Storm Environment Research Articles

Unclouding the Correlations: A Principal Component Analysis of Convective Environments

AbstractIn this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics.

Radar-Derived Convective Features and Atmospheric Environments of Hail-Producing Storms over the Contiguous United States

Abstract Hail smaller than 0.75 in. is known to cause economic impacts yet remains understudied due to report biases towards recording larger hail sizes (≤1 in.). In this study, we assembled ground hail reports during 2017–22 from the National Centers for Environmental Information (NCEI) Storm Data, Community Collaborative Rain, Hail and Snow Network (CoCoRaHS), and Meteorological Phenomena Identification Near the Ground (mPING) databases. Then, these reports are collocated with the attributes of radar-derived convective features from the Multi-Radar Multi-Sensor (MRMS) system and fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ERA5) atmospheric vertical profiles to construct a dataset describing properties of a full spectrum of hailstorms. The characteristics of radar reflectivity and atmospheric profiles are examined for hail of different sizes reported within selected regions over the contiguous United States (CONUS). In addition to the seasonal and diurnal variations, the morphology of convective features shows apparent regional differences from west to east in CONUS. The maximum expected size of hail (MESH) performance against reported hail sizes shows underestimation of hail with significant sizes and overestimation of small hail sizes. ERA5 vertical atmospheric profiles are explored to form relationships between storm environment and hail sizes. In addition to the relationships between wind shear and hail sizes, the roles of low-level relative humidity and freezing level height in regard to hail melting are discussed.

Should We Conserve Entropy or Energy when Computing CAPE with Mixed-Phase Precipitation Physics?

Abstract The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from nonequilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution-dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1%–3% as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 and 250 m using explicit first-order and second-order integration schemes, respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast to energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution, whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. Significance Statement Convective available potential energy (CAPE) is a measure of instability in the atmosphere that helps forecasters and researchers understand when and where thunderstorms will form. The purpose of this article is to identify the most efficient and accurate methods for computing CAPE. Two methods are considered here, one that relates to the entropy (a measure of thermodynamic disorder) of an air parcel and one that relates to the energy of an air parcel. Results indicate that the entropy method is most accurate and insensitive to the resolution of the data used for the calculation (which can vary considerably), whereas the energy method uses the least computation time.

A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model

AbstractLarge‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.

The Impact on Simulated Bow Echoes of Changing Grid Spacing from 3 km to 1 km in the WRF Model

Abstract Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

ERA5‐Based Significant Tornado Environments in Canada Between 1980 and 2020

AbstractThis study uses ERA5 close‐proximity soundings and associated convective parameters to characterize significant tornadic storm (F/EF2+) environments between 1980 and 2020 in parts of Canada. It is shown that ERA5 convective parameters are suitable to represent observed parameters, based on radiosonde comparisons. Results indicate that the eastern Canadian Prairies have nearly double the lifted condensation level with higher level of free convection compared to eastern Canada (southern Ontario/Quebec). Eastern Canada has more a humid boundary layer and free troposphere that can lead to warmer cold pools, favoring tornadogenesis. Central Canada (Manitoba) has the largest mixed‐layer (ML) convective available potential energy (CAPE) mainly due to a combination of regional differences in low level moisture and steeper mid‐level lapse rates in western Canada. Central continental U.S. and Canadian regions appear to have the highest (most negative) convective inhibition, leading to more explosive initiation. Mean bulk wind shear and storm relative helicity (SRH) increases from west to east, with eastern regions being significantly larger. The supercell composite and significant tornado parameters are generally less than U.S. magnitudes, particularly in western Canada, and would require recalibration for more practical use in Canada. Overall, western Canada significant tornadic storms are associated with more low‐level looping hodographs and dominated by thermodynamic influences compared to larger wind influences in eastern regions. This is likely due to more spring, late summer, and autumn events that typically have well‐developed synoptic systems (stronger wind shear) with overall less buoyant energy in eastern regions.

