scholarly journals Insights into wind turbine reflectivity and RCS and their variability using X-band weather radar observations

2020 ◽  
Author(s):  
Martin Lainer ◽  
Jordi Figueras i Ventura ◽  
Zaira Schauwecker ◽  
Marco Gabella ◽  
Montserrat F.-Bolaños ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Weather Radar ◽  
Least Square ◽  
Clear Correlation ◽  
Radar Observations ◽  
Ordinary Least Square ◽  
The North ◽  
X Band ◽  
Polarimetric Weather Radar

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the north-eastern part of Switzerland in March 2019 and March 2020. Based on the usage of a X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross section (RCS) of the turbine themselves, and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (Plan Position Indicator), RHI (Range-height Indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ respectively 44.1 dBsm. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH and RCS show different enhancements for specific relative orientations. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analyses, that the fast changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability of the observed returns.

scholarly journals Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations

2021 ◽  
Vol 14 (5) ◽  
pp. 3541-3560
Author(s):  
Martin Lainer ◽  
Jordi Figueras i Ventura ◽  
Zaira Schauwecker ◽  
Marco Gabella ◽  
Montserrat F.-Bolaños ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Cross Section ◽  
Wind Turbines ◽  
Weather Radar ◽  
Radar Cross Section ◽  
Least Square ◽  
Clear Correlation ◽  
Radar Observations ◽  
X Band ◽  
Polarimetric Weather Radar

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the northeastern part of Switzerland in March 2019 and March 2020. Based on the usage of an X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross-section (RCS) of the turbines themselves and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (plan position indicator), RHI (range–height indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ and 44.1 dBsm, respectively. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH show different enhancements for specific relative orientations. Some of them remain still for Doppler-filtered data, supporting the importance of the moving parts of the wind turbine for the radar returns. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analysis, that the fast-changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability in the observed returns.

scholarly journals X-Band Polarimetric Weather Radar Observations of a Hailstorm

2013 ◽  
Vol 30 (9) ◽  
pp. 2143-2151 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Françoise Honoré ◽  
Pierre Tabary
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Complementary Information ◽  
Radar Observations ◽  
National Network ◽  
X Band ◽  
Polarimetric Weather Radar

Abstract This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection.

scholarly journals Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

2021 ◽  
Vol 14 (2) ◽  
pp. 1075-1098
Author(s):  
Maryna Lukach ◽  
David Dufton ◽  
Jonathan Crosier ◽  
Joshua M. Hampton ◽  
Lindsay Bennett ◽  
...  
Keyword(s):  
Weather Radar ◽  
Original Data ◽  
Optimal Number ◽  
Vertical Profiles ◽  
Radar Observations ◽  
X Band ◽  
Novel Method ◽  
Microphysical Processes ◽  
Polarimetric Weather Radar ◽  
Temporal Dimensions

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

scholarly journals Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

2020 ◽  
Author(s):  
Maryna Lukach ◽  
David Dufton ◽  
Jonathan Crosier ◽  
Joshua M. Hampton ◽  
Lindsay Bennett ◽  
...  
Keyword(s):  
Weather Radar ◽  
Original Data ◽  
Optimal Number ◽  
Vertical Profiles ◽  
Radar Observations ◽  
X Band ◽  
Novel Method ◽  
Microphysical Processes ◽  
Polarimetric Weather Radar ◽  
Temporal Dimensions

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from Quasi-Vertical Profiles (QVP). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the NCAS X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

scholarly journals Radar Applications in Northern Scotland (RAiNS)

2020 ◽  
Author(s):  
Ryan R Neely ◽  
Louise Parry ◽  
David Dufton ◽  
Lindsay Bennett ◽  
Chris Collier
Keyword(s):  
High Resolution ◽  
Weather Radar ◽  
Polarimetric Radar ◽  
Summer Period ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimates ◽  
Radar Applications ◽  
Quantitative Precipitation Estimates ◽  
Hydrological Applications

AbstractThe Radar Applications in Northern Scotland (RAiNS) experiment took place from February to August 2016 near Inverness, Scotland. The campaign was motivated by the need to provide enhanced weather radar observations for hydrological applications for the Inverness region. Here we describe the campaign in detail and observations over the summer period of the campaign that show the improvements that high-resolution polarimetric radar observations may have on quantitative precipitation estimates in this region compared to concurrently generated operational radar quantitative precipitation estimates (QPE). We further provide suggestions of methods for generating QPE using dual-polarisation X-band radars in similar regions.

Iterative Bayesian Retrieval of Hydrometeor Content From X-Band Polarimetric Weather Radar

2010 ◽  
Vol 48 (8) ◽  
pp. 3059-3074 ◽  
Author(s):  
Frank Silvio Marzano ◽  
Giovanni Botta ◽  
Mario Montopoli
Keyword(s):  
Weather Radar ◽  
X Band ◽  
Polarimetric Weather Radar

Towards a multi-year urban precipitation climatology at 100 m scale using X-band radar observations

2021 ◽  
Author(s):  
Finn Burgemeister ◽  
Marco Clemens ◽  
Felix Ament
Keyword(s):  
Urban Area ◽  
Elevation Angle ◽  
Weather Radar ◽  
Rain Event ◽  
Data Set ◽  
Radar Observations ◽  
Precipitation Climatology ◽  
X Band ◽  
Time Periods ◽  
Radar Calibration

<p>An operational, single-polarized X-band weather radar provides measurements in Hamburg’s city center for almost eight years. This weather radar operates at an elevation angle (~3.5°) with a high temporal (30 s), range (60 m), and sampling (1°) resolution resulting<span> in a</span> high information density within <span>the</span> 20 km <span>scan radius</span>. <span>Studies on short time periods (several months) proofs the performance of this low-cost local area weather radar. </span><span>For example, a</span><span> case study on a tornado in a rain event demonstrates its refined resolution </span><span>compared to</span><span> the German nationwide C-band radars. </span><span>Now, we aim for a eight-year precipitation climatology with 100 m resolution. This data set will enable reliable studies on urban extreme precipitation. This presentation will describe h</span><span>ow we </span><span>can</span><span> infer a precipitation estimate based on multi-</span><span>year</span><span> weather radar observations in the urban area of Hamburg.</span></p><p>The single-polarization and <span>small</span> <span>wavelength</span> <span>comes along with</span> high resolution <span>but at the same time</span> high uncertainties. We address several sources of errors affecting th<span>e</span> radar-based <span>precipitation</span> estimate, like the radar calibration, alignment, attenuation, noise, non-meteorologial echoes, <span>and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at relevant heights reducing errors effectively concerning the radar calibration and required statistical relations (</span><span><em>k</em></span><span>-</span><span><em>Z</em></span><span> and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation). We outline the performance of the correction methods for long time periods and discuss open issues and limitations.</span></p><p><span>With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of </span><span>urban </span><span>precipitation will be possible. </span><span>This data set will be used for</span><span> further hydrological research in an urban area </span><span>within the project <em>Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides</em> of the</span><span> Cluster of Excellence <em>Climate Climatic Change, and Society</em> (CliCCS).</span></p>

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