Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5781-2018
© Author(s) 2018. 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-11-5781-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Oct 2018
Research article |
| 19 Oct 2018
| 19 Oct 2018
Analysis of the performance of a ship-borne scanning wind lidar in the Arctic and Antarctic
Department of Environmental Meteorology, University of Trier, Trier, Germany
Svenja H. E. Kohnemann
Department of Environmental Meteorology, University of Trier, Trier, Germany
Günther Heinemann
Department of Environmental Meteorology, University of Trier, Trier, Germany
Related authors
Lukrecia Stulic, Ralph Timmermann, Stephan Paul, Rolf Zentek, Günther Heinemann, and Torsten Kanzow
Ocean Sci., 19, 1791–1808, https://doi.org/10.5194/os-19-1791-2023, https://doi.org/10.5194/os-19-1791-2023, 2023
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In the southern Weddell Sea, the strong sea ice growth in coastal polynyas drives formation of dense shelf water. By using a sea ice–ice shelf–ocean model with representation of the changing icescape based on satellite data, we find that polynya sea ice growth depends on both the regional atmospheric forcing and the icescape. Not just strength but also location of the sea ice growth in polynyas affects properties of the dense shelf water and the basal melting of the Filchner–Ronne Ice Shelf.
Rolf Zentek and Günther Heinemann
Geosci. Model Dev., 13, 1809–1825, https://doi.org/10.5194/gmd-13-1809-2020, https://doi.org/10.5194/gmd-13-1809-2020, 2020
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We used the climate model CCLM to simulate the Weddell Sea region with a resolutions of 15 and 5 km. By adjusting the turbulence parametrization a warm bias over the Antarctic Plateau was removed. For sea ice we found a temperature bias around +/-1 K and a wind speed bias of 1 m s-1. Comparisons of radio soundings showed a bias around zero and a RMSE of 1–2 K for temperature and 3–4 m s-1 for wind speed. Comparison with wind Doppler lidar yielded almost no bias and a RMSE of ca. 2 m s-1.
Lukrecia Stulic, Ralph Timmermann, Stephan Paul, Rolf Zentek, Günther Heinemann, and Torsten Kanzow
Ocean Sci., 19, 1791–1808, https://doi.org/10.5194/os-19-1791-2023, https://doi.org/10.5194/os-19-1791-2023, 2023
Short summary
Short summary
In the southern Weddell Sea, the strong sea ice growth in coastal polynyas drives formation of dense shelf water. By using a sea ice–ice shelf–ocean model with representation of the changing icescape based on satellite data, we find that polynya sea ice growth depends on both the regional atmospheric forcing and the icescape. Not just strength but also location of the sea ice growth in polynyas affects properties of the dense shelf water and the basal melting of the Filchner–Ronne Ice Shelf.
Sascha Willmes, Günther Heinemann, and Frank Schnaase
The Cryosphere, 17, 3291–3308, https://doi.org/10.5194/tc-17-3291-2023, https://doi.org/10.5194/tc-17-3291-2023, 2023
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Sea ice is an important constituent of the global climate system. We here use satellite data to identify regions in the Arctic where the sea ice breaks up in so-called leads (i.e., linear cracks) regularly during winter. This information is important because leads determine, e.g., how much heat is exchanged between the ocean and the atmosphere. We here provide first insights into the reasons for the observed patterns in sea-ice leads and their relation to ocean currents and winds.
Rolf Zentek and Günther Heinemann
Geosci. Model Dev., 13, 1809–1825, https://doi.org/10.5194/gmd-13-1809-2020, https://doi.org/10.5194/gmd-13-1809-2020, 2020
Short summary
Short summary
We used the climate model CCLM to simulate the Weddell Sea region with a resolutions of 15 and 5 km. By adjusting the turbulence parametrization a warm bias over the Antarctic Plateau was removed. For sea ice we found a temperature bias around +/-1 K and a wind speed bias of 1 m s-1. Comparisons of radio soundings showed a bias around zero and a RMSE of 1–2 K for temperature and 3–4 m s-1 for wind speed. Comparison with wind Doppler lidar yielded almost no bias and a RMSE of ca. 2 m s-1.