Idealized Simulations of a Supercell Interacting with an Urban Area

Idealized simulations with a cloud-resolving model are conducted to examine the impact of a simplified city on the structure of a supercell thunderstorm. The simplified city is created by enhancing the surface roughness length and/or surface temperature relative to the surroundings. When the simplified city is both warmer and has larger surface roughness relative to its surroundings, the supercell that passes over it has a larger updraft helicity (at both midlevels and the surface) and enhanced precipitation and hail downwind of the city, all relative to the control simulation. The storm environment within the city has larger convective available potential energy which helps stimulate stronger low-level updrafts. Storm relative helicity (SRH) is actually reduced over the city, but enhanced in a narrow band on the northern edge of the city. This band of larger SRH is ingested by the primary updraft just prior to passing over the city, corresponding with enhancement to the near-surface mesocyclone. Additional simulations in which the simplified city is altered by removing either the heat island or surface roughness length gradient reveal that the presence of a heat island is most closely associated with enhancements in updraft helicity and low-level updrafts relative to the control simulation.

Study of Microphysical Signatures Based on Spectral Polarimetry during the RELAMPAGO Field Experiment in Argentina

Abstract Weather radars with dual-polarization capabilities enable the study of various characteristics of hydrometeors, including their size, shape, and orientation. Radar polarimetric measurements, coupled with Doppler information, allow for analysis in the spectral domain. This analysis can be leveraged to reveal valuable insight into the microphysics and kinematics of hydrometeors in precipitation systems. This paper uses spectral polarimetry to investigate precipitation microphysics and kinematics in storm environments observed during the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field experiment in Argentina. This study uses range–height indicator scan measurements from a C-band polarimetric Doppler weather radar deployed during the field campaign. In this work, the impact of storm dynamics on hydrometeors is studied, including the size sorting of hydrometeors due to vertical wind shear. In addition, particle microphysical processes because of aggregation and growth of ice crystals in anvil clouds, as well as graupel formation resulting from the riming of ice crystals and dendrites, are also analyzed here. The presence of different particle size distributions because of the mixing of hydrometeors in a sheared environment and resulting size sorting has been reported using spectral differential reflectivity (sZdr) slope. Spectral reflectivity sZh and sZdr have also been used to understand the signature of ice crystal aggregation in an anvil cloud. The regions of pristine ice crystals are identified from vertical profiles of spectral polarimetric variables in anvil cloud because of sZh < 0 dB and sZdr values around 2 dB. It is also found that the growth process of these ice crystals causes a skewed bimodal sZh spectrum due to the presence of both pristine ice crystals and dry snow. Next, graupel formation due to riming has been studied, and it is found that the riming process produces sZh values of about 10 dB and corresponding sZdr values of 1 dB. This positive sZdr indicates the presence of needle/columnar secondary ice particles formed by ice multiplication processes in the riming zones. Last, the temporal evolution of a storm is investigated by analyzing changes in hydrometeor types with time and their influence on the spectral polarimetric variables.

Automated and Objective Thunderstorm Identification and Tracking Using Geostationary Lightning Mapper (GLM) Data

Abstract A unique storm identification and tracking method is analyzed in varying storm environments within the United States spanning 273 hours in 2018. The methodology uses a quantity calculated through fusion of radar-based vertically integrated liquid (VIL) and satellite-based GLM flash rate density (FRD) called VILFRD to identify storms in space and time. This research analyzes GLM data use within VILFRD for the first time (method original: O), assesses four modifications to VILFRD implementation to find a more stable storm size with time (method new: N), larger storms (method original dilated: OD), or both (method new dilated: ND), and compares VILFRD methods with storm tracking using the 35-dBZ isosurface at −10°C (method non-VILFRD: NV). A case study analysis from 2019 is included to assess methods on a smaller scale and introduce a “lightning only” (LO) version of VILFRD. Large study results highlight that VILFRD-based storm identification produces smaller storms with more lightning than the NV method, and the NV method produces larger storms with a more stable size over time. Methods N and ND create smaller storm size fluctuations, but size changes more often. Dilation (OD, ND) creates larger storms and almost double the number of storms identified relative to nondilated methods (O, N, NV). The case study results closely resemble the large sample results and show that the LO method identifies storms with more lightning and shorter durations. Overall, these findings can aid in choice of storm tracking method based on desired user application and promote further testing of a lightning-only version of VILFRD.

