Study of Water Vapor Variations Associated with Meso-γ Scale Convection: Comparison between GNSS and Non-Hydrostatic Model Data

Oigawa, Masanori; Realini, Eugenio; Tsuda, Toshitaka

doi:10.2151/sola.2015-007

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…The zonal and meridional WVF profiles based on MAX-DOAS observations were averaged for each season (Figure 13). transport fluxes of the two cities experienced an increasing trend on the day before the precipitation, which is similar to the results of previous reports [43]. Moreover, the meridional vertically integrated water vapor transport fluxes of the two cities were positive before precipitation, which indicates that the transport of water vapor via southerly wind provides favorable conditions for precipitation in the two cities.…”

Section: Water Vapor Flux Profile In Qingdao and Xi'ansupporting

confidence: 89%

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

Ren

Xie

et al. 2021

Remote Sensing

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Water vapor transport affects regional precipitation and climate change. The measurement of precipitable water (PW) and water vapor flux (WVF) is of great importance for the study of precipitation and water vapor transport. This study presented a new method of computing PW and estimating WVF using the water vapor vertical column density (VCD) and profile retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS), combined with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 wind profiles. We applied our method to MAX-DOAS observations in the coastal (Qingdao) and inland (Xi’an) cities of China from June 2019 to May 2020 and compared the results to the ERA5 reanalysis datasets. Good agreement with ERA5 datasets was found; the correlation coefficient (r) of the PW and the zonal and meridional WVFs were r ≥ 0.92, r = 0.77, and r ≥ 0.89, respectively. The comparison results showed the feasibility and reliability of estimating PW and WVF using MAX-DOAS. Then, we analyzed the seasonal and diurnal climatology of the PW and WVFs in Qingdao and Xi’an. The results indicated that the seasonal and diurnal variations of the PW in the two cities were similar. The zonal water vapor transport of the two cities mainly involved eastward transport, Qingdao’s meridional water vapor mainly involved southward transport, and that of Xi’an mainly involved northward transport. The WVFs of the two cities were higher in the afternoon than in the morning, which may be related to wind speed. The results also indicated that the WVF transmitting belts appeared at around 2 and 1.4 km above the surface in Qingdao and around 2.8, 2.6, 1.6, and 1.0 km above the surface in Xi’an. Before precipitation, the WVF transmitting belt moved from near the ground to a high level, reaching its maximum at about 2 km, and the PW and meridional vertically integrated WVF increased. Finally, the sources and transports of water vapor during continuous precipitation and torrential rain were analyzed according to a 24 h backward trajectory. The air mass from the southeast accounted for more than 84% during continuous precipitation in Xi’an, while the air mass from the ocean accounted for more than 75% during torrential rain in Qingdao and was accompanied by a high-level ocean jet stream. As an optical remote sensing instrument, MAX-DOAS has the advantages of high spatiotemporal resolution, low cost, and easy maintenance. The application of MAX-DOAS to meteorological remote sensing provides a better method for evaluating the PW and WVF.

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Section: Water Vapor Flux Profile In Qingdao and Xi'ansupporting

confidence: 89%

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

Ren

Xie

et al. 2021

Remote Sensing

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“…In addition, RMS values of PWV CON and PWV SPD-H achieved maximum values, preceding the peak time of the surface rainfall at RISH. Oigawa et al (2015) analyzed a 250-m mesh model data simulated by JMA-NHM that successfully simulated the observed rapid increase in PWV prior to surface rainfall during the Uji heavy rainfall event. It was found that in the model, the local PWV maximum began to form about 16 min before the surface rainfall due to wind convergence near the ground.…”

Section: Gnss-derived Pwv Observed By the Uji Networkmentioning

confidence: 99%

Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system

Oigawa

Tsuda

Seko

et al. 2018

Earth Planets Space

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We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13-14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.

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“…Existing networks (not specifically designed for meteorological purposes) and specifically designed dense GNSS networks have been used for monitoring the distribution of water vapor in the atmosphere, with particular reference to its lower layer, that is, the troposphere. At least three different approaches have been applied: (1) investigation of the vertical column over a single station (Rocken et al 1997), (2) exploitation of existing national GNSS networks (Seko et al 2007;Inoue and Inoue 2007), and (3) implementation of specifically designed dense and hyperdense GNSS networks (Zhang et al 2008;Realini et al 2012;Sato et al 2013;Tsuda et al 2013;Oigawa et al 2015). In all aforementioned studies, which are surely not exhaustive, the water vapor content was monitored to support weather forecasting, which is useful for interpreting severe meteorological events.…”

Section: Gnss Meteorology: State Of the Artmentioning

confidence: 99%

“…• validation of GNSS-derived PWV with respect to radiosonde/radiometer PWV ); • feasibility study of extending the GNSS network to the entire urban area in high resolution by introducing several hundred low-cost and single-frequency receivers ); • exploitation of the network to study the correlation between PWV fluctuations and the occurrence of intense rain by focusing on heavy rainfall events (Oigawa et al 2015). …”

Section: Gnss Meteorology: State Of the Artmentioning

confidence: 99%

2D PWV monitoring of a wide and orographically complex area with a low dense GNSS network

Ferrando

Federici

Sguerso

2018

Earth Planets Space

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This study presents an innovative procedure to monitor the precipitable water vapor (PWV) content of a wide and orographically complex area with low-density networks. The procedure, termed G4M (global navigation satellite system, GNSS, for Meteorology), has been developed in a geographic information system (GIS) environment using the free and open source GRASS GIS software (https://grass.osgeo.org). The G4M input data are zenith total delay estimates obtained from GNSS permanent stations network adjustment and pressure (P) and temperature (T) observations using existing infrastructure networks with different geographic distributions in the study area. In spite of the wide sensor distribution, the procedure produces 2D maps with high spatiotemporal resolution (up to 250 m and 6 min) based on a simplified mathematical model including data interpolation, which was conceived by the authors to describe the atmosphere's physics. In addition to PWV maps, the procedure provides ∆PWV and heterogeneity index maps: the former represents PWV variations with respect to a "calm" moment, which are useful for monitoring the PWV evolution; and the latter are promising indicators to localize severe meteorological events in time and space. This innovative procedure is compared with meteorological simulations in this paper; in addition, an application to a severe event that occurred in Genoa (Italy) is presented.

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Study of Water Vapor Variations Associated with Meso-γ Scale Convection: Comparison between GNSS and Non-Hydrostatic Model Data

Cited by 5 publications

References 9 publications

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

Estimation of the Precipitable Water and Water Vapor Fluxes in the Coastal and Inland Cities of China Using MAX-DOAS

Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system

2D PWV monitoring of a wide and orographically complex area with a low dense GNSS network

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