Articles | Volume 15, issue 9
https://doi.org/10.5194/amt-15-2719-2022
© Author(s) 2022. 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-15-2719-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2022
Research article |
| 06 May 2022
| 06 May 2022
Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty
Katherine E. Lukens
CORRESPONDING AUTHOR
NOAA/NESDIS/Center for Satellite Applications and Research (STAR), College Park, Maryland 20740, USA
Cooperative Institute for Satellite Earth System Studies (CISESS), University of Maryland, College Park, Maryland 20740, USA
College of Computer, Mathematical, and Natural Sciences (CMNS), University of Maryland, College Park, Maryland 20740, USA
Kevin Garrett
NOAA/NESDIS/Center for Satellite Applications and Research (STAR), College Park, Maryland 20740, USA
NOAA/NESDIS/Center for Satellite Applications and Research (STAR), College Park, Maryland 20740, USA
Cooperative Institute for Satellite Earth System Studies (CISESS), University of Maryland, College Park, Maryland 20740, USA
David Santek
Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
Brett Hoover
Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
Ross N. Hoffman
NOAA/NESDIS/Center for Satellite Applications and Research (STAR), College Park, Maryland 20740, USA
Cooperative Institute for Satellite Earth System Studies (CISESS), University of Maryland, College Park, Maryland 20740, USA
Related authors
Hui Liu, Kevin Garrett, Kayo Ide, Ross N. Hoffman, and Katherine E. Lukens
Atmos. Meas. Tech., 15, 3925–3940, https://doi.org/10.5194/amt-15-3925-2022, https://doi.org/10.5194/amt-15-3925-2022, 2022
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A total least squares (TLS) regression is used to optimally estimate linear speed-dependent biases between Aeolus Level-2B winds and short-term (6 h) forecasts of NOAA’s FV3GFS. The winds for 1–7 September 2019 are examined. Clear speed-dependent biases for both Mie and Rayleigh winds are found, particularly in the tropics and Southern Hemisphere. Use of the TLS correction improves the forecast of the 26–28 November 2019 winter storm over the USA.
Hui Liu, Kevin Garrett, Kayo Ide, Ross N. Hoffman, and Katherine E. Lukens
Atmos. Meas. Tech., 15, 3925–3940, https://doi.org/10.5194/amt-15-3925-2022, https://doi.org/10.5194/amt-15-3925-2022, 2022
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A total least squares (TLS) regression is used to optimally estimate linear speed-dependent biases between Aeolus Level-2B winds and short-term (6 h) forecasts of NOAA’s FV3GFS. The winds for 1–7 September 2019 are examined. Clear speed-dependent biases for both Mie and Rayleigh winds are found, particularly in the tropics and Southern Hemisphere. Use of the TLS correction improves the forecast of the 26–28 November 2019 winter storm over the USA.
Erin Lynch, Daniel Kaufman, A. Surjalal Sharma, Eugenia Kalnay, and Kayo Ide
Nonlin. Processes Geophys., 23, 137–141, https://doi.org/10.5194/npg-23-137-2016, https://doi.org/10.5194/npg-23-137-2016, 2016
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In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the nearest-neighbor bred vectors are shown to be similar to bred vectors computed using evolution equations, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.
S. G. Penny, E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, and G. A. Chepurin
Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013, https://doi.org/10.5194/npg-20-1031-2013, 2013
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
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Evaluation of tropospheric water vapour and temperature profiles retrieved from Metop-A by the Infrared and Microwave Sounding scheme
Turbulence parameters measured by the Beijing mesosphere–stratosphere–troposphere radar in the troposphere and lower stratosphere with three models: comparison and analyses
Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data
Behavior and mechanisms of Doppler wind lidar error in varying stability regimes
Inter-comparison of atmospheric boundary layer (ABL) height estimates from different profiling sensors and models in the framework of HyMeX-SOP1
Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia
Comparison of global UV spectral irradiance measurements between a BTS CCD-array and a Brewer spectroradiometer
Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation
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Rainfall retrieval algorithm for commercial microwave links: stochastic calibration
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Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan
Monitoring the Tropospheric Monitoring Instrument (TROPOMI) short-wave infrared (SWIR) module instrument stability using desert sites
Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome
Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system
Validation of Aeolus winds using ground-based radars in Antarctica and in northern Sweden
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Sensitivity of Aeolus HLOS winds to temperature and pressure specification in the L2B processor
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Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica
Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia
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Validation of the TROPOspheric Monitoring Instrument (TROPOMI) surface UV radiation product
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Validation of Aeolus wind products above the Atlantic Ocean
Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy
Inter-calibration of nine UV sensing instruments over Antarctica and Greenland since 1980
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Validating HY-2A CMR precipitable water vapor using ground-based and shipborne GNSS observations
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Benjamin Witschas, Christian Lemmerz, Alexander Geiß, Oliver Lux, Uwe Marksteiner, Stephan Rahm, Oliver Reitebuch, Andreas Schäfler, and Fabian Weiler
Atmos. Meas. Tech., 15, 7049–7070, https://doi.org/10.5194/amt-15-7049-2022, https://doi.org/10.5194/amt-15-7049-2022, 2022
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In August 2018, the first wind lidar Aeolus was launched into space and has since then been providing data of the global wind field. The primary goal of Aeolus was the improvement of numerical weather prediction. To verify the quality of Aeolus wind data, DLR performed four airborne validation campaigns with two wind lidar systems. In this paper, we report on results from the two later campaigns, performed in Iceland and the tropics.
Olivier Bock, Pierre Bosser, and Carl Mears
Atmos. Meas. Tech., 15, 5643–5665, https://doi.org/10.5194/amt-15-5643-2022, https://doi.org/10.5194/amt-15-5643-2022, 2022
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Integrated water vapour measurements are often compared for the calibration and validation of instruments or techniques. Measurements made at different altitudes must be corrected to account for the vertical variation of water vapour. This paper shows that the widely used empirical correction model has severe limitations that are overcome using the proposed model. The method is applied to the inter-comparison of GPS and satellite microwave radiometer data in a tropical mountainous area.
Anthony J. Mannucci, Chi O. Ao, Byron A. Iijima, Thomas K. Meehan, Panagiotis Vergados, E. Robert Kursinski, and William S. Schreiner
Atmos. Meas. Tech., 15, 4971–4987, https://doi.org/10.5194/amt-15-4971-2022, https://doi.org/10.5194/amt-15-4971-2022, 2022
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The Global Positioning System (GPS) radio occultation (RO) technique is a satellite-based method for producing highly accurate vertical profiles of atmospheric temperature and pressure. RO profiles are used to monitor global climate trends, particularly in that region of the atmosphere that includes the lower stratosphere. Two data sets spanning 1995–1997 that were produced from the first RO satellite are highly accurate and can be used to assess global atmospheric models.
