Articles | Volume 18, issue 1
https://doi.org/10.5194/amt-18-57-2025
© Author(s) 2025. 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-18-57-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2025
Research article |
| 07 Jan 2025
| 07 Jan 2025
Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation
Endrit Shehaj
CORRESPONDING AUTHOR
STAR Laboratory, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Stephen Leroy
Atmospheric and Environmental Research, Lexington, MA 02421, USA
Kerri Cahoy
STAR Laboratory, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Alain Geiger
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Laura Crocetti
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Gregor Moeller
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Benedikt Soja
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Markus Rothacher
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, 8093, Switzerland
Related authors
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Juliana Chew, and Benedikt Soja
EGUsphere, https://doi.org/10.5194/egusphere-2025-1516, https://doi.org/10.5194/egusphere-2025-1516, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This work investigates the capability of machine learning models, trained with space and ground GNSS observations, to produce fields of refractivity and IWV that describe the spatiotemporal morphology of atmospheric rivers (ARs) and quantify moisture associated with them to a degree sufficient for atmospheric studies. The reconstructed fields can be used to monitor ARs. It studies what LEO radio occultation (RO) constellation is appropriate to quantify the structure, location and timing of ARs.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Juliana Chew, and Benedikt Soja
EGUsphere, https://doi.org/10.5194/egusphere-2025-1516, https://doi.org/10.5194/egusphere-2025-1516, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This work investigates the capability of machine learning models, trained with space and ground GNSS observations, to produce fields of refractivity and IWV that describe the spatiotemporal morphology of atmospheric rivers (ARs) and quantify moisture associated with them to a degree sufficient for atmospheric studies. The reconstructed fields can be used to monitor ARs. It studies what LEO radio occultation (RO) constellation is appropriate to quantify the structure, location and timing of ARs.
Sara Vannah, Stephen S. Leroy, Chi O. Ao, E. Robert Kursinski, Kevin J. Nelson, Kuo-Nung Wang, and Feiqin Xie
EGUsphere, https://doi.org/10.5194/egusphere-2024-4127, https://doi.org/10.5194/egusphere-2024-4127, 2025
Short summary
Short summary
Uncertainty estimation for GNSS radio occultation (RO) soundings in the planetary boundary layer (PBL) depends on the algorithms used to process the RO data. We compare the refractivity retrievals from three RO processing centers — each with their own retrieval algorithm — in the PBL, finding a strong underestimation of refractivity in regions with the strongest refractivity gradients, especially in JPL processing, as well as areas of weak overestimation of refractivity near the Poles.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary
Short summary
Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Nicholas Belsten, Mary Knapp, Rebecca Masterson, Cadence Payne, Kristen Ammons, Frank D. Lind, and Kerri Cahoy
Geosci. Instrum. Method. Data Syst., 12, 201–213, https://doi.org/10.5194/gi-12-201-2023, https://doi.org/10.5194/gi-12-201-2023, 2023
Short summary
Short summary
AERO and VISTA spacecraft will use commercial magnetometers to measure space weather events near Earth’s aurora. The small size of AERO and VISTA necessitate the use of magnetometers with small size, weight, and power. The magnetometers selected exhibit good precision, but additional calibration is needed to achieve good accuracy. This work evaluates a method for calibration by regression which has reduced the magnetic observed error by a factor of ca. 50, meeting mission requirements.
Alex Meredith, Stephen Leroy, Lucy Halperin, and Kerri Cahoy
Atmos. Meas. Tech., 16, 3345–3361, https://doi.org/10.5194/amt-16-3345-2023, https://doi.org/10.5194/amt-16-3345-2023, 2023
Short summary
Short summary
We developed a new efficient algorithm leveraging orbital dynamics to collocate radio occultation soundings with microwave radiance soundings. This new algorithm is 99 % accurate and is much faster than traditional collocation-finding approaches. Speeding up collocation finding is useful for calibrating and validating microwave radiometers and for data assimilation into numerical weather prediction models. Our algorithm can also be used to predict collocation yield for new satellite missions.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller
Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022, https://doi.org/10.5194/amt-15-5821-2022, 2022
Short summary
Short summary
This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
Short summary
Short summary
Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Natalia Hanna, Estera Trzcina, Gregor Möller, Witold Rohm, and Robert Weber
Atmos. Meas. Tech., 12, 4829–4848, https://doi.org/10.5194/amt-12-4829-2019, https://doi.org/10.5194/amt-12-4829-2019, 2019
Short summary
Short summary
In the study, the potential of GNSS tomography as an important supplementary data source for numerical weather prediction models was examined. We used two GNSS tomography models (TUW, WUELS) in different configurations. The GNSS tomography outputs were assimilated into the WRF model using a radio occultation observations operator (non-standard approach). Promising results show improvement in the weather forecasting of relative humidity and temperature during heavy-precipitation events.
