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Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change

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

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 12130

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Special Issue Editor

Dr. Quentin Laffineur

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Guest Editor

Royal Meteorological Institute of Belgium, Avenue Circulaire 3, 1180 Bruxelles, Belgium
Interests: remote sensing; meteorology; clouds–aerosols; climate change; boundary layer processes; land–atmosphere interactions; atmospheric chemistry

Special Issue Information

Dear Colleagues,

Water vapor is an active player in the radiative balance and the hydrological cycle of the climate system. It is also an important player as a chemical compound, both in the troposphere as a source of the hydroxyl radical and in the stratosphere where it has an influence on ozone depletion, especially in the Polar Regions. Water vapor concentrations can vary by orders of magnitude from place to place, especially in the lower atmosphere. Its measurement is therefore essential and determined by using a wide range of in-situ techniques, such as balloon-and-aircraft instruments, and remotely, by satellite and ground-based sensors.

These observations are fundamental for numerical weather prediction, climate and atmospheric chemistry models sensible to the high temporal and spatial variability of water vapor concentrations. In the context of climate change, observations are even more fundamental in the upper troposphere and lower stratosphere where increases in water vapor lead to radiative cooling at these levels and induce warming at the surface. Currently, all of the mechanisms that are driving changes in this part of the atmosphere are not fully understood.

This Special Issue invites research papers addressing one or more of the aspects of water vapor contributing to atmospheric phenomena on different time and space scales for today’s and future climates. Interdisciplinary approach studies will be greatly appreciated.

Dr. Quentin Laffineur
Guest Editor

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Keywords

hydrological cycle
climate change
remote sensing (ground-based, airborne, satellite)
troposhere–stratosphere
ozone depletion
climate model
numerical weather prediction
atmospheric chemistry model

Published Papers (6 papers)

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Research

17 pages, 1515 KiB

Open AccessArticle

Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project

by Pierre Bosser, Joël Van Baelen and Olivier Bousquet

Atmosphere 2022, 13(6), 903; https://doi.org/10.3390/atmos13060903 - 02 Jun 2022

Cited by 3 | Viewed by 1766

Abstract

The “Marion Dufresne Atmospheric Program-Indian Ocean” (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m

^{- 2}

shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m

^{- 2}

). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m

^{- 2}

, which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m

^{- 2}

). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m

^{- 2}

for the day+1 analyses, 0.7 kg m

^{- 2}

for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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17 pages, 3842 KiB

Open AccessArticle

Spatiotemporal Modes Characteristics and SARIMA Prediction of Total Column Water Vapor over China during 2002–2022 Based on AIRS Dataset

by Shanshan Shangguan, Han Lin, Yuanyuan Wei and Chaoli Tang

Atmosphere 2022, 13(6), 885; https://doi.org/10.3390/atmos13060885 - 30 May 2022

Cited by 2 | Viewed by 1601

Abstract

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m² per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed ‘bell-shaped’ curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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22 pages, 13490 KiB

Open AccessArticle

Evaluation of a Prototype Broadband Water-Vapour Profiling Differential Absorption Lidar at Cardington, UK

by Catherine Gaffard, Zhihong Li, Dawn Harrison, Raisa Lehtinen and Reijo Roininen

Atmosphere 2021, 12(11), 1521; https://doi.org/10.3390/atmos12111521 - 18 Nov 2021

Cited by 5 | Viewed by 1678

Abstract

For a one-month period in summer 2020, a prototype Vaisala broadband differential absorption lidar (BB-DIAL) was deployed at a Met Office research site. It was compared with in-situ observations of humidity (93 radiosonde ascents and 27 of uncrewed aerial vehicle flights) and the Met Office 1.5 km resolution numerical weather prediction (NWP) model: UK Variable resolution model (UKV). The BB-DIAL was able to collect data up to the cloud base, in all-weather situations including rain, when it was possible to reach 3 km. The average maximum height was 1300 m, with 75% of the data reaching 1000 m and 35% extending to 1500 m. Compared with radiosondes, the standard deviation for the water vapour is between 5% and 10%. The comparison with the UKV is very encouraging, with a correlation of 0.90. The error against the radiosonde is smaller than against the UKV, which is encouraging for assimilation the BB-DIAL data in UKV. Some data quality issues, such as an increase in error and variable bias in the region of overlap between the far field and close field, spurious oscillations and an unrealistic dry layer above fog are identified. Despite these issues, the overall results from this assessment are promising in terms of potential benefit, instrument reliability and capturing significant humidity changes in the boundary layer. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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12 pages, 1195 KiB

