Water vapor mixing ratio and temperature inter-comparison results in the framework of the Hydrological Cycle in the Mediterranean Experiment—Special Observation Period 1

Abstract

This paper reports results from an inter-comparison effort involving water vapor and temperature sensors, which took place in the NorthWestern Mediterranean in the period September-November 2012 in the framework of the first Special Observing Period of the Hydrological cycle in the Mediterranean Experiment. The involved sensors are the ground-based Raman lidars BASIL and WALI, the airborne water vapor differential absorption lidar LEANDRE 2, flying onboard the ATR42 aircraft, as well as additional water vapor and temperature sensors (radiosondes, aircraft in situ sensors, and a microwave radiometer). The main objective of the inter-comparison is the determination of the measurement uncertainty affecting these sensors. The effort benefitted from dedicated ATR42 flights in the framework of the EUropean Facility for Airborne Research (EUFAR) Project "WaLiTemp." Comparisons between BASIL and LEANDRE 2 in terms of water vapor mixing ratio indicate a vertically averaged mean bias, bias, and mean absolute bias, bias , between the two sensors of − 0.08 g kg −1 (or − 2.50%) and 0.67 g kg −1 (or 2.77%), respectively. For all sensors' pairs including LEANDRE 2, the inter-comparison range is 0.5-3 km, while for all other sensors' pairs, the inter-comparison range is 0.5-6 km. Comparisons between BASIL and the microwave radiometer indicate bias and bias values between the two sensors of − 0.02 g kg −1 (or − 1.11%) and 0.22 g kg −1 (or 7.31%), respectively, for water vapor mixing ratio measurements, and a value for both bias and bias of 0.62 K for temperature measurements. Comparisons of BASIL with the radiosondes indicate bias and bias values of 0.28 g kg −1 (or 1.56%) and 0.51 g kg −1 (or 6.66%), respectively, for water vapor mixing ratio measurements, and − 0.43 K and 0.77 K, respectively, for temperature measurements, while comparisons of BASIL with aircraft in situ sensors indicate bias and bias values of 0.22 g kg −1 (or 1.17%) and 0.43 g kg −1 (or 4.62%), respectively, for water vapor mixing ratio measurements, and 0.15 K and 0.47 K, respectively, for temperature measurements. Comparisons of LEANDRE 2 with the radiosondes result in bias and bias values of 0.21 g kg −1 (or 0.76%) and 1.10 g kg −1 (or 11.05%), respectively, while comparisons of LEANDRE 2 with the aircraft in situ sensors indicate a value of both bias and bias of 0.76 g kg −1 (or 8.9%). Comparisons of in situ sensors with the radiosondes reveal bias and bias values of 0.36 g kg −1 (or 2.26%) and 0.36 g kg −1 (or 4.72%),

Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations

“…The mean RMSD value within the boundary layer is ∼ 0.8 K for the temperature retrievals (Liljegren et al, 2005;Cimini et al, 2006;Löhnert et al, 2009;Löhnert and Maier, 2012). Bias values between MWR and Raman lidars are within ±0.4 g kg −1 (or ±20 %) for water vapour mixing ratio measurements with RMSD < 1 g kg −1 (25 %-55 %) and within 0-1.2 K for temperature measurements with RMSD ∼ 0.6-1.8 K (at 5 min integration time; Di Girolamo et al, 2020). When their results are compared to radiosonde data, Bianco et al (2017) find lower statistical differences for RASS than for MWR.…”

Measurements of Aerosol Size and Microphysical Properties: A Comparison Between Raman Lidar and Airborne Sensors

“…These measurements are used in the paper to infer aerosol size and microphysical properties. Before and after HyMeX‐SOP1, BASIL participated to a variety of other international field deployments (among others, Di Girolamo, Summa, Bhawar, et al., 2012; Di Girolamo et al., 2018; Di Girolamo et al., 2020; Summa et al., 2018; De Rosa, Di Girolamo, & Summa, 2018; De Rosa, Di Girolamo, Summa, Flamant et al., 2018).…”

A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative

“…Since more than 10 years, automated Raman lidar systems are operated in automatic mode at several observatories and research institutions (Goldsmith et al 1998;Balin et al 2004;Reichardt et al 2012;Dinoev et al 2013;Brocard et al 2013;Leuenberger et al 2020). Recently, also mobile systems became available which can be moved for field experiments: This is attested by the large data sets acquired by WALI and BASIL in the field during HyMeX SOP1 (Chazette et al 2016a, b;Di Girolamo et al 2020) or by the automated Raman lidar ARTHUS (Atmospheric Raman Temperature and Humidity Sounder, Lange et al 2019) of the University of Hohenheim that operated from a ship for over a month during the EUREC 4 A campaign (Stevens et al, 2021). Figure 3 shows examples of time-height cross-sections of WV mixing ratio measured with WALI from 17 September to 28 October 2012 over Menorca, Spain (Fig.…”

“…• The Weather Atmospheric LIdar (WALI, Chazette et al 2014) developed at LSCE, which was involved in the SOP1 of HyMeX (Chazette et al 2016a,b;Di Girolamo et al 2020) or during the Pollution in the ARCtic System (PARCS) project (Totems et al 2019) and recently during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) project (Chazette et al 2021) • The Airborne Lidar for Atmospheric Studies (ALiAS, Chazette et al 2012Chazette et al , 2017Chazette et al , 2019Chazette et al , 2020 developed at LSCE • The Lidar for Automatic Atmospheric Surveys using Raman Scattering (LAASURS; Baron et al 2020) developed at LSCE • The University of BASILicata ground-based Raman Lidar system (BASIL), which was involved in HyMeX (Di Girolamo et al, 2009Girolamo et al, , 2017Girolamo et al, , 2020Stelitano et al 2019) • The Atmospheric Raman Temperature and Humidity Sounder (ARTHUS, Lange et al 2019) of the University of Hohenheim…”