Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-515-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-515-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean
Simon Pfreundschuh
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
Clément Guilloteau
Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA 92697, USA
Paula J. Brown
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Christian D. Kummerow
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
Related authors
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros
Atmos. Meas. Tech., 15, 6907–6933, https://doi.org/10.5194/amt-15-6907-2022, https://doi.org/10.5194/amt-15-6907-2022, 2022
Short summary
Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based measurements of rain more accurate and reliable.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad
Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, https://doi.org/10.5194/amt-15-5033-2022, 2022
Short summary
Short summary
The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
Short summary
Short summary
We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan
Atmos. Meas. Tech., 14, 2957–2979, https://doi.org/10.5194/amt-14-2957-2021, https://doi.org/10.5194/amt-14-2957-2021, 2021
Short summary
Short summary
Currently, cloud contamination in microwave humidity channels is addressed using filtering schemes. We present an approach to correct the cloud-affected microwave humidity radiances using a Bayesian machine learning technique. The technique combines orthogonal information from microwave channels to obtain a probabilistic prediction of the clear-sky radiances. With this approach, we are able to predict bias-free clear-sky radiances with well-represented case-specific uncertainty estimates.
Patrick Eriksson, Anders Emrich, Kalle Kempe, Johan Riesbeck, Alhassan Aljarosha, Olivier Auriacombe, Joakim Kugelberg, Enne Hekma, Roland Albers, Axel Murk, Søren Møller Pedersen, Laurenz John, Jan Stake, Peter McEvoy, Bengt Rydberg, Adam Dybbroe, Anke Thoss, Alessio Canestri, Christophe Accadia, Paolo Colucci, Daniele Gherardi, and Ville Kangas
Atmos. Meas. Tech., 18, 4709–4729, https://doi.org/10.5194/amt-18-4709-2025, https://doi.org/10.5194/amt-18-4709-2025, 2025
Short summary
Short summary
The Arctic Weather Satellite (AWS), developed by the European Space Agency, highlights a new approach in satellite design, aiming to expand the network of operational microwave sensors cost-effectively. Launched in August 2024, AWS features a 19-channel microwave cross-track radiometer. Notably, it introduces groundbreaking channels at 325.15 GHz. In addition, AWS acts as the stepping stone to a suggested constellation of satellites, denoted as EUMETSAT Polar System Sterna.
Eleanor May and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2190, https://doi.org/10.5194/egusphere-2025-2190, 2025
Short summary
Short summary
The vertical distribution of atmospheric ice impacts Earth's weather and climate. The Ice Cloud Imager (ICI) will measure at microwave and sub-millimetre frequencies, which are well suited to detect atmospheric ice. In this study, a machine learning model is trained on ICI simulations. Results show that the vertical distribution of ice can be derived from ICI observations, and that ICI could offer a valuable data source that complements existing radar- and lidar-based measurements.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Chia-Pang Kuo and Christian Kummerow
Atmos. Meas. Tech., 17, 5637–5653, https://doi.org/10.5194/amt-17-5637-2024, https://doi.org/10.5194/amt-17-5637-2024, 2024
Short summary
Short summary
A small satellite about the size of a shoe box, named TEMPEST, carries only a microwave sensor and is designed to measure the water cycle of the Earth from space in an economical way compared with traditional satellites, which have additional infrared sensors. To overcome the limitation, extra infrared signals from GOES-R ABI are combined with TEMPEST microwave measurements. Compared with ground observations, improved humidity information is extracted from the merged TEMPEST and ABI signals.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Karina McCusker, Anthony J. Baran, Chris Westbrook, Stuart Fox, Patrick Eriksson, Richard Cotton, Julien Delanoë, and Florian Ewald
Atmos. Meas. Tech., 17, 3533–3552, https://doi.org/10.5194/amt-17-3533-2024, https://doi.org/10.5194/amt-17-3533-2024, 2024
Short summary
Short summary
Polarised radiative transfer simulations are performed using an atmospheric model based on in situ measurements. These are compared to large polarisation measurements to explore whether such measurements can provide information on cloud ice, e.g. particle shape and orientation. We find that using oriented particle models with shapes based on imagery generally allows for accurate simulations. However, results are sensitive to shape assumptions such as the choice of single crystals or aggregates.
