Hasil untuk "Meteorology. Climatology"

Jianping Guo, Y. Miao, Yong Zhang et al.

Abstract. The important roles of the planetary boundary layer (PBL) in climate, weather and air quality have long been recognized, but little is known about the PBL climatology in China. Using the fine-resolution sounding observations made across China and reanalysis data, we conducted a comprehensive investigation of the PBL in China from January 2011 to July 2015. The boundary layer height (BLH) is found to be generally higher in spring and summer than that in fall and winter. The comparison of seasonally averaged BLHs derived from observations and reanalysis, on average, shows good agreement, despite the pronounced inconsistence in some regions. The BLH, derived from soundings conducted three or four times daily in summer, tends to peak in the early afternoon, and the diurnal amplitude of BLH is higher in the northern and western subregions of China than other subregions. The meteorological influence on the annual cycle of BLH is investigated as well, showing that BLH at most sounding sites is negatively associated with the surface pressure and lower tropospheric stability, but positively associated with the near-surface wind speed and temperature. In addition, cloud tends to suppress the development of PBL, particularly in the early afternoon. This indicates that meteorology plays a significant role in the PBL processes. Overall, the key findings obtained from this study lay a solid foundation for us to gain a deep insight into the fundamentals of PBL in China, which helps to understand the roles that the PBL plays in the air pollution, weather and climate of China.

Nicolas Raillard, Coline Poppeschi, Tessa Chevallier et al.

The rapid expansion of the French offshore wind sector requires a critical reassessment of structural durability in the face of evolving marine conditions driven by climate change. Traditional design methodologies, which rely on the assumption of stationary environmental conditions, are no longer adequate. This study introduces a novel statistical framework to assess future changes in significant wave height by employing non-stationary Generalized Extreme Value (GEV) models applied to monthly maxima. This approach aims to reduce uncertainty and provide robust design tools adapted to the non-stationary conditions of the future. Based on CMIP6 climate models and reanalysis data, results reveal a projected trend towards a more pronounced seasonal contrast along the French Atlantic and English Channel coasts under future scenarios (SSP1-2.6 and SSP5-8.5), whereas the French Mediterranean Sea exhibits results that are more difficult to interpret, due to a weaker increase of extremes and large uncertainties (inter-model spread). Projections indicate more intense winters and calmer summers, along with a shift in the seasonal cycle. Overall, the multi-model ensemble suggests an increase in the design levels for extreme sea states. The research concludes by defining a new methodology for calculating an equivalent design level over the structure's operational lifespan. This tool is deemed essential for ensuring the resilience and economic viability of future offshore wind farms in a changing climate.

Pascal Meurer, Sebastian Buschow, Svenja Szemkus et al.

The increasing occurrence of extreme weather events since the beginning of the 21st century has led to the development of new methods to attribute extreme events to anthropogenic climate change. How the extreme event is defined has a major influence on the attribution result. A frequently disregarded or evaded aspect concerns the temporal dependence and the clustering of extremes. This study presents an approach for attributing complete time series during extreme events to anthropogenic forcing. The approach is based on a non-stationary Markov process using bivariate extreme value theory to model the temporal dependence of the time series. We calculate the likelihood ratio of an observational time series from ERA5 given the distributions as estimated from CMIP6 simulations with historical natural-only and natural and anthropogenic forcing scenarios. The spatial fields are condensed by the extremal pattern index as a compact description of spatial extremes. In addition, the study examines the extent to which attribution statements about the occurrence of extreme heat events change when the effect of the mean warming is eliminated. The resulting attribution statement provides very strong evidence for the scenario with anthropogenic drivers over Europe, especially since the beginning of the 21st century. For central and southern Europe, the influence of anthropogenic greenhouse gas emissions on heatwaves could already have been proven in the 1970s using today's methods. There is no reliable signal apart from a general increase in temperature, neither in terms of the temporal dependence of extreme heat days nor in terms of the shape of the extreme value distribution.

