Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2813-2021
© Author(s) 2021. 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-14-2813-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2021
Research article |
| 12 Apr 2021
| 12 Apr 2021
Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica
Evgenia Belova
CORRESPONDING AUTHOR
Swedish Institute of Space Physics, Kiruna, 98128, Sweden
Peter Voelger
Swedish Institute of Space Physics, Kiruna, 98128, Sweden
Sheila Kirkwood
Swedish Institute of Space Physics, Kiruna, 98128, Sweden
Susanna Hagelin
Swedish Meteorological and Hydrological Institute, Norrköping,
60176, Sweden
Magnus Lindskog
Swedish Meteorological and Hydrological Institute, Norrköping,
60176, Sweden
Heiner Körnich
Swedish Meteorological and Hydrological Institute, Norrköping,
60176, Sweden
Sourav Chatterjee
National Centre for Polar and Ocean Research, Ministry of Earth
Sciences, Vasco da Gama, Goa, 403804, India
Karathazhiyath Satheesan
Department of Atmospheric Sciences, School of Marine Sciences, Cochin
University of Science and Technology, Cochin, Kerala, 682 016, India
Related authors
Sheila Kirkwood, Evgenia Belova, Peter Voelger, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 16, 4215–4227, https://doi.org/10.5194/amt-16-4215-2023, https://doi.org/10.5194/amt-16-4215-2023, 2023
Short summary
Short summary
We compared 2 years of wind measurements by the Aeolus satellite with winds from two wind-profiler radars in Arctic Sweden and coastal Antarctica. Biases are similar in magnitude to results from other locations. They are smaller than in earlier studies due to more comparison points and improved criteria for data rejection. On the other hand, the standard deviation is somewhat higher because of degradation of the Aeolus lidar.
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
Short summary
Short summary
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.
Roshin P. Raj, Vidar S. Lien, Sourav Chatterjee, Saradhy Surendran, Antonio Bonaduce, and Laurent Bertino
State Planet Discuss., https://doi.org/10.5194/sp-2025-18, https://doi.org/10.5194/sp-2025-18, 2025
Preprint under review for SP
Short summary
Short summary
Dense water formed and exiting from the Barents Sea constitutes an important part of the global ocean circulation. Considering the significant impact of salinity changes in dense water formation, we investigate the salinity changes in the Barents Sea during the past 3 decades. Our results highlight the recent freshening and its drivers in the northern and southern Barents Sea and show its impact on the density of the waters exiting the Barents Sea.
Vidar S. Lien, Roshin P. Raj, and Sourav Chatterjee
State Planet, 4-osr8, 8, https://doi.org/10.5194/sp-4-osr8-8-2024, https://doi.org/10.5194/sp-4-osr8-8-2024, 2024
Short summary
Short summary
We find that major marine heatwaves are rather coherent throughout the Barents Sea, but surface marine heatwaves occur more frequently while heatwaves on the ocean floor have a longer duration. Moreover, we investigate the sensitivity to the choice of climatological average length when calculating marine heatwave statistics. Our results indicate that severe marine heatwaves may become more frequent in the future Barents Sea due to ongoing climate change.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Christoffer Hallgren, Heiner Körnich, Stefan Ivanell, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-129, https://doi.org/10.5194/wes-2023-129, 2023
Preprint withdrawn
Short summary
Short summary
Sometimes, the wind changes direction between the bottom and top part of a wind turbine. This affects both the power production and the loads on the turbine. In this study, a climatology of pronounced changes in wind direction across the rotor is created, focusing on Scandinavia. The weather conditions responsible for these changes in wind direction are investigated and the climatology is compared to measurements from two coastal sites, indicating an underestimation by the climatology.
Sheila Kirkwood, Evgenia Belova, Peter Voelger, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 16, 4215–4227, https://doi.org/10.5194/amt-16-4215-2023, https://doi.org/10.5194/amt-16-4215-2023, 2023
Short summary
Short summary
We compared 2 years of wind measurements by the Aeolus satellite with winds from two wind-profiler radars in Arctic Sweden and coastal Antarctica. Biases are similar in magnitude to results from other locations. They are smaller than in earlier studies due to more comparison points and improved criteria for data rejection. On the other hand, the standard deviation is somewhat higher because of degradation of the Aeolus lidar.
