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Brian H. Tang
,
Rosimar Rios-Berrios
, and
Jun A. Zhang

Abstract

This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θ e air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over three days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θ e azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θ e air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θ e air resulted in large ventilation at mid-to-upper levels. Additionally, at low-to-mid levels, there was evidence of mesoscale inflow of low-θ e air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam’s intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone’s core.

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Shawn S. Murdzek
,
Yvette P. Richardson
, and
Paul M. Markowski

Abstract

Previous work found that cold pools in ordinary convection are more sensitive to the microphysics scheme when the lifting condensation level (LCL) is higher owing to a greater evaporation potential, which magnifies microphysical uncertainties. In the current study, we explore whether the same reasoning can be applied to supercellular cold pools. To do this, four perturbed-microphysics ensembles are run, with each using an environment with a different LCL. Similar to ordinary convection, the sensitivity of supercellular cold pools to the microphysics increases with higher LCLs, though the physical reasoning for this increase in sensitivity differs from a previous study. Using buoyancy budgets along parcel trajectories that terminate in the cold pool, we find that negative buoyancy generated by microphysical cooling is partially countered by a decrease in environmental potential temperatures as the parcel descends. This partial erosion of negative buoyancy as parcels descend is most pronounced in the low-LCL storms, which have steeper vertical profiles of environmental potential temperature in the lower atmosphere. When this erosion is accounted for, the strength of the strongest cold pools in the low-LCL ensemble is reduced, resulting in a narrower distribution of cold pool strengths. This narrower distribution is indicative of reduced sensitivity to the microphysics. These results suggest that supercell behavior and supercell hazards (e.g., tornadoes) may be more predictable in low-LCL environments.

Significance Statement

Thunderstorms typically produce “pools” of cold air beneath them owing in part to the evaporation of rain and melting of ice produced by the storm. Past work has found that in computer simulations of thunderstorms, the cold pools that form beneath thunderstorms are sensitive to how rain and ice are modeled in the simulation. In this study, we show that in the strongest thunderstorms that are capable of producing tornadoes, this sensitivity is reduced when the humidity in the lowest few kilometers above the surface is increased. Exploring why the sensitivity is reduced when the humidity increases provides a deeper understanding of the relationship between humidity and cold pool strength, which is important for severe storm forecasting.

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Bruno S. Rojas
,
Anthony C. Didlake Jr.
, and
Jun A. Zhang

Abstract

The physical processes that govern eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are not yet fully understood. In particular, asymmetric structures within the TC inner core have an uncertain role in ERC dynamics. This study analyzes the kinematic and precipitation asymmetric structures during successive ERCs in Hurricane Ivan (2004) using airborne Doppler radar observations. The azimuthal locations of these asymmetries are analyzed relative to the deep-layer (850-200 hPa) environmental wind shear vector. Two ERCs were analyzed at different stages of their evolution. During the concentric eyewall stage of the first ERC, the outer eyewall updrafts were strongest in the left-of-shear half, which also coincided with mesoscale descending inflow (MDI) just radially outward. Enhanced low-level convergence, updrafts, and MDI were collocated in an zone spiraling inward towards the strongest outer eyewall updrafts, suggesting that the vertical velocity asymmetry in the outer eyewall was possibly forced by a stratiform-induced cold pool similar to MDI impacts seen in past TC studies. During the final stage of the second ERC, the outer eyewall (now the singular primary eyewall) experienced an upwind shift in the precipitation and vertical velocity asymmetries. The updraft maximum shifted from the downshear-left quadrant to the downshear-right quadrant, and the precipitation maximum (downwind of the updraft maximum) shifted from left-of-shear to the downshear direction. This shift corroborates previous studies, which hypothesize that at the end of an ERC, the forcing mechanism that drives the eyewall vertical velocity asymmetry transitions from MDI/cold-pool processes to direct interaction with the environmental wind shear.

