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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “equatorial waves”
Equatorial wave filtering during May-September 2020 in the ECMWF OSE experiments with and without Aeolus data
<p>These are files with the global analyses produced by the observing system experiment with and without Aeolus data, and decomposed using the MODES software. Every file contain the zonal and meridional wind components and the pseudo-geopotential. Note that the paper makes use of the zonal winds only. Files including "hel1" in their titles belong to the OSE without Aeolus winds whereas the files with "hel4" in their names are from OSE including Aeolus winds. </p> <p>File names starting with KW belong to the Kelvin waves, file names starting with All contain the total fields, IGMRG denoted the non-Rossby modes whereas Rot belongs to file names containing only the signal associated with the Rossby modes. The Kelvin wave analyses are updated for the period from 1 May to 30 September, whereas other files are available for the periods discussed in the paper.</p> <p>The two movies are named MODES_KW_May2Sep2020.gif and MODES_BalancedUwind_May2Sep2020.gif for the Kelvin and balanced (Rossby modes) zonal winds averaged within 15 degrees N and 15 degrees S, respectively. Individual figures which constitute the movies are available at https://modes.cen.uni-hamburg.de.</p> <p> </p> <p> </p>
Tropical cyclone and equatorial wave data in ERA5 (1980-2018)
<p><strong>The dataset consists of global tropical cyclone (TC) track and equatorial wave data derived from ERA5 (1980-2018). The dataset was originally used to produce the paper "Equatorial waves as useful precursors to tropical cyclone occurrence and intensification" published in Nature Communications in 2023. </strong>See the Methods section in the article for details.</p> <p><strong>Note</strong>: the TC data are produced by <strong>Kevin Hodges</strong> (Reading University) using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017); the wave data are produced by <strong>Gui-Ying Yang</strong> (Reading University) based on the method in Yang et al., 2003. ERA5 data is generated by ECMWF and distributed by the C3S CDS. ERA5 data used in this paper are archived from <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>.</p> <p> </p> <p><strong>TC track data </strong>(ERA5 TC-matched.zip)</p> <p>TCs are identified and tracked by cyclonic vorticity centres in the ECMWF fifth generation climate reanalysis (ERA5) from six‐hourly atmospheric data, using the TRACK method (Hodges and Emerton, 2015; Hodges et al., 2017). The TRACK scheme used in this paper includes the following processes. First, the vertical average of the relative vorticity between 850 and 600 hPa is obtained. This is then spatially filtered using spherical harmonics to T63 resolution; the large‐scale background with total wavenumbers n ≤ 5 is removed. Vorticity maxima in the Northern Hemisphere and minima in the Southern Hemisphere are determined on the T63 grid and then used as starting points to obtain the off‐grid locations using B‐spline interpolation and maximisation methods. Then, in the first instance, all positive vorticity centres that exceed 0.5 Cyclonic Vorticity Unit (CVU, with 1.0 CVU=1.0*10<sup>-5</sup> s<sup>-1</sup>) in the range 0°–60°N (negative vorticity centres that are below -0.5 CVU in the range 0°–60°S) are identified through the data time series. The tracking is performed by first initialising a set of tracks using a nearest neighbour method and then refining them by minimising a cost function for track smoothness subject to adaptive constraints on track smoothness and displacement distance in a time step. After the tracking is complete, the full T63 vorticity maxima at levels from 850 hPa up to 200 hPa (850, 700, 600, 500, 400, 300 and 200 hPa) are added to the tracks using a recursive search within a 5° radius (geodesic) of the tracked centre. This is used to test for the existence of a coherent vertical structure and a warm core.</p> <p>A matching process is applied to match the ERA5 tracks against the observed tracks, the Best Track from IBTrACS. An ERA5 track is matched to an IBTrACS track if the mean spatial separation is ≤5° over the corresponding paired track points and it is the track with the smallest separation. This process ensures that the ERA5 tracks are those storms actually ‘observed’ in IBTrACS (but they may have different lifecycle). This means that the ERA5 storm tracks have an extended lifecycle consistent, allowing the analysis of the “pre-TC” features. ERA5 “pre-TCG” is the first point of the above identified TC track in ERA5, which is at an earlier stage than the genesis in IBTrACS (normally the first track point reaching the tropical storm intensity). On average, the ERA5 pre-TCG events are 4.6 days earlier in time, and 2.7 CVU weaker in relative vorticity of the vortex, than the TCG events at the observed TCG time.