Tim Trent, Richard Siddens, Brian Kerridge, Marc Schroeder, Noëlle A. Scott, and John Remedios
EGUsphere, https://doi.org/10.5194/egusphere-2022-757, https://doi.org/10.5194/egusphere-2022-757, 2022
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Modern weather satellites provide essential information on our lower atmosphere's moisture content and temperature. This measurement record will span over 40 years, making it a valuable resource for climate studies. This study characterises atmospheric temperature and humidity profiles from a European Space Agency climate project. Using weather balloon measurements, we demonstrated the performance of this dataset was within the tolerances required for future climate studies.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
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Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
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The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Donato Summa, Fabio Madonna, Noemi Franco, Benedetto De Rosa, and Paolo Di Girolamo
Atmos. Meas. Tech., 15, 4153–4170, https://doi.org/10.5194/amt-15-4153-2022, https://doi.org/10.5194/amt-15-4153-2022, 2022
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The evolution of the atmospheric boundary layer height (ABLH) has an important impact on meteorology. However, the complexity of the phenomena occurring within the ABL and the influence of advection and local accumulation processes often prevent an unambiguous determination of the ABLH. The paper reports results from an inter-comparison effort involving different sensors and techniques to measure the ABLH. Correlations between the ABLH and other atmospheric variables are also assessed.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Carmen González, José M. Vilaplana, José A. Bogeat, and Antonio Serrano
Atmos. Meas. Tech., 15, 4125–4133, https://doi.org/10.5194/amt-15-4125-2022, https://doi.org/10.5194/amt-15-4125-2022, 2022
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Monitoring ultraviolet (UV) radiation is important since it can have harmful effects on the biosphere. Array spectroradiometers are increasingly used to measure UV as they are more versatile than scanning spectroradiometers. In this study, the long-term performance of the BTS-2048-UV-S-WP array spectroradiometer was assessed. The results show that the BTS can reliably measure both the UV index and UV radiation in the 300–360 nm range. Moreover, the BTS was stable and showed no seasonal behavior.
Charlotte Rahlves, Frank Beyrich, and Siegfried Raasch
Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, https://doi.org/10.5194/amt-15-2839-2022, 2022
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Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
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This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
Wagner Wolff, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 15, 485–502, https://doi.org/10.5194/amt-15-485-2022, https://doi.org/10.5194/amt-15-485-2022, 2022
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The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. Rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cell phone towers. This processing is a source of many uncertainties about input data, algorithm structures, parameters, CML network, and local climate. Application of a stochastic optimization method leads to improved CML rainfall estimates.
Songhua Wu, Kangwen Sun, Guangyao Dai, Xiaoye Wang, Xiaoying Liu, Bingyi Liu, Xiaoquan Song, Oliver Reitebuch, Rongzhong Li, Jiaping Yin, and Xitao Wang
Atmos. Meas. Tech., 15, 131–148, https://doi.org/10.5194/amt-15-131-2022, https://doi.org/10.5194/amt-15-131-2022, 2022
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During the VAL-OUC campaign, we established a coherent Doppler lidar (CDL) network over China to verify the Level 2B (L2B) products from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations, the L2B products from Aeolus are compared with those from CDLs. To our knowledge, the VAL-OUC campaign is the most extensive so far between CDLs and Aeolus in the lower troposphere for different atmospheric scenes. The vertical velocity impact on the HLOS retrieval from Aeolus is evaluated.
Karina Wilgan, Galina Dick, Florian Zus, and Jens Wickert
Atmos. Meas. Tech., 15, 21–39, https://doi.org/10.5194/amt-15-21-2022, https://doi.org/10.5194/amt-15-21-2022, 2022
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The assimilation of GNSS data in weather models has a positive impact on the forecasts. The impact is still limited due to using only the GPS zenith direction parameters. We calculate and validate more advanced tropospheric products from three satellite systems: the US American GPS, Russian GLONASS and European Galileo. The quality of all the solutions is comparable; however, combining more GNSS systems enhances the observations' geometry and improves the quality of the weather forecasts.
Hironori Iwai, Makoto Aoki, Mitsuru Oshiro, and Shoken Ishii
Atmos. Meas. Tech., 14, 7255–7275, https://doi.org/10.5194/amt-14-7255-2021, https://doi.org/10.5194/amt-14-7255-2021, 2021
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The first space-based Doppler wind lidar on board the Aeolus satellite was launched on 22 August 2018 to obtain global horizontal wind profiles. In this study, wind profilers, ground-based coherent Doppler wind lidars, and GPS radiosondes were used to validate the quality of Aeolus Level 2B wind products over Japan during three different periods. The results show that Aeolus can measure the horizontal winds over Japan accurately.
Tim A. van Kempen, Filippo Oggionni, and Richard M. van Hees
Atmos. Meas. Tech., 14, 6711–6722, https://doi.org/10.5194/amt-14-6711-2021, https://doi.org/10.5194/amt-14-6711-2021, 2021
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Validation of the instrument stability of the TROPOMI-SWIR module is done by monitoring a group of very stable and remote locations in the Saharan and Arabian deserts. These results confirm the excellent stability and lack of degradation of the TROPOMI-SWIR module derived from the internal calibration sources. The method was done for the first time on a spectrometer in the short-wave infrared and ensures TROPOMI-SWIR can be used for atmospheric research for years to come.
Susanna Hagelin, Roohollah Azad, Magnus Lindskog, Harald Schyberg, and Heiner Körnich
Atmos. Meas. Tech., 14, 5925–5938, https://doi.org/10.5194/amt-14-5925-2021, https://doi.org/10.5194/amt-14-5925-2021, 2021
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In this paper we study the impact of using wind observations from the Aeolus satellite, which provides wind speed profiles globally, in our numerical weather prediction system using a regional model covering the Nordic countries. The wind speed profiles from Aeolus are assimilated by the model, and we see that they have an impact on both the model analysis and forecast, though given the relatively few observations available the impact is often small.
Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min
Atmos. Meas. Tech., 14, 5735–5756, https://doi.org/10.5194/amt-14-5735-2021, https://doi.org/10.5194/amt-14-5735-2021, 2021
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Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.
Evgenia Belova, Sheila Kirkwood, Peter Voelger, Sourav Chatterjee, Karathazhiyath Satheesan, Susanna Hagelin, Magnus Lindskog, and Heiner Körnich
Atmos. Meas. Tech., 14, 5415–5428, https://doi.org/10.5194/amt-14-5415-2021, https://doi.org/10.5194/amt-14-5415-2021, 2021
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Wind measurements from two radars (ESRAD in Arctic Sweden and MARA at the Indian Antarctic station Maitri) are compared with lidar winds from the ESA satellite Aeolus, for July–December 2019. The aim is to check if Aeolus data processing is adequate for the sunlit conditions of polar summer. Agreement is generally good with bias in Aeolus winds < 1 m/s in most circumstances. The exception is a large bias (7 m/s) when the satellite has crossed a sunlit Antarctic ice cap before passing MARA.
