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Publications (7 of 7) Show all publications
Norin, L., Devasthale, A. & L'Ecuyer, T. S. (2017). The sensitivity of snowfall to weather states over Sweden. Atmospheric Measurement Techniques, 10(9), 3249-3263
Open this publication in new window or tab >>The sensitivity of snowfall to weather states over Sweden
2017 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 9, p. 3249-3263Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4296 (URN)10.5194/amt-10-3249-2017 (DOI)000409247900001 ()
Available from: 2017-09-19 Created: 2017-09-19 Last updated: 2017-09-19Bibliographically approved
Norin, L. (2017). Wind turbine impact on operational weather radar I/Q data: characterisation and filtering. Atmospheric Measurement Techniques, 10(5), 1739-1753
Open this publication in new window or tab >>Wind turbine impact on operational weather radar I/Q data: characterisation and filtering
2017 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 5, p. 1739-1753Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4109 (URN)10.5194/amt-10-1739-2017 (DOI)000400998100001 ()
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-07Bibliographically approved
Berg, P., Norin, L. & Olsson, J. (2016). Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden. Journal of Hydrology, 541, 6-13
Open this publication in new window or tab >>Creation of a high resolution precipitation data set by merging gridded gauge data and radar observations for Sweden
2016 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 541, p. 6-13Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Hydrology
Identifiers
urn:nbn:se:smhi:diva-3570 (URN)10.1016/j.jhydrol.2015.11.031 (DOI)000386421200002 ()
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-01-13Bibliographically approved
Norin, L. (2015). A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data. Atmospheric Measurement Techniques, 8(2), 593-609
Open this publication in new window or tab >>A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data
2015 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 8, no 2, p. 593-609Article in journal (Refereed) Published
Abstract [en]

In many countries wind turbines are rapidly growing in numbers as the demand for energy from renewable sources increases. The continued deployment of wind turbines can, however, be problematic for many radar systems, which are easily disturbed by turbines located in the radar line of sight. Wind turbines situated in the vicinity of Doppler weather radars can lead to erroneous precipitation estimates as well as to inaccurate wind and turbulence measurements. This paper presents a quantitative analysis of the impact of a wind farm, located in southeastern Sweden, on measurements from a nearby Doppler weather radar. The analysis is based on 6 years of operational radar data. In order to evaluate the impact of the wind farm, average values of all three spectral moments (the radar reflectivity factor, absolute radial velocity, and spectrum width) of the nearby Doppler weather radar were calculated, using data before and after the construction of the wind farm. It is shown that all spectral moments, from a large area at and downrange from the wind farm, were impacted by the wind turbines. It was also found that data from radar cells far above the wind farm (near 3 km altitude) were affected by the wind farm. It is shown that this in part can be explained by detection by the radar sidelobes and by scattering off increased levels of dust and turbulence. In a detailed analysis, using data from a single radar cell, frequency distributions of all spectral moments were used to study the competition between the weather signal and wind turbine clutter. It is shown that, when weather echoes give rise to higher reflectivity values than those of the wind farm, the negative impact of the wind turbines is greatly reduced for all spectral moments.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-2007 (URN)10.5194/amt-8-593-2015 (DOI)000350558300006 ()
Available from: 2016-04-13 Created: 2016-03-03 Last updated: 2017-11-30Bibliographically approved
Norin, L., Devasthale, A., L'Ecuyer, T. S., Wood, N. B. & Smalley, M. (2015). Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmospheric Measurement Techniques, 8(12), 5009-5021
Open this publication in new window or tab >>Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden
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2015 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 8, no 12, p. 5009-5021Article in journal (Refereed) Published
Abstract [en]

Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems-Swerad, the Swedish national weather radar network, and CloudSat - are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7 : 2 x 10(5) matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46-82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics-including the probability of detection, false alarm rate, hit rate, and Pierce's skill score - are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.

National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-1932 (URN)10.5194/amt-8-5009-2015 (DOI)000367384600001 ()
Available from: 2016-04-29 Created: 2016-03-03 Last updated: 2017-11-30Bibliographically approved
Devasthale, A. & Norin, L. (2014). The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network. ATMOSPHERIC MEASUREMENT TECHNIQUES, 7(6), 1605-1617
Open this publication in new window or tab >>The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network
2014 (English)In: ATMOSPHERIC MEASUREMENT TECHNIQUES, ISSN 1867-1381, Vol. 7, no 6, p. 1605-1617Article in journal (Refereed) Published
Abstract [en]

Using measurements from the national network of 12 weather radar stations for the 11-year period 2000-2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate-to high-intensity events (precipitation >0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation >1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:smhi:diva-148 (URN)10.5194/amt-7-1605-2014 (DOI)000339935900007 ()
Available from: 2015-04-08 Created: 2015-03-26 Last updated: 2016-04-07Bibliographically approved
de la Vega, D., Matthews, J. C. G., Norin, L. & Angulo, I. (2013). Mitigation Techniques to Reduce the Impact of Wind Turbines on Radar Services. Energies, 6(6), 2859-2873
Open this publication in new window or tab >>Mitigation Techniques to Reduce the Impact of Wind Turbines on Radar Services
2013 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 6, no 6, p. 2859-2873Article, review/survey (Refereed) Published
Abstract [en]

Radar services are occasionally affected by wind farms. This paper presents a comprehensive description of the effects that a wind farm may cause on the different radar services, and it compiles a review of the recent research results regarding the mitigation techniques to minimize this impact. Mitigation techniques to be applied at the wind farm and on the radar systems are described. The development of thorough impact studies before the wind farm is installed is presented as the best way to analyze in advance the potential for interference, and subsequently identify the possible solutions to allow the coexistence of wind farms and radar services.

Keywords
mitigation technique, impact study, weather radar, air traffic control radar, stealth technologies, data processing, adaptive clutter filters, adaptive scanning, gap filler radar
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-374 (URN)10.3390/en6062859 (DOI)000320773700009 ()
Available from: 2015-04-10 Created: 2015-03-31 Last updated: 2017-12-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4994-1659

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