Samuel Weber, Jan Beutel, Reto Da Forno, Alain Geiger, Stephan Gruber, Tonio Gsell, Andreas Hasler, Matthias Keller, Roman Lim, Philippe Limpach, Matthias Meyer, Igor Talzi, Lothar Thiele, Christian Tschudin, Andreas Vieli, Daniel Vonder Mühll, and Mustafa Yücel
Earth Syst. Sci. Data, 11, 1203–1237, https://doi.org/10.5194/essd-11-1203-2019, https://doi.org/10.5194/essd-11-1203-2019, 2019
Short summary
Short summary
In this paper, we describe a unique 10-year or more data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 m a.s.l. By documenting and sharing these data in this form, we contribute to facilitating future research based on them, e.g., in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.
Gregor Möller and Daniel Landskron
Atmos. Meas. Tech., 12, 23–34, https://doi.org/10.5194/amt-12-23-2019, https://doi.org/10.5194/amt-12-23-2019, 2019
Short summary
Short summary
The paper describes a ray-tracing approach for the proper reconstruction of GNSS signal paths through the lower atmosphere, identifies possible error sources during ray tracing and provides a strategy for reducing their effect on the GNSS tomography solution, thereby contributing to a more reliable reconstruction of the 3-D water vapor distribution in the lower atmosphere from GNSS measurements.
Hugues Brenot, Witold Rohm, Michal Kačmařík, Gregor Möller, André Sá, Damian Tondaś, Lukas Rapant, Riccardo Biondi, Toby Manning, and Cédric Champollion
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2018-292, https://doi.org/10.5194/amt-2018-292, 2018
Revised manuscript not accepted
Short summary
Short summary
The increasing number of navigation satellites orbiting the Earth and the continuous world wide deployment of dense networks will enable more present and future GNSS applications in the field of atmospheric monitoring. This study suggests some elements of progress in methodology to highlight the interest of ensemble tomography solution for improving the understanding of severe weather conditions, especially the initiation of the deep convection.
Ladina Steiner, Michael Meindl, Charles Fierz, and Alain Geiger
The Cryosphere, 12, 3161–3175, https://doi.org/10.5194/tc-12-3161-2018, https://doi.org/10.5194/tc-12-3161-2018, 2018
Short summary
Short summary
The amount of water stored in snow cover is of high importance for flood risks, climate change, and early-warning systems. We evaluate the potential of using GPS to estimate the stored water. We use GPS antennas buried underneath the snowpack and develop a model based on the path elongation of the GPS signals while propagating through the snowpack. The method works well over full seasons, including melt periods. Results correspond within 10 % to the state-of-the-art reference data.
Michal Kačmařík, Jan Douša, Galina Dick, Florian Zus, Hugues Brenot, Gregor Möller, Eric Pottiaux, Jan Kapłon, Paweł Hordyniec, Pavel Václavovic, and Laurent Morel
Atmos. Meas. Tech., 10, 2183–2208, https://doi.org/10.5194/amt-10-2183-2017, https://doi.org/10.5194/amt-10-2183-2017, 2017
Jan Douša, Galina Dick, Michal Kačmařík, Radmila Brožková, Florian Zus, Hugues Brenot, Anastasia Stoycheva, Gregor Möller, and Jan Kaplon
Atmos. Meas. Tech., 9, 2989–3008, https://doi.org/10.5194/amt-9-2989-2016, https://doi.org/10.5194/amt-9-2989-2016, 2016
Short summary
Short summary
GNSS products provide observations of atmospheric water vapour. Advanced tropospheric products focus on ultra-fast and high-resolution zenith total delays (ZTDs), horizontal gradients and slant delays, all suitable for rapid-cycle numerical weather prediction (NWP) and severe weather event monitoring. The GNSS4SWEC Benchmark provides a complex data set for developing and assessing these products, with initial focus on reference ZTDs and gradients derived from several NWP and dense GNSS networks.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Combining commercial microwave links and weather radar for classification of dry snow and rainfall
Improved consistency in solar-induced fluorescence retrievals from GOME-2A with the SIFTER v3 algorithm
An information content approach to diagnosing and improving CLIMCAPS retrieval consistency across instruments and satellites
Characterizing urban planetary boundary layer dynamics using 3-year Doppler wind lidar measurements in a western Yangtze River Delta city, China
Radar-based high-resolution ensemble precipitation analyses over the French Alps
Gravity waves above the northern Atlantic and Europe during streamer events using Aeolus
Observations of tall-building wakes using a scanning Doppler lidar
A new method to retrieve relative humidity profiles from a synergy of Raman lidar, microwave radiometer and satellite
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Best Estimate of the Planetary Boundary Layer Height from Multiple Remote Sensing Measurements
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Comparative experimental validation of microwave hyperspectral atmospheric soundings in clear-sky conditions
Propagating Information Content: An Example with Advection
Use of commercial microwave links as scintillometers: potential and limitations towards evaporation estimation
Determination of low-level temperature profiles from microwave radiometer observations during rain
Aeolus lidar surface return (LSR) at 355 nm as a new Aeolus Level-2A product
Sampling the diurnal and annual cycles of the Earth's energy imbalance with constellations of satellite-borne radiometers
Retrieval of top-of-atmosphere fluxes from combined EarthCARE lidar, imager, and broadband radiometer observations: the BMA-FLX product
Analysis of the measurement uncertainty for a 3D wind lidar
