Abstract

Abstract. The BASIL Raman lidar system entered the International Network for the Detection of Atmospheric Composition Change (NDACC) in 2012. Since then, measurements have been carried out routinely on a weekly basis. This paper reports specific measurement results from this effort, with a dedicated focus on temperature and water vapour profile measurements. The main objective of this research effort is to provide a characterisation of the system performance. The results illustrated in this publication demonstrate the ability of BASIL to perform measurements of the temperature profile up to 50 km and of the water vapour mixing ratio profile up to 15 km, when considering an integration time of 2 h and a vertical resolution of 150–600 m; the mean measurement accuracy, determined based on comparisons with simultaneous and co-located radiosondes, is 0.1 K (for the temperature profile) and 0.1 g kg−1 (for the water vapour mixing ratio profile) up to the upper troposphere. The relative humidity profiling capability up to the tropopause is also demonstrated by combining simultaneous temperature and water vapour profile measurements. Raman lidar measurements are compared with measurements from additional instruments, such as radiosondes and satellite sensors (IASI and AIRS), as well as with model reanalyses data (ECMWF and ECMWF-ERA). We focused our attention on six case studies collected during the first 2 years of system operation (November 2013–October 2015). Comparisons between BASIL and the different sensor/model data in terms of the water vapour mixing ratio indicate biases in the altitudinal interval between 2 and 15 km that are always within ±1 g kg−1 (or ±50 %), with minimum values being observed in the comparison between BASIL and radiosonde measurements (±20 % up to 15 km). Results also indicate a vertically averaged mean mutual bias of −0.026 g kg−1 (or −3.8 %), 0.263 g kg−1 (or 30.0 %), 0.361 g kg−1 (or 23.5 %), −0.297 g kg−1 (or −25 %) and −0.296 g kg−1 (or −29.6 %) when comparing BASIL with radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The vertically averaged mean absolute mutual biases are somewhat higher, i.e. 0.05 g kg−1(or 16.7 %), 0.39 g kg−1 (or 23.0 %), 0.57 g kg−1 (or 23.5 %), 0.32 g kg−1 (or 29.6 %) and 0.52 g kg−1 (or 53.3 %), when comparing BASIL with radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The comparisons in terms of temperature measurements indicate mutual biases in the altitudinal interval between 3 and 30 km that are always within ±3 K, with minimum values being observed in the comparison between BASIL and radiosonde measurements (±2 K within this same altitudinal interval). Results also reveal mutual biases within ±3 K up to 50 km for most sensor/model pairs. Furthermore, a vertically averaged mean mutual bias of −0.03, 0.21, 1.95, 0.14 and 0.43 K is found between BASIL and the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The vertically averaged absolute mean mutual biases between BASIL and the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA are 1.28, 1.30, 3.50, 1.76 and 1.63 K respectively. Based on the available dataset and benefiting from the fact that the BASIL Raman lidar could be compared with all other sensor/model data, it was possible to estimate the overall bias of all sensors/datasets: −0.04 g kg−1 ∕ 0.19 K, 0.20 g kg−1 ∕ 0.22 K, −0.31 g kg−1 ∕ −0.02 K, −0.40 g kg−1 ∕ −1.76 K, 0.25 g kg−1 ∕ 0.04 K and 0.25 g kg−1 ∕ −0.24 K for the water vapour mixing ratio/temperature profile measurements carried out by BASIL, the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively.

Highlights

  • Water vapour is the most important atmospheric greenhouse gas, and its increasing tropospheric concentration is primarily driven by human activities

  • A fundamental aspect of NDACC is represented by the high standard of quality of the data collected; we demonstrate, based on the results illustrated in this paper, that this standard is reached by BASILicata Raman lidar system (BASIL)

  • The systematic uncertainty affecting the temperature measurement at an altitude of 5 km below zref,2, i.e. at 50 km, is lower than 1 K, as clearly highlighted by the results reported in Sect. 6.1 and 6.2, which reveal deviations at this altitude between BASIL and the European Centre for Medium-Range Weather Forecasts (ECMWF) and ECMWF-ERA model reanalyses lower than 1 K for the case studies, i.e. considerably lower than the statistical uncertainty affecting BASIL temperature measurements at this altitude (±2 K)

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Summary

Introduction

Water vapour is the most important atmospheric greenhouse gas, and its increasing tropospheric concentration is primarily driven ( indirectly) by human activities. Despite the well recognised importance of having accurate tropospheric and stratospheric water vapour and temperature profile measurements, datasets of these variables and their long-term variability are limited, especially in the UTLS region. All of the above weather- and climate-related issues call for highly accurate measurements of both the water vapour and temperature profiles throughout the troposphere and stratosphere, with a specific focus on the UTLS region. These motivations pushed the Network for the Detection of Atmospheric Composition Change (NDACC), formerly the international Network for the Detection of Stratospheric Change (NCSC), to include water vapour and temperature lidars among its ensemble of instruments in the early 2000s. Niques considered to measure atmospheric thermodynamic variables; Sect. 5 defines the statistical quantities used in the intercomparison for the assessment of the measurement performance; Sect. 6 illustrates the intercomparison results and provides an assessment of the performance of the sensors and models considered; and Sect. 7 summarises all of the results reported and illustrates some possible future developments of the present study

The BASIL Raman lidar and its operation in the framework of NDACC
Water vapour mixing ratio
Temperature
Rotational Raman technique
Lidar integration technique
Relative humidity
Statistical quantities used for the intercomparison
Intercomparison results
Raman lidar calibration
Case studies
Findings
Summary
Full Text
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