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13 results for “Low Pressure System”
Monsoon low-pressure-system tracks over South Asia (1979-2019)
<p>Monsoon LPS tracks over South Asia, computed using ERA-Interim reanalysis data.<br> Tracking algorithm described in Hunt and Fletcher (2018) [doi:10.1007/s00382-019-04744-x] and Hunt <em>et al.</em> (2016) [doi:10.1175/MWR-D-15-0138.1].<br> <br> Description of fields:<br> <em>point_id</em>: unique integer identifier for each point<br> <em>time</em>: string with format DD/MM/YY HH:MM, denoting time of detected track point<br> <em>lon</em>: longitude of detected track point<br> <em>lat</em>: latitude of detected track point<br> <em>track_id</em>: unique integer identifier for each track (constituting a group of points)<br> <em>vort</em>: relative vorticity at 850 hPa for the given point (units: s<sup>-1</sup>)<br> <em>circulation</em>: vorticity integrated over the blob of positive vorticity containing the track point (units: arb)<br> <em>eccentricity</em>: eccentricity of the vorticity blob containing the track point<br> <em>category</em>: integer from 0-6 denoting the category of the LPS at the given point, approximately matching IMD criteria. 0: low-pressure area, 1: deep low-pressure area, 2: depression, 3: deep depression, 4: cyclonic storm, 5: severe cyclonic storm, 6: very severe cyclonic storm (and above).</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><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>
The System for Classification of Low-Pressure Systems (SyCLoPS) Dataset (Based on ERA5)
<p>This is the ERA5 System for Classification of Low-Pressure Systems (SyCLoPS) dataset<strong> version 6</strong>. Details of SyCLoPS algorithms are described in the paper titled <strong>The System for Classification of Low-Pressure Systems (SyCLoPS): An All-in-One Objective Framework for Large-scale Data sets </strong>published on <em>J. Geophys. Res. Atm.</em>:<strong> [<a href="https://doi.org/10.1029/2024JD041287">https://doi.org/10.1029/2024JD041287]</a></strong></p> <p><strong>Important: The most up-to-date SyCLoPS codes are now kept on GitHub: [<a href="https://github.com/yepkids/SyCLoPS">https://github.com/yepkids/SyCLoPS</a>] <br></strong>*Updates on GitHub: SyCLoPS can now run entirely in Python. See the GitHub README page for details.*</p> <p>SyCLoPS user manual: [<a href="https://climate.ucdavis.edu/syclops.php">https://climate.ucdavis.edu/syclops.php</a>]</p> <p><strong>Known issues</strong></p> <ol> <li>The master TE branch now lacks the ability to deal with large missing values in datasets (e.g. 1e20), this will result in unreasonable values in the classification process for some datasets. NaNs as missing values are safe to proceed with. We are working on this issue and users can expect a newer TE version with fixes in the near future. For now, users can install this fork of TempestExtremes via CMAKE, which can be found here: [<a href="https://github.com/yepkids/tempestextremes"><strong>https://github.com/yepkids/tempestextremes</strong></a>], to work around this problem. This fork provides a temporary solution that adds missing value support for operators used by SyCLoPS and has been tested. Note that this is not a stable release, and please report any problems with this fork to Yushan Han (yshhan@ucdavis.edu). You can also choose to convert all missing values in your input files to NaNs.</li> </ol> <p>TempestExtremes software (master branch): [<a href="https://github.com/ClimateGlobalChange/tempestextremes">https://github.com/ClimateGlobalChange/tempestextremes</a>] </p> <p><strong>Major updates and bug fixes in this version:</strong></p> <ol> <li><strong>The classified and input LPS dataset is now extended from 1979-2022 to 1970-2024 </strong>(See Chapter 3 of the manual on how to load and use the output classified catalog).