Dynamic Mapping of the Movement of Landfalling Atmospheric Rivers Over Southern California With GPS Data

Wang, Minghua; Wang, Jiexian; Bock, Yehuda; Liang, Hong; Dong, Danan; Fang, Peng

doi:10.1029/2018gl081318

Cited by 15 publications

(6 citation statements)

References 51 publications

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“…The present study, as well as other work utilizing data from the network (e.g., Martin et al, 2018; Ralph et al, 2013; Sterle et al, 2019; Sumargo et al, 2020; Wang et al, 2019; White et al, 2019), indicates the network is capable of achieving the goals outlined in Ralph et al (2014) and White et al (2013). These goals include the following: Improved understanding of physical processes, model limitations, and climate trend analysis (Ralph et al, 2014).…”

Section: Discussionsupporting

confidence: 78%

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Hatchett

Cao

Dawson

et al. 2020

Earth and Space Science

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Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

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Section: Discussionsupporting

confidence: 78%

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Hatchett

Cao

Dawson

et al. 2020

Earth and Space Science

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“…Significant changes of water vapour density can be found mostly at altitudes < 3 km. This is consistent with previous studies showing that most of the water vapour transport within atmospheric rivers occurs within the lowest 2.5 km of the atmosphere (Neiman et al ., 2008; Wang et al ., 2019a). It is interesting to see that, as shown in Figure 12f–j, the water vapour density experienced abruptly an increase in altitudes > 2 km at many places right of 119°W.…”

Section: Resultsmentioning

confidence: 99%

“…Means (2013) reported the benefits of using GPS IWV measurements in the diagnostic of the North American monsoon in California. Recently, Wang et al (2019a) proposed a method to map the movement of landfalling atmospheric river dynamically using high-rate GPS observations from a dense network. With the use of GPS-derived zenith tropospheric delay (ZTD), they successfully obtained isochrones that represent the arrival time of the atmospheric river over southern California.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of GPS tomography at retrieving water vapour fields during landfalling atmospheric rivers over southern California

Chen

Dai

et al. 2020

Meteorological Applications

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Accurate monitoring of water vapour evolution is essential to gain a better understanding of the atmospheric river movement, as well as predicting the severity and life cycle of heavy precipitation. The use of hundreds of ground Global Positioning System (GPS) stations in southern California provided a good opportunity to monitor the water vapour changes associated with landfalling atmospheric rivers. Based on a tomographic technique, the 15 min‐resolution water vapour density fields of southern California were reconstructed for February 2019. The assessments by radiosonde and the European Centre for Medium‐Range Weather Forecasts' (ECMWF) Re‐Analysis Interim (ERA‐Interim) database reanalysis show that tomography can retrieve the density profiles with an overall accuracy of about 1.1 g·m–3. In addition, both assessments indicate that the tomographic solutions of altitudes > 6 km are not reliable as relative root mean square error can reach up to 500% in the uppermost layer. Nevertheless, tomographic integrated water vapour is consistent with those derived from radiosonde and ERA‐Interim because their discrepancies are basically < 3 mm. The tomographic density fields were then applied to monitor water vapour evolution associated with two atmospheric river landfalls that occurred on February 2 and February 13, 2019. Results show that both atmospheric rivers made landfall from the northwest and passed over southern California in a southeasterly direction. The series of density profiles reveals that the high‐elevation mountains can block the atmospheric river movement, resulting in the concentration of water vapour at the foot of the mountains.

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“…Since the usefulness of GNSS for water vapor retrieval was demonstrated [6,9], it has been widely used in meteorological and environmental studies. These include investigations into the relation between PWV and precipitation [13][14][15], deep convections [16][17][18], the effects of incorporating the GNSS-derived PWV into numerical weather prediction systems [19,20], summer monsoon and atmospheric rivers [21][22][23], and drought monitoring [24]. Moreover, the long-time and high temporal resolution GNSS-derived PWV data have been utilized to analyze secular trends and diurnal variations of PWV [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

Wang,

Lv,

et al. 2024

Remote Sensing

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The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

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Dynamic Mapping of the Movement of Landfalling Atmospheric Rivers Over Southern California With GPS Data

Cited by 15 publications

References 51 publications

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Assessing the performance of GPS tomography at retrieving water vapour fields during landfalling atmospheric rivers over southern California

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

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