clouddrift.datasets#
This module provides functions to easily access ragged array datasets. If the datasets are not accessed via cloud storage platforms or are not found on the local filesystem, they will be downloaded from their upstream repositories and stored for later access (~/.clouddrift for UNIX-based systems).
Functions
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Returns the ANDRO as a ragged array Xarray dataset. |
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Returns the latest version of the NOAA Global Drifter Program (GDP) hourly dataset as a ragged array Xarray dataset. |
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Returns the NOAA Global Drifter Program (GDP) 6-hourly dataset as a ragged array Xarray dataset. |
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Returns the Grand LAgrangian Deployment (GLAD) dataset as a ragged array Xarray dataset. |
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Returns the revised hurricane database (HURDAT2) as a ragged array xarray dataset. |
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Returns the MOSAiC sea-ice drift dataset as a ragged array Xarray dataset. |
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Returns the Sofar Ocean Spotter drifters ragged array dataset as an Xarray dataset. |
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Returns the subsurface floats dataset as a ragged array Xarray dataset. |
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Returns the YoMaHa dataset as a ragged array Xarray dataset. |
- clouddrift.datasets.andro(decode_times: bool = False) Dataset[source]#
Returns the ANDRO as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local filesystem. If it is not found, the dataset will be downloaded using the corresponding adapter function and stored for later access. The upstream data is available at https://www.seanoe.org/data/00360/47077/.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
ANDRO dataset as a ragged array
Examples#
>>> from clouddrift.datasets import andro >>> ds = andro() >>> ds <xarray.Dataset> Dimensions: (obs: 1360753, traj: 9996) Coordinates: time_d (obs) datetime64[ns] ... time_s (obs) datetime64[ns] ... time_lp (obs) datetime64[ns] ... time_lc (obs) datetime64[ns] ... id (traj) int64 ... Dimensions without coordinates: obs, traj Data variables: (12/33) lon_d (obs) float64 ... lat_d (obs) float64 ... pres_d (obs) float32 ... temp_d (obs) float32 ... sal_d (obs) float32 ... ve_d (obs) float32 ... ... ... lon_lc (obs) float64 ... lat_lc (obs) float64 ... surf_fix (obs) int64 ... cycle (obs) int64 ... profile_id (obs) float32 ... rowsize (traj) int64 ... Attributes: title: ANDRO: An Argo-based deep displacement dataset history: 2022-03-04 date_created: 2023-12-08T00:52:00.937120 publisher_name: SEANOE (SEA scieNtific Open data Edition) publisher_url: https://www.seanoe.org/data/00360/47077/ license: freely available
Reference#
Ollitrault Michel, Rannou Philippe, Brion Emilie, Cabanes Cecile, Piron Anne, Reverdin Gilles, Kolodziejczyk Nicolas (2022). ANDRO: An Argo-based deep displacement dataset. SEANOE. https://doi.org/10.17882/47077
- clouddrift.datasets.gdp1h(decode_times: bool = True) Dataset[source]#
Returns the latest version of the NOAA Global Drifter Program (GDP) hourly dataset as a ragged array Xarray dataset.
The data is accessed from zarr archive hosted on a public AWS S3 bucket accessible at https://registry.opendata.aws/noaa-oar-hourly-gdp/. Original data source from NOAA NCEI is https://doi.org/10.25921/x46c-3620).
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
Hourly GDP dataset as a ragged array
Examples#
>>> from clouddrift.datasets import gdp1h >>> ds = gdp1h() >>> ds <xarray.Dataset> Dimensions: (traj: 19396, obs: 197214787) Coordinates: id (traj) int64 ... time (obs) datetime64[ns] ... Dimensions without coordinates: traj, obs Data variables: (12/60) BuoyTypeManufacturer (traj) |S20 ... BuoyTypeSensorArray (traj) |S20 ... CurrentProgram (traj) float32 ... DeployingCountry (traj) |S20 ... DeployingShip (traj) |S20 ... DeploymentComments (traj) |S20 ... ... ... start_lat (traj) float32 ... start_lon (traj) float32 ... typebuoy (traj) |S10 ... typedeath (traj) int8 ... ve (obs) float32 ... vn (obs) float32 ... Attributes: (12/16) Conventions: CF-1.6 acknowledgement: Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurio... contributor_name: NOAA Global Drifter Program contributor_role: Data Acquisition Center date_created: 2023-09-08T17:05:12.130123 doi: 10.25921/x46c-3620 ... ... processing_level: Level 2 QC by GDP drifter DAC publisher_email: aoml.dftr@noaa.gov publisher_name: GDP Drifter DAC publisher_url: https://www.aoml.noaa.gov/phod/gdp summary: Global Drifter Program hourly data title: Global Drifter Program hourly drifting buoy collection
See Also#
- clouddrift.datasets.gdp6h(decode_times: bool = True) Dataset[source]#
Returns the NOAA Global Drifter Program (GDP) 6-hourly dataset as a ragged array Xarray dataset.
