Can ERA5 reanalysis data characterize the pre-storm environment?

Wu, Jingyan; Guo, Jianping; Yun, Yuxing; Yang, Rongfang; Guo, Xiaoran; Meng, Deli; Sun, Yuping; Zhang, Zhen; Xu, Hui; Chen, Tianmeng

doi:10.1016/j.atmosres.2023.107108

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…Even though we used hourly reanalysis data here to study the impact of cloud parameters on lightning strength, the temporal resolution is too low to accurately describe the cloud microphysics before lightning strokes. With the finding that there were no distinct environment parameter changes in the ERA5 data before storms [87], a very minor impact may affect the results. However, Wu et al [87] also found the environmental parameters gradually increase from 30 min to 10 min before storms.…”

Section: Discussionmentioning

confidence: 93%

“…With the finding that there were no distinct environment parameter changes in the ERA5 data before storms [87], a very minor impact may affect the results. However, Wu et al [87] also found the environmental parameters gradually increase from 30 min to 10 min before storms. Therefore, improving the resolution of cloud data is an inevitable necessity for future work.…”

Section: Discussionmentioning

confidence: 93%

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Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

Wei,

Xu,

Sun

2024

Remote Sensing

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Lightning stroke strength, characterized by energy and peak currents, over the Tibetan Plateau (TP), is investigated by utilizing datasets from the World Wide Lightning Location Network and the Chinese Cloud-to-Ground Lightning Location System during 2016–2019. Focused on the south-central (SC) and southeast (SE) of the TP, it reveals that SE-TP experiences strokes with larger average energy and peak currents. Strong strokes (energy ≥ 100 kJ or peak currents ≥ |100| kA), exhibiting bimodal distribution in winter and summer, are more frequent and have larger average values over the SE-TP than the SC-TP, with diurnal distribution indicating peaks in energy and positive strokes in the middle of the night and negative strokes peaking in the morning. Utilizing the ECMWF/ERA-5 and MERRA-2 reanalysis, we find that stronger strokes correlate with thinner charge zone depths and larger CIWCFs but stable warm cloud depths and zero-degree levels over the SC-TP. Over the SE-TP, stronger strokes are associated with smaller CIWCFs and show turning points for warm cloud depths and zero-degree levels. Thicker charge zone depths correlate with stronger negative strokes but weaker positive strokes. Generating strokes of similar strength over the SC-TP requires larger CIWCFs, thinner warm cloud depths, and lower zero-degree levels than over the SE-TP.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 93%

Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

Wei,

Xu,

Sun

2024

Remote Sensing

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“…Therefore, this study uses ERA-5 data to produce a multidecadal hailday time series for Northern and Southern Switzerland from 1959 to 2022. Recent studies have shown that ERA-5 can be considered as one of the most reliable reanalysis in representing convective storm environments (Li et al, 2020;Taszarek et al, 2020b;Pilguj et al, 2022;Varga and Breuer, 2022;Wu et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

A modelled multi-decadal hailday time series for Switzerland

Wilhelm,

Schwierz,

Schröer

et al. 2024

Preprint

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Abstract. In Switzerland, hail is one of the costliest natural hazards, causing extensive damage to agriculture, cars, and infrastructure each year. In a warming climate, hail frequency and its patterns of occurrence are expected to change, which is why understanding the long-term variability and its drivers is essential. Therefore, this study presents new multidecadal daily hail time series for Northern and Southern Switzerland from 1959 to 2022. Daily radar hail proxies and environmental predictor variables from ERA-5 reanalysis are used to build an ensemble statistical model for predicting past hail occurrence. Haildays are identified from operational radar-derived "Probability of Hail" (POH) data for two study regions, namely the north and south of the Swiss Alps. We use data from 2002–2022 during the convective season from April to September. The decision hailday YES / NO is based on surpassing a POH ≥ 80 % for a certain minimum footprint area of the domains. Separate logistic regression models and GAM´s are built for each domain and combined in an ensemble model to reconstruct the final time series. Overall, the models are able to describe the observed time series well. Historical hail reports are used for comparing years with the most and least haildays. For the northern and southern domains, the time series both show a significant positive trend in yearly aggregated haildays from 1959 to 2022. The trend is still positive and significant when looking at the period 1979–2022. In all models, the trends are driven by moisture and instability predictors. In the last two decades, we can see an increase in haildays at the beginning of the hail season and an earlier and longer peak, however, there is no systematic shift in the seasonal cycle. With this time series, we can now study the local and remote drivers of the interannual variability and seasonality of Swiss hail occurrence.

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“…There are numerous studies in the literature that have utilized these indices for different regions. Wu et al (2024) calculated CAPE, CIN, KI, and LI using data from 120 radiosonde stations across China. Spatially varying index results were obtained for pre-storm, storm onset, and post-storm periods in the analyses conducted for the warm season.…”

Section: Introductionmentioning

confidence: 99%

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

Yavuz

2024

Pure Appl. Geophys.

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This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

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Can ERA5 reanalysis data characterize the pre-storm environment?

Cited by 7 publications

References 49 publications

Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

A modelled multi-decadal hailday time series for Switzerland

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

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