Ramashray Yadav, Ram Kumar Giri, and Virendra Singh
Atmos. Meas. Tech., 14, 4857–4877, https://doi.org/10.5194/amt-14-4857-2021, https://doi.org/10.5194/amt-14-4857-2021, 2021
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We performed an intercomparison of seasonal and annual studies of retrievals of integrated precipitable water vapor (IPWV) carried out by INSAT-3DR satellite-borne infrared radiometer sounding and CAMS reanalysis data with ground-based Indian GNSS data. The magnitude and sign of the bias of INSAT-3DR and CAMS with respect to GNSS IPWV differs from station to station and season to season. A statistical evaluation of the collocated data sets was done to improve day-to-day weather forecasting.
Matic Šavli, Vivien Pourret, Christophe Payan, and Jean-François Mahfouf
Atmos. Meas. Tech., 14, 4721–4736, https://doi.org/10.5194/amt-14-4721-2021, https://doi.org/10.5194/amt-14-4721-2021, 2021
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The ESA's Aeolus satellite wind retrieval is provided through a series of processors. It depends on the temperature and pressure specification, which, however, are not measured by the satellite. The numerical weather predicted values are used instead, but these are erroneous. This article studies the sensitivity of the wind retrieval by introducing errors in temperature and pressure. This has been found to be small for Aeolus but is expected to be more crucial for future missions.
Kristopher M. Bedka, Amin R. Nehrir, Michael Kavaya, Rory Barton-Grimley, Mark Beaubien, Brian Carroll, James Collins, John Cooney, G. David Emmitt, Steven Greco, Susan Kooi, Tsengdar Lee, Zhaoyan Liu, Sharon Rodier, and Gail Skofronick-Jackson
Atmos. Meas. Tech., 14, 4305–4334, https://doi.org/10.5194/amt-14-4305-2021, https://doi.org/10.5194/amt-14-4305-2021, 2021
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This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar Observatory (HALO) measurement capabilities across a range of atmospheric conditions, compares DAWN and HALO measurements with Aeolus satellite Doppler wind lidar to gain an initial perspective of Aeolus performance, and discusses how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, water vapour, and aerosol profile observations.
Emranul Sarkar, Alexander Kozlovsky, Thomas Ulich, Ilkka Virtanen, Mark Lester, and Bernd Kaifler
Atmos. Meas. Tech., 14, 4157–4169, https://doi.org/10.5194/amt-14-4157-2021, https://doi.org/10.5194/amt-14-4157-2021, 2021
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The biasing effect in meteor radar temperature has been a pressing issue for the last 2 decades. This paper has addressed the underlying reasons for such a biasing effect on both theoretical and experimental grounds. An improved statistical method has been developed which allows atmospheric temperatures at around 90 km to be measured with meteor radar in an independent way such that any subsequent bias correction or calibration is no longer required.
Wei Zhong, Xianghui Xue, Wen Yi, Iain M. Reid, Tingdi Chen, and Xiankang Dou
Atmos. Meas. Tech., 14, 3973–3988, https://doi.org/10.5194/amt-14-3973-2021, https://doi.org/10.5194/amt-14-3973-2021, 2021
Evgenia Belova, Peter Voelger, Sheila Kirkwood, Susanna Hagelin, Magnus Lindskog, Heiner Körnich, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 14, 2813–2825, https://doi.org/10.5194/amt-14-2813-2021, https://doi.org/10.5194/amt-14-2813-2021, 2021
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We validate horizontal wind measurements at altitudes of 0.5–14 km made with atmospheric radars: ESRAD located near Kiruna in the Swedish Arctic and MARA at the Indian research station Maitri in Antarctica, by comparison with radiosondes, the regional model HARMONIE-AROME and the ECMWF ERA5 reanalysis. Good agreement was found in general, and radar bias and uncertainty were estimated. These radars are planned to be used for validation of winds measured by lidar by the ESA satellite Aeolus.
Gizachew Kabite Wedajo, Misgana Kebede Muleta, and Berhan Gessesse Awoke
Atmos. Meas. Tech., 14, 2299–2316, https://doi.org/10.5194/amt-14-2299-2021, https://doi.org/10.5194/amt-14-2299-2021, 2021
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Satellite rainfall estimates (SREs) are alternative data sources for data-scarce basins. However, the accuracy of the products is plagued by multiple sources of errors. Therefore, SREs should be evaluated for particular basins before being used for other applications. The results of the study showed that CHIRPS2 and IMERG6 estimated rainfall and predicted hydrologic simulations well for Dhidhessa River Basin, which shows remote sensing technology could improve hydrologic studies.
Steven Knoop, Fred C. Bosveld, Marijn J. de Haij, and Arnoud Apituley
Atmos. Meas. Tech., 14, 2219–2235, https://doi.org/10.5194/amt-14-2219-2021, https://doi.org/10.5194/amt-14-2219-2021, 2021
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Doppler wind lidars are laser-based remote sensing instruments that measure the wind up to a few hundred metres or even a few kilometres. Their data can improve weather models and help forecasters. To investigate their accuracy and required meteorological conditions, we have carried out a 2-year measurement campaign of a wind lidar at our Cabauw test site and made a comparison with cup anemometers and wind vanes at several levels in a 213 m tall meteorological mast.
Joaquim V. Teixeira, Hai Nguyen, Derek J. Posselt, Hui Su, and Longtao Wu
Atmos. Meas. Tech., 14, 1941–1957, https://doi.org/10.5194/amt-14-1941-2021, https://doi.org/10.5194/amt-14-1941-2021, 2021
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Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite observations. Accurately characterizing the uncertainties in AMVs is essential in assimilating them into data assimilation models. We develop a machine-learning-based approach for error characterization which involves Gaussian mixture model clustering and random forest using a simulation dataset of water vapor, AMVs, and true winds. We show that our method improves on existing AMV error characterizations.
Giovanni Martucci, Francisco Navas-Guzmán, Ludovic Renaud, Gonzague Romanens, S. Mahagammulla Gamage, Maxime Hervo, Pierre Jeannet, and Alexander Haefele
Atmos. Meas. Tech., 14, 1333–1353, https://doi.org/10.5194/amt-14-1333-2021, https://doi.org/10.5194/amt-14-1333-2021, 2021
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This article presents a validation of 1.5 years of pure rotational temperature data measured by the Raman lidar RALMO installed at the MeteoSwiss station of Payerne. The statistical results are in terms of bias and standard deviation with respect to two well-established radiosounding systems. The statistics are divided into daytime (bias = 0.28 K, SD = 0.62±0.03 K) and nighttime (bias = 0.29 K, SD = 0.66±0.06 K). The lidar temperature profiles are applied to cloud supersaturation studies.
Anders Tegtmeier Pedersen and Michael Courtney
Atmos. Meas. Tech., 14, 889–903, https://doi.org/10.5194/amt-14-889-2021, https://doi.org/10.5194/amt-14-889-2021, 2021
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This paper suggests and describes a method for calibrating wind lidars using a rotating flywheel. An uncertainty analysis shows that a standard uncertainty of 0.1 % can be achieved, with the main contributor being the width of the laser beam which is in agreement with experimental results. The method can potentially lower the calibration uncertainty of wind lidars, which today is often based on cup anemometers, and thus lead to better wind assessments and perhaps more widespread use.