Samuel Davidson, Zachary J. Suriano

Abstract Rain-on-snow events, characterized by rainfall occurring over existing snowpack, have the potential to trigger significant hydrological and environmental impacts including snowmelt, flooding, landslides, and other natural hazards. Despite prior evidence suggesting the Connecticut River watershed has endured socioeconomic and environmental setbacks due to rain-on-snow events in the past, limited prior research has been performed on the role of rain-on-snow events within the region’s hydroclimatology. A clearer understanding of the occurrence and impacts of these events is essential for risk and water resource managers. This study created a climatology of rain-on-snow events in the Connecticut River watershed from 1981 to 2022, focusing on their spatiotemporal variability, trends, and hydrological impacts. Using daily gridded observations of snow water equivalent, precipitation, and temperature, rain-on-snow events were identified in the region, followed by an analysis of their frequency, intensity, and temporal patterns. Results revealed that higher elevations experienced more frequent rain-on-snow events and a higher average magnitude of snow water equivalent loss, while precipitation decreased with increasing latitude. There were significant decreasing trends in annual rain-on-snow frequency for parts of Vermont and Massachusetts, significant increasing (decreasing) trends in event-based snow water equivalent loss for parts of the lower (upper) watershed, and significant increasing trends in event-based precipitation for many parts of the watershed. The findings from this research will play a crucial role in future mitigation strategies, water resource management, and resilience within the watershed.

C. Álvarez-Garretón, P. Mendoza, J. Boisier et al.

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.

Duc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia et al.

This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from climate reanalysis datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) dataset during the 1980-2020 period, we demonstrate that ResNet can reasonably reproduce the overall TCG climatology in the WNP, capturing both its seasonality and spatial distribution. Our sensitivity analyses and optimizations show that this TCG reconstruction depends on both the type of TCG climatology that one wishes to reconstruct and the strategies used to label TCG data. Of interest, analyses of different input features reveal that DL-based reconstruction of TCG climatology needs only a subset of channels rather than all available data, which is consistent with previous modeling and observational studies of TCG. These results not only enhance our understanding of the TCG process but also provide a promising pathway for predicting or downscaling TCG climatology based on large-scale environments from global model forecasts or climate output. Overall, our study demonstrates that DL can offer an effective approach for studying TC climatology beyond the traditional physical-based simulations and vortex-tracking algorithms used in current climate model analyses.

J. Hernández-Bernal, A. Spiga, A. Chatain et al.

We investigate Gravity Waves (GWs) in the lower atmosphere of Mars based on pressure timeseries acquired by the InSight lander. We compile a climatology showing that most GW activity detected at the InSight landing site takes place after the sunrise and sunset, they are almost absent during the aphelion season, and more prominent around the equinoxes, with variations during dust events and interannual variations. We find GWs with coherent phases in different sols, and a previously unnoticed coincidence of GW activity with those moments in which the diurnal cycle (of tidal origin) exhibits the fastest increases in absolute pressure. We explore the possibility that some of these GWs might actually be high-order harmonics of thermal tides transiently interfering constructively to produce relevant meteorological patterns, and discuss other interpretations based on wind patterns. The so-called Terminator Waves observed on Earth might also explain some of our observations.

Sinesipho Ngamile, Sinesipho Ngamile, Mahlatse Kganyago et al.

IntroductionWater quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS.MethodsHigh-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions.ResultsThe results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. Specifically, Model 2 achieved an R2 of 0.88 and an RMSE of 1.37 for DO, while Model 1 achieved an R2 of 0.63 and an RMSE of 291.48 for EC. For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions using Model 2 (R2p = 0.55; RMSE = 118.19). Due to the over-pixelation of other smaller water bodies within the COHWHS in Sentinel-2 imagery, Cradlemoon Lake was selected to show distinct seasonal (high- and low-flow) and spatial variations in optically and non-optically active water quality parameters.DiscussionVariations in the results were influenced by runoff dynamics and upstream pollution: lower Temperatures and suspended solids under low-flow conditions increased DO concentrations, whereas higher suspended solid concentrations under high-flow conditions likely reduced light penetration, resulting in lower spectral reflectance and chlorophyll-a levels. These findings highlight the potential of Sentinel-2 MSI data and machine learning models for monitoring dynamic water quality variations in freshwater ecosystems.

Wenhao Zhang, Yao Li, Bo Li et al.

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product.

Iñigo Gómara, Gianni Bellocchi, Raphaël Martin et al.

Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959-2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995-2015 average being 29% higher than the 1959-1979 average. Such increase seems consistent with observed rising trends in temperature and CO2, and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value<0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection.

Rana Muhammad Adnan, Wang Mo, Ozgur Kisi et al.

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation.

Xiaoxuan Bai, Peng Li, Weiqing Zhou et al.