Peter Voelger and Peter Dalin
Atmos. Chem. Phys., 23, 5551–5565, https://doi.org/10.5194/acp-23-5551-2023, https://doi.org/10.5194/acp-23-5551-2023, 2023
Short summary
Short summary
We examined 11 winters of lidar measurements of polar stratospheric clouds (PSCs), performed in Kiruna, northern Sweden. We discriminated cases with and without mountain lee waves present. We found that under mountain-lee-wave conditions PSCs are on average at higher altitudes and are more likely to contain ice. Without such waves present, most PSCs consist of nitric acid.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
Short summary
Short summary
As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
Short summary
Short summary
Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Cited articles
Belu, R. G., Hocking, W. K., Donaldson, N., and Thayaparan, T.: Comparisons
of CLOVAR windprofiler horizontal winds with radiosondes and CMC regional
analyses, Atmos. Ocean, 39, 107–126,
https://doi.org/10.1080/07055900.2001.9649669, 2001.
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,
Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K.,
Lenderink, G., Niemelä, S., Pagh Nielsen, K., Onvlee, J., Rontu, L.,
Samuelsson, P., Santos Muñoz, D., Subias, A., Tijm, S., Toll, V., Yang,
X., and Ødegaard Køltzow, M.: The HARMONIE-AROME model configuration in
the ALADIN-HIRLAM NWP system, Mon. Weather Rev., 145, 1919–1935,
https://doi.org/10.1175/MWR-D-16-0417.1, 2017.
Briggs, B. H.: Radar observations of atmospheric winds and turbulence: a
comparison of techniques, J. Atmos. Terr. Phys., 42, 823–833,
https://doi.org/10.1016/0021-9169(80)90086-0, 1980.
Briggs, B. H.: The analysis of spaced sensor records by correlation
technique, in: Middle Atmosphere Program, Handbook for MAP, 13: Ground-based
Techniques, edited by: Vincent, R. A., NASA, Washington, USA, 166–186, 1984.
Briggs, B. H., Phillips, G. J., and Shinn, D. H.: The analysis of observations
on spaced receivers of the fading of radio signals, Proc. Phys. Soc. B, 63,
106–121, https://doi.org/10.1088/0370-1301/63/2/305, 1950.
Chilson, P. B., Kirkwood, S., and Nilsson, A.: The Esrange MST radar: a
brief introduction and procedure for range validation using balloons, Radio
Sci., 34, 427–436, https://doi.org/10.1029/1998RS900023, 1999.
Copernicus Climate Change Service: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Climate Data Store (CDS), https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 20 December 2019), 2017.
ESA: Applications: Aeolus, available at: http://www.esa.int/Applications/Observing_the_Earth/Aeolus (last access: 19 September 2020), 2018.
Fischer, C., Montmerle, T., Berre, L., Auger, L., and Stefanescu, S.: An
overview of the variational assimilation in the ALADIN/France numerical
weather-prediction system, Q. J. Roy. Meteor. Soc., 131, 3477–3492,
https://doi.org/10.1256/qj.05.115, 2005.
Gage, K. S., McAfee, J. R., and Carter, D. A.: A Comparison of Winds Observed
at Christmas island using a Wind-Profiling Doppler Radar with NMC and ECMWF
Analyses, B. Am. Meteorol. Soc., 69, 1041–1047,
https://doi.org/10.1175/1520-0477(1988)069<1041:ACOWOA>2.0.CO;2, 1988.
Giard, D. and Bazile, E.: Implementation of a new assimilation scheme for
soil and surface variables in a global NWP model, Mon. Weather Rev., 128, 997–1015, https://doi.org/10.1175/1520-0493(2000)128<0997:IOANAS>2.0.CO;2, 2000.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.‐N.: The ERA5 global reanalysis, Q.
J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hocking, W. K., Kelley, M., Rogers, R., Brown, W. O. J., Moorcroft, D., and
St. Maurice, J.-P.: Resolute Bay VHF radar: A multipurpose tool for studies
of tropospheric motions, middle atmosphere dynamics, meteor physics, and
ionospheric physics, Radio Sci., 36, 1839–1857,
https://doi.org/10.1029/2000RS001005, 2001.
Hocking, W. K., Röttger, J., Palmer, R. D., Sato, T., and Chilson, P.
B.: Atmospheric radar: application and science of MST radars in the Earth's
mesosphere, stratosphere, troposphere, and weakly ionized regions, Cambridge
University Press, Cambridge, UK, and New York, USA,
2016.
Holdsworth, D. A.: Signal analysis with applications to atmospheric radars,
PhD thesis, University of Adelaide, Adelaide, Australia, 371 pp., 1995.
Holdsworth, D. A.: Influence of instrumental effects upon the full
correlation analysis, Radio Sci., 34, 643–655,
https://doi.org/10.1029/1999RS900001, 1999.