The data is accessed from a public HTTPS server at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) accessible at https://www.aoml.noaa.gov/phod/gdp/index.php. It should be noted that the data loading method is platform dependent. Linux and Darwin (macOS) machines lazy load the datasets leveraging the byte-range feature of the netCDF-c library (dataset loading engine used by xarray). Windows machines download the entire dataset into a memory buffer which is then passed to xarray.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
6-hourly GDP dataset as a ragged array
Examples#
>>> from clouddrift.datasets import gdp6h >>> ds = gdp6h() >>> ds <xarray.Dataset> Size: 2GB Dimensions: (traj: 27647, obs: 46535470) Coordinates: id (traj) int64 221kB ... time (obs) datetime64[ns] 372MB ... Dimensions without coordinates: traj, obs Data variables: (12/49) BuoyTypeManufacturer (traj) |S20 553kB ... BuoyTypeSensorArray (traj) |S20 553kB ... CurrentProgram (traj) float64 221kB ... DeployingCountry (traj) |S20 553kB ... DeployingShip (traj) |S20 553kB ... DeploymentComments (traj) |S20 553kB ... ... ... start_lon (traj) float32 111kB ... temp (obs) float32 186MB ... typebuoy (traj) |S10 276kB ... typedeath (traj) int8 28kB ... ve (obs) float32 186MB ... vn (obs) float32 186MB ... Attributes: (12/18) Conventions: CF-1.6 acknowledgement: Lumpkin, Rick; Centurioni, Luca (2019). NOAA Global... contributor_name: NOAA Global Drifter Program contributor_role: Data Acquisition Center date_created: 2024-04-04T13:44:01.176967 doi: 10.25921/7ntx-z961 ... ... publisher_name: GDP Drifter DAC publisher_url: https://www.aoml.noaa.gov/phod/gdp summary: Global Drifter Program six-hourly data time_coverage_end: 2023-10-18:18:00:00Z time_coverage_start: 1979-02-15:00:00:00Z title: Global Drifter Program drifting buoy collection
See Also#
- clouddrift.datasets.glad(decode_times: bool = True) Dataset[source]#
Returns the Grand LAgrangian Deployment (GLAD) dataset as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local filesystem. If it is not found, the dataset will be downloaded using the corresponding adapter function and stored for later access.
The upstream data is available at https://doi.org/10.7266/N7VD6WC8.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
GLAD dataset as a ragged array
Examples#
>>> from clouddrift.datasets import glad >>> ds = glad() >>> ds <xarray.Dataset> Dimensions: (obs: 1602883, traj: 297) Coordinates: time (obs) datetime64[ns] ... id (traj) object ... Data variables: latitude (obs) float32 ... longitude (obs) float32 ... position_error (obs) float32 ... u (obs) float32 ... v (obs) float32 ... velocity_error (obs) float32 ... rowsize (traj) int64 ... Attributes: title: GLAD experiment CODE-style drifter trajectories (low-pass f... institution: Consortium for Advanced Research on Transport of Hydrocarbo... source: CODE-style drifters history: Downloaded from https://data.gulfresearchinitiative.org/dat... references: Özgökmen, Tamay. 2013. GLAD experiment CODE-style drifter t...
Reference#
Özgökmen, Tamay. 2013. GLAD experiment CODE-style drifter trajectories (low-pass filtered, 15 minute interval records), northern Gulf of Mexico near DeSoto Canyon, July-October 2012. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7VD6WC8
- clouddrift.datasets.hurdat2(basin: Literal['atlantic', 'pacific', 'both'] = 'both', decode_times: bool = True) DataArray[source]#
Returns the revised hurricane database (HURDAT2) as a ragged array xarray dataset.
The function will first look for the ragged array dataset on the local filesystem. If it is not found, the dataset will be downloaded using the corresponding adapter function and stored for later access.
The upstream data is available at https://www.aoml.noaa.gov/hrd/hurdat/hurdat2.html.
Parameters#
- basin“atlantic”, “pacific”, “both” (default)
Specify which ocean basin to download storm data for. Current available options are the Atlantic Ocean “atlantic”, Pacific Ocean “pacific” and, both “both” to download storm data for both oceans.
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
HURDAT2 dataset as a ragged array.
Standard usage of the dataset.