Anne Klosterhalfen, Alexander Graf, Nicolas Brüggemann, Clemens Drüe, Odilia Esser, María P. González-Dugo, Günther Heinemann, Cor M. J. Jacobs, Matthias Mauder, Arnold F. Moene, Patrizia Ney, Thomas Pütz, Corinna Rebmann, Mario Ramos Rodríguez, Todd M. Scanlon, Marius Schmidt, Rainer Steinbrecher, Christoph K. Thomas, Veronika Valler, Matthias J. Zeeman, and Harry Vereecken
Biogeosciences, 16, 1111–1132, https://doi.org/10.5194/bg-16-1111-2019, https://doi.org/10.5194/bg-16-1111-2019, 2019
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To obtain magnitudes of flux components of H2O and CO2 (e.g., transpiration, soil respiration), we applied source partitioning approaches after Scanlon and Kustas (2010) and after Thomas et al. (2008) to high-frequency eddy covariance measurements of 12 study sites covering various ecosystems (croplands, grasslands, and forests) in different climatic regions. We analyzed the interrelations among turbulence, site characteristics, and the performance of both partitioning methods.
Andreas Preußer, Günther Heinemann, Sascha Willmes, and Stephan Paul
The Cryosphere, 10, 3021–3042, https://doi.org/10.5194/tc-10-3021-2016, https://doi.org/10.5194/tc-10-3021-2016, 2016
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We present spatial and temporal characteristics of 17 Arctic polynya regions. By using an energy balance model, daily thin-ice thickness distributions are derived from TIR satellite and atmospheric reanalysis data. All polynyas combined yield an average ice production of about 1811 km3 per winter. Interestingly, we find distinct regional differences in calculated trends over the last 13 years. Finally, we set a special focus on the Laptev Sea region and its relation to the Transpolar Drift.
Oliver Gutjahr, Günther Heinemann, Andreas Preußer, Sascha Willmes, and Clemens Drüe
The Cryosphere, 10, 2999–3019, https://doi.org/10.5194/tc-10-2999-2016, https://doi.org/10.5194/tc-10-2999-2016, 2016
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We estimated the formation of new sea ice within polynyas in the Laptev Sea (Siberia) with the regional climate model COSMO-CLM at 5 km horizontal resolution. Fractional sea ice and the representation of thin ice is often neglected in atmospheric models. Our study demonstrates, however, that the way thin ice in polynyas is represented in the model considerably affects the amount of newly formed sea-ice and the air–ice–ocean heat flux. Both processes impact the Arctic sea-ice budget.
S. Paul, S. Willmes, and G. Heinemann
The Cryosphere, 9, 2027–2041, https://doi.org/10.5194/tc-9-2027-2015, https://doi.org/10.5194/tc-9-2027-2015, 2015
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We established a 13-year-long MODIS-derived thin-ice thickness data set from which we derived information about polynya dynamics in the southern Weddell Sea. In contrast to other studies, we do not focus on a single region but instead discuss polynya dynamics for the complete coastal area. The higher spatial resolution of MODIS compared to passive-microwave sensors enables us to resolve even very narrow coastal polynyas that would remain otherwise undetected.
A. Preußer, S. Willmes, G. Heinemann, and S. Paul
The Cryosphere, 9, 1063–1073, https://doi.org/10.5194/tc-9-1063-2015, https://doi.org/10.5194/tc-9-1063-2015, 2015
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The Storfjorden polynya (Svalbard) forms regularly under the influence of strong north-easterly winds. In this study, spatial and temporal characteristics for the period 2002/03-2013/14 were inferred from daily calculated thin-ice thickness distributions, based on MODIS ice surface temperatures and ERA-interim reanalysis.
With an estimated average ice production of 28.3km³/winter, this polynya system is of particular interest regarding its potential contribution to deep water formation.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
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Zhen Li, Ad Stoffelen, Anton Verhoef, Zhixiong Wang, Jian Shang, and Honggang Yin
Atmos. Meas. Tech., 16, 4769–4783, https://doi.org/10.5194/amt-16-4769-2023, https://doi.org/10.5194/amt-16-4769-2023, 2023
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WindRAD (Wind Radar) is the first dual-frequency rotating fan-beam scatterometer in orbit. We observe non-linearity in the backscatter distribution. Therefore, higher-order calibration (HOC) is proposed, which removes the non-linearities per incidence angle. The combination of HOC and NOCant is discussed. It can remove not only the non-linearity but also the anomalous harmonic azimuth dependencies caused by the antenna rotation; hence the optimal winds can be achieved with this combination.
Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli
Atmos. Meas. Tech., 16, 4409–4422, https://doi.org/10.5194/amt-16-4409-2023, https://doi.org/10.5194/amt-16-4409-2023, 2023
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A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
Carsten Schmidt, Lisa Küchelbacher, Sabine Wüst, and Michael Bittner
Atmos. Meas. Tech., 16, 4331–4356, https://doi.org/10.5194/amt-16-4331-2023, https://doi.org/10.5194/amt-16-4331-2023, 2023
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Two identical instruments in a parallel setup were used to observe the mesospheric OH airglow for more than 10 years (2009–2020) at 47.42°N, 10.98°E. This allows unique analyses of data quality aspects and their impact on the obtained results. During solar cycle 24 the influence of the sun was strong (∼6 K per 100 sfu). A quasi-2-year oscillation (QBO) of ±1 K is observed mainly during the maximum of the solar cycle. Unlike the stratospheric QBO the variation has a period of or below 24 months.
Jason N. S. Cole, Howard W. Barker, Zhipeng Qu, Najda Villefranque, and Mark W. Shephard
Atmos. Meas. Tech., 16, 4271–4288, https://doi.org/10.5194/amt-16-4271-2023, https://doi.org/10.5194/amt-16-4271-2023, 2023
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Measurements from the EarthCARE satellite mission will be used to retrieve profiles of cloud and aerosol properties. These retrievals are combined with auxiliary information about surface properties and atmospheric state, e.g., temperature and water vapor. This information allows computation of 1D and 3D solar and thermal radiative transfer for small domains, which are compared with coincident radiometer observations to continually assess EarthCARE retrievals.
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
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A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, André Ehrlich, and Manfred Wendisch
Atmos. Meas. Tech., 16, 3915–3930, https://doi.org/10.5194/amt-16-3915-2023, https://doi.org/10.5194/amt-16-3915-2023, 2023
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Snow layer melting and melt pond formation on Arctic sea ice are important seasonal processes affecting the surface reflection and energy budget. Sea ice reflectivity was surveyed by airborne imaging spectrometers in May–June 2017. Adapted retrieval approaches were applied to find snow layer liquid water fraction, snow grain effective radius, and melt pond depth. The retrievals show the potential and limitations of spectral airborne imaging to map melting snow layer and melt pond properties.
Mohamed Mossad, Irina Strelnikova, Robin Wing, and Gerd Baumgarten
EGUsphere, https://doi.org/10.5194/egusphere-2023-1598, https://doi.org/10.5194/egusphere-2023-1598, 2023
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This paper is a numerical study addressing the problem of observational gaps in atmospheric measurements. We have tested three commonly used techniques, namely the FFT, GLS and HSF. We have found the GLS is a more reliable method for identifying the periodic components of gapped GW time series, particularly when dealing with high-frequency waves. The Haar Structure Function (HSF) is good for producing spectra with negative slopes, the GLS excels for flat spectra and positive slopes.
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023, https://doi.org/10.5194/amt-16-3715-2023, 2023
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This paper provides a new method to retrieve wind profiles from coherent Doppler lidar (CDL) measurements. It takes advantage of layer-to-layer correlation in wind profiles to provide continuous profiles of up to 3 km by filling in the gaps where the CDL signal is too small to retrieve reliable results by itself. Comparison with the current method and collocated radiosonde wind measurements showed excellent agreement with no degradation in results where the current method gives valid results.
Jake J. Gristey, K. Sebastian Schmidt, Hong Chen, Daniel R. Feldman, Bruce C. Kindel, Joshua Mauss, Mathew van den Heever, Maria Z. Hakuba, and Peter Pilewskie
Atmos. Meas. Tech., 16, 3609–3630, https://doi.org/10.5194/amt-16-3609-2023, https://doi.org/10.5194/amt-16-3609-2023, 2023
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The concept of a satellite-based camera is demonstrated for sampling the angular distribution of outgoing radiance from Earth needed to generate data products for new radiation budget spectral channels.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-155, https://doi.org/10.5194/amt-2023-155, 2023
Revised manuscript accepted for AMT
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A common method to retrieve important information about the microphysical structure of rain (DSD retrievals) requires a constrained relationship between the drop size distribution parameters. The most widely accepted empirical relationship is between μ and Λ. The relationship shows variability across the different types of rainfall (convective or stratiform). The new proposed power-law model to represent the μ-Λ relation provide a better physical interpretation of the relationship coefficients.