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network

Abstract Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database from GOES-12/13 to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

An Analytic Formula for Entraining CAPE in Midlatitude Storm Environments

Abstract This article introduces an analytic formula for entraining convective available potential energy (ECAPE) with an entrainment rate that is determined directly from an environmental sounding, rather than prescribed by the formula user. Entrainment is connected to the background environment using an eddy diffusivity approximation for lateral mixing, updraft geometry assumptions, and mass continuity. These approximations result in a direct correspondence between the storm-relative flow and the updraft radius and an inverse scaling between the updraft radius squared and entrainment rate. The aforementioned concepts, combined with the assumption of adiabatic conservation of moist static energy, yield an explicit analytic equation for ECAPE that depends entirely on state variables in an atmospheric profile and a few constant parameters with values that are established in past literature. Using a simplified Bernoulli-like equation, the ECAPE formula is modified to account for updraft enhancement via kinetic energy extracted from the cloud’s background environment. CAPE and ECAPE can be viewed as predictors of the maximum vertical velocity wmax in an updraft. Hence, these formulas are evaluated using wmax from past numerical modeling studies. Both of the new formulas improve predictions of wmax substantially over commonly used diagnostic parameters, including undiluted CAPE and ECAPE with a constant prescribed entrainment rate. The formula that incorporates environmental kinetic energy contribution to the updraft correctly predicts instances of exceedance of by wmax, and provides a conceptual explanation for why such exceedance is rare among past simulations. These formulas are potentially useful in nowcasting and forecasting thunderstorms and as thunderstorm proxies in climate change studies. Significance Statement Substantial mixing occurs between the upward-moving air currents in thunderstorms (updrafts) and the surrounding comparatively dry environmental air, through a process called entrainment. Entrainment controls thunderstorm intensity via its diluting effect on the buoyancy of air within updrafts. A challenge to representing entrainment in forecasting and predictions of the intensity of updrafts in future climates is to determine how much entrainment will occur in a given thunderstorm environment without a computationally expensive high-resolution simulation. To address this gap, this article derives a new formula that computes entrainment from the properties of a single environmental profile. This formula is shown to predict updraft vertical velocity more accurately than past diagnostics, and can be used in forecasting and climate prediction to improve predictions of thunderstorm behavior and impacts.

Factors Affecting the Rapid Recovery of CAPE on 31 March 2016 during VORTEX-Southeast

Abstract In this study, we analyze various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. As a case study, the relative roles of direct insolation and near-surface moisture advection in the recovery CAPE on 31 March 2016 in northern Alabama are examined using VORTEX-Southeast (VORTEX-SE) observations and numerical simulations. In between rounds of nontornadic morning storms and tornadic evening storms, CAPE over the VORTEX-SE domain increased from near zero to at least 500 J kg−1. A timeline of the day’s events is provided with a focus on the evolution of the lower levels of the atmosphere. We focus on its responses to solar insolation and moisture advection, which we hypothesize as the main mechanisms behind the recovery of CAPE. Data from the University of Massachusetts S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), and high-resolution EnKF analyses from the Advanced Regional Prediction System (ARPS) are used to characterize the boundary layer evolution in the pre-tornadic storm environment. It is found that insolation-driven surface diabatic heating was the primary driver of rapid CAPE recovery on this day. The methodology developed in this case can be applied in other scenarios to diagnose the primary drivers of CAPE development. Significance Statement The mechanisms by which atmospheric instability recovers can vary widely and are often a source of uncertainty in forecasting. We want to understand how and why the environment destabilized enough to produce an evening tornado following morning storms on 31 March 2016. To do this, we used model data and observations from a collocated radar and profiler. It was found that heating from the sun at the surface was the primary cause of destabilization in the environment.

