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Calvin M. Elkins
and
Deanna A. Hence

Abstract

Frequent deep convective thunderstorms and mesoscale convective systems make the Córdoba region, near the Sierras de Córdoba mountain range, one of the most active areas on Earth for hail activity. Analysis of hail observations from trained observers and social media reports cross-referenced with operational radar observations identified the convective characteristics of hail-producing convective systems in central Argentina over a 6-month period divided into early (October–December 2018) and late seasons (January–March 2019). Reflectivity and dual-polarization characteristics from the Córdoba operational radar [Radar Meteorológico Argentina (RMA1)] were used to identify the convective modes of convective cells at time of positive hail indicators. Analysis of ERA5 upper-air and surface data examined convective environments of hail events and identified representative dynamic and thermodynamic environments. A majority of early season hail-producing cells were classified as discrete convection, while discrete and multicell occurrence evened out in the late season. Most hail-producing cells initiated directly adjacent to the Sierras in the late season, while cell initiation and hail production is further spread out in the early season. Dividing convective events into dynamic/thermodynamic regimes based on values of 1000 J kg−1 of CAPE and vertical wind shear of 20 m s−1 results in most early season events reflecting shear-dominant characteristics (low CAPE, high shear) and most late-season events exhibiting CAPE-dominant characteristics (high CAPE, low shear). Strength and placement of low-level temperature and moisture anomalies/advection and upper-level jets largely defined the differences in the dominant regimes.

Significance Statement

This study used regional radar data alongside hail reports from trained observers and social media to better understand the types and timing of storms identified as producing hail, given the lower resolution of satellite studies. Dividing the hail season (October–December; January–March) showed that within hail season, early season storms tended to be singular storms that formed across the region in environments with strong vertical winds and weak instability. Late-season storms were a mix of singular storms and multicellular storm systems focused on the mountains in weak vertical winds and strong instability. These results show differences from satellite studies and identify key representative hail-producing radar features and environmental regimes for this region, which could guide hail risk analysis within the severe-weather season.

Open access
Mohammad Hadavi
and
Djordje Romanic

Abstract

Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19,132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in performance of the random forest (RF) model is observed when the Support Vector Machine Synthetic Minority Oversampling Technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts towards the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application.

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Shoobhangi Tyagi
,
Sandeep Sahany
,
Dharmendra Saraswat
,
Saroj Kanta Mishra
,
Amlendu Dubey
, and
Dev Niyogi

Abstract

The 2015 Paris Agreement outlined limiting global warming to 1.5°C relative to the preindustrial levels, necessitating the development of regional climate adaptation strategies. This requires a comprehensive understanding of how the 1.5°C rise in global temperature would translate across different regions. However, its implications on critical agricultural components, particularly blue and green water, remains understudied. This study investigates these changes using a rice-growing semiarid region in central India. The aim of this study is to initiate a discussion on the regional response of blue–green water at specific warming levels. Using different global climate models (GCMs) and shared socioeconomic pathways (SSPs), the study estimated the time frame for reaching the 1.5°C warming level and subsequently investigated changes in regional precipitation, temperature, surface runoff, and blue–green water. The results reveal projected reductions in precipitation and surface runoff by approximately 5%–15% and 10%–35%, respectively, along with decrease in green and blue water by approximately 12%–1% and 40%–10%, respectively, across different GCMs and SSPs. These findings highlight 1) the susceptibility of blue–green water to the 1.5°C global warming level, 2) the narrow time frame available for the region to develop the adaptive strategies, 3) the influence of warm semiarid climate on the blue–green water dynamics, and 4) the uncertainty associated with regional assessment of a specific warming level. This study provides new insights for shaping food security strategies over highly vulnerable semiarid regions and is expected to serve as a reference for other regional blue/green water assessment studies.

Significance Statement

This study helps to drive home the message that a global agreement to limit the warming level to 1.5°C does not mean local-scale temperature (and associated hydrological) impacts would be limited to those levels. The regional changes can be more exaggerated and uncertain, and they also depend on the choice of the climate model and region. Therefore, local-scale vulnerability assessments must focus on the multidimensional assessment of a 1.5°C warmer world involving different climate models, climate-sensitive components, and regions. This information is relevant for managing vulnerable agricultural systems. This study is among the first to investigate the critical agricultural components such as the blue–green water over a semiarid Indian region, and the findings and methodology are expected to be transferable for performing regional-scale assessments elsewhere.

