diff --git a/joss.06733/10.21105.joss.06733.crossref.xml b/joss.06733/10.21105.joss.06733.crossref.xml new file mode 100644 index 0000000000..a1bd947a55 --- /dev/null +++ b/joss.06733/10.21105.joss.06733.crossref.xml @@ -0,0 +1,271 @@ + + + + 20240620141848-ddca0188f23e5c692e95bf52f5a8a554795cb77d + 20240620141848 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 06 + 2024 + + + 9 + + 98 + + + + MWRpy: A Python package for processing microwave +radiometer data + + + + Tobias + Marke + https://orcid.org/0000-0001-7804-9056 + + + Ulrich + Löhnert + https://orcid.org/0000-0002-9023-0269 + + + Simo + Tukiainen + https://orcid.org/0000-0002-0651-4622 + + + Tuomas + Siipola + https://orcid.org/0009-0004-7757-0893 + + + Bernhard + Pospichal + https://orcid.org/0000-0001-9517-8300 + + + + 06 + 20 + 2024 + + + 6733 + + + 10.21105/joss.06733 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.11614185 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6733 + + + + 10.21105/joss.06733 + https://joss.theoj.org/papers/10.21105/joss.06733 + + + https://joss.theoj.org/papers/10.21105/joss.06733.pdf + + + + + + Ground-based microwave radiometer +reprocessing mwr_pro + Löhnert + 10.5281/zenodo.7973553 + 2023 + Löhnert, U. (2023). Ground-based +microwave radiometer reprocessing mwr_pro (Version v04). Zenodo. +https://doi.org/10.5281/zenodo.7973553 + + + Cloudnet: Continuous evaluation of cloud +profiles in seven operational models using ground-based +observations + Illingworth + Bulletin of the American Meteorological +Society + 6 + 88 + 10.1175/BAMS-88-6-883 + 2007 + Illingworth, A. J., & Coauthors. +(2007). Cloudnet: Continuous evaluation of cloud profiles in seven +operational models using ground-based observations. Bulletin of the +American Meteorological Society, 88(6), 883–898. +https://doi.org/10.1175/BAMS-88-6-883 + + + Aerosol, Clouds and Trace Gases Research +Infrastructure – ACTRIS, the European research infrastructure supporting +atmospheric science + Laj + Bulletin of the American Meteorological +Society + 10.1175/BAMS-D-23-0064.1 + 2024 + Laj, P., & Coauthors. (2024). +Aerosol, Clouds and Trace Gases Research Infrastructure – ACTRIS, the +European research infrastructure supporting atmospheric science. +Bulletin of the American Meteorological Society. +https://doi.org/10.1175/BAMS-D-23-0064.1 + + + CloudnetPy: A Python package for processing +cloud remote sensing data + Tukiainen + Journal of Open Source +Software + 53 + 5 + 10.21105/joss.02123 + 2020 + Tukiainen, S., O’Connor, E., & +Korpinen, A. (2020). CloudnetPy: A Python package for processing cloud +remote sensing data. Journal of Open Source Software, 5(53), 2123. +https://doi.org/10.21105/joss.02123 + + + A review of surface-based microwave and +millimeter-wave radiometric remote sensing of the +troposphere + Westwater + URSI Radio Science Bulletin + 310 + 2004 + 10.23919/URSIRSB.2004.7909438 + 2004 + Westwater, E. R., Crewell, S., & +Mätzler, C. (2004). A review of surface-based microwave and +millimeter-wave radiometric remote sensing of the troposphere. URSI +Radio Science Bulletin, 2004(310), 59–80. +https://doi.org/10.23919/URSIRSB.2004.7909438 + + + EUMETNET opens to microwave radiometers for +operational thermodynamical profiling in Europe + Rüfenacht + Bulletin of Atmospheric Science and +Technology + 4 + 2 + 10.1007/s42865-021-00033-w + 2021 + Rüfenacht, R., Haefele, A., +Pospichal, B., Cimini, D., Bircher-Adrot, S., Turp, M., & Sugier, J. +(2021). EUMETNET opens to microwave radiometers for operational +thermodynamical profiling in Europe. Bulletin of Atmospheric Science and +Technology, 2(4). +https://doi.org/10.1007/s42865-021-00033-w + + + Accuracy of boundary layer temperature +profiles retrieved with multifrequency multiangle microwave +radiometry + Crewell + IEEE Transactions on Geoscience and Remote +Sensing + 7 + 45 + 10.1109/TGRS.2006.888434 + 2007 + Crewell, S., & Löhnert, U. +(2007). Accuracy of boundary layer temperature profiles retrieved with +multifrequency multiangle microwave radiometry. IEEE Transactions on +Geoscience and Remote Sensing, 45(7), 2195–2201. +https://doi.org/10.1109/TGRS.2006.888434 + + + Accuracy of cloud liquid water path from +ground-based microwave radiometry 2. Sensor accuracy and +synergy + Crewell + Radio Science + 3 + 38 + 10.1029/2002RS002634 + 2003 + Crewell, S., & Löhnert, U. +(2003). Accuracy of cloud liquid water path from ground-based microwave +radiometry 2. Sensor accuracy and synergy. Radio Science, 38(3). +https://doi.org/10.1029/2002RS002634 + + + Operational profiling of temperature using +ground-based microwave radiometry at Payerne: Prospects and +challenges + Löhnert + Atmospheric Measurement +Techniques + 5 + 5 + 10.5194/amt-5-1121-2012 + 2012 + Löhnert, U., & Maier, O. (2012). +Operational profiling of temperature using ground-based microwave +radiometry at Payerne: Prospects and challenges. Atmospheric Measurement +Techniques, 5(5), 1121–1134. +https://doi.org/10.5194/amt-5-1121-2012 + + + PyRTlib: An educational Python-based library +for non-scattering atmospheric microwave radiative transfer +computations + Larosa + Geoscientific Model +Development + 5 + 17 + 10.5194/gmd-17-2053-2024 + 2024 + Larosa, S., Cimini, D., Gallucci, D., +Nilo, S. T., & Romano, F. (2024). PyRTlib: An educational +Python-based library for non-scattering atmospheric microwave radiative +transfer computations. Geoscientific Model Development, 17(5), +2053–2076. +https://doi.org/10.5194/gmd-17-2053-2024 + + + + + + diff --git a/joss.06733/10.21105.joss.06733.pdf b/joss.06733/10.21105.joss.06733.pdf new file mode 100644 index 0000000000..20fdde0c68 Binary files /dev/null and b/joss.06733/10.21105.joss.06733.pdf differ diff --git a/joss.06733/paper.jats/10.21105.joss.06733.jats b/joss.06733/paper.jats/10.21105.joss.06733.jats new file mode 100644 index 0000000000..2f8c57f2c0 --- /dev/null +++ b/joss.06733/paper.jats/10.21105.joss.06733.jats @@ -0,0 +1,466 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6733 +10.21105/joss.06733 + +MWRpy: A Python package for processing microwave +radiometer data + + + +https://orcid.org/0000-0001-7804-9056 + +Marke +Tobias + + +* + + +https://orcid.org/0000-0002-9023-0269 + +Löhnert +Ulrich + + + + +https://orcid.org/0000-0002-0651-4622 + +Tukiainen +Simo + + + + +https://orcid.org/0009-0004-7757-0893 + +Siipola +Tuomas + + + + +https://orcid.org/0000-0001-9517-8300 + +Pospichal +Bernhard + + + + + +Institute for Geophysics and Meteorology, University of +Cologne, Germany + + + + +Finnish Meteorological Institute, Helsinki, +Finland + + + + +* E-mail: + + +21 +2 +2024 + +9 +98 +6733 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +meteorology +remote sensing +microwave radiometer + + + + + + Summary +