Kaisa Lakkala, Jukka Kujanpää, Colette Brogniez, Nicolas Henriot, Antti Arola, Margit Aun, Frédérique Auriol, Alkiviadis F. Bais, Germar Bernhard, Veerle De Bock, Maxime Catalfamo, Christine Deroo, Henri Diémoz, Luca Egli, Jean-Baptiste Forestier, Ilias Fountoulakis, Katerina Garane, Rosa Delia Garcia, Julian Gröbner, Seppo Hassinen, Anu Heikkilä, Stuart Henderson, Gregor Hülsen, Bjørn Johnsen, Niilo Kalakoski, Angelos Karanikolas, Tomi Karppinen, Kevin Lamy, Sergio F. León-Luis, Anders V. Lindfors, Jean-Marc Metzger, Fanny Minvielle, Harel B. Muskatel, Thierry Portafaix, Alberto Redondas, Ricardo Sanchez, Anna Maria Siani, Tove Svendby, and Johanna Tamminen
Atmos. Meas. Tech., 13, 6999–7024, https://doi.org/10.5194/amt-13-6999-2020, https://doi.org/10.5194/amt-13-6999-2020, 2020
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The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite was launched on 13 October 2017 to provide the atmospheric composition for atmosphere and climate research. Ground-based data from 25 sites located in Arctic, subarctic, temperate, equatorial and Antarctic
areas were used for the validation of the TROPOMI surface ultraviolet (UV) radiation product. For most sites 60 %–80 % of TROPOMI data was within ± 20 % of ground-based data.
Pauline Martinet, Domenico Cimini, Frédéric Burnet, Benjamin Ménétrier, Yann Michel, and Vinciane Unger
Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, https://doi.org/10.5194/amt-13-6593-2020, 2020
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Each year large human and economical losses are due to fog episodes. However, fog forecasts remain quite inaccurate, partly due to a lack of observations in the atmospheric boundary layer. The benefit of ground-based microwave radiometers has been investigated and has demonstrated their capability of significantly improving the initial state of temperature and liquid water content profiles in current numerical weather prediction models, paving the way for improved fog forecasts in the future.
Holger Baars, Alina Herzog, Birgit Heese, Kevin Ohneiser, Karsten Hanbuch, Julian Hofer, Zhenping Yin, Ronny Engelmann, and Ulla Wandinger
Atmos. Meas. Tech., 13, 6007–6024, https://doi.org/10.5194/amt-13-6007-2020, https://doi.org/10.5194/amt-13-6007-2020, 2020
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A first validation for the European satellite Aeolus is presented. Aeolus is the first satellite that can actively measure horizontal wind profiles from space.
Radiosonde launches on board the German research vessel Polarstern have been utilized to validate Aeolus observations over the Atlantic Ocean, a region where almost no other reference measurements are available. It is shown that Aeolus is able to measure accurately atmospheric winds and thus may significantly improve weather forecasts.
Giacomo Roversi, Pier Paolo Alberoni, Anna Fornasiero, and Federico Porcù
Atmos. Meas. Tech., 13, 5779–5797, https://doi.org/10.5194/amt-13-5779-2020, https://doi.org/10.5194/amt-13-5779-2020, 2020
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The microwave signal travelling between two antennas of the commercial mobile backhaul network is strongly attenuated by rainfall. The open-source RAINLINK algorithm extracts rainfall rate maps, processing the attenuation data recorded by the transmission system. In this work, we applied RAINLINK to 357 Vodafone links in northern Italy and compared the outputs with the operational rain products of the local weather service (Arpae), outlining pros and cons and discussing error structure.
Clark J. Weaver, Pawan K. Bhartia, Dong L. Wu, Gordon J. Labow, and David E. Haffner
Atmos. Meas. Tech., 13, 5715–5723, https://doi.org/10.5194/amt-13-5715-2020, https://doi.org/10.5194/amt-13-5715-2020, 2020
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Currently, we do not know whether clouds will accelerate or moderate climate. We look to the past and ask whether cloudiness has changed over the last 4 decades. Using a suite of nine satellite instruments, we need to ensure that the first satellite, which was launched in 1980 and died in 1991, observed the same measurement as the eight other satellite instruments used in the record. If the instruments were measuring length and observing a 1.00 m long stick, they would all see 0.99 to 1.01 m.
Samuel Favrichon, Carlos Jimenez, and Catherine Prigent
Atmos. Meas. Tech., 13, 5481–5490, https://doi.org/10.5194/amt-13-5481-2020, https://doi.org/10.5194/amt-13-5481-2020, 2020
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Long-term monitoring of satellite-derived variables is necessary for a better understanding of the evolution of Earth parameters at global scale. However different instruments' observations used over the years need to be inter-calibrated with each other to provide meaningful information. This paper describes how a linear correction can improve the observations from the Scanning Multichannel Microwave Radiometer over continental surfaces to be more consistent with more recent radiometers.
Zhilu Wu, Yanxiong Liu, Yang Liu, Jungang Wang, Xiufeng He, Wenxue Xu, Maorong Ge, and Harald Schuh
Atmos. Meas. Tech., 13, 4963–4972, https://doi.org/10.5194/amt-13-4963-2020, https://doi.org/10.5194/amt-13-4963-2020, 2020
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The HY-2A calibration microwave radiometer (CMR) water vapor product is validated using ground-based GNSS observations along the coastline and shipborne GNSS observations over the Indian Ocean. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV, with 2.67 mm in rms within 100 km and an RMS of 1.57 mm with shipborne GNSS for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well.
Viswanathan Bringi, Kumar Vijay Mishra, Merhala Thurai, Patrick C. Kennedy, and Timothy H. Raupach
Atmos. Meas. Tech., 13, 4727–4750, https://doi.org/10.5194/amt-13-4727-2020, https://doi.org/10.5194/amt-13-4727-2020, 2020
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The raindrop size distribution and its moments are fundamental in many areas, such as radar measurement of rainfall using polarimetry and numerical modeling of the microphysical processes of rain formation and evolution. We develop a technique which uses advanced radar measurements and complete drop size distributions using two collocated instruments to retrieve the lower-order moments such as total drop concentration and rain water content. We demonstrate a proof-of-concept using a case study.
Allan C. Just, Yang Liu, Meytar Sorek-Hamer, Johnathan Rush, Michael Dorman, Robert Chatfield, Yujie Wang, Alexei Lyapustin, and Itai Kloog
Atmos. Meas. Tech., 13, 4669–4681, https://doi.org/10.5194/amt-13-4669-2020, https://doi.org/10.5194/amt-13-4669-2020, 2020
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A flexible machine-learning model was fit to explain the differences between estimates of water vapor from satellites versus ground stations in Northeastern USA. We use nine variables derived from the satellite acquisition and ground characteristics to explain this measurement error. Our results showed overall good agreement, but data from the Terra satellite were drifting too high in recent summers. Our model reduces measurement error and works well in new locations in the northeast.