Improving solution availability and temporal consistency of an optimal-estimation physical retrieval for ground-based thermodynamic boundary layer profiling
Reconstruction of 3D precipitation measurements from FY-3G MWRI-RM imaging and sounding channels
An improved geolocation methodology for spaceborne radar and lidar systems
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
An LES Exploration of the Assumptions used in Retrieving Entrainment from a Mixing Diagram Approach with Ground-Based Remote Sensors
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
TanSat-2: a new satellite for mapping solar-induced chlorophyll fluorescence at both red and far-red bands with high spatio-temporal resolution
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Evolution of Wind Field in the Atmospheric Boundary Layer with using of Multiple Sources Observations during the Transit of Super Typhoon Doksuri (2305)
Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)
Drop size distribution retrieval using dual-polarization radar at C-band and S-band
Thermal tides in the middle atmosphere at mid-latitudes measured with a ground-based microwave radiometer
Global sensitivity analysis of simulated remote sensing polarimetric observations over snow
Improving the Gaussianity of radar reflectivity departures between observations and simulations using symmetric rain rates
On the temperature stability requirements of free-running Nd:YAG lasers for atmospheric temperature profiling through the rotational Raman technique
Limitations in wavelet analysis of non-stationary atmospheric gravity wave signatures in temperature profiles
A new non-linearity correction method for the spectrum from the Geostationary Inferometric Infrared Sounder on board Fengyun-4 satellites and its preliminary assessments
Observing atmospheric rivers using multi-GNSS airborne radio occultation: system description and data evaluation
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Unfiltering of the EarthCARE Broadband Radiometer (BBR) observations: the BM-RAD product
Variance estimations in the presence of intermittent interference and their applications to incoherent scatter radar signal processing
A clustering-based method for identifying and tracking squall lines
A multi-instrument fuzzy logic boundary-layer-top detection algorithm
Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models
Scale separation for gravity wave analysis from 3D temperature observations in the mesosphere and lower thermosphere (MLT) region
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Observed impact of the GNSS clock data rate on Radio Occultation bending angles for Sentinel-6A and COSMIC-2
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco (C. Z.) van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen
Atmos. Meas. Tech., 18, 2279–2293, https://doi.org/10.5194/amt-18-2279-2025, https://doi.org/10.5194/amt-18-2279-2025, 2025
Short summary
Short summary
We present a novel method for classifying rain and snow by combining data from commercial microwave links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.
Juliëtte C. S. Anema, K. Folkert Boersma, Lieuwe G. Tilstra, Olaf N. E. Tuinder, and Willem W. Verstraeten
Atmos. Meas. Tech., 18, 1961–1979, https://doi.org/10.5194/amt-18-1961-2025, https://doi.org/10.5194/amt-18-1961-2025, 2025
Short summary
Short summary
Long-term records of plant fluorescence offer vital insights into changing vegetation activity. The GOME-2A sensor provides extensive global observations but suffers from calibration and instrument degradation, which affects data consistency. This study presents the SIFTER v3 algorithm, which effectively resolves these issues and includes other improvements, resulting in robust, accurate, and consistent GOME-2A fluorescence measurements from 2007 to 2017.
Nadia Smith and Christopher D. Barnet
Atmos. Meas. Tech., 18, 1823–1839, https://doi.org/10.5194/amt-18-1823-2025, https://doi.org/10.5194/amt-18-1823-2025, 2025
Short summary
Short summary
CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.
Tianwen Wei, Mengya Wang, Kenan Wu, Jinlong Yuan, Haiyun Xia, and Simone Lolli
Atmos. Meas. Tech., 18, 1841–1857, https://doi.org/10.5194/amt-18-1841-2025, https://doi.org/10.5194/amt-18-1841-2025, 2025
Short summary
Short summary
This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Atmos. Meas. Tech., 18, 1731–1755, https://doi.org/10.5194/amt-18-1731-2025, https://doi.org/10.5194/amt-18-1731-2025, 2025
Short summary
Short summary
This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
Sabine Wüst, Lisa Küchelbacher, Franziska Trinkl, and Michael Bittner
Atmos. Meas. Tech., 18, 1591–1607, https://doi.org/10.5194/amt-18-1591-2025, https://doi.org/10.5194/amt-18-1591-2025, 2025
Short summary
Short summary
Information on the energy transported by atmospheric gravity waves (GWs) is crucial for improving atmosphere models. Most space-based studies report the potential energy. We use Aeolus wind data to estimate the kinetic energy (density). However, the data quality is a challenge for such analyses, as the accuracy of the data is in the range of typical GW amplitudes. We find a temporal coincidence between enhanced or breaking planetary waves and enhanced gravity wave kinetic energy density.
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025, https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Short summary
A Doppler lidar was placed in a highly built-up area in London to measure wakes from tall buildings during a period of 1 year. We were able to detect wakes and assess their dependence on wind speed, wind direction, and atmospheric stability.