>>> from clouddrift.datasets import hurdat2 >>> ds = hurdat2() >>> ds <xarray.Dataset> Dimensions: (traj: ..., obs: ...) Coordinates: atcf_identifier (traj) <U8 ... time (obs) datetime64[ns] ... Dimensions without coordinates: traj, obs Data variables: (12/23) basin (traj) <U2 ... year (traj) int64 ... rowsize (traj) int64 ... record_identifier (obs) <U1 ... system_status (obs) <U2 ... ... ... max_med_wind_radius_nw (obs) float64 ... max_high_wind_radius_ne (obs) float64 ... max_high_wind_radius_se (obs) float64 ... max_high_wind_radius_sw (obs) float64 ... max_high_wind_radius_nw (obs) float64 ... max_sustained_wind_speed_radius (obs) float64 ... Attributes: title: HURricane DATa 2nd generation (HURDAT2) date_created: ... publisher_name: NOAA AOML Hurricane Research Division publisher_email: AOML.HRDWebmaster@noaa.gov publisher_url: https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html institution: NOAA Atlantic Oceanographic and Meteorological Laboratory summary: The National Hurricane Center (NHC) conducts a post-sto... ...
To only retrieve records for the Atlantic Ocean basin.
>>> from clouddrift.datasets import hurdat2 >>> ds = hurdat2(basin="atlantic") >>> ds <xarray.Dataset> ...
Reference#
- clouddrift.datasets.mosaic(decode_times: bool = True) Dataset[source]#
Returns the MOSAiC sea-ice drift dataset as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local filesystem. If it is not found, the dataset will be downloaded using the corresponding adapter function and stored for later access.
The upstream data is available at https://arcticdata.io/catalog/view/doi:10.18739/A2KP7TS83.
Reference#
Angela Bliss, Jennifer Hutchings, Philip Anderson, Philipp Anhaus, Hans Jakob Belter, Jørgen Berge, Vladimir Bessonov, Bin Cheng, Sylvia Cole, Dave Costa, Finlo Cottier, Christopher J Cox, Pedro R De La Torre, Dmitry V Divine, Gilbert Emzivat, Ying-Chih Fang, Steven Fons, Michael Gallagher, Maxime Geoffrey, Mats A Granskog, … Guangyu Zuo. (2022). Sea ice drift tracks from the Distributed Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition 2019 - 2021. Arctic Data Center. doi:10.18739/A2KP7TS83.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
MOSAiC sea-ice drift dataset as a ragged array
Examples#
>>> from clouddrift.datasets import mosaic >>> ds = mosaic() >>> ds <xarray.Dataset> Dimensions: (obs: 1926226, traj: 216) Coordinates: time (obs) datetime64[ns] ... id (traj) object ... Dimensions without coordinates: obs, traj Data variables: (12/19) latitude (obs) float64 ... longitude (obs) float64 ... Deployment Leg (traj) int64 ... DN Station ID (traj) object ... IMEI (traj) object ... Deployment Date (traj) datetime64[ns] ... ... ... Buoy Type (traj) object ... Manufacturer (traj) object ... Model (traj) object ... PI (traj) object ... Data Authors (traj) object ... rowsize (traj) int64 ...
- clouddrift.datasets.spotters(decode_times: bool = True) Dataset[source]#
Returns the Sofar Ocean Spotter drifters ragged array dataset as an Xarray dataset.
The data is accessed from a zarr archive hosted on a public AWS S3 bucket accessible at https://sofar-spotter-archive.s3.amazonaws.com/spotter_data_bulk_zarr.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
Sofar ocean floats dataset as a ragged array
Examples#
>>> from clouddrift.datasets import spotters >>> ds = spotters() >>> ds <xarray.Dataset> Dimensions: (index: 6390651, trajectory: 871) Coordinates: time (index) datetime64[ns] ... * trajectory (trajectory) object 'SPOT-010001' ... 'SPOT-1975' Dimensions without coordinates: index Data variables: latitude (index) float64 ... longitude (index) float64 ... meanDirection (index) float64 ... meanDirectionalSpread (index) float64 ... meanPeriod (index) float64 ... peakDirection (index) float64 ... peakDirectionalSpread (index) float64 ... peakPeriod (index) float64 ... rowsize (trajectory) int64 ... significantWaveHeight (index) float64 ... Attributes: author: Isabel A. Houghton creation_date: 2023-10-18 00:43:55.333537 email: isabel.houghton@sofarocean.com institution: Sofar Ocean references: https://content.sofarocean.com/hubfs/Spotter%20product%20... source: Spotter wave buoy title: Sofar Spotter Data Archive - Bulk Wave Parameters
- clouddrift.datasets.subsurface_floats(decode_times: bool = True) Dataset[source]#
Returns the subsurface floats dataset as a ragged array Xarray dataset.