Michael Kiefer, Dale F. Hurst, Gabriele P. Stiller, Stefan Lossow, Holger Vömel, John Anderson, Faiza Azam, Jean-Loup Bertaux, Laurent Blanot, Klaus Bramstedt, John P. Burrows, Robert Damadeo, Bianca Maria Dinelli, Patrick Eriksson, Maya García-Comas, John C. Gille, Mark Hervig, Yasuko Kasai, Farahnaz Khosrawi, Donal Murtagh, Gerald E. Nedoluha, Stefan Noël, Piera Raspollini, William G. Read, Karen H. Rosenlof, Alexei Rozanov, Christopher E. Sioris, Takafumi Sugita, Thomas von Clarmann, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 16, 4589–4642, https://doi.org/10.5194/amt-16-4589-2023, https://doi.org/10.5194/amt-16-4589-2023, 2023
Short summary
Short summary
We quantify biases and drifts (and their uncertainties) between the stratospheric water vapor measurement records of 15 satellite-based instruments (SATs, with 31 different retrievals) and balloon-borne frost point hygrometers (FPs) launched at 27 globally distributed stations. These comparisons of measurements during the period 2000–2016 are made using robust, consistent statistical methods. With some exceptions, the biases and drifts determined for most SAT–FP pairs are < 10 % and < 1 % yr−1.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
Short summary
Short summary
Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros
Atmos. Meas. Tech., 15, 6907–6933, https://doi.org/10.5194/amt-15-6907-2022, https://doi.org/10.5194/amt-15-6907-2022, 2022
Short summary
Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based measurements of rain more accurate and reliable.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad
Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, https://doi.org/10.5194/amt-15-5033-2022, 2022
Short summary
Short summary
The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.
William G. Read, Gabriele Stiller, Stefan Lossow, Michael Kiefer, Farahnaz Khosrawi, Dale Hurst, Holger Vömel, Karen Rosenlof, Bianca M. Dinelli, Piera Raspollini, Gerald E. Nedoluha, John C. Gille, Yasuko Kasai, Patrick Eriksson, Christopher E. Sioris, Kaley A. Walker, Katja Weigel, John P. Burrows, and Alexei Rozanov
Atmos. Meas. Tech., 15, 3377–3400, https://doi.org/10.5194/amt-15-3377-2022, https://doi.org/10.5194/amt-15-3377-2022, 2022
Short summary
Short summary
This paper attempts to provide an assessment of the accuracy of 21 satellite-based instruments that remotely measure atmospheric humidity in the upper troposphere of the Earth's atmosphere. The instruments made their measurements from 1984 to the present time; however, most of these instruments began operations after 2000, and only a few are still operational. The objective of this study is to quantify the accuracy of each satellite humidity data set.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
Short summary
Short summary
We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Jie Gong, Dong L. Wu, and Patrick Eriksson
Earth Syst. Sci. Data, 13, 5369–5387, https://doi.org/10.5194/essd-13-5369-2021, https://doi.org/10.5194/essd-13-5369-2021, 2021
Short summary
Short summary
Launched from the International Space Station, the IceCube radiometer orbited the Earth for 15 months and collected the first spaceborne radiance measurements at 874–883 GHz. This channel is uniquely important to fill in the sensitivity gap between operational visible–infrared and microwave remote sensing for atmospheric cloud ice and snow. This paper delivers the IceCube Level 1 radiance data processing algorithm and provides a data quality evaluation and discussion on its scientific merit.
Francesco Grieco, Kristell Pérot, Donal Murtagh, Patrick Eriksson, Bengt Rydberg, Michael Kiefer, Maya Garcia-Comas, Alyn Lambert, and Kaley A. Walker
Atmos. Meas. Tech., 14, 5823–5857, https://doi.org/10.5194/amt-14-5823-2021, https://doi.org/10.5194/amt-14-5823-2021, 2021
Short summary
Short summary
We present improved Odin/SMR mesospheric H2O concentration and temperature data sets, reprocessed assuming a bigger sideband leakage of the instrument. The validation study shows how the improved SMR data sets agree better with other instruments' observations than the old SMR version did. Given their unique time extension and geographical coverage, and H2O being a good tracer of mesospheric circulation, the new data sets are valuable for the study of dynamical processes and multi-year trends.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-97, https://doi.org/10.5194/amt-2021-97, 2021
Preprint withdrawn
Short summary
Short summary
Latent heating released from phase changes of water is an important factor in driving convection, and thus used in short-term weather forecast models to initiate convection. Typically, radars have been used to retrieve latent heating to be used in the forecast model, but continuous radar data are only available over land. Therefore, this study uses geostationary satellite data to retrieve latent heating so that it can be used to initiate convection in regions where radar data are not available.
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, https://doi.org/10.5194/amt-14-3427-2021, 2021
Short summary
Short summary
Oriented nonspherical ice particles induce polarization that is ignored when cloud-sensitive satellite observations are used in numerical weather prediction systems. We present a simple approach for approximating particle orientation, requiring minor adaption of software and no additional calculation burden. With this approach, the system realistically simulates the observed polarization patterns, increasing the physical consistency between instruments with different polarizations.
Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan
Atmos. Meas. Tech., 14, 2957–2979, https://doi.org/10.5194/amt-14-2957-2021, https://doi.org/10.5194/amt-14-2957-2021, 2021
Short summary
Short summary
Currently, cloud contamination in microwave humidity channels is addressed using filtering schemes. We present an approach to correct the cloud-affected microwave humidity radiances using a Bayesian machine learning technique. The technique combines orthogonal information from microwave channels to obtain a probabilistic prediction of the clear-sky radiances. With this approach, we are able to predict bias-free clear-sky radiances with well-represented case-specific uncertainty estimates.
Yoonjin Lee, Christian D. Kummerow, and Imme Ebert-Uphoff
Atmos. Meas. Tech., 14, 2699–2716, https://doi.org/10.5194/amt-14-2699-2021, https://doi.org/10.5194/amt-14-2699-2021, 2021
Short summary
Short summary
Convective clouds are usually associated with intense rain that can cause severe damage, and thus it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling areas. The results look promising when compared to radar-derived products, which are commonly used for detecting convection.
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech., 13, 6933–6944, https://doi.org/10.5194/amt-13-6933-2020, https://doi.org/10.5194/amt-13-6933-2020, 2020
Short summary
Short summary
Raindrops become flattened due to aerodynamic drag as they increase in mass and fall speed. This study calculated the electromagnetic interaction between microwave radiation and non-spheroidal raindrops. The calculations are made publicly available to the scientific community, in order to promote accurate representations of raindrops in measurements. Tests show that the drop shape can have a noticeable effect on microwave observations of heavy rainfall.
Cited articles
Anonymous referee: Referee comment 2, Comment on egusphere-2023-1310, https://doi.org/10.5194/egusphere-2023-1310-RC2, 2023. a, b
Boukabara, S.-A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti, C., Kongoli, C., Chen, R., Liu, Q., Yan, B., Weng, F., Ferraro, R., Kleespies, T. J., and Meng, H.: MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System, IEEE T. Geosci. Remote, 49, 3249–3272, https://doi.org/10.1109/TGRS.2011.2158438, 2011. a
Cifelli, R., Chandrasekar, V., Lim, S., Kennedy, P. C., Wang, Y., and Rutledge, S. A.: A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events, J. Atmos. Ocean. Tech., 28, 352–364, https://doi.org/10.1175/2010JTECHA1488.1, 2011. a
GPM Science Team: GPM SSMIS on F17 (GPROF) Climate-based Radiometer Precipitation Profiling 1.5 hours 12 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SSMIS/F17/GPROFCLIM/2A/07, 2022a. a
GPM Science Team: GPM AMSR-2 on GCOM-W1 (GPROF) Climate-based Radiometer Precipitation Profiling L2A 1.5 hours 10 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/AMSR2/GCOMW1/GPROFCLIM/2A/07, 2022b. a
GPM Science Team: GPM GMI (GPROF) Climate-based Radiometer Precipitation Profiling L2A 1.5 hours 4 km x 4 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/GMI/GPROFCLIM/2A/07, 2022c. a
GPM Science Team: GPM MHS on NOAA-19 (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 17 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/NOAA19/GPROFCLIM/2A/07, 2022d. a
GPM Science Team: GPM ATMS on SUOMI-NPP (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 16 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/ATMS/NPP/GPROFCLIM/2A/07, 2022e. a
Grecu, M., Olson, W. S., Munchak, S. J., Ringerud, S., Liao, L., Haddad, Z., Kelley, B. L., and McLaughlin, S. F.: The GPM Combined Algorithm, J. Atmos. Ocean. Tech., 33, 2225–2245, https://doi.org/10.1175/JTECH-D-16-0019.1, 2016. a, b, c
Guilloteau, C., Foufoula-Georgiou, E., and Kummerow, C. D.: Global multiscale evaluation of satellite passive microwave retrieval of precipitation during the TRMM and GPM eras: Effective resolution and regional diagnostics for future algorithm development, J. Hydrometeorol., 18, 3051–3070, https://doi.org/10.1175/JHM-D-17-0087.1, 2017. a