Holdsworth, D. A. and Reid, I. M.: A simple model of atmospheric radar
backscatter: description and application to the full correlation analysis of
spaced antenna data, Radio Sci., 30, 1263–1280,
https://doi.org/10.1029/95RS00645, 1995.
Jasperson, W. H.: Mesoscale time and space wind variability, J. Appl.
Meteorol., 21, 831–839,
https://doi.org/10.1175/1520-0450(1982)021%3C0831:MTASWV%3E2.0.CO;2,
1982.
Kirkwood, S., Wolf, I., Nilsson, H., Dalin, P., Mikhaylova, D., and Belova,
E.: Polar mesosphere summer echoes at Wasa, Antarctica (73∘S):
First observations and comparison with 68∘N, Geophys. Res. Lett.,
34, L15803, https://doi.org/10.1029/2007GL030516, 2007.
Kirkwood, S., Mihalikova, M., Rao, T. N., and Satheesan, K.: Turbulence associated with mountain waves over Northern Scandinavia – a case study using the ESRAD VHF radar and the WRF mesoscale model, Atmos. Chem. Phys., 10, 3583–3599, https://doi.org/10.5194/acp-10-3583-2010, 2010.
Kudeki, E., Rastogi, P. K., and Sürücü, F.: Systematic Errors in
Radar Wind Estimation: Implications for Comparative Measurements, Radio
Sci., 28, 169–179, https://doi.org/10.1029/92RS01931, 1993.
MacKinnon, A. D.: VHF boundary layer radar and RASS, PhD thesis, University of
Adelaide, Adelaide, Australia, 286 pp., 2001.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
Müller, M., Homleid, M., Ivarsson, K., Køltzow, M. Ø., Lindskog,
M., Midtbø, K., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D.,
Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes,
O.: AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction
Model, Weather Forecast., 32, 609–627, https://doi.org/10.1175/WAF-D-16-0099.1, 2017.
Rabier, F., Thepaut, J.-N., and Courtier, P.: Extended assimilation and
forecast experiments with a four-dimensional
variational assimilation system, Q. J. Roy. Meteor. Soc., 124, 1861–1888,
https://doi.org/10.1002/qj.49712455005, 1998.
Reid, I. M., Holdsworth, D. A., Kovalam, S., Vincent, R. A., and Stickland,
J.: Mount Gambier (38∘ S, 141∘ E) prototype VHF wind
profiler, Radio Sci., 40, RS5007, https://doi.org/10.1029/2004RS003055, 2005.
Rust, D. W., Burgess, D. W., Madox, R. A., Showell, L. C., Marshall, T. C., and Lauritsen, D. K.: Testing a mobile version of a
cross-chain LORAN atmospheric (M-CLASS) sounding system, B. Am. Meteorol.
Soc., 71, 173–181,
https://doi.org/10.1175/1520-0477(1990)071<0173:TAMVOA>2.0.CO;2,
1990.
Schafer, R., Avery, S. K., and Gage, K. S.: A Comparison of VHF Wind Profiler
Observations and the NCEP–NCAR Reanalysis over the Tropical Pacific, J.
Appl. Meteor., 42, 873–889,
https://doi.org/10.1175/1520-0450(2003)042<0873:ACOVWP>2.0.CO;2, 2003.
Stober, G., Latteck, R., Rapp, M., Singer, W., and Zecha, M.: MAARSY – the
new MST radar on Andøya: first results of spaced antenna and Doppler
measurements of atmospheric winds in the troposphere and mesosphere using a
partial array, Adv. Radio Sci., 10, 291–298,
https://doi.org/10.5194/ars-10-291-2012, 2012.
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 Parrinello, T.: ESA's space-based Doppler wind lidar mission Aeolus – First wind and aerosol product assessment results, EPJ Web Conf., 237, 01007, https://doi.org/10.1051/epjconf/202023701007, 2020.
Vincent, R. A., May, P. T., Hocking, W. K., Elford, W. G., Candy, B. H., and
Briggs, B. H.: First results with the Adelaide VHF radar: spaced antenna
studies of tropospheric winds, J. Atmos. Terr. Phys., 49, 353–366,
https://doi.org/10.1016/0021-9169(87)90030-4, 1987.
WMO: WIGOS: WMO Integrated Global Observing System, Final report of the
Fifth WMO Workshop on the Impact of Various Observing Systems on Numerical
Weather Prediction, WMO Tech. Report 2012-1,
World Meteorological Organization, Geneva, Switzerland,
23 pp., 2012.
Short summary
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.
We validate horizontal wind measurements at altitudes of 0.5–14 km made with atmospheric...