Chengli Ji, Qiankai Jin, Feilong Li, Yuyang Liu, Zhicheng Wang, Jiajia Mao, Xiaoyu Ren, Yan Xiang, Wanlin Jian, Peitao Zhao, and Zhenyi Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1171, https://doi.org/10.5194/egusphere-2025-1171, 2025
Short summary
Short summary
This study presents the humidity measurements with a synergetic algorithm combining Raman lidar, MVR, and satellite. The results from 47 sites in China show the best correlation over 0.9 concerning the radiosonde measurements. It validates the RH accuracy with various data integrations. Three representative sites present the different seasonal characteristics indicating the geographic and height influences on the RH vertical distribution.
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025, https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Short summary
This paper presents a supervised-machine-learning approach for the automatic isolation of nocturnal low-level jets (NLLJs) using observations from a radar wind profiler. This analysis isolated 90 southwesterly NLLJs observed from May to September 2017–2021, highlighting key features in the evolution and morphology of the mid-Atlantic NLLJ.
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025, https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
Short summary
This study explores how the joints in a weather radar's protective cover affect its measurements. We developed a new method to correct these errors, improving the accuracy of the radar's data. Our method was tested during an intense cyclone on Réunion Island, demonstrating significant improvements in data accuracy. This research is crucial for enhancing weather predictions and understanding, particularly in challenging terrains.
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025, https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Short summary
Global Navigation Satellite System radio occultation data help monitor climate and weather prediction but are affected by residual ionospheric errors (RIEs). A new excess-phase-gradient method detects and corrects RIEs, showing both positive and negative values, varying by latitude, time, and solar activity. Tests show that RIE impacts polar stratosphere temperatures in models, with differences up to 3–4 K. This highlights the need for RIE correction to improve the accuracy of data assimilation.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3959, https://doi.org/10.5194/egusphere-2024-3959, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 18, 471–485, https://doi.org/10.5194/amt-18-471-2025, https://doi.org/10.5194/amt-18-471-2025, 2025
Short summary
Short summary
This study evaluates and compares a new microwave hyperspectrometer with an infrared hyperspectrometer for clear-sky temperature and water vapor retrievals. The analysis reveals that the information content of the infrared hyperspectrometer exceeds that of the microwave hyperspectrometer and provides higher vertical resolution in ground-based zenith measurements. Leveraging the ground–airborne synergy between the two instruments yielded optimal sounding results.
David D. Turner, Maria P. Cadeddu, Julia Simonson, and Timothy J. Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2024-4124, https://doi.org/10.5194/egusphere-2024-4124, 2025
Short summary
Short summary
When deriving a geophysical variable from remote sensors, the uncertainty and information content are critical. The latter quantify specifies what fraction of a real perturbation would be observed in the derived variable. This paper outlines, for the first time, a methodology for propagating the information content from multiple remote sensors into a derived product, using horizontal advection as an example.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2974, https://doi.org/10.5194/egusphere-2024-2974, 2025
Short summary
Short summary
Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024, https://doi.org/10.5194/amt-17-7169-2024, 2024
Short summary
Short summary
Microwave radiometers are usually not able to provide atmospheric quantities such as temperature profiles during rain. We present a method based on a selection of specific frequencies and elevation angles from microwave radiometer observations. A comparison with a numerical weather prediction model shows the presented method allows low-level temperature profiles during rain to be resolved, with rain rates of up to 2.5 mm h−1,, which was not possible before with state-of-the-art retrievals.
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024, https://doi.org/10.5194/amt-17-7183-2024, 2024
Short summary
Short summary
The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024, https://doi.org/10.5194/amt-17-7077-2024, 2024
Short summary
Short summary
The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back into space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024, https://doi.org/10.5194/amt-17-7007-2024, 2024
Short summary
Short summary
This paper focuses on the BMA-FLX processor, in which thermal and solar top-of-atmosphere radiative fluxes are obtained from longwave and shortwave radiances measured along track by the EarthCARE Broadband Radiometer (BBR). The BBR measurements, at three fixed viewing angles (fore, nadir, aft), are co-registered either at the surface or at a reference level. A combined flux from the three BRR views is obtained. The algorithm has been successfully validated against test scenes.
Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek
Atmos. Meas. Tech., 17, 6913–6931, https://doi.org/10.5194/amt-17-6913-2024, https://doi.org/10.5194/amt-17-6913-2024, 2024
Short summary
Short summary
Three-dimensional (3D) wind velocity measurements are of major importance for the characterization of atmospheric turbulence. This paper presents a detailed study of the measurement uncertainty of a three-beam wind lidar designed for mounting on airborne platforms. Considering the geometrical constraints, the analysis provides quantitative estimates for the measurement uncertainty of all components of the 3D wind vector. As a result, we propose optimized post-processing for error reduction.
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
Short summary
Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-175, https://doi.org/10.5194/amt-2024-175, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the FengYun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Short summary
The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
Short summary
We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2894, https://doi.org/10.5194/egusphere-2024-2894, 2024
Short summary
Short summary
Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024, https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Short summary
The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
Short summary
Short summary
The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024, https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary
Short summary
We investigate the capabilities of a proposed satellite mission, CAIRT, for observing gravity waves throughout the middle atmosphere and present the necessary methodology for in-depth wave analysis. Our findings suggest that such a satellite mission is highly capable of resolving individual wave parameters and could give new insights into the role of gravity waves in general atmospheric circulation and atmospheric processes.