</li> <li>Note: This release will have a slight difference in the number of nodes found and some tracks compared to previous releases for the overlapped period. This is partly due to the extension of the tracks at the beginning of 1979 and the end of 2022, and also to the use of the newly developed "--mergeequal" argument for ERA5 MSLP nodes (this is to avoid some rare cases where two nodes with exactly the same MSLP values are close to each other but are not merged; see the manual section 2.2 for more details). </li> </ol> <p><strong>The following files can be obtained from version 4:</strong></p> <ol> <li>The labeled size blobs of each year: "<strong>size_blobs_1979_2022.tar.gz</strong>"</li> <li>The labeled precipitation blobs of each year: "<strong>preci_blobs_1979_2022.tar.gz</strong>"</li> </ol> <p>Please contact Yushan Han (yshhan@ucdavis.edu) if you have questions about the SyCLoPS framework. Please contact Paul Ullrich (paullrich@ucdavis.edu) if you have any questions about the TE software.</p> <p>See below for a table of atmospheric variables required for SyCLoPS and a flowchart of the classification process. See the SyCLoPS manual for more details.</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>
Global track dataset of monsoon low pressure systems
<p>This dataset contains the tracks and intensities of low pressure system (LPS) in the global tropics (35ºS-35ºN), as identified in five atmospheric reanalyses (ERA5, ERA-Interim, JRA55, MERRA2, and CFSR) using the algorithm described in the paper titled <strong>Assessing historical variability of South Asian monsoon lows and depressions with an optimized tracking algorithm</strong>. Tracking of LPS was performed using an automated Lagrangian pointwise feature tracker, TempestExtremes (Ullrich & Zarzycki, 2017), with criteria chosen to best match a subjectively analyzed LPS dataset while minimizing disagreement between four atmospheric reanalyses. A full description of the algorithm and dataset is described in the preprint *(<a href="https://doi.org/10.1029/2020JD032977">https://doi.org/10.1029/2020JD032977</a>)</p> <p> </p> <p><strong>Files:</strong></p> <ul> <li><em><strong>Header.txt</strong>: contains the names of columns of the LPS dataset files</em></li> <li><em><strong>LPS_Global_ERA5.dat</strong>: LPS track file for ERA5, 1979-2019, hourly resolution</em></li> <li><em><strong>LPS_Global_ERA-Interim.dat</strong>: LPS track file for ERA-Interim, 1979-2018, six-hourly resolution</em></li> <li><em><strong>LPS_Global_JRA55.dat</strong>: LPS track file for JRA55, 1958-2019, six-hourly resolution</em></li> <li><em><strong>LPS_Global_CFSR.dat</strong>: LPS track file for CFSR, 1979-2010, six-hourly resolution</em></li> <li><em><strong>LPS_Global_MERRA2.dat</strong>: LPS track file for MERRA2, 1980-2019, three-hourly resolution</em></li> </ul> <p><strong><em>Extented Data for ERA5:</em></strong></p> <ul> <li><em>ERA5 tracks for 1940-2022 are available on <strong><a href="https://boos.berkeley.edu">https://boos.berkeley.edu</a> </strong>in the "Data & Tools" section.</em></li> </ul> <p><strong>Script:</strong></p> <ul> <li><em><strong>Run_tempest_lps.sh</strong>: TempestExtremes script to track LPS in reanalysis dataset. </em></li> </ul> <p>Additionally, four python scripts are available to subset the dataset:</p> <ol> <li><em><strong>Python_Moist_LPS_heat_low.py</strong>: Python script to create two separate files for moist LPS and heat lows</em></li> <li><em><strong>Python_Low_Depression.py</strong>: Python script to create two separate files for monsoon lows and monsoon depressions</em></li> <li><em><strong>Python_Region.py</strong>: Python script to create a separate file for a region</em></li> <li><em><strong>Python_Season.py</strong>: Python script to create a separate file for a season</em></li> </ol> <p> </p> <ul> <li> </li> </ul> <p>Note: The TempestExtremes software can be obtained from GitHub at <a href="https://github.com/ClimateGlobalChange/tempestextremes">https://github.com/ClimateGlobalChange/tempestextremes</a>.</p> <p>For further details, contact S. Vishnu (<em>vishnuedv@gmail.com</em>) or William R Boos (<em>billboos@alum.mit.edu</em>).</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>
Influence of the Southeast Asian mountains on the statistics of monsoon low pressure systems over India
<p>Data used in the letter titled "Influence of the Southeast Asian mountains on the statistics of monsoon low pressure systems over India".</p>
Hydrocortisone Treatment In Systemic Low Blood Pressure During Hypothermia in Asphyxiated Newborns
ClinicalTrials.gov study NCT02700828. IPD Sharing: NO. Countries: 1. Publications: 10.