Hans Gleisner, Kent B. Lauritsen, Johannes K. Nielsen, and Stig Syndergaard
Atmos. Meas. Tech., 13, 3081–3098, https://doi.org/10.5194/amt-13-3081-2020, https://doi.org/10.5194/amt-13-3081-2020, 2020
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Data from GPS radio occultation (RO) instruments aboard a series of satellites have been reprocessed by the ROM SAF. We describe the monthly mean RO climate data records (CDRs) and the methods for removing sampling errors. The quality of the CDRs is evaluated, with a focus on systematic differences between satellite missions. Between 8 and 30 km, the data quality and the inter-mission differences are small enough to allow the generation of combined multi-mission data records starting in 2001.
Andrea K. Steiner, Florian Ladstädter, Chi O. Ao, Hans Gleisner, Shu-Peng Ho, Doug Hunt, Torsten Schmidt, Ulrich Foelsche, Gottfried Kirchengast, Ying-Hwa Kuo, Kent B. Lauritsen, Anthony J. Mannucci, Johannes K. Nielsen, William Schreiner, Marc Schwärz, Sergey Sokolovskiy, Stig Syndergaard, and Jens Wickert
Atmos. Meas. Tech., 13, 2547–2575, https://doi.org/10.5194/amt-13-2547-2020, https://doi.org/10.5194/amt-13-2547-2020, 2020
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High-quality observations are critically important for monitoring the Earth’s changing climate. We provide information on the consistency and long-term stability of observations from GPS radio occultation (RO). We assess, for the first time, RO records from multiple RO missions and all major RO data providers. Our results quantify where RO can be used for reliable trend assessment and confirm its climate quality.
Benjamin Witschas, Christian Lemmerz, Alexander Geiß, Oliver Lux, Uwe Marksteiner, Stephan Rahm, Oliver Reitebuch, and Fabian Weiler
Atmos. Meas. Tech., 13, 2381–2396, https://doi.org/10.5194/amt-13-2381-2020, https://doi.org/10.5194/amt-13-2381-2020, 2020
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Aeolus, the first ever wind lidar in space, has been providing wind profiles on a global scale since its launch. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (DLR) recently performed two airborne campaigns over central Europe deploying two different Doppler wind lidars. A total of 10 satellite underflights were performed and used to validate the early-stage wind data product of Aeolus by means of collocated airborne wind lidar observations.
Oliver Lux, Christian Lemmerz, Fabian Weiler, Uwe Marksteiner, Benjamin Witschas, Stephan Rahm, Alexander Geiß, and Oliver Reitebuch
Atmos. Meas. Tech., 13, 2075–2097, https://doi.org/10.5194/amt-13-2075-2020, https://doi.org/10.5194/amt-13-2075-2020, 2020
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This work reports on the first airborne validation campaign of ESA’s Earth Explorer mission Aeolus, conducted in central Europe during the commissioning phase in November 2018. After presenting the methodology used to compare the data sets from the satellite, the airborne wind lidar and the ECWMF model, the wind results from the underflights performed are analyzed and discussed, providing a first assessment of the accuracy and precision of the preliminary Aeolus wind data.
Ramon Padullés, Chi O. Ao, F. Joseph Turk, Manuel de la Torre Juárez, Byron Iijima, Kuo Nung Wang, and Estel Cardellach
Atmos. Meas. Tech., 13, 1299–1313, https://doi.org/10.5194/amt-13-1299-2020, https://doi.org/10.5194/amt-13-1299-2020, 2020
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In this study we thoroughly address the calibration and validation of the new polarimetric radio occultation (PRO) observables. These represent an innovative way to obtain vertical profiles of precipitation along with thermodynamic observations of the same scene. First we perform the on-orbit calibration of the measurement. Then, we show how the PRO observables are sensitive to the presence and intensity of rain by looking for coincident precipitation measurements from independent missions.
Freek Liefhebber, Sarah Lammens, Paul W. G. Brussee, André Bos, Viju O. John, Frank Rüthrich, Jacobus Onderwaater, Michael G. Grant, and Jörg Schulz
Atmos. Meas. Tech., 13, 1167–1179, https://doi.org/10.5194/amt-13-1167-2020, https://doi.org/10.5194/amt-13-1167-2020, 2020
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The paper addresses the need for automatic quality control of a whole series of Earth observation (EO) time series extending a period of over 40 years. Such a dataset is valuable and may provide important information about trends related to geo-physical processes. Furthermore, as the dataset is that large, there is a need to completely automate the processes, as otherwise the effort would become impracticable. The result is a system with a high probability of detection and low false alarm rate.
Felix Erdmann, Eric Defer, Olivier Caumont, Richard J. Blakeslee, Stéphane Pédeboy, and Sylvain Coquillat
Atmos. Meas. Tech., 13, 853–875, https://doi.org/10.5194/amt-13-853-2020, https://doi.org/10.5194/amt-13-853-2020, 2020
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This article compares lightning observations from an optical sensor onboard the International Space Station to two ground-based networks using different radio frequencies. The location and timing of coincident flashes agree well for the three instruments. Differences exist for the detected number of flashes and the characteristics. Small flashes in particular are not always detected by all three instruments. About half of the flashes at altitudes below 10 km are not seen by the satellite sensor.
Irene Crisologo and Maik Heistermann
Atmos. Meas. Tech., 13, 645–659, https://doi.org/10.5194/amt-13-645-2020, https://doi.org/10.5194/amt-13-645-2020, 2020
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Archives of radar observations often suffer from errors, one of which is calibration. However, it is possible to correct them after the fact by using satellite radars as a calibration reference. We propose improvements to this calibration method by considering factors that affect the data quality, such that poor quality data gets filtered out in the bias calculation by assigning weights. We also show that the bias can be interpolated in time even for days when there are no satellite data.
Zachary Fasnacht, Alexander Vasilkov, David Haffner, Wenhan Qin, Joanna Joiner, Nickolay Krotkov, Andrew M. Sayer, and Robert Spurr
Atmos. Meas. Tech., 12, 6749–6769, https://doi.org/10.5194/amt-12-6749-2019, https://doi.org/10.5194/amt-12-6749-2019, 2019
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The anisotropy of Earth's surface reflection plays an important role in satellite-based retrievals of cloud, aerosol, and trace gases. Most current ultraviolet and visible satellite retrievals utilize climatological surface reflectivity databases that do not account for surface anisotropy. The GLER concept was introduced to account for such features. Here we evaluate GLER for water surfaces by comparing with OMI measurements and show that it captures these surface anisotropy features.
Cited articles
Abdalla, S., de Kloe, J., Flament, T., Krisch, I., Marksteiner, U., Reitebuch, O., Rennie, M., Weiler, F., and Witschas, B.: Verification report of first Reprocessing campaign for FM-B covering the time period 2019-06 to 2019-12. Tech. rep., Aeolus Data Innovation Science Cluster DISC, Version 1.0, REF: AED-TN-ECMWF-GEN-040, internal document available for registered Aeolus Cal/Val teams, summary of this document available at:
https://earth.esa.int/eogateway/documents/20142/0/Aeolus-Summary-Reprocessing-1-DISC.pdf (last access: 4 January 2022), 2020.
