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Mahsa Payami
,
Yunsoo Choi
,
Ahmed Khan Salman
,
Seyedali Mousavinezhad
,
Jincheol Park
, and
Arman Pouyaei

Abstract

In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a 1-dimensional Convolutional Neural Network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorology, and land use land cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an Index of Agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal 3-fold cross-validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley Additive Explanations analysis. The results revealed solar radiation reaching the surface, Planetary Boundary Layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times faster in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well.

Open access
Nicholas M. Leonardo
and
Brian A. Colle

Abstract

Nested idealized baroclinic wave simulations at 4-km and 800-m grid spacing are used to analyze the precipitation structures and their evolution in the comma head of a developing extratropical cyclone. After the cyclone spins up by hour 120, snow multi-bands develop within a wedge-shaped region east of the near-surface low center within a region of 700-500-hPa potential and conditional instability. The cells deepen and elongate northeastward as they propagate north. There is also an increase in 600-500-hPa southwesterly vertical wind shear prior to band development. The system stops producing bands 12 hours later as the differential moisture advection weakens, and the instability is depleted by the convection.

Sensitivity experiments are run in which the initial stability and horizontal temperature gradient of the baroclinic wave are adjusted by 5-10%. A 10% decrease in initial instability results in less than half the control run potential instability by 120 h and the cyclone fails to produce multi-bands. Meanwhile, a 5% decrease in instability delays the development of multi-bands by 18 h. Meanwhile, decreasing the initial horizontal temperature gradient by 10% delays the growth of vertical shear and instability, corresponding to multi-bands developing 12-18 hours later. Conversely, increasing the horizontal temperature gradient by 10% corresponds to greater vertical shear, resulting in more prolific multi-band activity developing ∼12 hours earlier. Overall, the relatively large changes in band characteristics over a ∼12-hour period (120-133 h) and band evolutions for the sensitivity experiments highlight the potential predictability challenges.

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Isaac Davis
and
Brian Medeiros

Abstract

The Community Earth System Model, version 2 (CESM2) has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late 21st Century in four Shared Socioeconomic Pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

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Jingyi Wen
,
Zhiyong Meng
,
Lanqiang Bai
, and
Ruilin Zhou

Abstract

This study documents the features of tornadoes, their parent storms and the environments of the only two documented tornado outbreak events in China. The two events were associated with tropical cyclone (TC) Yagi on 12 August 2018, with 11 tornadoes, and with an extratropical cyclone (EC) on 11 July 2021 (EC 711), with 13 tornadoes. Most tornadoes in TC Yagi were spawned from discrete mini-supercells, while a majority of tornadoes in EC 711 were produced from supercells imbedded in QLCSs or cloud clusters. In both events, the high-tornado-density area was better collocated with K index rather than MLCAPE, and with entraining rather than non-entraining parameters possibly due to their sensitivity to mid-level moisture. EC 711 had a larger displacement between maximum entraining CAPE and vertical wind shear than TC Yagi, with the maximum entraining CAPE better collocated with the high-tornado-density area than vertical wind shear. Relative to TC Yagi, EC 711 had stronger entraining CAPE, 0–1-km storm relative helicity, 0–6-km vertical wind shear, and composite parameters such as entraining significant tornado parameter, which caused its generally stronger tornado vortex signatures (TVSs) and mesocyclones with a larger diameter and longer lifespan. No significant differences were found in composite parameter of these two events from U.S. statistics. Although obvious dry air intrusions were observed in both events, no apparent impact was observed on the potential of tornado outbreak in EC 711. In TC Yagi, however, the dry air intrusion may have helped tornado outbreak due to cloudiness erosion and thus increase in surface temperature and low-level lapse rate.

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Gijs de Boer
,
Brian J. Butterworth
,
Jack S. Elston
,
Adam Houston
,
Elizabeth Pillar-Little
,
Brian Argrow
,
Tyler M. Bell
,
Phillip Chilson
,
Christopher Choate
,
Brian R. Greene
,
Ashraful Islam
,
Ryan Martz
,
Michael Rhodes
,
Daniel Rico
,
Maciej Stachura
,
Francesca M. Lappin
,
Antonio R. Segales
,
Seabrooke Whyte
, and
Matthew Wilson

Abstract

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Open access
Maria Laura Poletti
,
Martina Lagasio
,
Antonio Parodi
,
Massimo Milelli
,
Vincenzo Mazzarella
,
Stefano Federico
,
Lorenzo Campo
,
Marco Falzacappa
, and
Francesco Silvestro

Abstract

Flood forecast remains a significant challenge, particularly when dealing with basins characterized by small drainage areas (i.e. 103 km2 or lower with response time in the range 0.5-10 h) especially because of the rainfall prediction uncertainties (Buzzi et al., 2014) . This study aims to investigate the performances of streamflow predictions using two short-term rainfall forecast methods.