Hegerl, G. C., Black, E., Allan, R. P., Ingram, W. J., Polson, D., Trenberth, K. E., Chadwick, R. S., Arkin, P. A., Sarojini, B. B., Becker, A., Dai, A., Durack, P. J., Easterling, D., Fowler, H. J., Kendon, E. J., Huffman, G. J., Liu, C., Marsh, R., New, M., Osborn, T. J., Skliris, N., Stott, P. A., Vidale, P.-L., Wijffels, S. E., Wilcox, L. J., Willett, K. M., and Zhang, X.: Challenges in Quantifying Changes in the Global Water Cycle, B. Am. Meteorol. Soc., 96, 1097–1115, https://doi.org/10.1175/BAMS-D-13-00212.1, 2015. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in Python, Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), Springer International Publishing, Cham, ISBN 978-3-030-24568-9, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Kerr, C. A. and Alsheimer, F.: Storm-Scale Predictability and Analysis of the 13 April 2020 Central Savannah River Area Tornado Outbreak, Weather Forecast., 37, 901–913, https://doi.org/10.1175/WAF-D-21-0185.1, 2022. a
Kirschbaum, D. B., Huffman, G. J., Adler, R. F., Braun, S., Garrett, K., Jones, E., McNally, A., Skofronick-Jackson, G., Stocker, E., Wu, H., and Zaitchik, B. F.: NASA's Remotely Sensed Precipitation: A Reservoir for Applications Users, B. Am. Meteorol. Soc., 98, 1169–1184, https://doi.org/10.1175/BAMS-D-15-00296.1, 2017. a
Kirstetter, P.-E., Hong, Y., Gourley, J. J., Chen, S., Flamig, Z., Zhang, J., Schwaller, M., Petersen, W., and Amitai, E.: Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE, J. Hydrometeorol., 13, 1285–1300, https://doi.org/10.1175/JHM-D-11-0139.1, 2012. a, b, c
Kummerow, C. D., Randel, D. L., Kulie, M., Wang, N.-Y., Ferraro, R., Joseph Munchak, S., and Petkovic, V.: The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme, J. Atmos. Ocean. Tech., 32, 2265–2280, https://doi.org/10.1175/JTECH-D-15-0039.1, 2015. a
Met Office: Cartopy: a cartographic python library with a matplotlib interface, Exeter, Devon, http://scitools.org.uk/cartopy (last access: 1 April 2023), 2010–2015. a
Olson, W.: GPM DPR and GMI Combined Precipitation L2B 1.5 hours 5 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/DPRGMI/CMB/2B/07, 2022. a
Perez, F. and Granger, B. E.: IPython: A System for Interactive Scientific Computing, Comput. Sci. Eng., 9, 21–29, https://doi.org/10.1109/MCSE.2007.53, 2007. a
Pfreundschuh, S.: GPROF-NN: A neural network based implementation of the Goddard Profiling Algorithm (v0.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.10257786, 2023. a, b
Raspaud, M., Hoese, D., Lahtinen, P., Finkensieper, S., Holl, G., Proud, S., Dybbroe, A., Meraner, A., Feltz, J., Zhang, X., Joro, S., Roberts, W., Ørum Rasmussen, L., Strandgren, BENR0, Méndez, J. H. B., Zhu, Y., Daruwala, R., Jasmin, T., mherbertson, Kliche, C., Barnie, T., Sigurðsson, E., Garcia, R. K., Leppelt, T., TT, ColinDuff, Egede, U., LTMeyer, and Itkin, M.: pytroll/satpy: Version 0.33.1, Zenodo [code], https://doi.org/10.5281/zenodo.5789830, 2021. a
Ryzhkov, A. V., Giangrande, S. E., Melnikov, V. M., and Schuur, T. J.: Calibration Issues of Dual-Polarization Radar Measurements, J. Atmos. Ocean. Tech., 22, 1138–1155, https://doi.org/10.1175/JTECH1772.1, 2005. a
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) Mission for Science and Society, B. Am. Meteorol. Soc., 98, 1679–1695, https://doi.org/10.1175/BAMS-D-15-00306.1, 2017. a
Tapiador, F., Navarro, A., Levizzani, V., García-Ortega, E., Huffman, G., Kidd, C., Kucera, P., Kummerow, C., Masunaga, H., Petersen, W., Roca, R., Sánchez, J.-L., Tao, W.-K., and Turk, F.: Global precipitation measurements for validating climate models, Atmos. Res., 197, 1–20, https://doi.org/10.1016/j.atmosres.2017.06.021, 2017. a
The Python Language Foundation: The Python Language Reference, Python Language Foundation, https://docs.python.org/3/reference/index.html (last access: 1 April 2023), 2018. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Executive editor
This work validates retrievals from the latest operational version of the NASA Goddard Profiling Algorithm (GPROF), currently version 7, and it quantitatively demonstrates the performance improvements with respect to previous versions (GPROF v05) at various spatial and temporal resolutions. It also presents new, not yet operationally implemented, machine learning based versions of GPROF (GPROF-NN).
This work validates retrievals from the latest operational version of the NASA Goddard Profiling...
Short summary
The latest version of the GPROF retrieval algorithm that produces global precipitation estimates using observations from the Global Precipitation Measurement mission is validated against ground-based radars. The validation shows that the algorithm accurately estimates precipitation on scales ranging from continental to regional. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.
The latest version of the GPROF retrieval algorithm that produces global precipitation estimates...