Research on Atmospheric Water Vapor: Monitoring and Characteristics

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (10 November 2022) | Viewed by 1642

Special Issue Editors

Department of Physics, University of Extremadura, 06006 Badajoz, Spain
Interests: atmospheric water vapor; ground-based GNSS signal processing; remote sensing

Special Issue Information

Dear Colleagues,

Water vapor is a well-known trace gas. Despite its low concentration in the atmosphere, it has a paramount importance in many processes like the hydrological cycle, energy transportation or greenhouse effect. For instance, it is main natural contributor to the greenhouse effect, being one of the main absorbers of infrared radiation from the Earth, part of which is emitted back to the Earth’s surface. Moreover, water vapor varies widely at different scales in the spatial and temporal domains, and therefore it is difficult to study and needs continuous monitoring through different kinds of instrumentation. There is no single instrument able to catch all the variation of the water vapor fields, and therefore the use of coincident data-sets is very valuable in the study of this gas.

We present a Special Issue of the journal Atmosphere titled Research on Atmospheric Water Vapor: Monitoring and Characteristics. This Special Issue aims to present the state-of-the-art in topics related to any water vapor instrumentation (radiosondes, microwave radiometers, photometers, global navigation satellite systems, and so on) and their validation, as well as studies on spatio-temporal analysis, trend analysis, and the study of the water vapor cycle at different time scales. It also covers studies using numerical weather prediction models, assimilation experiments, and the study of the radiative effects of water vapor.

Dr. Javier Vaquero-Martínez
Dr. Manuel Antón
Guest Editors

Manuscript Submission Information

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Keywords

  • water vapor
  • spatio-temporal analysis
  • validation
  • trends
  • instrumentation
  • modeling

Published Papers (1 paper)

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Research

14 pages, 5495 KiB  
Article
Evaluation of Water Vapor Product from TROPOMI and GOME-2 Satellites against Ground-Based GNSS Data over Europe
Atmosphere 2022, 13(7), 1079; https://doi.org/10.3390/atmos13071079 - 08 Jul 2022
Cited by 1 | Viewed by 1126
Abstract
A novel integrated water vapor (IWV) product from TROPOspheric Monitoring Instrument (TROPOMI) is validated together with a Global Ozone Monitoring Instrument-2 (GOME-2) standard product. As reference, ground-based Global Navigation Satellite Systems (GNSS) IWV data in 235 European stations from May 2018 to May [...] Read more.
A novel integrated water vapor (IWV) product from TROPOspheric Monitoring Instrument (TROPOMI) is validated together with a Global Ozone Monitoring Instrument-2 (GOME-2) standard product. As reference, ground-based Global Navigation Satellite Systems (GNSS) IWV data in 235 European stations from May 2018 to May 2019 are used. Under cloud free situations, a general comparison is carried out. It suggests that TROPOMI IWV exhibits less bias than GOME-2 and better results in the dispersion and regression parameters. Moreover, TROPOMI presents more homogeneous results along the different stations. However, TROPOMI is found to be overestimating the IWV uncertainties and being, therefore, too conservative in the confidence interval considered. The dependence of satellite product performance on several variables is also discussed. TROPOMI IWV shows wet bias of 5.7% or less for IWV < 10 mm (TROPOMI) and dry bias of up to −3% (TROPOMI). In contrast, GOME-2 shows wet bias of 30% or less for IWV < 25 mm (GOME-2) and dry bias of −12.3% for IWV > 25 mm. In addition, relative standard deviation (rSD) increases as IWV increases. In addition, the dependence on solar zenith angle (SZA) was also analyzed, as solar radiation bands are used in the retrieval algorithm of both instruments. Relative mean bias error (rMBE) shows positive values for GOME-2, slightly increasing with SZA, while TROPOMI shows more stable values. However, under high SZA, GOME-2 IWV exhibits a steep increase in rMBE (overestimation), while TROPOMI IWV exhibits a moderate decrease (underestimation). rSD is slightly increasing with SZA. The influence of cloudiness on satellite IWV observations is such that TROPOMI tends to overestimate IWV more as cloudiness increases, especially for high IWV. In the case of GOME-2, the rSD slightly increases with cloudiness, but TROPOMI rSD has a marked increase with increasing cloudiness. TROPOMI IWV is an important source of information about moisture, but its algorithm could still benefit from further improvement to respond better to cloudy situations. Full article
(This article belongs to the Special Issue Research on Atmospheric Water Vapor: Monitoring and Characteristics)
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