Open AccessArticle

An Observational Study of GPS-Derived Integrated Water Vapor over India

by Kaushik Gopalan, Bipasha Paul Shukla, Som Sharma, Prashant Kumar, Abhineet Shyam, Amita Gaur and Surendra Sunda

Atmosphere 2021, 12(10), 1303; https://doi.org/10.3390/atmos12101303 - 07 Oct 2021

Cited by 2 | Viewed by 1623

Abstract

This study describes the process of deriving integrated water vapor (IWV) from (a) a set of 18 GPS receivers that were installed at different airports across India and (b) a pair of GPS receivers located in Ahmedabad situated around 8 km apart. The Zenith Tropospheric Delay was estimated from the GPS observations using the GAMIT software. Further, IWV was estimated from the ZTD values using surface temperature and pressure from ERA-I reanalysis as additional inputs. The IWV estimates for 1 year—March 2013 to February 2014—were compared with ECMWF Reanalysis Interim (ERA-I) reanalysis as well as radiosonde soundings. The Root Mean Squared Error (RMSE) was ≈6 mm or better for most stations. The IWV estimates for July 2013 were assimilated into the WRF model and had a positive impact on model analysis of IWV. The forecasted rain improved by up to 3–4 mm/day in some regions as a result of GPS-derived IWV estimates. For the Ahmedabad receivers, the GPS-derived IWV was compared with IWV from ERA-I reanalysis and was found to have a RMSE of ≈7.7 mm which is <20% of the mean value. The study demonstrates that the observed IWV variation is consistent with rainfall patterns over Ahmedabad. The rise and dips in the IWV correlate well with the active-break cycle in the monsoon rain. The study demonstrates the value of local measurements of IWV with high temporal frequency, as they are more likely to respond to fast-moving weather phenomena such as rainfall. Thus, the GPS-derived IWV measurements are likely to have significant value in the short-term forecasts of precipitation. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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22 pages, 2582 KiB

Open AccessArticle

Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

by Jan El Kassar, Cintia Carbajal Henken, Rene Preusker and Jürgen Fischer

Atmosphere 2021, 12(10), 1256; https://doi.org/10.3390/atmos12101256 - 27 Sep 2021

Cited by 1 | Viewed by 1917

Abstract

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m

^{2}

, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m

^{2}

and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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17 pages, 9681 KiB

Open AccessArticle

Analysis of the Precipitable Water Vapor Observation in Yunnan–Guizhou Plateau during the Convective Weather System in Summer

by Heng Hu, Yunchang Cao, Chuang Shi, Yong Lei, Hao Wen, Hong Liang, Manhong Tu, Xiaomin Wan, Haishen Wang, Jingshu Liang and Panpan Zhao

Atmosphere 2021, 12(8), 1085; https://doi.org/10.3390/atmos12081085 - 23 Aug 2021

Cited by 2 | Viewed by 2084

Abstract

The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in the Yunnan–Guizhou Plateau. The correlation coefficients of the two datasets ranged between 0.804 and 0.878, the standard deviations ranged between 4.686 and 7.338 mm, and the monthly average deviations ranged between −4.153 and 9.459 mm, which increased with the altitude of the station. Matching the quality-controlled ground precipitation data with the PWV in time and space revealed that most precipitation occurred when the PWV was between 30 and 65 mm and roughly met the normal distribution. We used the vertical integral of divergence of moisture flux (∇_p) and S-band Doppler radar networking products combined with the PWV to study the convergence and divergence process and the water vapor delivery conditions during the deep convective weather process from August 24 to 26, 2020, which can be used to analyze the real-time observation capability and continuity of PWV in small-scale and mesoscale weather processes. Furthermore, the 1 h precipitation and the cloud top temperature (ctt) data at the same site were used to demonstrate the effect of PWV on the transit of convective weather systems from different time–space scales. Full article

(This article belongs to the Special Issue Atmospheric Water Vapor Observation, Simulation, Prediction and Responses to Climate Change)

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