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-117, https://doi.org/10.5194/amt-2023-117, 2023
Revised manuscript accepted for AMT
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The WIVERN mission, one of the four candidates to be the ESA’s Earth Explorer 11 mission, aims at providing measurements of horizontal winds in cloud and precipitating systems through a conically scanning W-band Doppler radar. This work discusses four methods that can be used to correct the antenna mispointing errors of the WIVERN Doppler radar. The proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-161, https://doi.org/10.5194/amt-2023-161, 2023
Revised manuscript accepted for AMT
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The manuscript addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilises a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regards.
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
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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.
René Sedlak, Andreas Welscher, Patrick Hannawald, Sabine Wüst, Rainer Lienhart, and Michael Bittner
Atmos. Meas. Tech., 16, 3141–3153, https://doi.org/10.5194/amt-16-3141-2023, https://doi.org/10.5194/amt-16-3141-2023, 2023
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We show that machine learning can help in classifying images of the OH* airglow, a thin layer in the middle atmosphere (ca. 86 km height) emitting infrared radiation, in an efficient way. By doing this,
dynamicepisodes of strong movement in the OH* airglow caused predominantly by waves can be extracted automatically from large data sets. Within these dynamic episodes, turbulent wave breaking can also be found. We use these observations of turbulence to derive the energy released by waves.
Roberto Cremonini, Tanel Voormansik, Piia Post, and Dmitri Moisseev
Atmos. Meas. Tech., 16, 2943–2956, https://doi.org/10.5194/amt-16-2943-2023, https://doi.org/10.5194/amt-16-2943-2023, 2023
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Extreme rainfall for a specific location is commonly evaluated when designing stormwater management systems. This study investigates the use of quantitative precipitation estimations (QPEs) based on polarimetric weather radar data, without rain gauge corrections, to estimate 1 h rainfall total maxima in Italy and Estonia. We show that dual-polarization weather radar provides reliable QPEs and effective estimations of return periods for extreme rainfall in climatologically homogeneous regions.
Kuo-Nung Wang, Chi O. Ao, Mary G. Morris, George A. Hajj, Marcin J. Kurowski, Francis J. Turk, and Angelyn W. Moore
EGUsphere, https://doi.org/10.5194/egusphere-2023-85, https://doi.org/10.5194/egusphere-2023-85, 2023
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In this article we described a joint retrieval approach combining two techniques, RO and MWR, to obtain high vertical resolution and solve for temperature and moisture independently. The results show that the complicated structure in the lower troposphere can be better resolved with much smaller biases, and the RO/MWR combination is the most stable scenario in our sensitivity analysis. This approach is also applied to real data (COSMIC-2/Suomi-NPP) to show the promise of joint RO/MWR retrieval.
Vinícius Ludwig-Barbosa, Joel Rasch, Thomas Sievert, Anders Carlström, Mats I. Pettersson, Viet Thuy Vu, and Jacob Christensen
Atmos. Meas. Tech., 16, 1849–1864, https://doi.org/10.5194/amt-16-1849-2023, https://doi.org/10.5194/amt-16-1849-2023, 2023
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In this paper, the back-propagation method's capabilities and limitations regarding the location of irregularity regions in the ionosphere, e.g. equatorial plasma bubbles, are evaluated. The assessment was performed with simulations in which different scenarios were assumed. The results showed that the location estimate is possible if the amplitude of the ionospheric disturbance is stronger than the instrument noise level. Further, multiple patches can be located if regions are well separated.
Lars Norin
Atmos. Meas. Tech., 16, 1789–1801, https://doi.org/10.5194/amt-16-1789-2023, https://doi.org/10.5194/amt-16-1789-2023, 2023
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The atmosphere can cause radar beams to bend more or less towards the ground. When the atmosphere differs from standard atmospheric conditions, the propagation is considered anomalous. Radars affected by anomalous propagation can observe ground clutter far beyond the radar horizon. Here, 4.5 years' worth of data from five operational Swedish weather radars are presented. Analyses of the data reveal a strong seasonal cycle and weaker diurnal cycle in ground clutter from across nearby waters.