Alekseev, G., Kuzmina, S., Bobylev, L., Urazgildeeva, A., and Gnatiuk, N.:
Impact of atmospheric heat and moisture transport on the Arctic warming,
Int. J. Climatol., 39, 3582–3592, https://doi.org/10.1002/joc.6040, 2018.
Bedka, K. M., Velden, C. S. Petersen, R. A. Feltz, W. F., and Mecikalski, J.
R.: Comparisons of Satellite-Derived Atmospheric Motion Vectors,
Rawinsondes, and NOAA Wind Profiler Observations, J. Appl. Meteorol. Clim.,
48, 1542–1561, https://doi.org/10.1175/2009JAMC1867.1, 2009.
Berger, H., Langland, R., Velden, C. S., Reynolds, C. A., and Pauley, P. M.:
Impact of enhanced satellite-derived atmospheric motion vector observations
on numerical tropical cyclone track forecasts in the western North Pacific
during TPARC/TCS-08, J. Appl. Meteorol. Clim., 50, 2309–2318,
https://doi.org/10.1175/JAMC-D-11-019.1, 2011.
Bormann, N., Kelly, G., and Thépaut, J.-N.: Characterising and
correcting speed biases in atmospheric motion vectors within the ECMWF
system, in: Sixth Int. Winds Workshop, 7–10 May 2002, Madison, WI, USA, EUMETSAT, 113–120, http://cimss.ssec.wisc.edu/iwwg/iww6/session3/bormann_1_bias.pdf (last access: 18 April 2021), 2002.
Bormann, N., Saarinen, S., Kelly, G., and Thepaut, J.-N.: The Spatial
Structure of Observation Errors in Atmospheric Motion Vectors from
Geostationary Satellite Data, Mon. Weather Rev., 131, 706–718,
https://doi.org/10.1175/1520-0493(2003)131<0706:TSSOOE>2.0.CO;2,
2003.
Boukabara, S. A., Zhu, T., Tolman, H. L., Lord, S., Goodman, S., Atlas, R.,
Goldberg, M., Auligne, T., Pierce, B., Cucurull, L., Zupanski, M., Zhang,
M., Moradi, I., Otkin, J., Santek, D., Hoover, B., Pu, Z., Zhan, X., Hain,
C., Kalnay, E., Hotta, D., Nolin, S., Bayler, E., Mehra, A., Casey, S. P.
F., Lindsey, D., Grasso, L., Kumar, V. K., Powell, A., Xu, J., Greenwald,
T., Zajic, J., Li, J., Li, J., Li, B., Liu, J., Fang, L., Wang, P., and
Chen, T.-C.: S4: An O2R/R2O infrastructure for optimizing satellite data
utilization in NOAA numerical modeling systems. a step toward bridging the
gap between research and operations, B. Am. Meteorol. Soc., 97,
2359–2378, https://doi.org/10.1175/bams-d-14-00188.1, 2016.
Chen, S., Cao, R., Xie, Y., Zhang, Y., Tan, W., Chen, H., Guo, P., and Zhao, P.: Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data, Atmos. Chem. Phys., 21, 11489–11504, https://doi.org/10.5194/acp-21-11489-2021, 2021.
Conger, K.: “Master”, “slave” and the fight over offensive terms in
computing, The New York Times, 13 April 2021, https://www.nytimes.com/2021/04/13/technology/racist-computer-engineering-terms-ietf.html (last access: 4 January 2022), 2021.
Cordoba, M., Dance, S. L., Kelly, G. A., Nichols, N. K., and Walker, J. A.:
Diagnosing atmospheric motion vector observation errors for an operational
high-resolution data assimilation system, Q. J. Roy. Meteor. Soc., 143,
333–341, https://doi.org/10.1002/qj.2925, 2017.
Cotton, J., Doherty, A., Lean, K., Forsythe, M., and Cress, A.: NWP SAF AMV
monitoring: the 9th Analysis Report (AR9), Tech. rep., NWP SAF, Version 1.0,
REF: NWPSAF-MO-TR-039, https://nwp-saf.eumetsat.int/site/monitoring/winds-quality-evaluation/amv/amv-analysis-reports/ (last access: 9 May 2021), 2020.
Cotton, J., Doherty, A., and Lean, K.: Characterising AMV errors using the NWP SAF monitoring, in: 15th IWWG Workshop, 12–16 April 2021, Virtual, https://www.ssec.wisc.edu/meetings/iwwg/2021-meeting/presentations/oral-cotton/ (last access: 3 January 2022), 2021.
Daniels, J., Bresky, W., Bailey, A., Allegrino, A., Wanzong, S., and Velden,
C.: Introducing Atmospheric Motion Vectors Derived from the GOES-16 Advanced
Baseline Imager (ABI), in: 14th International Winds Workshop, 17 June 2020, Jeju City, South Korea, CIMSS, ESA, http://cimss.ssec.wisc.edu/iwwg/iww14/talks/01_Monday/1400_IWW14_ABI_AMVs_Daniels.pdf (last access: 20 February 2021), 2018.
de Kloe, J., Stoffelen, A., Tan, D., Andersson, E., Rennie, M., Dabas, A.,
Poli, P., and Huber, D.: Aeolus Data Innovation Science Cluster DISC
ADM-Aeolus Level-2B/2C Processor Input/Output Data Definitions Interface
Control Document. Tech. rep., KMNI, Aeolus, DISC, REF: AED-SD-ECMWF-L2B-037, https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD.pdf (last access: 17 April 2021),
2020.
ESA-ESRIN: Aeolus Cal/Val and Science Workshop 2019 Summary, in: Aeolus CAL/VAL and Science Workshop 2019, 26–29 March 2019, Frascati, Italy, https://az659834.vo.msecnd.net/eventsairwesteuprod/production-nikal-public/3cb005cf00ea441d97eb5cadb5f3c78c (last access: 25 April 2022), 2019.
European Space Agency (ESA): Aeolus L2B Earth Explorer data set, ESA [data set], https://aeolus-ds.eo.esa.int/oads/access/ (last access: 25 February 2021), 2020.
Garrett, K., Liu, H., Ide, K., Hoffman, R., and Lukens, K. E.: Optimization
and Impact Assessment of Aeolus HLOS Wind Data Assimilation in NOAA's Global
Forecast System, Q. J. Roy. Meteor. Soc., in review, 2022.
Grieger, J., Leckebusch, G. C., and Ulbrich, U.: Net Precipitation of
Antarctica: Thermodynamical and Dynamical Parts of the Climate Change
Signal, J. Climate, 29, 907–924, https://doi.org/10.1175/JCLI-D-14-00787.1, 2016.