Performance analysis of four PPP service software under different intensity geomagnetic storms

Currently, many precise point positioning (PPP) services provide through software or online websites for GNSS positioning studies and applications. Various PPP service software has different positioning performances due to their respective algorithms and versions. In addition, the positioning accuracy and reliability of PPP services lack systematic evaluation, especially in the case of geomagnetic storms. This study presents the PPP performance analysis of the common GNSS PPP software as CSRS-PPP (https://www.geod.nrcan.gc.ca/), APPS (https://apps.gdgps.net/), Net_Diff (http://center.shao.ac.cn/shao_gnss_ac/), and GAMP (https://www.ngs.noaa.gov/gps-toolbox/GAMP) under different geomagnetic storm intensities. The PPP experiments involve data from 916, 885, and 676 GPS stations data on 17 March 2015 (severe), 26 August 2018 (strong), and 15 January 2022 (moderate), respectively. The results showed that the positioning performance of the four PPP software is different in the magnetic storm environment. We found that during geomagnetic storms, the CSRS-PPP software has the highest positioning accuracy and reliability, while the APPS software shows the worst positioning performance. The performance of Net_Diff and GAMP during geomagnetic storms are comparable but are better than that of APPS. Statistical results indicate that the average values of 3D RMS of CSRS-PPP, APPS, Net_Diff, and GAMP under severe storm are 0.14 m, 0.60 m, 0.40 m, and 0.33 m, under strong storm are 0.18 m, 0.65 m, 0.27 m, and 0.33 m, and under moderate storm are 0.25 m, 1.66 m, 0.26 m, and 0.34 m, respectively. The main reason why the CSRS-PPP has the best positioning accuracy under different intensity storms is that it uses the ambiguity-fixed PPP solution, while the other three software use float solution.

Entrainment in a Simulated Supercell Thunderstorm. Part III: The Influence of Decreased Environmental Humidity and General Effects upon Precipitation Efficiency

Abstract Entrainment is a key process that can modulate the intensity of supercells, and a better understanding of its impact could help improve forecasts of thunderstorms and the precipitation they produce. In Part III of this series, the three distinct mechanisms of entrainment identified during the mature stage of idealized supercell thunderstorms in Part I (overturning “ribbons” of horizontal vorticity, “disorganized turbulent eddies,” and the “storm-relative airstream”) are examined as the absolute humidity of the environment is decreased. The existence of these mechanisms in a more realistic simulated storm environment is also established. Entrainment is calculated as fluxes of air across the storm core surface; passive fluid tracers help determine the resulting dilution of the storm updraft. Model microphysical rates are used to examine the direct impacts of entrainment on hydrometeors within the storm updraft as well as precipitation that falls to the ground. Results show that as mixed-layer humidity decreases, the “ribbons” and turbulent eddy mechanisms decrease in intensity, but their effects on precipitation production change little. With decreasing humidity in the 3–4 km AGL layer, the storm-relative airstream entrains less humid low-level air into the storm core, decreasing the vertical mass flux, and therefore the precipitation produced by the storm. When the humidity in the mid- to upper troposphere (4–20 km AGL) is decreased, precipitation is significantly reduced, but not due to the effects of the entrained air. Rather, enhanced evaporation and sublimation of falling precipitation decreases the overall precipitation efficiency of the storm.

Extreme Convective Rainfall and Flooding from Winter Season Extratropical Cyclones in the Mid-Atlantic Region of the United States

Abstract Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods.

