Pypromice: A Python Package for Processing Automated Weather Station Data
The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cr...
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| Vydáno v: | Journal of open source software Ročník 8; číslo 86; s. 5298 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Goddard Space Flight Center
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01.06.2023
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| ISSN: | 2475-9066, 2475-9066 |
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| Abstract | The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020).
A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice, which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021). |
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| AbstractList | The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020).
A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice, which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021). |
| Audience | PUBLIC |
| Author | Vandecrux, Baptiste Ahlstrøm, Andreas P Wright, Patrick J Fausto, Robert S How, Penelope R Mankoff, Kenneth D |
| Author_xml | – sequence: 1 givenname: Penelope R orcidid: 0000-0002-8088-8497 surname: How fullname: How, Penelope R organization: Geological Survey of Denmark and Greenland – sequence: 2 givenname: Patrick J orcidid: 0000-0003-2999-9076 surname: Wright fullname: Wright, Patrick J organization: Geological Survey of Denmark and Greenland – sequence: 3 givenname: Kenneth D orcidid: 0000-0001-5453-2019 surname: Mankoff fullname: Mankoff, Kenneth D organization: Autonomic Integra – sequence: 4 givenname: Baptiste orcidid: 0000-0002-4169-8973 surname: Vandecrux fullname: Vandecrux, Baptiste organization: Geological Survey of Denmark and Greenland – sequence: 5 givenname: Robert S orcidid: 0000-0003-1317-8185 surname: Fausto fullname: Fausto, Robert S organization: Geological Survey of Denmark and Greenland – sequence: 6 givenname: Andreas P orcidid: 0000-0001-8235-8070 surname: Ahlstrøm fullname: Ahlstrøm, Andreas P organization: Geological Survey of Denmark and Greenland |
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| Cites_doi | 10.3389/feart.2022.970026 10.5194/essd-13-3819-2021 10.1038/nclimate2899 10.1175/BAMS-D-22-0082.1 10.5281/zenodo.3509134 10.1038/s41558-022-01441-2 10.22008/FK2/IPOHT5 10.22008/FK2/IW73UU 10.22008/FK2/VVXGUT 10.34194/geusb.v15.5045 10.17897/E594-NV64 10.5334/jors.148 10.25923/c430-hb50 10.1038/s41467-022-34049-3 |
| ContentType | Journal Article |
| Copyright | Copyright Determination: MAY_INCLUDE_COPYRIGHT_MATERIAL -- Approver Comments: No civil servants / IPAs are listed as authors; no NASA ownership rights |
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| Keywords | Pypromice Glaciology Climate Weather Station Data Kalaallit-Nunaat Data Processing Gc-Net Geus Greenland Python |
| Language | English |
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| References | The pandas development team (pandas-decpandas-2020) 2020 Fausto (fausto-programme-2021) 2021; 13 Ahlstrøm (ahlstrom-programme-2008) 2008; 15 Steffen (steffen-gcnet-2023) 2023 Hoyer (hoyer-xarray-2017) 2017; 5 Machguth (macguth-greenland-2016) 2016; 6 Zender (zender-jaws-2019) How (how-pypromice-2022) 2023 GEM (gem-glaciobasis-2020) 2020 Messerli (messerli-snow-2022) 2022; 10 Easterbrook (easterbrook-pywws-2023) Moon (moon-greenland-2022a) 2022 Moon (moon-greenland-2022b) 2022; 103 Vandecrux (vandecrux-gcnet-2020) Oehri (oehri_vegetation_2022) 2022; 13 Steffen (steffen-greenland-1996) 1996; 96 Box (box-greenland-2022) 2022; 12 How (how-one-boom-2022) 2023 |
| References_xml | – ident: easterbrook-pywws-2023 article-title: Python software for USB Wireless Weather Stations – volume: 10 issn: 2296-6463 year: 2022 ident: messerli-snow-2022 article-title: Snow cover evolution at Qasigiannguit Glacier, Southwest Greenland: A comparison of time-lapse imagery and mass balance data publication-title: Frontiers in Earth Science doi: 10.3389/feart.2022.970026 – volume: 96 year: 1996 ident: steffen-greenland-1996 article-title: Greenland Climate Network: GC-Net, in US Army Cold Regions Reattach and Engineering (CRREL) publication-title: CRREL Special Report – volume: 13 issn: 1866-3508 issue: 8 year: 2021 ident: fausto-programme-2021 article-title: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data publication-title: Earth System Science Data doi: 10.5194/essd-13-3819-2021 – volume: 6 year: 2016 ident: macguth-greenland-2016 article-title: Greenland meltwater storage in firn limited by near-surface ice formation publication-title: Nature Climate Change doi: 10.1038/nclimate2899 – ident: zender-jaws-2019 article-title: JAWS: An extensible toolkit to harmonize and analyze polar automatic weather station datasets, manuscript in preparation for geosci. Model dev. – volume: 103 year: 2022 ident: moon-greenland-2022b article-title: Greenland Ice Sheet. In: State of the Climate in 2021 - The Arctic publication-title: Bulletin of the American Meteorological Society doi: 10.1175/BAMS-D-22-0082.1 – year: 2020 ident: pandas-decpandas-2020 article-title: Pandas-dec/pandas: Pandas doi: 10.5281/zenodo.3509134 – volume: 12 year: 2022 ident: box-greenland-2022 article-title: Greenland Ice Sheet climate disequilibrium and committed sea-level rise publication-title: Nature Climate Change doi: 10.1038/s41558-022-01441-2 – ident: vandecrux-gcnet-2020 article-title: The GC-Net Level 1 dataset and processing scripts – year: 2023 ident: how-pypromice-2022 article-title: pypromice doi: 10.22008/FK2/IPOHT5 – year: 2023 ident: how-one-boom-2022 article-title: PROMICE and GC-Net automated weather station data in Greenland doi: 10.22008/FK2/IW73UU – year: 2023 ident: steffen-gcnet-2023 article-title: GC-Net Level 1 automated weather station data doi: 10.22008/FK2/VVXGUT – volume: 15 year: 2008 ident: ahlstrom-programme-2008 article-title: A new programme for monitoring the mass loss of the Greenland Ice Sheet publication-title: GEUS Bulletin doi: 10.34194/geusb.v15.5045 – year: 2020 ident: gem-glaciobasis-2020 article-title: GlacioBasis Zackenberg - snow cover - snow depth radar publication-title: Dataset doi: 10.17897/E594-NV64 – volume: 5 issn: 2049-9647 issue: 1 year: 2017 ident: hoyer-xarray-2017 article-title: xarray: N-d labeled Arrays and Datasets in Python publication-title: Journal of Open Research Software doi: 10.5334/jors.148 – year: 2022 ident: moon-greenland-2022a article-title: Greenland Ice Sheet publication-title: Arctic report card 2022 doi: 10.25923/c430-hb50 – volume: 13 issue: 1 year: 2022 ident: oehri_vegetation_2022 article-title: Vegetation type is an important predictor of the arctic summer land surface energy budget publication-title: Nature Communications doi: 10.1038/s41467-022-34049-3 |
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| Title | Pypromice: A Python Package for Processing Automated Weather Station Data |
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