-1</sup>)<br> <em>mcz_cape</em>: mean CAPE over the monsoon trough (J kg<sup>-1</sup>)<br> <em>mean_dthetae_dp_900_750</em>: d(theta_e)/dp between 900 and 750 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_dthetae_dp_750_500</em>: d(theta_e)/dp between 750 and 500 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_land_skt</em>: land surface temperature (K; NaN over ocean)<br> <em>mean_sst</em>: sea surface temperature (K; NaN over land)<br> <em>mean_swvl1</em>: soil moisture in the top layer (m<sup>3</sup> m<sup>-3</sup>; <7 cm; NaN over ocean)<br> <em>mean_swvl2</em>: soil moisture in the second layer (m<sup>3</sup> m<sup>-3</sup>; 7-28 cm; NaN over ocean)<br> <em>mean_swvl1_grad</em>: mean absolute horizontal gradient of mean_swvl1 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>mean_swvl2_grad</em>: mean absolute horizontal gradient of mean_swvl2 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>olr_90</em>: 90th percentile of negative OLR (i.e. ~90th percentile of cloud top height) (W m<sup>-2</sup>)<br> <em>olr_75</em>: 75th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>olr_50</em>: 50th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>qshear_850_background</em>: qshear\_850 averaged over the previous ten days (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850_background</em>: ushear\_850 averaged over the previous ten days (m<sup>3</sup> s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_q_850</em>: 850 hPa specific humidity (m<sup>3</sup> m<sup>-3</sup>)<br> <em>orography_height</em>: elevation of land surface under LPS centre (m)<br> <em>peak_vorticity</em>: largest value of mean_vort_850} attained by a given LPS (10<sup>-5</sup> s<sup>-1</sup>)<br> <em>reached_peak</em>: False if peak\_vorticity has not been reached yet, else True<br> <em>mean_prcp_400</em>: mean precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_prcp_800</em>: mean precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_400</em>: maximum precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_800</em>: maximum precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_vimfd_400</em>: vertically integrated moisture flux convergence (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_v200</em>: 200 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v500</em>: 500 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v850</em>: 850 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_u500</em>: 500 hPa zonal wind speed (m s<sup>-1</sup>)<br> <em>zonal_speed</em>: zonal (x) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>merid_speed</em>: meridional (y) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>mean_prcp_imerg</em>: as mean_prcp_400 but computed using IMERG data, rather than ERA5 (kg m<sup>-2</sup> hr<sup>-1</sup>)</p> <p> </p> <p>qshear_850, ushear_850 and their backgrounds are averaged over 5° longitude either side of the LPS centre, with the gradient computed between 10°N and 27°N, reflecting the moisture and zonal wind gradients across the monsoon region.</p>
Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s
<p>LPS tracks for the manuscript "Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s"</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.