High-temporal resolution and timely emission estimates are essential for developing refined air quality management policies. Considering the advantages of extensive coverage, high reliability, and near real-time capabilities, in this work, electric power big data (EPBD) was first employed to obtain accurate hourly resolved facility-level air pollutant emissions information from the cement industries in Tangshan City, China. Then, the simulation optimization was elucidated by coupling the data with the weather research and forecasting (WRF)-community multiscale air quality (CMAQ) model. Simulation results based on estimated emissions effectively captured the hourly variation, with the NMB within ±50% for NO₂ and PM_2.5 and R greater than 0.6 for SO₂. Hourly PM_2.5 emissions from clinker production enterprises exhibited a relatively smooth pattern, whereas those from separate cement grinding stations displayed a distinct diurnal variation. Despite the remaining underestimation and/or overestimation of the simulation concentration, the emission inventory based on EPBD demonstrates an enhancement in simulation results, with RMSE, NMB, and NME decreasing by 9.6%, 15.8%, and 11.2%, respectively. Thus, the exploitation of the vast application potential of EPBD in the field of environmental protection could help to support the precise prevention and control of air pollution, with the possibility of the early achievement of carbon peaking and carbon neutrality targets in China and other developing countries.

Grzegorz Urban, Michał K. Kowalewski, Jakub Sawicki et al.

At the Polish Institute of Meteorology and Water Management – National Research Institute (the Polish acronym: IMGW-PIB), experimental parallel measurements of daily precipitation totals were carried out from August 2021 – September 2022. Measurements were made with 5 types of amateur rain gauges (A – CliMET CM1016; B – TFA Dostmann 47.1008; C – TFA Dostmann 47.1000; D – Davis 6466 AeroCone Rain Collector; E – TFA Dostmann 47.3005.01) and a manual Hellmann rain gauge (H), which was taken as reference. The results showed that the most frequent differences in the rain gauges tested are very small differences (in daily rainfall totals Δ≤0.1 mm). They account for 63–86% of the days. On the other hand, large (1.0<Δ≤5.0 mm) and very large (Δ>5.0 mm) differences are most frequent in type D, respectively: 12% and 5% of days. This type, as the only one of the tested types, is characterised by the occurrence of days with very large differences with respect to rain gauge H. The tested rain gauges of the same type do not show the same sign of difference with respect to the reference rain gauge, as there are both negative and positive differences. The C type rain gauge shows the smallest average difference of daily precipitation totals against the H-type rain gauge (+0.04 mm). On the other hand, the average differences for types A and B are negative and amount to –0.1 and –0.2 mm, respectively. In contrast, the average difference for type D is +0.2 mm. Some amateur rain gauges perform better or similar to operationally used rain gauges of automatic networks. Types A, C and B can be used in voluntary networks with appropriate and continuous human supervision and their results used, as an additional source of information, for operational purposes at IMGW-PIB or other hydrological and meteorological services. The rain gauge D is useful only during warm season. Type E was not suitable for the conditions of the experiment for technical reasons.

Jianping Guo, Y. Miao, Yong Zhang et al.

The important roles of planetary boundary layer (PBL) in climate, weather and air quality 15 have long been recognized, but little is known about the PBL climatology in China. Using the fineresolution sounding observations made across China and reanalysis data, we conducted a comprehensive investigation of the PBL in China from January 2011 to July 2015. The boundary layer height (BLH) is found to be generally higher in spring and summer than that in fall and winter. The comparison of seasonally averaged BLH derived from observations and reanalysis shows good 20 agreement. The BLH derived from threeor four-times-daily soundings in summer tends to peak in the early afternoon, and the diurnal amplitude of BLH is higher in the northern and western sub-regions of China than other sub-regions. The meteorological influence on the annual cycle of BLH is investigated 4 as well, showing that BLH at most sounding sites is negatively associated with the surface pressure and lower tropospheric stability, but positively associated with the near-surface wind speed and temperature. This indicates that meteorology plays a significant role in the PBL processes. Overall, the key findings obtained from this study lay a solid foundation for us to gain a deep insight into the fundamentals of PBL in China, which helps to understand the roles of PBL playing in the air pollution, weather and 5 climate of China.

M. Saft

Elin Törnquist, Wagner Costa Santos, Timothy Pogue et al.