Xiaoye Wang, Jing Xu, Songhua Wu, Qichao Wang, Guangyao Dai, Peizhi Zhu, Zhizhong Su, Sai Chen, Xiaomeng Shi, and Mengqi Fan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-156, https://doi.org/10.5194/amt-2024-156, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
In this paper, we proposed a data fusion method to obtain the no-blind zone wind speed profiles covering the whole Atmospheric Boundary Layer based on the joint measurements of coherent Doppler lidar (CDL), radar wind profiler (RWP) and automatic weather station (AWS). Since above instruments are widely deployed in China, we believe this method has broad application prospects on the improvement of the boundary layer parameterization scheme in numerical forecast models.
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024, https://doi.org/10.5194/amt-17-5601-2024, 2024
Short summary
Short summary
This study evaluates the use of background surface reflectance (BSR) derived from a semi-empirical bidirectional reflectance distribution function (BRDF) model based on GEMS satellite images. Analysis shows that BSR provides improved accuracy and stability compared to Lambertian-equivalent reflectivity (LER). These results indicate that BSR can significantly enhance climate analysis and air quality monitoring, making it a promising tool for accurate environmental satellite applications.
Daniel Durbin, Yadong Wang, and Pao-Liang Chang
Atmos. Meas. Tech., 17, 5397–5411, https://doi.org/10.5194/amt-17-5397-2024, https://doi.org/10.5194/amt-17-5397-2024, 2024
Short summary
Short summary
A method for determining drop size distributions (DSDs) for rain using radar measurements from two frequencies at two polarizations is presented. Following some preprocessing and quality control, radar measurements are incorporated into a model that uses swarm intelligence to seek the most suitable DSD to produce the input measurements.
Witali Krochin, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 17, 5015–5028, https://doi.org/10.5194/amt-17-5015-2024, https://doi.org/10.5194/amt-17-5015-2024, 2024
Short summary
Short summary
Atmospheric tides are global-scale oscillations with periods of a fraction of a day. Their observation in the middle atmosphere is challenging and rare, as it requires continuous measurements with a high temporal resolution. In this paper, temperature time series of a ground-based microwave radiometer were analyzed with a spectral filter to derive thermal tide amplitudes and phases in an altitude range of 25–50 km at the geographical locations of Payerne and Bern (Switzerland).
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024, https://doi.org/10.5194/amt-17-4737-2024, 2024
Short summary
Short summary
We analyze simulated polarization observations over snow to investigate the capabilities of remote sensing to determine surface and atmospheric properties in snow-covered regions. Polarization measurements are demonstrated to aid in the determination of snow grain shape, ice crystal roughness, and the vertical distribution of impurities in the snow–atmosphere system, data that are critical for estimating snow albedo for use in climate models.
Yudong Gao, Lidou Huyan, Zheng Wu, and Bojun Liu
Atmos. Meas. Tech., 17, 4675–4686, https://doi.org/10.5194/amt-17-4675-2024, https://doi.org/10.5194/amt-17-4675-2024, 2024
Short summary
Short summary
A symmetric error model built by symmetric rain rates handles the non-Gaussian error structure of the reflectivity error. The accuracy and linearization of rain rates can further improve the Gaussianity.
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024, https://doi.org/10.5194/amt-17-4687-2024, 2024
Short summary
Short summary
We study how the spectral characteristics of a solid-state laser in an atmospheric temperature profiling lidar using the Raman technique impact the temperature retrieval accuracy. We find that the spectral widening, with respect to a seeded laser, has virtually no impact, while crystal-rod temperature variations in the laser must be kept within a range of 1 K for the uncertainty in the atmospheric temperature below 1 K. The study is carried out through spectroscopy simulations.
Robert Reichert, Natalie Kaifler, and Bernd Kaifler
Atmos. Meas. Tech., 17, 4659–4673, https://doi.org/10.5194/amt-17-4659-2024, https://doi.org/10.5194/amt-17-4659-2024, 2024
Short summary
Short summary
Imagine you want to determine how quickly the pitch of a passing ambulance’s siren changes. If the vehicle is traveling slowly, the pitch changes only slightly, but if it is traveling fast, the pitch also changes rapidly. In a similar way, the wind in the middle atmosphere modulates the wavelength of atmospheric gravity waves. We have investigated the question of how strong the maximum wind may be so that the change in wavelength can still be determined with the help of wavelet transformation.
Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui
Atmos. Meas. Tech., 17, 4613–4627, https://doi.org/10.5194/amt-17-4613-2024, https://doi.org/10.5194/amt-17-4613-2024, 2024
Short summary
Short summary
Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level. Different from the classical method, a new NL correction method for a spaceborne Fourier transform spectrometer is proposed. To overcome the inaccurate linear coefficient from two-point calibration influencing NL correction, an iteration algorithm is established that is suitable for NL correction of both infrared and microwave sensors.