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Jingnan Wang
,
Xiaodong Wang
,
Jiping Guan
,
Lifeng Zhang
,
Tao Chang
, and
Wei Yu

Abstract

The forecast uncertainty, particularly for precipitation, serves as a crucial indicator of the reliability of deterministic forecasts. Traditionally, forecast uncertainty is estimated by ensemble forecasting, which is computationally expensive since the forecast model is run multiple times with perturbations. Recently, deep learning methods have been explored to learn the statistical properties of ensemble prediction systems due to their low computational costs. However, accurately and effectively capturing the uncertainty information in precipitation forecasts remains challenging. In this study, we present a novel spatiotemporal transformer network (ST-TransNet) as an alternative approach to estimate uncertainty with ensemble spread and probabilistic forecasts, by learning from historical ensemble forecasts. ST-TransNet features a hierarchical structure for extracting multiscale features and incorporates a spatiotemporal transformer module with window-based attention to capture correlations in both spatial and temporal dimensions. Additionally, window-based attention can not only extract local precipitation patterns but also reduce computational costs. The proposed ST-TransNet is evaluated on the TIGGE ensemble forecast dataset and Global Precipitation Measurement (GPM) precipitation products. Results show that ST-TransNet outperforms both traditional and deep learning methods across various metrics. Case studies further demonstrate its ability to generate reasonable and accurate spread and probability forecasts from a single deterministic precipitation forecast. It demonstrates the capacity and efficiency of neural networks in estimating precipitation forecast uncertainty.

Open access
Wataru Mashiko

Abstract

A quasi-linear mesoscale convective system that remained nearly stationary (hereafter referred to as a stationary QLCS) for almost 8 h in southern Kyushu, Japan, caused torrential rainfall exceeding 350 mm on 11 July 2021. The stationary QLCS consisted of several rainbands organized by back-building convection. As the cold pool intensified, the system attained a more widespread structure, leaning on the upshear side. To elucidate the mechanism responsible for the upshear tilt, numerical simulations with 250-m horizontal grid spacing were conducted, including sensitivity experiments in which the evaporative cooling rates from rain were reduced to modify the cold pool intensity. Results show that the cold pool is critical to the organization of the QLCS, and the structure normal to it is mainly governed by the balance between the low-level shear magnitude and cold pool intensity, supporting the application of so-called Rotunno-Klemp-Weisman (RKW) theory to this event. The cause of the weak updraft that accompanies the leaning system over the strong cold pool was also investigated, analyzing the trajectories, vertical momentum equation, and pressure perturbation field using the anelastic equation. It is revealed that the updraft travels a longer distance through the tilted system experiencing more mixing with the ambient air, which results in less thermal buoyancy. In addition, the updraft is decelerated by the downward perturbation gradient force owing to the vertical buoyancy gradient around the sloping surface of the cold pool and the dynamical effect caused by the baroclinity-associated strong horizontal vorticity around the cold pool leading edge.

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David I. Duncan
,
Niels Bormann
,
Alan J. Geer
, and
Peter Weston

Abstract

Humidity sounder radiances are currently thinned to 110 km spacing prior to assimilation at ECMWF and used with no averaging applied. In this paper, the thinning scale and possible averaging of all-sky humidity sounder observations into “superobs” are considered. The short- and medium-range forecast impacts of changing the thinning and averaging scales of humidity sounder radiances prior to the data assimilation are investigated separately and then together. Superobbing acts as a low-pass filter and provides smoother images of departures, decreasing the effective sensor noise and thus the standard deviation of background departures, marginally for 183GHz channels (5-15%) and significantly for 118GHz channels (5-55%). Observations are thus more representative of the model effective resolution, with a better utilisation of total information content than thinning native-resolution radiances, as less information is discarded. Whether changed in isolation or combination, the additions of data via superobbing and finer thinning are both shown to markedly improve background fits to independent observations, indicative of better short-range forecasts of humidity and improved winds via the 4D-Var tracer effect. Wind forecasts in the Southern Hemisphere are slightly improved in the medium-range. A final configuration is tested at the resolution of the current operational model, with humidity sounder radiances averaged into 50 km superobs with 70 km spacing. This provides about 140% more radiances for assimilation and more finely detailed maps to analyse mesoscale features. The forecast impact of this change is larger in testing with higher model and data assimilation resolutions, showing the scale-dependence of such decisions and the expected performance in an operational configuration.

