Abstract
Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work.
Highlights
Water vapor, as an important atmospheric component, plays a key role in the radiative energy exchange processes that occur there [1,2]
The hourly averaged Vertical Column Densities (VCDs) retrieved by MAX-DOAS were validated using Aerosol Robotic Network (AERONET) Level 1.5 precipitable water data under clear sky conditions
The hourly averaged error in the water vapor VCDs observed by MAX-DOAS ranges from 1.20 × 1021 to 9.89 × 1021, with a mean error of 4.45 × 1021 and a median error of 4.22 × 1021, while the hourly averaged error of the AERONET data ranges from 4.09 × 1019 to 2.57 × 1022 with a mean error of 2.21 × 1021 and a median error of 9.50 × 1020
Summary
As an important atmospheric component, plays a key role in the radiative energy exchange processes that occur there [1,2]. A large portion of the energy transferred between the ground surface and the atmosphere takes the form of latent heat, which determines the global distribution of clouds [3]. As the most important greenhouse gas, water vapor’s atmospheric concentration increases drastically with temperature, and this increase can amplify global warming processes [5,6]. Water vapor has a critical influence on atmospheric pollution [7]. It has been demonstrated that under high relative humidity, the volume of aerosol particles can be doubled through the absorption of water vapor onto their surfaces, thereby promoting secondary aerosol formation [8]
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