Bing Cao, Jennifer S. Haase, Michael J. Murphy Jr., and Anna M. Wilson
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-119, https://doi.org/10.5194/amt-2024-119, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This paper describes an Airborne Radio Occultation (ARO) observation system installed on reconnaissance aircraft that uses GPS signal refraction in the atmosphere to retrieve information about the temperature and moisture in the storm environment as far away as 400 km surrounding the flight track. The characteristics and quality of 1700 ARO refractivity profiles were assessed. These observations are collected to help understand atmospheric rivers and improve their forecasting.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary
Short summary
Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Almudena Velázquez Blázquez, Edward Baudrez, Nicolas Clerbaux, and Carlos Domenech
Atmos. Meas. Tech., 17, 4245–4256, https://doi.org/10.5194/amt-17-4245-2024, https://doi.org/10.5194/amt-17-4245-2024, 2024
Short summary
Short summary
The Broadband Radiometer measures shortwave and total-wave radiances filtered by the spectral response of the instrument. To obtain unfiltered solar and thermal radiances, the effect of the spectral response needs to be corrected for, done within the BM-RAD processor. Errors in the unfiltering are propagated into fluxes; thus, accurate unfiltering is required for their proper estimation (within BMA-FLX). Unfiltering errors are estimated to be <0.5 % for the shortwave and <0.1 % for the longwave.
Qihou Zhou, Yanlin Li, and Yun Gong
Atmos. Meas. Tech., 17, 4197–4209, https://doi.org/10.5194/amt-17-4197-2024, https://doi.org/10.5194/amt-17-4197-2024, 2024
Short summary
Short summary
We discuss several robust estimators to compute the variance of a normally distributed random variable to deal with interference. Compared to rank-based estimators, the methods based on the geometric mean are more accurate and are computationally more efficient. We apply three robust estimators to incoherent scatter power and velocity processing, along with the traditional sample mean estimator. The best estimator is a hybrid estimator that combines the sample mean and a robust estimator.
Zhao Shi, Yuxiang Wen, and Jianxin He
Atmos. Meas. Tech., 17, 4121–4135, https://doi.org/10.5194/amt-17-4121-2024, https://doi.org/10.5194/amt-17-4121-2024, 2024
Short summary
Short summary
The squall line is a type of convective system. Squall lines are often associated with damaging weather, so identifying and tracking squall lines plays an important role in early meteorological disaster warnings. A clustering-based method is proposed in this article. It can identify the squall lines within the radar scanning range with an accuracy rate of 95.93 %. It can also provide the three-dimensional structure and movement tracking results for each squall line.
Elizabeth N. Smith and Jacob T. Carlin
Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024, https://doi.org/10.5194/amt-17-4087-2024, 2024
Short summary
Short summary
Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
Short summary
Short summary
The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Björn Linder, Peter Preusse, Qiuyu Chen, Ole Martin Christensen, Lukas Krasauskas, Linda Megner, Manfred Ern, and Jörg Gumbel
Atmos. Meas. Tech., 17, 3829–3841, https://doi.org/10.5194/amt-17-3829-2024, https://doi.org/10.5194/amt-17-3829-2024, 2024
Short summary
Short summary
The Swedish research satellite MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is designed to study atmospheric waves in the mesosphere and lower thermosphere. These waves perturb the temperature field, and thus, by observing three-dimensional temperature fluctuations, their properties can be quantified. This pre-study uses synthetic MATS data generated from a general circulation model to investigate how well wave properties can be retrieved.
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024, https://doi.org/10.5194/amt-17-3605-2024, 2024
Short summary
Short summary
This research examines the characteristics of low-level GNSS radio occultation (RO) refractivity bias over ocean and land and its dependency on the RO retrieval uncertainty, atmospheric temperature, and moisture. We propose methods for estimating the region-dependent refractivity bias. Our methods can be applied to calibrate the refractivity bias under different atmospheric conditions and thus improve the applications of the GNSS RO data in the deep troposphere.
Sebastiano Padovan, Axel Von Engeln, Saverio Paolella, Yago Andres, Chad R. Galley, Riccardo Notarpietro, Veronica Rivas Boscán, Francisco Sancho, Francisco Martin Alemany, Nicolas Morew, and Christian Marquardt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-80, https://doi.org/10.5194/amt-2024-80, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Radio Occultation (RO) measurements are an important contribution to numerical weather predictions and long-term climate studies. Using more than a hundred thousand occultations recorded by instruments onboard the Sentinel-6A and Cosmic-2 satellites, this work studies the effects of the clock data rate of the Global Navigation Satellite System on the RO data quality. GLONASS occultations benefit of high-rate clock data (1 second), GPS occultation have high quality already at 30 seconds.
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024, https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Short summary
This study introduces and evaluates a new ocean surface wind speed product from the NASA Langley Research Center (LARC) airborne High-Spectral-Resolution Lidar – Generation 2 (HSRL-2) during the NASA ACTIVATE mission. We show that HSRL-2 surface wind speed data are accurate when compared to ground-truth dropsonde measurements. Therefore, the HSRL-2 instrument is able obtain accurate, high-resolution surface wind speed data in airborne field campaigns.