Liqin Jin, Jakob Mann, Nikolas Angelou, and Mikael Sjöholm
EGUsphere, https://doi.org/10.5194/egusphere-2023-464, https://doi.org/10.5194/egusphere-2023-464, 2023
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By sampling the spectra of a Doppler lidar faster than the raindrop's beam transit time, the rain signal can be filtered away and the bias on the wind velocity estimation can be reduced. In the method we propose, 3 kHz spectra are normalized with their peak values before retrieving the radial wind velocity. In three hours period, we have observed a significant reduction of the bias of the lidar data relative to the sonic. The tendency is that the more it rains, the more the bias is reduced.
Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Robin Wing, Jean-Pierre Cammas, Yann Hello, and Philippe Keckhut
Atmos. Meas. Tech., 16, 997–1016, https://doi.org/10.5194/amt-16-997-2023, https://doi.org/10.5194/amt-16-997-2023, 2023
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Aeolus is the first spaceborne wind lidar providing global wind measurements since 2018. This study offers a comprehensive analysis of Aeolus instrument performance, using ground-based wind lidars and meteorological radiosondes, at tropical and mid-latitudes sites. The analysis allows assessing the long-term evolution of the satellite's performance for more than 3 years. The results will help further elaborate the understanding of the error sources and the behavior of the Doppler wind lidar.
Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne
Atmos. Meas. Tech., 16, 911–940, https://doi.org/10.5194/amt-16-911-2023, https://doi.org/10.5194/amt-16-911-2023, 2023
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Better understanding and modeling snowfall properties and processes is relevant to many fields, ranging from weather forecasting to aircraft safety. Meteorological radars can be used to gain insights into the microphysics of snowfall. In this work, we propose a new method to retrieve snowfall properties from measurements of radars with different frequencies. It relies on an original deep-learning framework, which incorporates knowledge of the underlying physics, i.e., electromagnetic scattering.
Chia-Lun Tsai, Kwonil Kim, Yu-Chieng Liou, and GyuWon Lee
Atmos. Meas. Tech., 16, 845–869, https://doi.org/10.5194/amt-16-845-2023, https://doi.org/10.5194/amt-16-845-2023, 2023
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Since the winds in clear-air conditions usually play an important role in the initiation of various weather systems and phenomena, the modified Wind Synthesis System using Doppler Measurements (WISSDOM) synthesis scheme was developed to derive high-quality and high-spatial-resolution 3D winds under clear-air conditions. The performance and accuracy of derived 3D winds from this modified scheme were evaluated with an extreme strong wind event over complex terrain in Pyeongchang, South Korea.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
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Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Michael Frech, Cornelius Hald, Maximilian Schaper, Bertram Lange, and Benjamin Rohrdantz
Atmos. Meas. Tech., 16, 295–309, https://doi.org/10.5194/amt-16-295-2023, https://doi.org/10.5194/amt-16-295-2023, 2023
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Weather radar data are the backbone of a lot of meteorological products. In order to obtain a better low-level coverage with radar data, additional systems have to be included. The frequency range in which radars are allowed to operate is limited. A potential radar-to-radar interference has to be avoided. The paper derives guidelines on how additional radars can be included into a C-band weather radar network and how interferences can be avoided.
Yeeun Lee, Myoung-Hwan Ahn, Mina Kang, and Mijin Eo
Atmos. Meas. Tech., 16, 153–168, https://doi.org/10.5194/amt-16-153-2023, https://doi.org/10.5194/amt-16-153-2023, 2023
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This study aims to verify that a partly defective hyperspectral measurement can be successfully reproduced with concise machine learning models coupled with principal component analysis. Evaluation of the approach is performed with radiances and retrieval results of ozone and cloud properties. Considering that GEMS is the first geostationary UV–VIS hyperspectral spectrometer, we expect our findings can be introduced further to similar geostationary environmental instruments to be launched soon.
Mathias Gergely, Maximilian Schaper, Matthias Toussaint, and Michael Frech
Atmos. Meas. Tech., 15, 7315–7335, https://doi.org/10.5194/amt-15-7315-2022, https://doi.org/10.5194/amt-15-7315-2022, 2022
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This study presents the new vertically pointing birdbath scan of the German C-band radar network, which provides high-resolution profiles of precipitating clouds above all DWD weather radars since the spring of 2021. Our AI-based postprocessing method for filtering and analyzing the recorded radar data offers a unique quantitative view into a wide range of precipitation events from snowfall over stratiform rain to intense frontal showers and will be used to complement DWD's operational services.