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Julio C. Marín
,
Felipe Gutiérrez
,
Vittorio A. Gensini
,
Bradford S. Barrett
,
Diana Pozo
,
Martín Jacques-Coper
, and
Daniel Veloso-Aguila

Abstract

Tornadoes in Chile seem to develop in what are called “high-shear, low-CAPE” (HSLC) environments. An analysis of convective parameters from the ERA5 reanalysis during sixteen notable tornadoes in Chile showed that several increased markedly before the time of the reports. The significant tornado parameter (STP) was able to discriminate the timing and location of the tornadoes, even though it was not created with that goal. We established thresholds for the Severe Hazards in Environments with Reduced Buoyancy (SHERBE) parameter (≥1) and the STP (≤−0.3) to further identify days favorable for tornado activity in Chile. The SHERBE and STP parameters were then used to conduct a climatological analysis from 1959–2021 of the seasonal, interannual, and latitudinal variation of the environments that might favor tornadoes. Both parameters were found to have a strong annual cycle. The largest magnitudes of STP were found to be generally confined to south-central Chile, in agreement with the (sparse) tornado record. The probability of a day with both SHERBE and STP values beyond their thresholds was greatest between May and August, which aligns with the months with the most tornado reports. The number of days with both SHERBE and STP beyond their respective thresholds was found to fluctuate interanually. This result warrants further study given the known interannual variability of synoptic and mesoscale weather in Chile. The results of this study extend our understanding of tornado environments in Chile and provide insight into their spatio-temporal variability.

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Kelsey Malloy
and
Michael K. Tippett

Abstract

Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index.

Significance Statement

Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

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Jake P. Mulholland
,
Christopher J. Nowotarski
,
John M. Peters
,
Hugh Morrison
, and
Erik R. Nielsen

Abstract

Vertical wind shear is known to affect supercell thunderstorms by displacing updraft hydrometeor mass downshear, thereby facilitating the storms’ longevity. Shear also impacts the size of supercell updrafts, with stronger shear leading to wider, less dilute, and stronger updrafts with likely greater hydrometeor production. To more clearly define the role of shear across different vertical layers on hydrometeor concentrations and displacements relative to supercell updrafts, a suite of idealized numerical model simulations of supercells was conducted. Shear magnitudes were systematically varied across the 0–1 km, 1–6 km, and 6–12 km AGL layers while the thermodynamic environment was held fixed. Simulations show that as shear magnitude increases, especially from 1–6 km, updrafts become wider and less dilute with an increase in hydrometeor loading, along with an increase in the low-level precipitation area/rate and total precipitation accumulation. Even with greater updraft hydrometeor loading amid stronger shear, updrafts are more intense in stronger shear simulations due to larger thermal buoyancy owing to wider, less dilute updraft cores. Furthermore, downshear hydrometeor displacements are larger in environments with stronger 1–6 km shear. In contrast, there is relatively less sensitivity of hydrometeor concentrations and displacements to variations in either 0–1 km or 6–12 km shear. Results are consistent across free tropospheric relative humidity sensitivity simulations, which show an increase in updraft size and hydrometeor mass with increasing free tropospheric relative humidity owing to a reduction in entrainment-driven dilution for wider updrafts in moister environments.

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Rumeng Li
,
Juanzhen Sun
,
Qinghong Zhang
,
Anders A. Jensen
, and
Sarah Tessendorf

Abstract

Explicit simulation of hailstorms remains a challenge partly due to the lack of accurate representations of both initial conditions and microphysical processes. Using a moderate hailstorm case that occurred in Beijing on June 10, 2016, the impact of the initial conditions on explicit hail prediction has been studied in part I of this two-part work via high-resolution data assimilation. This part II paper examines the role of improved graupel treatment by comparing the recently upgraded Thompson-Eidhammer microphysics scheme (MP38) with two previous versions. MP38 is a double-moment hail-aware scheme with the ability to additionally predict graupel number concentration and density. This case study showed that the addition of these predictive variables improved the simulation of the mass-weighted mean diameter of hail and thereby reduced the overestimation of hail size. However, the hail size was significantly underpredicted without the prediction of hail density, indicating that both quantities must be prognosed for skillful hail prediction. It was further shown that the revised graupel treatment also influenced hailstorm dynamics. The smaller hail size in MP38 led to a stronger graupel melting process, which further promoted a stronger cold pool and downdraft. By assessing the efficiency of the upgraded Thompson-Eidhammer microphysics scheme, the current study shed some light on the importance of accurate representation of microphysical processes in numerical models for explicit hailstorm prediction.

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