Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Shen, Feifei; Min, Jinzhong; Hong, Li; Xu, Dongmei; Shu, Aiqing; Zhai, Danhua; Guo, Yakai; Song, Lixin

doi:10.3390/atmos12070853

Cited by 6 publications

(3 citation statements)

References 31 publications

(44 reference statements)

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“…With the control variables as hydrometeor mixing ratios, the track forecast of Typhoon Chanthu is improved significantly to assimilate radar radial velocity and reflectivity [7]. Similar results are reflected in the paper by [8]. These studies illustrate the importance of radar data assimilation in typhoon forecasting from different perspectives.…”

Section: Introductionsupporting

confidence: 70%

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

2022

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The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast.

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Section: Introductionsupporting

confidence: 70%

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

2022

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“…In meteorological time series, Autoregressive Integrated Moving Average (ARIMA) can be utilized to predict changes in the water resources [103]. Recently data assimilation has been implemented in shallow water models [104], in-situ remote sensing data surface water temperature [105], water temperature [106], spatio-temporal & real-time measurements [99], discharge forecasting [107] and the control of hydrometeorological variables [108]. The use of artificial neural network (ANN) ensemble models with their ability to combine data-driven models with one prediction rather than using a single model is gaining in popularity [109].…”

Section: Water Quality and Quantity Modellingmentioning

confidence: 99%

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

2023

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The complex interactions from anthropogenic activities, climate change, sedimentation and the input of wastewater has significantly affected the aquatic environment and entire ecosystem. Over the years, the researchers have investigated water monitoring approaches in terms of traditional monitoring or even integrated systems to handle such an environmental assessment and predictions based on warning systems. However, research into the selection and optimisation of water monitoring systems by the combination of parallel approach in terms of sampling techniques, process analysis and results is limited. The research objectives of the present study are to evaluate the existing water monitoring systems based on the latest approach and then provide insights into factors affecting sensor implementation at sampling locations. Here we summarize the advancement and trends of various water monitoring systems as well as the suitability of sensor placement in the area by reviewing more than 300 papers published between 2011 and 2022. The research highlights the urgency of an integrative approach with regard to water monitoring systems including water quality model and water quantity model. A framework is proposed to incorporate all water monitoring approaches, sampling techniques, and predictive models to provide comprehensive information about environmental assessment. It was observed that the urgency of model-based approaches as verification and fusion of data assemble has the ability to improve the performances of the systems. Furthermore, integrated systems with the inclusion of a separate modeling approach through integrated -semi-mechanistic models, data science and artificial intelligence are recommended in the future. Overall, this study provides guidelines for achieving standardized water management by implementing integrated water monitoring systems INDEX TERMS infrastructure, integrated monitoring, water monitoring system, modelling, water quality

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“…Hence, many scholars have carried out analyses and research studies on the influence mechanisms of different terrains on precipitation through numerical simulation experiments (Chao et al, 2018;He et al, 2013;He et al, 2015;. The research on multi-source observation data assimilation technology under complex terrain is also helpful to deeply understand the evolution mechanism of topographic precipitation (Shen et al, 2020;Zhong, 2020;Shen et al, 2021). Pontoppidan et al (2017) conducted a series of simulation experiments with different resolutions on a heavy rainfall event in western Norway in October 2014.…”

Section: Introductionmentioning

confidence: 99%

A high-resolution numerical simulation of the “7.19” extraordinary heavy rainfall in Henan Province under topographic effect

Wang³

2023

Front. Earth Sci.

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Introduction: In this study, based on the ERA5 dataset, the orographic precipitation characteristics during an extraordinary heavy rainfall event in Henan Province on 19 July 2016 are analyzed.Methods: In addition, by using the WRF model and the NCEP GDAS/FNL dataset, a high-resolution numerical simulation is carried out, including a series of sensitivity experiments on the Taihang Mountains, Yunmeng Mountain, and low-altitude areas.Results and Discussion: The distribution of observed precipitation shows that the precipitation over the Taihang Mountains in northern Henan during the daytime on July 19 is significantly enhanced due to the topographic effect compared with that over the plain on the east. The simulation results demonstrate that the distribution of simulated precipitation over the steep terrains on the east side of the Taihang Mountains is closer to the observational precipitation when SRTM-90m is introduced to the WRF model. When the altitude of the Taihang Mountains is lowered by 50%, the accumulated precipitation along the Taihang Mountains in the daytime on July 19 decreases by 26%, accompanied by the northward movement of the heavy precipitation center over the northern Taihang Mountains. In addition, Yunmeng Mountain plays an important role in blocking or diverting the easterly and southeasterly low-level jet. When the terrain elevation of Yunmeng Mountain is reduced, the convergence zone of the wind field in the low level moves to the areas along the Taihang Mountains, which leads to the expansion of the area with heavy rainfall, with the average accumulated precipitation in mountain areas increasing by 7%. In addition, when the low altitude terrain in the study region is reduced by 50%, the center of heavy precipitation obviously moves westward. Combined with the diagnostic analysis of the topographically forced vertical velocity, the results show that the uplift of the low-level jet caused by the low-altitude terrain in northern Henan is one of the reasons that affect the location of the heavy rainfall center.

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Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Cited by 6 publications

References 31 publications

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

A high-resolution numerical simulation of the “7.19” extraordinary heavy rainfall in Henan Province under topographic effect

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