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Christopher J. Schultz
,
Phillip M. Bitzer
,
Michael Antia
,
Jonathan L. Case
, and
Christopher R. Hain

Abstract

Twenty-six years of lightning data were paired with over 68 000 lightning-initiated wildfire (LIW) reports to understand lightning flash characteristics responsible for ignition in between 1995 and 2020. Results indicate that 92% of LIW were started by negative cloud-to-ground (CG) lightning flashes and 57% were single stroke flashes. Moreover, 62% of LIW reports did not have a positive CG within 10 km of the start location, contrary to the science literature’s suggestion that positive CG flashes are a dominant fire-starting mechanism. Nearly ⅓ of wildfire events were holdovers, meaning 1 or more days elapsed between lightning occurrence and fire report. However, fires that were reported less than a day after lightning occurrence statistically burned more acreage. Peak current was not found to be a statistically significant delineator between fire starters and non–fire starters for negative CGs but was for positive CGs. Results highlighted the need for reassessing the role of positive CG lightning and subsequently long-continuing current in wildfire ignition started by lightning. One potential outcome of this study’s results is the development of real-time tools to identify ignition potential during lightning events to aid in fire mitigation efforts.

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Laurence Coursol
,
Sylvain Heilliette
, and
Pierre Gauthier

Abstract

With hyperspectral instruments measuring radiation emitted by the earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for CrIS was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati (2022) has 224 channels also present in to our optimal selection which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3 % and 4.5 % for temperature and humidity respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices.

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Lu Yang
,
Linye Song
,
Mingxuan Chen
, and
Conglan Cheng

Abstract

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions.

Significance Statement

The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.

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Ricardo C. Muñoz
and
Laurence Armi

Abstract

Raco is a local wind occurring in central Chile where the Maipo River Canyon exits into the Santiago valley. The intensification of the easterly down-canyon flow starts at any time during some cold season nights, accompanied by increases in temperature and drops in humidity. The hypothesis of the raco being a gap wind controlled by the narrowest section in the 12-km canyon exit corridor is tested with data from two events in July 2018 and July 2019. The data are analyzed in the framework of hydraulic theory, and a subcritical-to-supercritical transition is documented to occur at the narrows of the gap where the Froude number is close to unity, confirmed by radiosondes launched in the narrows in 2019. For the raco flow, the sum of potential and kinetic energy is conserved upstream of the narrows, while the acceleration occurring farther downstream loses a large fraction of energy to frictional dissipation. The raco events occur under the influence of regional subsidence, but a differential nocturnal warming of the in-canyon air mass is responsible for a pressure gradient driving the raco. In the 2019 case, a ceilometer mounted on an instrumented pickup truck documented the structure and movement of the interface between the raco air and the cold-air pool (CAP) existing over the valley to the west. Together with a radiosonde launched near the CAP–raco surface front, the observations reveal the intense shear-driven mixing taking place at the interface and the factors supporting the establishment of a stationary front.

Open access
Free access
Tim Cowan
,
Matthew C. Wheeler
,
David H. Cobon
,
John B. Gaughan
,
Andrew G. Marshall
,
Wendy Sharples
,
Jillian McCulloch
, and
Chelsea Jarvis

Abstract

Exposure to weather extremes, such as heat waves, can cause discomfort, harm or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure to chill risk for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multi-year climatology of three cattle thermal heat stress indices across Australia in the warm season months (October to March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the 7 historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events, and importantly, further research on standardizing the risk classifications of these thermal indices for cattle in Australia’s variable climate.

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Nicholas S. Grondin
and
Kelsey N. Ellis

Abstract

In this study, we investigated the translation speed and intensity change characteristics for landfalling North American tropical cyclones (TCs) from 1971 to 2020. We calculated three variables—intensity change, mean translation speed, and translation speed change—prior to each TC landfall and investigated the climatology of these variables for seven coastal segments. We found that lower-latitude segments generally had greater positive intensity changes prior to landfall, and higher-latitude segments had greater translation speeds. Longitude primarily influenced translation speed changes, with landfalling TCs along the Atlantic coast of the United States notably accelerating prior to landfall. Temporal trends in each of these variables were inconsistent geographically, but most segments showed an increase in positive intensity changes over time, demonstrating the increasing likelihood of intensifying TCs before landfall in recent years. We defined extreme intensification and extreme weakening as the 90th and 10th percentile of all landfalling TC intensity changes, respectively. We found that extreme intensification and weakening have been increasing and decreasing in frequency, respectively. Results from this study can be used in a variety of future applications, including in operational forecasting and model production, and provide a baseline for climate attribution studies investigating extreme intensity change events.

Significance Statement

Landfalling tropical cyclones pose significant hazards to life and property in coastal regions across North America. This study develops a comprehensive climatology of intensity change and translation speed of tropical cyclones during the final 36 h prior to landfall. We found that incidences of extreme intensification and weakening of landfalling North American tropical cyclones have increased and decreased, respectively, since 1971. We also identify lower-latitude coastal segments tend to average faster translation speeds that higher-latitude segments, while segments on the east coast of the United States tended to average a greater acceleration. These results show the importance of looking at tropical cyclone characteristics regionally and provide a useful baseline to assess how tropical cyclone risk is changing in a warming climate.

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