Ground-based passive microwave radiometers (MWRs) are deployed to + obtain information on the vertical structure of temperature and water + vapor mostly in the lower troposphere. In addition, they are used to + derive the total column-integrated liquid water content of the + atmosphere, referred to as liquid water path (LWP). MWRs measure + radiances, given as brightness temperatures + ( + + TB), + typically in two frequency ranges along absorption features of water + vapor and oxygen, as well as in window regions where the observations + are sensitive to liquid water clouds. Profiles of temperature and + humidity are retrieved together with the vertically integrated water + vapor content (IWV) and LWP (e.g., Crewell & Löhnert + (2003), + Löhnert & Maier + (2012)). + A specific elevation scanning configuration allows for an improved + resolution for temperature profiles in the atmospheric boundary-layer + (Crewell + & Löhnert, 2007). The instruments can be operated + continuously and provide temporally highly resolved observations of up + to 1 + + s, + which makes them a valuable tool for improving numerical weather + forecasts and climate models by studying the atmospheric water cycle, + including cloud dynamics + (Westwater + et al., 2004).

+

One widely used application exploiting MWR data is the synergistic + algorithm Cloudnet + (Illingworth + & Coauthors, 2007), which classifies hydrometeors in the + atmosphere by combining several ground-based remote sensing + instruments. As part of the European Aerosol, Clouds and Trace Gases + Research Infrastructure (ACTRIS, Laj & Coauthors + (2024)), the + Centre for Cloud Remote Sensing (CCRES) is aiming to provide + continuous and long-term data of cloud properties and the + thermodynamic state of the atmosphere, with Cloudnet being one of the + key tools. For atmospheric observatories, MWRs are therefore mandatory + to qualify as an ACTRIS-CCRES compatible station. The ACTRIS Central + Facility responsible for MWRs in the network is hosted within ACTRIS + Germany (ACTRIS-D).

+

The European cloud remote sensing network will encompass around 30 + stations, including mobile platforms, and covering different + climatological zones. This network configuration enables + investigations of similarities of atmospheric processes and long-term + trends between those sites. Some of the participating stations have + been operational for more than a decade and Cloudnet products were + derived based on their individual setups and processing algorithms. To + ensure that the generated data sets are comparable, station operators + are required to follow the ACTRIS-CCRES standard operating procedures + and send raw data files to the central cloud remote sensing data + center unit (CLU, http://cloudnet.fmi.fi). CLU provides data storage + and provision, but also the centralized processing, including + visualization, in order to harmonize the data streams.

+ + + Statement of need +

MWRpy + addresses the needs of a centralized processing, quality control of + MWR raw data, and deriving standardized output of meteorological + variables. The Python code is an advancement of the Interactive Data + Language (IDL)-based processing software mwr_pro + (Löhnert, + 2023) and is able to handle raw data from HATPRO manufactured + by Radiometer Physics GmbH (RPG, https://www.radiometer-physics.de/), + which is so far the only instrument type in the network. The output + format, including metadata information, variable names, and file + naming, is designed to be compliant with the data structure and naming + convention developed together with the EUMETNET Profiling Programme + E-PROFILE + (Rüfenacht + et al., 2021), which is establishing a MWR network with the + focus on near-real-time data provision. The processing chain in + E-PROFILE consists of a package to convert instrument generated files + into a common netCDF format using the convention shared with + MWRpy (mwr_raw2l1) and a second tool to run an + optimal estimation retrieval approach for advanced products + (mwr_l12l2). Both modules are designed to be implemented in the + central data hub of E-PROFILE for operational near-real-time data + processing in the network of the European Meteorological + Services1. As a research + infrastructure, ACTRIS is pursuing a different approach for the + product generation, which is based on statistical retrieval, while + still allowing stations to be part of both networks. In this way, + MWRpy + improves data compatibility and fosters cross network + collaborations.

+

The processing chain of + MWRpy + is replacing the mode of operation in Cloudnet, which previously + relied on pre-processed and non-harmonized MWR data, and therefore + contributes to more ACTRIS data consistency. Statistical analysis of + these consistent long-term data sets is expected to be beneficial not + only for atmospheric studies, but also for improving knowledge on + instrument operation and maintenance by monitoring key parameters from + the instrument and mandatory regular absolute calibrations + (approximately every 6 months). Future developments include the + support of further instrument types, if present in the network. + Furthermore, the flexible design of the code enables updating the + retrievals of meteorological variables, which will be derived from a + common statistical approach. For that, a training data set is derived + from a climatology of the atmospheric state (e.g. profiles from + radiosondes or model reanalysis) and simulated + + + TB + coming from a microwave radiative transfer model like + PyRTlib + (Larosa + et al., 2024). PyRTlib, as a Python + library for non-scattering atmospheric microwave radiative transfer + calculations, takes various input profiles to compute down- and + upwelling + + TB + for microwave sensors from different observational platforms using + built-in atmospheric absorption models. This output, together with the + climatology, can then be used for retrieval training (not included in + MWRpy) + to update existing coefficients in the ACTRIS network.

+ + + Code design +

MWRpy + is designed to be used as a stand-alone software since it covers the + full processing and visualization chain from raw data to higher level + products, but it is also embedded in the Python implementation of the + Cloudnet processing scheme CloudnetPy + (Tukiainen + et al., 2020). At first, data quality control is performed on + the mandatory data fields of measured + + TB + at various frequencies and instrument specific housekeeping data to + generate quality flags. In the next step, auxiliary data (e.g., from a + weather station) are combined to produce daily netCDF files. + Subsequently advanced meteorological variables are derived by applying + coefficients from the statistical retrieval approaches and are stored + as separate daily files for variables originating from elevation scans + (e.g., temperature profiles) and all remaining measuring modes + (including vertical stare for e.g., LWP). Within the Cloudnet + processing framework the output of + MWRpy + is then harmonized and utilized by CloudnetPy, + together with data streams from other ACTRIS-CCRES instruments, like + cloud radar, to derive synergy products. All files, including + calibration and retrieval information, and corresponding + visualizations are stored in the Cloudnet data portal and accessible + through an API.