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024, https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary
Short summary
We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.
Cited articles
Aichinger-Rosenberger, M., Brockmann, E., Crocetti, L., Soja, B., and Moeller, G.: Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland, Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022, 2022.
Crocetti, L., Schartner, M., and Soja, B.: Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning, Remote Sens., 13, 3906, https://doi.org/10.3390/rs13193906, 2021.
Crocetti, L., Schartner, M., Zus, F., Zhang, W., Moeller, G., Navarro, V., See, L., Schindler, K., and Soja, B.: Global, spatially explicit modelling of zenith wet delay with XGBoost, J. Geod., 98, 23, https://doi.org/10.1007/s00190-024-01829-2, 2024.
ECMWF: IFS documentation, https://www.ecmwf.int/en/publications/ifs-documentation (last access: 31 July 2023), 2023.
Foelsche, U., Borsche, M., Steiner, A., Gobiet, A., Pirscher, B., Kirchengast, G., Wickert, J., and Schmidt, T.: Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite, Clim. Dynam., 31, 49–65, https://doi.org/10.1007/s00382-007-0337-7, 2008.
Foelsche, U., Scherllin-Pirscher, B., Ladstädter, F., Steiner, A. K., and Kirchengast, G.: Refractivity and temperature climate records from multiple radio occultation satellites consistent within 0.05 %, Atmos. Meas. Tech., 4, 2007–2018, https://doi.org/10.5194/amt-4-2007-2011, 2011.
Gou, J., Rösch, C., Shehaj, E., Chen, K., Kiani Shahvandi, M., Soja, B., and Rothacher, M.: Modelling the differences between ultra-rapid and final orbit products of GPS satellites using machine learning approaches, Remote Sens., 15, 5585, https://doi.org/10.3390/rs15235585, 2023.
Gou, J., Soja, B.: Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms, Nat. Water, 2, 139–150, https://doi.org/10.1038/s44221-024-00194-w, 2024.
Hassanien, A. E.: Machine Learning Paradigms: Theory and Application, Springer, https://doi.org/10.1007/978-3-030-02357-7, 2018.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning (2nd edition), Springer-Verlag, https://doi.org/10.1007/978-0-387-84858-7, 2009.
Haykin, S.O.: Neural Networks and Learning Machines, Pearson, https://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf (last access: 10 December 2024), 2009.
Ho, S. P., Zhou, X., Shao, X., Zhang, B., Adhikari, L., Kireev, S., He, Y., Yoe, J. G., Xia-Serafino, W., and Lynch, E.: Initial Assessment of the COSMIC-2/FORMOSAT-7 Neutral Atmosphere Data Quality in NESDIS/STAR Using In Situ and Satellite Data, Remote Sens., 12, 4099, https://doi.org/10.3390/rs12244099, 2020.
Kiani Shahvandi, M., Schartner, M., and Soja, B.: Neural ODE Differential Learning and Its Application in Polar Motion Prediction, JGR Solid Earth, 127, e2022JB024775, https://doi.org/10.1029/2022JB024775, 2022.
Kiani Shahvandi, M., Dill, R., Dobslaw, H., Kehm, A., Bloßfeld, M., Schartner, M., Mishra, S., and Soja, B.: Geophysically informed machine learning for improving rapid estimation and short-term prediction of Earth orientation pa-rameters, JGR Solid Earth, 128, e2023JB026720, https://doi.org/10.1029/2023JB026720, 2023.
Kitpracha, C., Modiri, S., Asgarimchr, M., Heinkelmann, R., and Schuh, H.: Machine Learning based prediction of atmospheric zenith wet delay: A study using GNSS measurements in Wettzell and co-located VLBI observations, Vol. 21, EGU2019-4127, 2019.
Kursinski, E., Hajj, G., Schofield, J., Linfield, R., and Hardy, K.: Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System, J. Geophys. Res., 102 (D19), 23429–23465, https://doi.org/10.1029/97JD01569, 1997.
Kursinski, E., Hajj, G., Leroy, S., and Herman, B.: The GPS radio occultation technique, Terr. Atmos. Ocean. Sci., 11, 53–114, https://doi.org/10.3319/TAO.2000.11.1.53(COSMIC), 2000.
Leroy, S.: The measurement of geopotential heights by GPS radio occultation, J. Geophys. Res., 102, 6971–6986, 1997.
Leroy, S., Ao, C., and Verkhoglyadova, O.: Mapping GPS Radio Occultation Data by Bayesian Interpolation, J. Atmos. Ocean. Technol., 29, 1062–1074, https://doi.org/10.1175/JTECH-D-11-00179.1, 2012.
Leroy, S., Ao, C., Verkhoglyadova, O., and Oyola, M.: Analyzing the Diurnal Cycle by Bayesian Interpolation on a Sphere for Mapping GNSS Radio Occultation Data, J. Atmos. Oceab. Technol., 38, 951–961, https://doi.org/10.1175/JTECH-D-20-0031.1, 2021.
MacKay, D.: Bayesian Interpolation, Neural Comput., 4, 415–447, 1992.
Mannucci, A. J., Ao, C., and Williamson, W.: GNSS Radio Occultation, in Position, Navigation, and Timing Technologies in the 21st Century: Integrated Satellite Navigation, Sensor Systems, and Civil Applications, Volume 1, 1st Edition, edited by: Morton, J. Y., van Diggelen, Fm., Spilker, Jr. J. J., and Parkinson, B. W., 1168 pp., John Wiley & Sons, https://doi.org/10.1002/9781119458449, 2021.
Melbourne, W. G.: Radio Occultations Using Earth Satellites: A Wave Theory Treatment, Deep Space Communications and Navigation Series, Monograph 6, Jet Proupulison Laboratory, California Institute of Technology, https://descanso.jpl.nasa.gov/monograph/series6/Full_Version_rev2.pdf (last access: 10 December 2024), 2004.
Miotti, L., Shehaj, E., Geiger, A., D'Aronco, S., Wegner, J. D., Moeller, G., and Rothacher, M.: Tropospheric delays derived from ground meteorological parameters: comparison between machine learning and empirical model approaches, 2020 European Navigation Conference (ENC), Dresden, Germany, 1–10 pp., https://doi.org/10.23919/ENC48637.2020.9317442, 2020.
Muir, J. and Tkalcic, H.: A method of spherical harmonic analysis in the geosciences via hierarchical Bayesian inference, Geophys. J. Int., 203, 1164–1171, 2015.
Natras, R., Soja, B., and Schmidt, M.: Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting, Remote Sens., 14, 3547, https://doi.org/10.3390/rs14153547, 2022.
Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S.: Activation Functions: Comparison of Trends in Practice and Research for Deep Learning, https://doi.org/10.48550/arXiv.1811.03378, 2018.
Rueger, J. M.: Refractive index formulae for radio waves. Proceedings of the FIG XXII International Congress, Washington D.C., USA, paper in JS28, 12 pp., https://api.semanticscholar.org/CorpusID:126525251 (last access: 10 December 2024), 2002.
Schreiner, W. S., Weiss, J. P., Anthes, R. A., Braun, J., Chu, V., Fong, J., Hunt, D., Kuo, Y.-H., Meehan, T., Serafino, W., Sjoberg, J., Sokolovskiy, S., Talaat, E., Wee, T. K., and Zeng, Z.: COSMIC-2 Radio Occultation Constellation: First Results, Geophys. Res. Lett., 47 , e2019GL086841, https://doi.org/10.1029/2019GL086841, 2020.
Shamshiri, R., Motagh, M., and Nahavandchi, H.: A machine learning-based regression technique for prediction of tropospheric phase delay on large-scale Sentinel-1 InSAR time-series, EGU, https://meetingorganizer.copernicus.org/EGU2019/EGU2019-10739.pdf (last access: 10 December 2024), 2019.
Shehaj, E.: Space Geodetic Techniques for Retrieval of High-Resolution Atmospheric Water Vapor Fields, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-b-000618346, 2023.
Shehaj, E.: GNSS Radio Occultation Climatologies mapped by Machine Learning and Bayesian Interpolation (dataset and code example), ETH Zurich [data set, code], https://doi.org/10.3929/ethz-b-000670139, 2024.
Shehaj, E., Miotti, L., Geiger, A., D'Aronco, S., Wegner, J.D., Moeller, G., Soja, B., and Rothacher, M.: High-Resolution Tropospheric Refractivity Fields by Combining Machine Learning and Collocation Methods to Correct Earth Observation Data, Acta Astronaut., 204, 591–598, https://doi.org/10.1016/j.actaastro.2022.10.007, 2023.
Shi, J., Li, X., Li, L., Ouyang, C., and Xu, C.: An Efficient Deep Learning-Based Troposphere ZTD Dataset Generation Method for Massive GNSS CORS Stations, IEEE T. Geosci. Remote Sens., 61, 1–11, https://doi.org/10.1109/TGRS.2023.3276874, 2023.
Stanford CS: CS231n Convolutional Neural Networks for Visual Recognition, https://cs231n.github.io/ (last access: 15 February 2023), 2023.
UCAR: Constellation Observing System for Meteorology, Ionosphere and Climate, https://www.cosmic.ucar.edu/global-navigation-satellite-system-gnss-background/cosmic-2 (last access: 7 November 2022), 2022a.
UCAR: COSMIC-2 Data, Index of /gnss-ro/cosmic2/nrt/level2/, https://data.cosmic.ucar.edu/gnss-ro/cosmic2/nrt/level2/ (last acess: 7 November 2022), 2022b.
Wang, J. X., Wu, J. L., and Xiao, H.: Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data, Phys. Rev. Fluids, 2, 034603, https://doi.org/10.1103/PhysRevFluids.2.034603, 2017.
Zhang, B. and Yao, Y.: Precipitable water vapor fusion based on a generalized regression neural network, J. Geod., 95, 36, https://doi.org/10.1007/s00190-021-01482-z, 2021.
Short summary
This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
This work investigates whether machine learning (ML) can offer an alternative to existing...