Kenneth A. Brown and Thomas G. Herges
Atmos. Meas. Tech., 15, 7211–7234, https://doi.org/10.5194/amt-15-7211-2022, https://doi.org/10.5194/amt-15-7211-2022, 2022
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The character of the airflow around and within wind farms has a significant impact on the energy output and longevity of the wind turbines in the farm. For both research and control purposes, accurate measurements of the wind speed are required, and these are often accomplished with remote sensing devices. This article pertains to a field experiment of a lidar mounted to a wind turbine and demonstrates three data post-processing techniques with efficacy at extracting useful airflow information.
Xavier Calbet, Cintia Carbajal Henken, Sergio DeSouza-Machado, Bomin Sun, and Tony Reale
Atmos. Meas. Tech., 15, 7105–7118, https://doi.org/10.5194/amt-15-7105-2022, https://doi.org/10.5194/amt-15-7105-2022, 2022
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Water vapor concentration in the atmosphere at small scales (< 6 km) is considered. The measurements show Gaussian random field behavior following Kolmogorov's theory of turbulence two-thirds law. These properties can be useful when estimating the water vapor variability within a given observed satellite scene or when different water vapor measurements have to be merged consistently.
Qiuyu Chen, Konstantin Ntokas, Björn Linder, Lukas Krasauskas, Manfred Ern, Peter Preusse, Jörn Ungermann, Erich Becker, Martin Kaufmann, and Martin Riese
Atmos. Meas. Tech., 15, 7071–7103, https://doi.org/10.5194/amt-15-7071-2022, https://doi.org/10.5194/amt-15-7071-2022, 2022
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Observations of phase speed and direction spectra as well as zonal mean net gravity wave momentum flux are required to understand how gravity waves reach the mesosphere–lower thermosphere and how they there interact with background flow. To this end we propose flying two CubeSats, each deploying a spatial heterodyne spectrometer for limb observation of the airglow. End-to-end simulations demonstrate that individual gravity waves are retrieved faithfully for the expected instrument performance.
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
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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.
Maximilian Schaper, Michael Frech, David Michaelis, Cornelius Hald, and Benjamin Rohrdantz
Atmos. Meas. Tech., 15, 6625–6642, https://doi.org/10.5194/amt-15-6625-2022, https://doi.org/10.5194/amt-15-6625-2022, 2022
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C-band weather radar data are commonly compromised by radio frequency interference (RFI) from external sources. It is not possible to separate a superimposed interference signal from the radar data. Therefore, the best course of action is to shut down RFI sources as quickly as possible. An automated RFI detection algorithm has been developed. Since its implementation, persistent RFI sources are eliminated much more quickly, while the number of short-lived RFI sources keeps steadily increasing.
Oliver Lux, Benjamin Witschas, Alexander Geiß, Christian Lemmerz, Fabian Weiler, Uwe Marksteiner, Stephan Rahm, Andreas Schäfler, and Oliver Reitebuch
Atmos. Meas. Tech., 15, 6467–6488, https://doi.org/10.5194/amt-15-6467-2022, https://doi.org/10.5194/amt-15-6467-2022, 2022
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We discuss the influence of different quality control schemes on the results of Aeolus wind product validation and present statistical tools for ensuring consistency and comparability among diverse validation studies with regard to the specific error characteristics of the Rayleigh-clear and Mie-cloudy winds. The developed methods are applied for the validation of Aeolus winds against an ECMWF model background and airborne wind lidar data from the Joint Aeolus Tropical Atlantic Campaign.
Alex T. Chartier, Thomas R. Hanley, and Daniel J. Emmons
Atmos. Meas. Tech., 15, 6387–6393, https://doi.org/10.5194/amt-15-6387-2022, https://doi.org/10.5194/amt-15-6387-2022, 2022
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This is a study of anomalous long-distance (>1000 km) radio propagation that was identified in United States Coast Guard monitors of automatic identification system (AIS) shipping transmissions at 162 MHz. Our results indicate this long-distance propagation is caused by dense sporadic E layers in the daytime ionosphere, which were observed by nearby ionosondes at the same time. This finding is surprising because it indicates these sporadic E layers may be far more dense than previously thought.