Hoffman, R. N., Lukens, K. E., Ide, K., and Garrett, K.: A Collocation Study
of Atmospheric Motion Vectors (AMVs) Compared to Aeolus Wind Profiles with a
Feature Track Correction (FTC) Observation Operator, Q. J. Roy. Meteor. Soc., 148, 321–337, https://doi.org/10.1002/qj.4207, 2022.
Hoskins, B. J. and Hodges, K. I.: A new perspectives on Southern Hemisphere
storm tracks, J. Climate, 18, 4108–4129, https://doi.org/10.1175/JCLI3570.1, 2005.
Jung, J., Le Marshall, J., Daniels, J., and Riishojgaard, L. P.:
Investigating height assignment type errors in the NCEP global forecasting
system, in: 10th International Wind Workshop, 22–26 February 2010, Tokyo, Japan, EUMETSAT P.56, https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_p56_s3_04_jung_v.pdf (last access: 22 February 2021), 2010.
Key, J., Santek, D., and Dworak, R.: Polar winds from shortwave infrared
cloud tracking, in: Proc. 13th Int. Winds Workshop, Monterey,
California, USA, 1–6, https://www.researchgate.net/profile/Jeffrey-Key-2/publication/309727571_Polar_winds_from_shortwave_infrared_cloud_tracking/links/581f84da08aea429b29907fd/Polar-winds-from-shortwave-infrared-cloud-tracking.pdf (last access: 7 September 2021), 2016.
Le Marshall, J., Jung, J., Zapotocny, T., Redder, C., Dunn, M., Daniels, J.,
and Riishojgaard, L. P.: Impact of MODIS atmospheric motion vectors on a
global NWP system, Aust. Met. Mag., 57, 45–51, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.222.6537&rep=rep1&type=pdf (last access: 9 May 2021), 2008.
Liu, B., Guo, J., Gong, W., Zhang, Y., Shi, L., Ma, Y., Li, J., Guo, X., Stoffelen, A., de Leeuw, G., and Xu, X.: Intercomparison of wind observations from ESA’s satellite mission Aeolus, ERA5 reanalysis and radiosonde over China, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-26, in review, 2022.
Liu, H., Garrett, K., Ide, K., Hoffman, R., and Lukens, K.: A Statistically Optimal Analysis of Systematic Differences between Aeolus HLOS Winds and NOAA’s Global Forecast System, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-20, in review, 2022.
Lux, O., Lemmerz, C., Weiler, F., Marksteiner, U., Witschas, B., Rahm, S., Geiß, A., and Reitebuch, O.: Intercomparison of wind observations from the European Space Agency's Aeolus satellite mission and the ALADIN Airborne Demonstrator, Atmos. Meas. Tech., 13, 2075–2097, https://doi.org/10.5194/amt-13-2075-2020, 2020.
Martin, A., Weissmann, M., Reitebuch, O., Rennie, M., Geiß, A., and Cress, A.: Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents, Atmos. Meas. Tech., 14, 2167–2183, https://doi.org/10.5194/amt-14-2167-2021, 2021.
Nakamura, H. and Shimpo, A.: Seasonal Variations in the Southern Hemisphere
Storm Tracks and Jet Streams as Revealed in a Reanalysis Dataset, J.
Climate, 17, 1828–1844, https://doi.org/10.1175/1520-0442(2004)017<1828:SVITSH>2.0.CO;2, 2004.
National Academies of Sciences, Engineering, and Medicine: Thriving on Our
Changing Planet: A Decadal Strategy for Earth Observation from Space.
National Academies Press, Washington, DC, USA, https://doi.org/10.17226/24938, 2018.
Posselt, D., Wu, L., Mueller, K., Huang, L., Irion, F. W., Brown, S., Su,
H., Santek, D., and Velden, C. S.: Quantitative Assessment of
State-Dependent Atmospheric Motion Vector Uncertainties. J. Appl. Meteorol.
Clim., 58, 2479–2495, https://doi.org/10.1175/JAMC-D-19-0166.1, 2019.
Reitebuch, O., Lemmerz, C., Nagel, E., Paffrath, U., Durand, Y., Endemann,
M., Fabre, F., and Chaloupy, M.: The Airborne Demonstrator for the
Direct-Detection Doppler Wind Lidar ALADIN on ADM-Aeolus. Part I: Instrument
Design and Comparison to Satellite Instrument, J. Atmos. Ocean. Tech., 26,
2501–2515, https://doi.org/10.1175/2009JTECHA1309.1, 2009.
Rennie, M. and Isaksen, L.: The NWP impact of Aeolus Level-2B winds at
ECMWF. Tech. rep., Aeolus Data Innovation Science Cluster DISC, Ref:
AED-TN-ECMWF-NWP-025, https://doi.org/10.21957/alift7mhr, 2020a.
Rennie, M. and Isaksen, L.: Assessment of the Impact of Aeolus Doppler Wind
Lidar Observations for Use in Numerical Weather Prediction at ECMWF, in: EGU
2020, 28 April 2020, Virtual, Copernicus, https://presentations.copernicus.org/EGU2020/EGU2020-5340_presentation.pdf (last access: 20 February 2021), 2020b.
Rennie, M., Tan, D., Andersson, E., Poli, P., Dabas, A., de Kloe, J., and
Stoffelen, A.: Aeolus Level-2B Algorithm Theoretical Basis Document:
Mathematical Description of the Aeolus Level-2B Processor, Tech. rep., ESA,
Version 3.4, Ref: AED-SD-ECMWF-L2B-038, https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-Algorithm-ATBD.pdf, last access: 20 November 2020.
Salonen, K., Cotton, J., Bormann, N., and Forsythe, M.: Characterizing AMV
height-assignment error by comparing best-fit pressure statistics from the
Met Office and ECMWF data assimilation systems, J. Appl. Meteorol. Clim.,
54, 225–242, https://doi.org/10.1175/JAMC-D-14-0025.1, 2015.
Santek, D., García-Pereda, J., Velden, C., Genkova, I., Wanzong, S.,
Stettner, D., and Mindock, M.: A new atmospheric motion vector intercomparison study, in: 12th International Winds Workshop, 16–20 June 2014, Copenhagen, Denmark, CIMSS, Technical
Report, http://www.nwcsaf.org/aemetRest/downloadAttachment/225 (last access: 18 December 2020), 2014.
Santek, D., Dworak, R., Nebuda, S., Wanzong, S., Borde, R., Genkova, I.,
García-Pereda, J., Negri, R. G., Carranza, M., Nonaka, K., Shimoji, K.,
Oh, S. M., Lee, B.-I., Chung, S.-R., Daniels, J., and Bresky, W.: 2018
Atmospheric Motion Vector (AMV) Intercomparison Study, Remote Sens., 11,
2240, https://doi.org/10.3390/rs11192240, 2019.
Santek, D., Hoover, B., Zhang, H., and Moeller, C.: Evaluation of Aeolus
Winds by Comparing to AIRS 3D Winds, Rawinsondes, and Reanalysis Grids, in:
15th International Winds Workshop, 12–16 April 2021, Virtual, CIMSS, https://www.ssec.wisc.edu/meetings/iwwg/2021-meeting/presentations/oral-santek/, last access: 9 May 2021.