These methods utilize a combination of nowcasting extrapolation algorithm and numerical weather predictions by employing three-dimensional variational assimilation system and nudging assimilation techniques, meteorological radar and lightning data are frequently updated, allowing new forecasts with high temporal frequency (i.e. 1-3 hours). A distributed hydrological model is used to convert rainfall forecasts in streamflow prediction. The potential of assimilating radar and lightning data or radar data alone, is also discussed.

A hindcast experiment on two rainy periods in the north-west region of Italy was designed. The selected skill scores were analyzed to assess their degradation with increasing lead time, and the results were further aggregated based on basin dimensions to investigate the catchment integration effect. The findings indicate that both rainfall forecast methods yield good performance, with neither definitively outperforming the other. Furthermore, the results demonstrate that, on average, assimilating both radar and lightning data enhances the performance.

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Olivia Gozdz
,
Martha W. Buckley
, and
Timothy DelSole

Abstract

The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere-ocean-ice model to the same atmosphere-ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

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Zeying Huang
,
Jungmin Lim
, and
Mark Skidmore

Abstract

Extreme heat events stress the body and can result in fatalities, especially for those with underlying health problems. Air pollution is another threat to health and is an important confounder of extreme heat risks. However, previous empirical studies that have addressed the joint health impacts of air pollution and heat rarely considered the endogeneity and spillover effects of air pollution. To fill this research gap, this article investigates the interconnected impacts of extreme heat and fine particulate matter (PM2.5) on all-cause and cause-specific mortality. We correct the endogeneity of PM2.5 by applying the control function approach and explore transboundary externalities of all-source PM2.5 and wildfire-caused PM2.5. We use a county-year balanced panel dataset covering 2,992 United States counties from 2001 through 2011. Results show that extreme heat and air pollution exacerbate each other and jointly increase mortality. Specifically, a one standard deviation (SD) increase in the heat index results in 0.60% (95% confidence interval: 0.26% - 0.97%), 2.14% (1.34% - 2.94%), and 0.86% (0.41% - 1.34%) more all-cause fatalities, fatalities from respiratory system diseases, fatalities from circulatory system diseases, respectively. A one SD increase in PM2.5 results in 5.75% (3.61% - 7.90%), 6.99% (3.01% - 11.15%), and 2.93% (0.66% - 5.28%) additional fatalities, respectively. Failure to consider the endogeneity of PM2.5 leads to a substantial underestimation of PM2.5 risk. In addition, our instrumental variable strategy offers evidence of spillover effects from PM2.5 and wildfires.

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Yankun Gong
,
Zhiwu Chen
,
Ruixiang Zhao
,
Jiexin Xu
,
Juan Li
,
Jiesuo Xie
,
Yinghui He
,
Xiao-Hua Zhu
,
Yuhan Sun
, and
Shuqun Cai

Abstract

Joint effects of winds and tides on near-inertial internal waves (NIWs) are numerically investigated via a series of three-dimensional quasi-realistic simulations in the northern South China Sea (NSCS). Model results demonstrate that in the presence of wind-induced NIWs, more tidal energy is transferred to NIWs, while in the presence of tide-induced NIWs, the extreme wind (cyclone) would inject less near-inertial kinetic energy (NIKE). The interaction between wind-induced and tide-induced NIWs produces total NIKE more (or less) than a linear superposition of that generated by wind and tide forcing alone at different sites in the NSCS. Specifically, near the Luzon Strait, both tides and winds make positive contributions to the local near-inertial energy input, resulting in more than 30% enhancement of total NIKE (>0.5 kJ m−2). However, in some deep-water regions along the cyclone paths, energy is transferred from cyclones to NIWs and also from NIWs to internal tides. Due to this “energy pipeline” effect, tide- and wind-induced NIWs contribute to weakening of total NIKE (∼0.3 kJ m−2 or 30%). Additionally, sensitivity experiments with varying initial tidal phases indicate that the interaction between wind-induced NIKE and tide-induced NIKE is robust in most model domain (over 80%) under different phase alignments between wind- and tide-induced NIWs. From the perspective of cyclones, tide-induced NIKE is comparable to wind-induced NIKE in the Luzon Strait before the arrival of cyclones, while tide-induced NIKE is two orders of magnitude smaller than wind-induced NIKE in most of the NSCS after the arrival of cyclones. Overall, our results highlight the joint effects of wind and tide forcing on the local NIW dynamics in the NSCS.

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Wen-Shu Lin
,
Joel R. Norris
,
Michael J. DeFlorio
, and
F. Martin Ralph

Abstract

We apply the Ralph et al. (2019) scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast are highest in Oregon and Washington and lowest in southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

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