+ +

Flowchart of the MWRpy processing + chain (including main functions), with the last two steps being + exclusive for the CloudnetPy + implementation.

+ + + + + Acknowledgements +

This work is funded by the Federal Ministry of Education and + Research (BMBF) under the FONA Strategy “Research for Sustainability” + and part of the implementation of ACTRIS Germany (ACTRIS-D) under the + research grant no. 01LK2002F. The operation of the Central Facilities + is supported by the Federal Ministry for the Environment, Nature + Conservation, Nuclear Safety and Consumer Protection (BMUV). The + implementation and operation of ACTRIS-D is co-funded by 11 German + research performing organizations.

+ + + + + + + + + LöhnertU. + + Ground-based microwave radiometer reprocessing mwr_pro + Zenodo + 2023 + https://doi.org/10.5281/zenodo.7973553 + 10.5281/zenodo.7973553 + + + + + + IllingworthA. J. + Coauthors + + Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations + Bulletin of the American Meteorological Society + 2007 + 88 + 6 + https://doi.org/10.1175/BAMS-88-6-883 + 10.1175/BAMS-88-6-883 + 883 + 898 + + + + + + LajP. + Coauthors + + Aerosol, Clouds and Trace Gases Research Infrastructure – ACTRIS, the European research infrastructure supporting atmospheric science + Bulletin of the American Meteorological Society + 2024 + https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-23-0064.1/BAMS-D-23-0064.1.xml + 10.1175/BAMS-D-23-0064.1 + + + + + + TukiainenS. + O’ConnorE. + KorpinenA. + + CloudnetPy: A Python package for processing cloud remote sensing data + Journal of Open Source Software + The Open Journal + 2020 + 5 + 53 + https://doi.org/10.21105/joss.02123 + 10.21105/joss.02123 + 2123 + + + + + + + WestwaterE. R. + CrewellS. + MätzlerC. + + A review of surface-based microwave and millimeter-wave radiometric remote sensing of the troposphere + URSI Radio Science Bulletin + 2004 + 2004 + 310 + 10.23919/URSIRSB.2004.7909438 + 59 + 80 + + + + + + RüfenachtR. + HaefeleA. + PospichalB. + CiminiD. + Bircher-AdrotS. + TurpM. + SugierJ. + + EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe + Bulletin of Atmospheric Science and Technology + 2021 + 2 + 4 + https://doi.org/10.1007/s42865-021-00033-w + 10.1007/s42865-021-00033-w + + + + + + CrewellS. + LöhnertU. + + Accuracy of boundary layer temperature profiles retrieved with multifrequency multiangle microwave radiometry + IEEE Transactions on Geoscience and Remote Sensing + 2007 + 45 + 7 + 10.1109/TGRS.2006.888434 + 2195 + 2201 + + + + + + CrewellS. + LöhnertU. + + Accuracy of cloud liquid water path from ground-based microwave radiometry 2. Sensor accuracy and synergy + Radio Science + 2003 + 38 + 3 + https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2002RS002634 + 10.1029/2002RS002634 + + + + + + + + LöhnertU. + MaierO. + + Operational profiling of temperature using ground-based microwave radiometry at Payerne: Prospects and challenges + Atmospheric Measurement Techniques + 2012 + 5 + 5 + https://amt.copernicus.org/articles/5/1121/2012/ + 10.5194/amt-5-1121-2012 + 1121 + 1134 + + + + + + LarosaS. + CiminiD. + GallucciD. + NiloS. T. + RomanoF. + + PyRTlib: An educational Python-based library for non-scattering atmospheric microwave radiative transfer computations + Geoscientific Model Development + 2024 + 17 + 5 + https://gmd.copernicus.org/articles/17/2053/2024/ + 10.5194/gmd-17-2053-2024 + 2053 + 2076 + + + + + +

E-PROFILE developed code for MWR processing + (mwr_raw2l1, mwr_l12l2) can be found at + https://github.com/MeteoSwiss

+ + + +
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