Schmetz, J., Holmlund, K., Hoffman, J., Strauss, B., Mason, B., Gaertner,
V., Koch, A., and Van De Berg, L.: Operational cloud-motion winds from
Meteosat infrared images, J. Appl. Meteorol., 32, 1206–1225, https://doi.org/10.1175/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2, 1993.
Schmetz, J., Holmlund, K., Roesli, H. P., and Levizzani, V.: On the Use of
Rapid Scans, Proceedings of the Fifth International Winds Workshop, Lorne,
Australia, 28 February–3 March 2000, EUM P28, EUMETSAT, 227–234, http://cimss.ssec.wisc.edu/iwwg/iww5/S5-2_Schmetz-OnTheUse.pdf (last access: 4 January 2022), 2000.
Stoffelen, A., Pailleux, J., Källén, E., Vaughan, J. M., Isaksen,
L., Flamant, P., Wergen, W., Andersson, E., Schyberg, H., Culoma, A.,
Meynart, R., Endemann, M., and Ingmann, P.: The atmospheric dynamics mission
for global wind field measurement, B. Am. Meteorol. Soc., 86, 73–87,
https://doi.org/10.1175/BAMS-86-1-73, 2005.
Straume, A. G., Elfving, A., Wernham, D., Kanitz, T., de Bruin, F., Buscaglione, F., von Bismarck, J., and Lengert, W.: Status of
ESA's Doppler Wind Lidar Mission Aeolus, in: 14th International Winds
Workshop, 23–27 April 2018, Jeju City, South Korea, ESA, http://cimss.ssec.wisc.edu/iwwg/iww14/talks/04_Thursday/1000_IWW14_Aeolus_Straume.pdf (last access: 28 August 2019), 2018.
Straume, A. G., Rennie, M., Isaksen, L., de Kloe, J., Marseille, G.-J.,
Stoffelen, A., Flament, T., Stieglitz, H., Dabas, A., Huber, D., Reitebuch,
O., Lemmerz, C., Lux, O., Marksteiner, U., Weiler, F., Witschas, B.,
Meringer, M., Schmidt, K., Nikolaus, I., Geiss, A., Flamant, P., Kanitz, T.,
Wernham, D., von Bismarck, J., Bley, S., Fehr, T., Floberghagen, R., and
Parinello, T.: ESA's space-based Doppler wind lidar mission Aeolus – first
wind and aerosol product assessment results, EPJ Web Conferences, 237,
01007, https://doi.org/10.1051/epjconf/202023701007, 2020.
Straume, A. G., Parrinello, T., von Bismarck, J., Bley, S., Ehlers, F., and
the Aeolus teams: ESA's Wind Lidar Mission Aeolus – status and scientific
exploitation after 2.5 years in space, in: 15th International Winds Workshop, 12–16 April 2021, Virtual, ESA, https://www.ssec.wisc.edu/meetings/wp-content/uploads/sites/33/2021/02/IWW15_Presentation_AG_Straume.pdf, last access: 9 May 2021.
Straume-Lindner, A. G.: Aeolus Sensor and Product Description. Tech. rep.,
European Space Agency – European Space Research and Technology Centre, the
Netherlands, REF: AE-SU-ESA-GS-000, https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Sensor-and-Product-Description.pdf (last access: 9 May 2021), 2018.
Trenberth, K.: Storm tracks in the Southern Hemisphere, J. Atmos. Sci.,
48, 2159–2178, https://doi.org/10.1175/1520-0469(1991)048<2159:STITSH>2.0.CO;2, 1991.
Velden, C., Daniels, J., Stettner, D., Santek, D., Key, J., Dunion, J.,
Holmlund, K., Dengel, G., Bresky, W., and Menzel, P.: Recent innovations in
deriving tropospheric winds from meteorological satellites, B. Am.
Meteorol. Soc., 86, 205–223, https://doi.org/10.1175/BAMS-86-2-205, 2005.
Velden, C. S. and Bedka, K. M.: Identifying the Uncertainty in Determining
Satellite-Derived Atmospheric Motion Vector Height Attribution, J. Meteorol.
Clim., 48, 450–463, https://doi.org/10.1175/2008JAMC1957.1, 2009.
Velden, C. S. and Holmlund, K.: Report from the working group on
verification and quality indices (WG II), in: 4th International Winds
Workshop, 20–23 October 1998, Saanenmöser, Switzerland, EUMETSAT, https://cimss.ssec.wisc.edu/iwwg/iww4/p19-20_WGReport3.pdf (last access: 5 January 2022), 1998.
Velden, C. S., Hayden, C. M., Nieman, S. J., Menzel, W. P., Wanzong, S., and
Goerss, J. S.: Upper-tropospheric winds derived from geostationary satellite
water vapor observations, B. Am. Meteorol. Soc., 78, 173–195,
https://doi.org/10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2, 1997.
von Bremen, L.: Using simulated satellite images to improve the
characterization of Atmospheric Motion Vectors (AMVs) and their errors for
Numerical Weather Prediction, NWP SAF, Version 1.4, REF: NWPSAF-EC-VS-015, http://research.metoffice.gov.uk/research/interproj/nwpsaf/vs.html (last access: 7 September 2021), 2008.
Weiler, F., Rennie, M., Kanitz, T., Isaksen, L., Checa, E., de Kloe, J., Okunde, N., and Reitebuch, O.: Correction of wind bias for the lidar on board Aeolus using telescope temperatures, Atmos. Meas. Tech., 14, 7167–7185, https://doi.org/10.5194/amt-14-7167-2021, 2021.
Wilks, D.: Statistical Methods in the Atmospheric Sciences, vol. 100, 3rd
edn., Academic Press, ISBN 9780123850225, 2011.
Wu, T.-C., Liu, H., Majumdar, S. J., Velden, C. S., and Anderson, J. L.:
Influence of assimilating satellite-derived atmospheric motion vector
observations on numerical analyses and forecasts of tropical cyclone track
and intensity, Mon. Weather Rev., 142, 49–71, https://doi.org/10.1175/MWR-D-13-00023.1,
2014.
Zuev, V. V. and Savelieva, E.: The cause of the spring strengthening of the
Antarctic polar vortex, Dyn. Atmos. Oceans, 87, 101097, https://doi.org/10.1016/j.dynatmoce.2019.101097, 2019.
Short summary
Winds that are crucial to weather forecasting derived from two different techniques – tracking satellite images (AMVs) and direct measurement of molecular and aerosol motions by Doppler lidar (Aeolus satellite winds) – are compared. We find that AMVs and Aeolus winds are highly correlated. Aeolus Mie-cloudy winds have great potential value as a comparison standard for AMVs. Larger differences are found in the Southern Hemisphere related to higher wind speed and higher vertical variation in wind.
Winds that are crucial to weather forecasting derived from two different techniques – tracking...