</p> <p> </p> <p><strong>Wave data (</strong>qvr_coefficient_YYYY_k3-40_p2-10_28plev_era5.nc<strong>)</strong></p> <p>In this dataset, dynamical equatorial waves are derived by projecting global wind and geopotential height data onto an orthogonal basis defined by the horizontal equatorial wave structures obtained from the theory of disturbances to a resting atmosphere on the equatorial <em>β</em>-plane. Six‐hourly horizontal winds and geopotential height in ERA5 are used here. This method identifies horizontal wind (<em>u, v</em>) and geopotential height (<em>Z</em>) structures associated with distinct equatorial waves. Potential equatorial waves are identified by projecting <em>u</em>, <em>v</em> and <em>Z</em> in the tropics (24°S–24°N) at each pressure level onto the different equatorial wave modes, using their sinusoidal structure in the zonal direction and parabolic cylinder functions in the meridional direction. These basis functions used for the wave projection are orthogonal, meaning that the wave structures here are orthogonal since they are pre-described as a series of the basis functions. In the parabolic cylinder functions, the meridional trapping scale is y<sub>0</sub>=6°. Before the projection, a broad-band spectral filter, with wavenumber 3 to 40 and period 2 to 10 days, is applied to separate eastward and westward moving waves.</p> <p>This ERA5 wave dataset contains three equatorial wave modes: westward-moving mixed Rossby-gravity (WMRG) and meridional mode number n=1 and 2 Rossby (R1 and R2) waves. The dataset spans 39 years from 1980 to 2018 covering all seasons, with a 6-hourly interval at 1° resolution on 28 pressure levels from 1000 to 70 hPa. </p> <p> </p> <p>Reference </p> <p>Hodges, K. I., & Emerton, R. (2015). The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF ensemble and deterministic prediction systems. Part I: Tropical cyclone stage. <em>Monthly Weather Review</em>, 143(12), 5091-5114.</p> <p>Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets?. <em>Journal of Climate</em>, 30(14), 5243-5264.</p> <p>Yang, G. Y., Hoskins, B., & Slingo, J. (2003). Convectively coupled equatorial waves: A new methodology for identifying wave structures in observational data. <em>Journal of the atmospheric sciences</em>, 60(14), 1637-1654.</p> <p>Feng, X., Yang, G.Y., Hodges, K., & Methven, J. (2023). Equatorial waves as useful precursors to tropical cyclone occurrence and intensification. <em>Nature Communications</em>.</p>
Data for GRL Article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model"
This repository contains mandatory material to reproduce the results of the GRL article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model" [Paper #2021GL093265RR].
Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
<p>The dataset is the wave-induced geopotential output from linear ocean models and potential energy flux by a group-velocity-based wave energy flux scheme in the period (1992–2016), which is involved in building a lightweight model, as well as showing a simple instance of utilizing the wave energy transfer for the prediction of Atlantic Niño/Niñas.</p>
Dataset for "Contributions of Parameterized Gravity Waves and Resolved Equatorial Waves to the QBO Period in a Future Climate of CESM2" by Lee et al. (2024)
Open the record for dataset details and reuse information.
Record-Breaking Precipitation in Indonesia's Capital Jakarta in January 2020 Linked to the Northerly Surge, Equatorial Waves, and MJO
<p>Collection of the dataset used in "Record-Breaking Precipitation in Indonesia's capital Jakarta in January 2020 Linked to the Northerly Surge, Equatorial Waves, and MJO" research letter [<a href="https://doi.org/10.1029/2022GL101513">https://doi.org/10.1029/2022GL101513</a>]. The file consisted of rain gauges, radar reflectivity, atmospheric reanalysis model, blackbody temperature of clouds, bandpass filtered data, HYSPLIT backward trajectory data, and other supplement data.</p>
Additional data for "Record-Breaking Precipitation in Indonesia's Capital of Jakarta in Early January 2020 Linked to the Northerly Surge, Equatorial Waves, and MJO" containing Cross Equatorial Northerly Surge record data
<p>Data containing the record of the daily amplitude of active Cross Equatorial Northerly Surge events on December, January, and February of 1979 to 2021. This amplitude was calculated based on the ERA5 reanalysis dataset. The rules for recording an event with the next event separated by a minimum of 1 day interval of amplitude below the threeshold.</p>
Dataset (part 1) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"
<p>Dataset (part 1) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"</p>
Dataset (part 2) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"
<p>Dataset (part 2) for "Quasilinear diffusion of protons by equatorial magnetosonic waves at quasi-perpendicular propagation"</p>
Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
Open the record for dataset details and reuse information.
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.