The data is accessed from a public HTTPS server at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) accessible at https://www.aoml.noaa.gov/phod/gdp/index.php.
The upstream data is available at https://www.aoml.noaa.gov/phod/float_traj/files/allFloats_12122017.mat.
This dataset of subsurface float observations was compiled by the WOCE Subsurface Float Data Assembly Center (WFDAC) in Woods Hole maintained by Andree Ramsey and Heather Furey and copied to NOAA/AOML in October 2014 (version 1) and in December 2017 (version 2). Subsequent updates will be included as additional appropriate float data, quality controlled by the appropriate principal investigators, is submitted for inclusion.
Note that these observations are collected by ALACE/RAFOS/Eurofloat-style acoustically-tracked, neutrally-buoyant subsurface floats which collect data while drifting beneath the ocean surface. These data are the result of the effort and resources of many individuals and institutions. You are encouraged to acknowledge the work of the data originators and Data Centers in publications arising from use of these data.
The float data were originally divided by project at the WFDAC. Here they have been compiled in a single Matlab data set. See here for more information on the variables contained in these files.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
Subsurface floats dataset as a ragged array
Examples#
>>> from clouddrift.datasets import subsurface_floats >>> ds = subsurface_floats() >>> ds <xarray.Dataset> Dimensions: (traj: 2193, obs: 1402840) Coordinates: id (traj) uint16 ... time (obs) datetime64[ns] ... Dimensions without coordinates: traj, obs Data variables: (12/13) expList (traj) object ... expName (traj) object ... expOrg (traj) object ... expPI (traj) object ... indexExp (traj) uint8 ... fltType (traj) object ... ... ... lon (obs) float64 ... lat (obs) float64 ... pres (obs) float64 ... temp (obs) float64 ... ve (obs) float64 ... vn (obs) float64 ... Attributes: title: Subsurface float trajectories dataset history: December 2017 (version 2) date_created: 2023-11-14T22:30:38.831656 publisher_name: WOCE Subsurface Float Data Assembly Center and NOAA AOML publisher_url: https://www.aoml.noaa.gov/phod/float_traj/data.php license: freely available acknowledgement: Maintained by Andree Ramsey and Heather Furey from the ...
References#
WOCE Subsurface Float Data Assembly Center (WFDAC) https://www.aoml.noaa.gov/phod/float_traj/index.php
- clouddrift.datasets.yomaha(decode_times: bool = True) Dataset[source]#
Returns the YoMaHa dataset as a ragged array Xarray dataset.
The function will first look for the ragged-array dataset on the local filesystem. If it is not found, the dataset will be downloaded using the corresponding adapter function and stored for later access. The upstream data is available at http://apdrc.soest.hawaii.edu/projects/yomaha/.
Parameters#
- decode_timesbool, optional
If True, decode the time coordinate into a datetime object. If False, the time coordinate will be an int64 or float64 array of increments since the origin time indicated in the units attribute. Default is True.
Returns#
- xarray.Dataset
YoMaHa’07 dataset as a ragged array
Examples#
>>> from clouddrift.datasets import yomaha >>> ds = yomaha() >>> ds <xarray.Dataset> Dimensions: (obs: 1926743, traj: 12196) Coordinates: time_d (obs) datetime64[ns] ... time_s (obs) datetime64[ns] ... time_lp (obs) datetime64[ns] ... time_lc (obs) datetime64[ns] ... id (traj) int64 ... Dimensions without coordinates: obs, traj Data variables: (12/27) lon_d (obs) float64 ... lat_d (obs) float64 ... pres_d (obs) float32 ... ve_d (obs) float32 ... vn_d (obs) float32 ... err_ve_d (obs) float32 ... ... ... cycle (obs) int64 ... time_inv (obs) int64 ... rowsize (traj) int64 ... wmo_id (traj) int64 ... dac_id (traj) int64 ... float_type (traj) int64 ... Attributes: title: YoMaHa'07: Velocity data assessed from trajectories of A... history: Dataset updated on Tue Jun 28 03:14:34 HST 2022 date_created: 2023-12-08T00:52:08.478075 publisher_name: Asia-Pacific Data Research Center publisher_url: http://apdrc.soest.hawaii.edu/index.php license: Creative Commons Attribution 4.0 International License..
Reference#
Lebedev, K. V., Yoshinari, H., Maximenko, N. A., & Hacker, P. W. (2007). Velocity data assessed from trajectories of Argo floats at parking level and at the sea surface. IPRC Technical Note, 4(2), 1-16.