Improvement of Rainfall Forecast by Assimilations of Ground-Based GPS Data and Radio Occultation Data

Seko, Hiromu; Kunii, Masaru; Shoji, Yasuhiro; Saitō, Kazuo

doi:10.2151/sola.2010-021

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Based on this, it can be concluded that GPS-derived PWV can resolve the synoptic signature for the dynamics and precursors to severe weather, and also has the potential to strengthen NWP models and forecasts in view of severe weather. The signature of PWV when influenced by rain intensity was also supported by similar severe weather studies conducted by [5]- [7], [29] using GPS-derived ZTDs. However, the limitation of this 1-D solution is that the mesoscale storm processes cannot be fully resolved due to the integral observation geometry of GPS-derived PWV.…”

Section: B Case Study 2: January 2011 Severe Storm and Precipitationsupporting

confidence: 74%

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Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Zhang

Manning

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for ∼70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather.Index Terms-Global positioning system (GPS), precipitable water vapour (PWV), severe weather, tomogr...

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Section: B Case Study 2: January 2011 Severe Storm and Precipitationsupporting

confidence: 74%

“…Concept studies have identified the potential benefit for tropospheric tomography including [2], [10]- [12], [15], [21], [26], [29], [35]. The dense and well-distributed ground-based GPS CORS networks in Australia can be used to provide wet refractivity fields at a high spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Zhang

Manning

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The assimilation period of this study was as short as 3 d, and number of the ensemble member was only 20. The slant water vapour data such as SWV or slant wet delay, which have the information of anisotropic distribution of water vapour around GPS receivers, is expected to improve rainfall forecasts by intensifying the horizontal gradient of water vapour if SWV is used instead of PWV (Bauer et al, 2011; Seko et al, 2010). Doppler radar data (e.g.…”

Section: Discussionmentioning

confidence: 99%

Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008

Seko

Miyoshi

Shoji

et al. 2011

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TWe performed data assimilation experiments using data from an intense rainfall event in western Japan on 28 July 2008 with the Local Ensemble Transform Kalman Filter (LETKF) and analysed fields of the mesoscale four-dimensional variational assimilation (4DVAR) system of the Japan Meteorological Agency (JMA). We supplemented conventional observation data with precipitable water vapour (PWV) data derived from the Global Positioning System (GPS) Earth Observation Network of the Geospatial Information Authority of Japan. Because the LETKF system assimilates fewer data than the 4DVAR system, ensemble mean fields of the LETKF cycle experiment were replaced with analysed fields from the 4DVAR system each day at 12 UTC. PWV values were converted to relative humidity profiles for assimilation by the LETKF. The addition of PWV data tended to increase low-level water vapour and improve the precipitation forecast. We attempted to reproduce the intense rainfall band using downscale forecast experiments with the JMA non-hydrostatic model (JMANHM) with grid spacings of 5 and 1.6 km. The experiment with 5-km resolution generated a rainfall band in western Japan that was not reproduced using conventional data, although the rainfall was smaller than observations. The experiment with 1.6-km resolution faithfully reproduced the observed band of intense rainfall.

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“…On 28 Seko et al (2010) used the Meso 4D-Var system for a heavy rainfall event in northern Japan on 16 July 2004 to investigate the impacts of three kinds of GPS-derived water vapor data: TPWV, slant water vapor along the path from the GPS satellite to the receiver (SWV), and radio occultation (RO) data along the path from the GPS satellite to the CHAllenging Minisatellite Payload (CHAMP) satellite. When SWV and RO data were assimilated simultaneously, both the rainfall region and rainfall amount were similar to the observed ones.…”

Section: Mesoscale Assimilation Of Gps Datamentioning

confidence: 99%

The JMA Nonhydrostatic Model and Its Applications to Operation and Research

Saitō¹

2012

Atmospheric Model Applications

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Improvement of Rainfall Forecast by Assimilations of Ground-Based GPS Data and Radio Occultation Data

Cited by 4 publications

References 7 publications

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements

Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008

The JMA Nonhydrostatic Model and Its Applications to Operation and Research

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