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D. L. Suhas
and
William R. Boos

Abstract

Synoptic-scale vortices known as monsoon low pressure systems (LPS) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–2022. The GEFS successfully predicted about half the observed LPS genesis events one to two days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a False Alarm Ratio (FAR) around 50% for one- to two-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing that of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPS form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias-correction.

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Hao Huang
,
Shi Qiu
,
Zhi Zeng
,
Pengyang Song
,
Jiaqi Guo
, and
Xueen Chen

Abstract

The characteristics of modulated internal solitary waves (ISWs) under the influence of one mesoscale eddy pair in the Luzon Strait, involving one anticyclonic eddy (AE) and one cyclonic eddy (CE) induced by the Kuroshio intrusion, were investigated using a nested high-resolution numerical model in the northeastern South China Sea (SCS). The presence of mesoscale eddies greatly impacts the nonlinear evolution of type-a and type-b ISWs. The eddy pair contributes to distinct wave properties and energy evolutions. Compared to type-b waves, type-a waves display more pronounced modulatory characteristics with a larger spatial scale. CE currents and horizontal inhomogeneous stratification are crucial in modulating the wave behaviors, which induce large-amplitude depression ISWs. The AE thereafter yields retardation effects on the wave energy evolution. The average depth-integrated available potential and kinetic energy showed relative increases of −66.12% and −46.07%, respectively, for type-a waves, and −24.26% and −20.15% for type-b waves along the propagation path up to the AE core. The deformed and distorted ISW crest lines propagating further northward exhibit a more dramatic shoaling evolution. The maximum total energies of type-a and b waves at the north station are approximately 13.5 and 3.5 times greater than those at the south station on the continental shelf of the Dongsha Atoll. This work provides essential insights into modulated ISW dynamics under the mesoscale eddy pair within the northeastern SCS deep basin.

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Xiong Xiong
,
Jiang Zhongbao
,
Tang Hongsheng
,
An Ran
,
Liu Yuzhu
, and
Ye Xiaoling

Abstract

This article aims to improve the quality control (QC) of surface daily temperature observations over complex physical geography. A new QC method based on multi-verse optimization algorithm, variational modal decomposition and kernel extreme learning machine was employed to identify potential outliers (the MVO-VMD-KELM method). For the selected six regions with complex physical geography, the inverse distance weighting (IDW), the spatial regression test (SRT), the kernel extreme learning machine (KELM), and the empirical mode decomposition improved KELM (EMD-KELM) methods were employed to test the proposed method. The results indicate that the MVO-VMD-KELM method outperformed other methods in all the cases. The MVO-VMD-KELM method yielded better mean absolute error (MAE), root mean square error (RMSE), index of agreement (IOA) and Nash-Sutcliffe model efficiency coefficient (NSC) values than others via the analysis of evaluation metrics for different cases. The comparison results led to the recommendation that the proposed method is an effective quality control method in identifying the seeded errors for the surface daily temperature observations.

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Andrea W. Harris
and
Jennifer Albrecht

Abstract

Temperature-related illness (TRI) encompasses heat-related illness, such as heat exhaustion and heatstroke, and cold-related illness, such as frostbite and hypothermia. TRI is typically the result of exposure to ambient weather conditions; because of this, unhoused individuals are hypothesized to have higher risk of TRI. However, no national epidemiological studies have been completed to determine this risk. The objective of this study was to determine the association between homelessness and emergency department (ED) diagnosis of TRI in the United States. We conducted a cross-sectional study of adult ED visits in the U.S. from 2005 through 2020 using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationally representative sample of non-federal ED patient visits. Housing status (housed vs. unhoused) was measured using NHAMCS patient residence category, with blank responses excluded. TRI was defined as ED clinician diagnosis of heat- or cold-related illness using ICD-9 and ICD-10 codes. Multivariable logistic regression was used to determine adjusted odds of TRI by housing status. There were 323,606 non-pediatric ED visits in the NHAMCS sample. TRI diagnosis was present in 288 (0.09%) visits. 4099 visits (0.9%) were categorized as unhoused. After adjusting for sex, mental health diagnosis, and alcohol or substance use or use disorder, the odds of TRI diagnosis in unhoused individuals was 4.08 (95% CI 2.09,7.95) compared to housed individuals. We found a higher adjusted odds of TRI diagnosis at an ED visit among unhoused individuals compared with housed individuals.

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ShaoPeng Che
,
Kai Kuang
, and
Shujun Liu

Abstract

Nongovernmental organizations (NGOs) have increasingly played pivotal roles in shaping climate agendas and mobilizing individuals to engage in environmental initiatives. However, the nature of NGOs’ online interaction with users, especially in developing countries, remains largely unexplored. This study focused on the dynamics of engagement between a Chinese NGO, Chinese Weather Enthusiasts (CWE), and Chinese youth on the social media platform of Bilibili. The research comprised two main components. First, named entity recognition was employed to analyze weather-related terms in CWE’s posts on Bilibili and dynamic topic modeling was utilized to uncover shifts in thematic focus. Subsequently, descriptive analysis and negative binomial regression were employed to investigate the correlation between weather types and user engagement metrics. The study unveiled two noteworthy findings: first, CWE posts are closely linked to short-term weather, providing timely content that may meet the public’s demand for climate information. Second, the engagement of Chinese youth users is not affected by extreme weather types. Future research should continue to elucidate strategies that NGOs can employ to enhance online engagement among youth users.

Significance Statement

This study seeks to contribute to the current literature of climate communication by investigating how NGOs engage with Chinese youth on social media, an area that has received scant attention thus far. Focusing on an influential Chinese climate NGO, CWE, and its interactions with Chinese youth on the social media platform of Bilibili, this research sheds lights on strategies to communicate information related to extreme weather to this demographic. Examining factors that influence online user engagement offers both theoretical insights about the mechanisms of climate communication and practical implications for NGOs and policymakers to mobilize youth for environmental initiatives. The findings also underscore the importance of tailoring climate communication to align with the daily experiences of the target audience and public-centric approaches in climate communication strategies.

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Iréne Lake
and
Melissa S. Bukovsky
Open access
Ruth Mottram
,
Michiel van den Broeke
,
Andrew Meijers
,
Christian Rodehacke
,
Rebecca L. Dell
,
Anna E. Hogg
,
Benjamin J. Davison
,
Stef Lhermitte
,
Nicolaj Hansen
,
Jose Abraham Torres Alavez
, and
Martin Olesen
Open access
Ryan A. Sobash
and
David A. Ahijevych

Abstract

The High Resolution Rapid Refresh (HRRR) model provides hourly-updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–2020 HRRR version 4 (HRRRv4) forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts (NNPFs) for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day, but were all superior to the UH forecast. NNPFs valid at hours between 18 UTC – 00 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 06–12 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–2020 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts.

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