Machine learning for time-series forecasting remains a key area of research. Despite successful application of many machine learning techniques, relating computational efficiency to forecast error remains an under-explored domain. This paper addresses this topic through a series of real-time experiments to quantify the relationship between computational cost and forecast error using meteorological nowcasting as an example use-case. We employ a variety of popular regression techniques (XGBoost, FC-MLP, Transformer, and LSTM) for multi-horizon, short-term forecasting of three variables (temperature, wind speed, and cloud cover) for multiple locations. During a 5-day live experiment, 4000 data sources were streamed for training and inferencing 144 models per hour. These models were parameterized to explore forecast error for two computational cost minimization methods: a novel auto-adaptive data reduction technique (Variance Horizon) and a performance-based concept drift-detection mechanism. Forecast error of all model variations were benchmarked in real-time against a state-of-the-art numerical weather prediction model. Performance was assessed using classical and novel evaluation metrics. Results indicate that using the Variance Horizon reduced computational usage by more than 50\%, while increasing between 0-15\% in error. Meanwhile, performance-based retraining reduced computational usage by up to 90\% while \emph{also} improving forecast error by up to 10\%. Finally, the combination of both the Variance Horizon and performance-based retraining outperformed other model configurations by up to 99.7\% when considering error normalized to computational usage.

Maggie Bailey, Douglas Nychka, Manajit Sengupta et al.

Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM output from their native grids and at differing spatial resolutions to a common grid in order to be comparable to observed data and (2) bias correcting RCM data, via quantile mapping, for example, for future modeling and analysis. The uncertainty associated with (1) is not always considered for downstream operations in (2). This work examines this uncertainty, which is not often made available to the user of a regridded data product. This analysis is applied to RCM solar radiation data from the NA-CORDEX data archive and observed data from the National Solar Radiation Database housed at the National Renewable Energy Lab. A case study of the mentioned methods over California is presented.

Sándor Baran, Mária Lakatos

To be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water- and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill, and reliability of visibility predictions are far reduced compared to other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or non-parametric approaches, including also machine learning-based techniques. As visibility observations - according to the suggestion of the World Meteorological Organization - are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post-processing results in further substantial improvement in forecast skill and in general, POLR models are superior to their MLP counterparts.

Yopi Ilhamsyah, Yustya Rahmy, Marwan Marwan et al.

The objective is to analyse temperature changes and their future projection in Aceh. The activities consist of collecting past and future temperature data, preparing materials for processing, and analyzing past and future temperature data (climate change projections). The data used are monthly average temperature data from data global climate model, e.g., csiromk3.6-hist-1986-2005-tas, csiromk3.6-rcp45- 2016-2035-tas, csiromk3.6-rcp45-2046-2065-tas, csiromk3.6-rcp45-2081-2100- tas, csiromk3.6-rcp85-2016-2035-tas, csiromk3.6-rcp85-2046-2065-tas, and csiromk3.6-rcp85-2081-2100-tas. The study began with reading climate data in NetCDF format using GRADS software, data processing using CDO software, providing a coordinate system using QGIS software, making climate change projection maps using ArcGIS software, and making climate change graphs using spreadsheet programs. Two scenarios, i.e., RCP4.5 and RCP8.5 are used to analyse the projected temperature changes in the short-term (2016 – 2035), medium-term (2046-2065), and long-term (2081-2100). The results show that the RCP4.5 projection shows a lower change in temperature rise than the RCP 8.5. A change in a temperature rise of up to 5°C was found in the RCP8.5 scenario.

Jessica Voveris, Mark Serreze

Arctic rain-on-snow (ROS) events can have significant impacts on Arctic wildlife and socio-economic systems. This study addresses the meteorology of two different Arctic ROS events. The first, occurring near Nuuk, Greenland, generated significant impacts, including slush avalanches. The second, less severe, event occurred within the community of Iqaluit, Nunavut, Canada. This research utilizes atmospheric reanalysis, automated surface observation station data and atmospheric soundings to determine the meteorological conditions driving these events and the differences between each case. In both cases, atmospheric blocking played a leading role in ROS initiation, with atmospheric rivers – narrow bands of high water vapor transport, typically originating from the tropics and subtropics – having both direct and indirect effects. Cyclone-induced low-level jets and resultant ‘warm noses’ of higher air temperatures and moisture transport were other key features in ROS generation. To our knowledge, our study is the first to visualize how the varying strength and manifestation of these coupled features contribute to differences in the severity of Arctic ROS events. The meteorological drivers identified here find support from other studies on Arctic ROS events and are similar to weather features associated with Arctic precipitation events of extreme magnitude.