Low-pressure system (LPS) tracks in ERA5 over South Asia (1979-2025) v4.1
<h1>Low-Pressure System (LPS) tracks over South Asia — v4.1</h1> <p><strong>Region:</strong> South Asia (India, Sri Lanka, Pakistan; tracks may extend beyond these borders)<br><strong>Data source:</strong> 3-hourly ERA5 reanalysis<br><strong>Method:</strong> Automated LoG tracking following Hunt & Fletcher (2018, doi:10.1007/s00382-019-04744-x) and Hunt et al. (2016, doi:10.1175/MWR-D-15-0138.1)<br><br></p> <h2>Coverage</h2> <ul> <li><strong>Temporal span:</strong> <strong>1940-01-01</strong> to <strong>2025-08-31</strong> (inclusive)</li> <li><strong>Frame epoch:</strong> <code>frame</code> is hours since <strong>1940-01-01 00:00 UTC</strong></li> <li><strong>Timestep:</strong> hourly detections</li> </ul> <h3>Variables (columns)</h3> <ul> <li><code>x</code> — longitude (deg E)</li> <li><code>y</code> — latitude (deg N)</li> <li><code>frame</code> — hours since 1940-01-01 00:00 UTC (int)</li> <li><code>year</code>, <code>month</code>, <code>day</code>, <code>hour</code> — UTC components of detection time</li> <li><code>max_vort</code> — maximum relative vorticity within ~4° (~400–450 km) of the LPS centre from the <strong>850–700 hPa mean</strong>; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>mean_vort_850</code>, <code>mean_vort_700</code>, <code>mean_vort_500</code> — mean relative vorticity within ~4° at each level; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>precip_1hr</code> — mean precipitation rate within ~8° (~800–900 km) over the hour; <strong>mm h⁻¹</strong> </li> <li><code>precip_24hr</code> — mean of the <strong>calendar-day</strong> 24-h precipitation sum within ~8°; <strong>mm day⁻¹</strong></li> <li><code>mean_wind</code>, <code>max_wind</code> — 10 m wind speed within ~4° (area-mean and maximum); <strong>m s⁻¹</strong></li> <li><code>min_mslp</code> — minimum mean sea-level pressure within ~4°; <strong>hPa</strong></li> <li><code>mslp_contours</code> — count of <strong>closed</strong> 2 hPa isobars that enclose the LPS centre (dimensionless)</li> <li><code>over_land</code> — 1 if the centre is over land (Natural Earth 1:50 m), else 0</li> <li><code>category</code> — intensity class (IMD-style): 1=L, 2=D, 3=DD, 4=CS, 5=SCS, 6=VSCS, 7=SuCS</li> <li><code>track_id</code> — unique integer identifier for each track (all points with the same <code>track_id</code> belong to the same system)</li> </ul> <h3>Intensity category (<code>category</code>)</h3> <p>IMD-style classes, derived from the number of enclosing closed isobars and 10 m wind (over ocean) / combination with isobars (over land).<br>Mapping used in figures/legends:</p> <ul> <li><strong>1 = L</strong> (Low)</li> <li><strong>2 = D</strong> (Depression)</li> <li><strong>3 = DD</strong> (Deep Depression)</li> <li><strong>4 = CS</strong> (Cyclonic Storm)</li> <li><strong>5 = SCS</strong> (Severe Cyclonic Storm)</li> <li><strong>6 = VSCS</strong> (Very Severe Cyclonic Storm)</li> <li><strong>7 = SuCS</strong> (Super Cyclonic Storm)<br><br></li> </ul> <p><strong>Notes:</strong></p> <ul> <li>Over <strong>ocean</strong>, thresholds follow IMD (knots converted to m s⁻¹): 17, 28, 34, 48, 64, 119 kt breakpoints.</li> <li>Over <strong>land</strong>, the number of enclosing closed isobars (2 hPa spacing) informs classification together with wind.</li> </ul>
Comparison of Systemic Response After Laparoscopies Performed With Standard and Low-Pressure Pneumoperitoneum
ClinicalTrials.gov study NCT00567125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of the Ultravision™ System in Low Pressure Laparoscopic Cholecystectomy Compared to Airseal® IFS
ClinicalTrials.gov study NCT04162106. IPD Sharing: NO. Countries: 1. Publications: 0.
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