Winds and temperatures of the Arctic middle atmosphere during January measured by Doppler lidar
Abstract. We present an extensive data set of simultaneous temperature and wind measurements in the Arctic middle atmosphere. It consists of more than 300 h of Doppler Rayleigh lidar observations obtained during three January seasons (2012, 2014, and 2015) and covers the altitude range from 30 km up to about 85 km. The data set reveals large year-to-year variations in monthly mean temperatures and winds, which in 2012 are affected by a sudden stratospheric warming. The temporal evolution of winds and temperatures after that warming are studied over a period of 2 weeks, showing an elevated stratopause and the reformation of the polar vortex. The monthly mean temperatures and winds are compared to data extracted from the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Horizontal Wind Model (HWM07). Lidar and ECMWF data show good agreement of mean zonal and meridional winds below  ≈ 55 km altitude, but we also find mean temperature, zonal wind, and meridional wind differences of up to 20 K, 20 m s−1, and 5 m s−1, respectively. Differences between lidar observations and HWM07 data are up to 30 m s−1. From the fluctuations of temperatures and winds within single nights we extract the potential and kinetic gravity wave energy density (GWED) per unit mass. It shows that the kinetic GWED is typically 5 to 10 times larger than the potential GWED, the total GWED increases with altitude with a scale height of  ≈ 16 km. Since temporal fluctuations of winds and temperatures are underestimated in ECMWF, the total GWED is underestimated as well by a factor of 3–10 above 50 km altitude. Similarly, we estimate the energy density per unit mass for large-scale waves (LWED) from the fluctuations of nightly mean temperatures and winds. The total LWED is roughly constant with altitude. The ratio of kinetic to potential LWED varies with altitude over 2 orders of magnitude. LWEDs from ECMWF data show results similar to the lidar data. From the comparison of GWED and LWED, it follows that large-scale waves carry about 2 to 5 times more energy than gravity waves.
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm
Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. This paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on  $z$ -normalization, shape-based distance metric solution, and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using commercial Windcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100 000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.
A study of a self-generated stratospheric sudden warming and its mesospheric-lower thermospheric impacts using the coupled TIME-GCM/CCM3
[1] A stratospheric sudden warming episode was self-consistently generated in the coupled National Center for Atmospheric Research Thermosphere, Ionosphere, Mesosphere, and Electrodynamics General Circulation Model/Climate Community Model version 3 (TIME-GCM/CCM3). Taking advantage of the unique vertical range of the coupled model (ground to 500 km), we were able to study the coupling of the lower and upper atmosphere in this warming episode. Planetary wave 1 is the dominant wave component in this warming event. Analysis of the wave phase structure and the wave amplitude indicates that the wave may experience resonant amplification prior to the peak warming. The mean wind in the high-latitude winter stratopause and mesosphere decelerates and reverses to westward due to planetary wave forcing and forms a critical layer near the zero wind lines. The wind deceleration and reversal also change the filtering of gravity waves by allowing more eastward gravity waves to propagate into the mesosphere and lower thermosphere (MLT), which causes eastward forcing and reverses the westward jet in the MLT. This also changes the meridional circulation in the upper mesosphere from poleward/downward to equatorward/upward, causing a depletion of the peak atomic oxygen layer at 97 km and significant reduction of green line airglow emission at high latitudes and midlatitudes. Planetary waves forced in situ by filtered gravity waves in the MLT grow in the warming episode. Their growth and interaction with tides create diurnal and semidiurnal variabilities in the zonal mean zonal wind.