Indirect Effects of Binary Typhoons on an Extreme Rainfall Event in Henan Province, China From 19 to 21 July 2021. 3. Sensitivities to Microphysics Schemes

Xu, Hongxiong; Zhao, Dajun; Yin, Jinfang; Duan, Yihong; Li, Ying; Zhou, Linli

doi:10.1029/2022jd037936

Cited by 10 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results also warrant the necessity of employing multi‐moment cloud microphysics schemes for models attempting to simulate extreme rainfall events. The microphysics parameterization can have substantial impact on extreme rainfall simulation, for example, by modulating latent heating, flows, as well as convergence zones (H. Xu et al., 2023). Our results suggest the importance of parameterizing sorting process, which can be represented in multi‐moment schemes instead of single‐moment ones, in extreme rainfall simulation.…”

Section: Discussionmentioning

confidence: 99%

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Li,

Yin,

Kumjian

2024

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

In this study, we present radar polarimetric characterizations of the storm producing 201.9 mm hourly rainfall on 20 July 2021 in Zhengzhou, China. We employed the separation signatures of enhanced polarimetric observations to investigate hydrometeor size sorting processes, and developed an algorithm to quantify the size sorting directions. Analysis of coupled polarimetric observations unraveled multi‐directional size sorting (MSS) occurred as a low‐level differential reflectivity ZDR backwards arc signature encompassing the rainfall center during the most intensive rainfall period. The rainfall intensification is in step with the increase of size sorting directions. Model simulations with two‐moment microphysics scheme suggest that the presence of arc‐shaped updrafts is conducive to MSS and increased rain rates around the rainfall center. This work sheds novel insights into the kinematics‐driven microphysics in extreme rainfall storms, warranting the potential of using coupled polarimetric signatures for warning catastrophic extreme rainfall events.

show abstract

Section: Discussionmentioning

confidence: 99%

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Li,

Yin,

Kumjian

2024

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…In contrast, traditional regional weather models, such as the Weather Research and Forecasting (WRF) model, recognized as one of the most widely used regional weather models, extensively employed for kilometer (Xu et al , 2023 and sub-kilometer scale forecasting (Bryan et al 2003, Xu and, excel in capturing finer atmospheric structures (Zhang and Zhang 2011) and enhancing the spatial resolution (Vukicevic et al 2022). This capability is key for accurately representing meso-and smallscale meteorological events and effectively improving extreme precipitation forecasts (Ding et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

Xu,

Zhao,

Zhao

et al. 2024

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

In the realm of weather forecasting, the implementation of Artificial Intelligence (AI) represents a transformative approach. However, AI weather forecasting method still faces challenges in accurately predicting meso- and smaller-scale processes and failing to directly capture extreme precipitation due to regression algorithm's nature, coarse resolution, and limitations in key variables like precipitation. Therefore, we propose a state-of-the-art technology which integrates the strengths of the Pangu-Weather AI weather forecasting with the traditional regional weather model, focusing specifically on enhancing the prediction of extreme precipitation events, as mainly exemplified by an unprecedented precipitation in North China from 29 July to 1 August 2023, and an additional extraordinary precipitation event as a supplementary validation to further ensure the accuracy of this technology. The results show that the AI-driven approach exhibits superior performance in capturing the spatial and temporal dynamics of extreme precipitation events. Remarkably, with a threshold of 400 mm, the AI-driven model secures a Threat Score (TS) of 0.1 for forecast lead time reaching up to 8.5 days. This performance notably surpasses the performance of traditional GFS-Driven models, which achieve a similar TS only within a limited 3-day forecast lead time. This considerable enhancement in forecast accuracy, especially over extended lead times illustrates the AI-driven model’s potential to advance in long-term forecasts of extreme precipitation, previously considered challenging, emphasizing the potential of AI in augmenting and refining traditional weather prediction.

show abstract

“…Recently, companion research to this study (Xu et al, 2023), hereafter referred to as Part 3, examined the sensitivity of different microphysics schemes to the southerly flow and its indirect effect on the extreme rainfall event. Specifically, the Thompson, Morrison and WDM6 schemes, popular within the Weather Research and Forecasting (WRF) model, were investigated.…”

Section: Introductionmentioning

confidence: 99%

Indirect effects of latent heat of condensation on the simulation of the 2021 catastrophic Henan rainfall event in central China

Xu,

Zhao,

Gao

et al. 2024

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

Cloud‐related latent heat is critical to the development and maintenance of atmospheric circulation. However, it is unclear how sensitive the key large‐scale weather systems of an extreme rainfall event could be to the variation in cloud‐related latent heat. In a companion study, by investigating the indirect effects of different microphysics schemes, we hypothesized that the latent heat of condensation (CT) would play a role in the ability to simulate the extreme rainfall event that took place in Henan Province, central China, on July 19–21, 2021. To examine the proposed hypothesis, the impacts of varying the CT and the collection of cloud water by rainwater (ACW) in the Thompson, WDM6, and Morrison microphysics schemes on the simulation of the southerly flow and its indirect effect on this extreme rainfall case were studied using the Advanced Research Weather Research and Forecasting (ARW‐WRF) model. It was found that directly changing the CT and ACW had significant effects on the southerly flow and precipitation. An increase in CT generally led to a reduced temperature gradient, a smaller pressure gradient, a stronger southerly flow, and a precipitation surge of 32%–50%. Similar outcomes resulted from increasing ACW in the WDM6 scheme, which effectively diminished the cloud mixing ratio, generated higher CT, and consequently intensified the southerly flow and precipitation by 31%. This study confirmed that the accurate simulation of microphysical processes, along with latent heating, is desirable for more realistically reproducing the key weather systems in the simulation of extreme rainfall.

show abstract

Indirect Effects of Binary Typhoons on an Extreme Rainfall Event in Henan Province, China From 19 to 21 July 2021. 3. Sensitivities to Microphysics Schemes

Cited by 10 publications

References 45 publications

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

Indirect effects of latent heat of condensation on the simulation of the 2021 catastrophic Henan rainfall event in central China

Contact Info

Product

Resources

About

Indirect Effects of Binary Typhoons on an Extreme Rainfall Event in Henan Province, China From 19 to 21 July 2021. 3. Sensitivities to Microphysics Schemes

Cited by 10 publications

References 45 publications

ZDR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

ZDR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model

Indirect effects of latent heat of condensation on the simulation of the 2021 catastrophic Henan rainfall event in central China

Contact Info

Product

Resources

About

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall

Z_DR Backwards Arc: Evidence of Multi‐Directional Size Sorting in the Storm Producing 201.9 mm Hourly Rainfall