The Hydrometeorology of Extreme Floods in the Lower Mississippi River

Abstract The Lower Mississippi River has experienced a cluster of extreme floods during the past two decades. The Bonnet Carré spillway, which is located on the Mississippi River immediately upstream of New Orleans, has been operated 15 times since its completion in 1931, with 7 occurrences after 2008. In this study, we examine rainfall and atmospheric water balance components associated with Lower Mississippi River flooding in 2008, 2011, and 2015–19. We focus on multiple time scales—1, 3, 7, and 14 days—reflecting contributions from individual storm systems and the aggregate contributions from a sequence of storm systems. Atmospheric water balance variables—integrated water vapor flux (IVT) and precipitable water—are central to our assessment of the storm environment for Lower Mississippi flood events. We find anomalously large IVT corridors accompany the critical periods of heavy rainfall and are organized in southwest–northeast orientation over the Mississippi domain. Atmospheric rivers play an important role as agents of extremes in water vapor flux and rainfall. We conduct climatological analyses of IVT and precipitable water extremes across the four time scales using 40 years of North American Regional Reanalysis (NARR) fields from 1979 to 2018. We find significant increasing trends in both variables at all time scales. Increases in IVT especially cover large regions of the Mississippi domain. The findings point to increased vulnerability faced by the Mississippi flood control system in the current and future climate.

Impact of Chronic Rhinosinusitis with Nasal Polyposis on IL-12 and IL-8.

The pathophysiology of Chronic Rhinosinusitis is coordinated by distinct inflammatory reactions in different individuals. Inflammatory environments with a predominance of Th2 lymphocytes tend also to be rich in eosinophils. These environments are common during the formation of nasal polyps associated with aspirin intolerance, which is also marked by an increase in inflammatory mediators, especially IL-4, IL-5, and IL-13. Despite the significance of these inflammatory mediators, the relevance of IL-12 subunits' presence within eosinophilic nasal polyps, however, has been less studied. The current study aims to evaluate the presence of IL-12 subunits, IL-12p40 and IL-12p70, in eosinophilic nasal polyps and their correlations with IL-8 presence. We compared the concentrations of IL-8, IL12p40, and IL12p70 among samples of eosinophilic nasal polypoid tissue, eosinophilic nasal polypoid tissue associated with aspirin intolerance, and healthy nasal mucosa, using an indirect immunoassay (ELISA) kit. When compared to healthy nasal mucosa, there was a lower concentration of IL-8 in Chronic Rhinosinusitis with Nasal Polyp (CRSwNP) tissue. Aspirin Intolerant polypoid tissue also presented a lower concentration of IL-12 subunits compared to healthy nasal mucosa. There was no significant correlation between IL-8 and IL-12 in the eosinophilic polypoid conditions. In CRSwNP, there is a reduction in IL-8 and IL-12 subunits compared to control, with a lack of correlation between IL-12 and IL-8. The lack of correlation can be justified by a type two inflammatory storm environment.

Biases in CMIP6 Historical U.S. Severe Convective Storm Environments Driven by Biases in Mean‐State Near‐Surface Moist Static Energy

AbstractThis work evaluates how well Coupled Model Intercomparison Project 6 models reproduce the climatology of North American severe convective storm (SCS) environments in ERA5 reanalysis and examines what drives biases across models. Biases in spring SCS environments vary widely in magnitude and spatial pattern, though most models do well in reproducing the climatological pattern and a few (MPI and CNRM) also reproduce the overall magnitude. SCS biases are driven by biases in extreme convective available potential energy. These biases are ultimately found to be driven by biases in mean‐state near‐surface moist static energy, indicating that the SCS environments depend strongly on the near‐surface mean state. Results are similar for fall, but not summer or winter when free‐tropospheric biases are also important. Biases differ strongly across parent models but weakly across child models of the same parent. These outcomes help identify models well‐suited for studying climate effects on SCS environments.

Does ERA5 Mark a New Era for Resolving the Tropical Cyclone Environment?

Abstract The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications.