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  • Author or Editor: Carmen Córdoba-Jabonero x
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Daniel Toledo, Carmen Córdoba-Jabonero, and Manuel Gil-Ojeda


Several procedures are widely applied to estimate the atmospheric boundary layer (ABL) top height by using aerosols as tracers from lidar measurements. These methods represent different mathematical approaches, relying on either the abrupt step of the aerosol concentration between the ABL and the free troposphere (FT) or the statistical analysis of vertical variations of the aerosol concentration. An alternative method—the cluster analysis (CA)—has been applied to lidar measurements for the first time, emerging as a useful and robust approach for calculating the ABL height, taking the advantage of both previous variables: the vertical aerosol distribution as obtained from the lidar range-corrected signal (RCS) and the statistical analysis of the RCS profiles in terms of its variance to determine a region of high aerosol loading variability. CA limitations under real situations are also tested, and the effects in ABL height determination of both noise and cloud contamination in RCS are examined. In particular, CA results are weakly sensitive to the signal noise due to the basic features of this statistical method. In addition, differences in the ABL top height, as estimated under cloudy and clear skies, have been found to be lower than 1.8% for a high RCS signal, while no effect is observed for low RCS cloud conditions. Moreover, the CA performance on the ABL top height determination for real cases is also presented, showing the reliable CA skills in reproducing the ABL evolution.

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Carmen Cordoba-Jabonero, Manuel Gil, Margarita Yela, Marion Maturilli, and Roland Neuber


The potential of a new improved version of micropulse lidar (MPL-4) on polar stratospheric cloud (PSC) detection is evaluated in the Arctic over Ny-Ålesund (79°N, 12°E), Norway. The campaign took place from January to February 2007 in the frame of the International Polar Year (IPY) activities. Collocated Alfred Wegener Institute (AWI) Koldewey Aerosol Raman Lidar (KARL) devoted to long-term Arctic PSC monitoring is used for validation purposes. PSC detection is based on lidar retrievals of both backscattering ratio R and volume depolarization ratio δV. Two episodes were unequivocally attributed to PSCs: 21–22 January and 5–6 February 2007, showing a good correlation between MPL-4 and KARL backscattering ratio datasets (mean correlation coefficient = 0.92 ± 0.03). PSC layered structures were characterized for four observational periods coincident with KARL measurements. Also, PSC type classification was determined depending on the retrieved R and δV values as compared with those obtained by KARL long-term Arctic PSC measurements. Tropospheric cloud cover from lidar observations and both ECMWF potential vorticity and temperature at 475 K, in addition to temperature profiles from AWI daily radiosoundings, are also reported. Height-resolved and temporal evolution of both PSC episodes obtained from MPL-4 measurements clearly show that MPL-4 is a suitable instrument to provide long-term PSC statistic monitoring in polar regions. These results are the first reported on PSC detection in the Arctic by using a low-energy and highly pulsed lidar operating on autonomous and full-time continuous mode MPL-4.

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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang


Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

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