Johannes K. Nielsen, Hans Gleisner, Stig Syndergaard, and Kent B. Lauritsen
Atmos. Meas. Tech., 15, 6243–6256, https://doi.org/10.5194/amt-15-6243-2022, https://doi.org/10.5194/amt-15-6243-2022, 2022
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This paper provides a new way to estimate uncertainties and error correlations. The method is a generalization of a known method called the
three-cornered hat: Instead of calculating uncertainties from assumed knowledge about the observation method, uncertainties and error correlations are estimated statistically from tree independent observation series, measuring the same variable. The results are useful for future estimation of atmospheric-specific humidity from the bending of radio waves.
Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell
Atmos. Meas. Tech., 15, 6035–6050, https://doi.org/10.5194/amt-15-6035-2022, https://doi.org/10.5194/amt-15-6035-2022, 2022
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Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.
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
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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.
Gunter Stober, Alan Liu, Alexander Kozlovsky, Zishun Qiao, Ales Kuchar, Christoph Jacobi, Chris Meek, Diego Janches, Guiping Liu, Masaki Tsutsumi, Njål Gulbrandsen, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, and Nicholas Mitchell
Atmos. Meas. Tech., 15, 5769–5792, https://doi.org/10.5194/amt-15-5769-2022, https://doi.org/10.5194/amt-15-5769-2022, 2022
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Precise and accurate measurements of vertical winds at the mesosphere and lower thermosphere are rare. Although meteor radars have been used for decades to observe horizontal winds, their ability to derive reliable vertical wind measurements was always questioned. In this article, we provide mathematical concepts to retrieve mathematically and physically consistent solutions, which are compared to the state-of-the-art non-hydrostatic model UA-ICON.
Benjamin Schumacher, Marwan Katurji, Jiawei Zhang, Peyman Zawar-Reza, Benjamin Adams, and Matthias Zeeman
Atmos. Meas. Tech., 15, 5681–5700, https://doi.org/10.5194/amt-15-5681-2022, https://doi.org/10.5194/amt-15-5681-2022, 2022
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This investigation presents adaptive thermal image velocimetry (A-TIV), a newly developed algorithm to spatially measure near-surface atmospheric velocities using an infrared camera mounted on uncrewed aerial vehicles. A validation and accuracy assessment of the retrieved velocity fields shows the successful application of the algorithm over short-cut grass and turf surfaces in dry conditions. This provides new opportunities for atmospheric scientists to study surface–atmosphere interactions.
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, and Luke R. Allen
Atmos. Meas. Tech., 15, 5515–5525, https://doi.org/10.5194/amt-15-5515-2022, https://doi.org/10.5194/amt-15-5515-2022, 2022
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Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.
Willem J. Marais and Matthew Hayman
Atmos. Meas. Tech., 15, 5159–5180, https://doi.org/10.5194/amt-15-5159-2022, https://doi.org/10.5194/amt-15-5159-2022, 2022
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For atmospheric science and weather prediction, it is important to make water vapor measurements in real time. A low-cost lidar instrument has been developed by Montana State University and the National Center for Atmospheric Research. We developed an advanced signal-processing method to extend the scientific capability of the lidar instrument. With the new method we show that the maximum altitude at which the MPD can make water vapor measurements can be extended up to 8 km.
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
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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.
Christos Gatidis, Marc Schleiss, and Christine Unal
Atmos. Meas. Tech., 15, 4951–4969, https://doi.org/10.5194/amt-15-4951-2022, https://doi.org/10.5194/amt-15-4951-2022, 2022
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Knowledge of the raindrop size distribution (DSD) is crucial for understanding rainfall microphysics and quantifying uncertainty in quantitative precipitation estimates. In this study a general overview of the DSD retrieval approach from a polarimetric radar is discussed, highlighting sensitivity to potential sources of errors, either directly linked to the radar measurements or indirectly through the critical modeling assumptions behind the method such as the shape–size (μ–Λ) relationship.
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(last access: 2 January 2018), 2013. a
Short summary
The performance of the lidar measurements in comparison with radio soundings generally shows small RMSD (bias) for wind speed of around 1 m s−1 (0.1 m s−1) and for a wind direction of around 10° (1°). The post-processing of the non-motion-stabilized data shows comparably high quality to studies with motion stabilized systems. Ship-based doppler lidar measurements can contribute to filling the data gap over oceans, particularly in polar regions.
The performance of the lidar measurements in comparison with radio soundings generally shows...
