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Dylan W. Reif
,
Howard B. Bluestein
, and
David B. Parsons

Abstract

This study creates a composite sounding for nocturnal convection initiation (CI) events under weakly forced conditions and utilizes an idealized numerical simulation to assess the impact of atmospheric bores on these environments. Thirteen soundings were used to create this composite sounding. Common conditions associated with these weakly forced environments include a nocturnal low-level jet and a Brunt–Väisälä frequency of 0.011 s−1 above 900 hPa. The median lift needed for parcels to realize any convective instability is 490 m, the median convective available potential energy of these convectively unstable parcels is 992 J kg−1, and the median initial pressure of these parcels is 800 hPa. An idealized numerical simulation was utilized to examine the potential influence of bores on CI in an environment based on composite sounding. The characteristics of the simulated bore were representative of observed bores. The vertical velocities associated with this simulated bore were between 1 and 2 m s−1, and the net upward displacement of parcels was between 400 and 650 m. The vertical displacement of air parcels has two notable phases: lift by the bore itself and smaller-scale lift that occurs 100–150 km ahead of the bore passage. The prebore lift is between 50 and 200 m and appears to be related to low-frequency waves ahead of the bores. The lift with these waves was maximized in the low to midtroposphere between 1 and 4 km AGL, and this lift may play a role in assisting CI in these otherwise weakly forced environments.

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Milind Sharma
,
Robin L. Tanamachi
, and
Eric C. Bruning

Abstract

The dual-polarization radar characteristics of severe storms are commonly used as indicators to estimate the size and intensity of deep convective updrafts. In this study, we track rapid fluctuations in updraft intensity and size by objectively identifying polarimetric fingerprints such as Z DR and K DP columns, which serve as proxies for mixed-phase updraft strength. We quantify the volume of Z DR and K DP columns to evaluate their utility in diagnosing temporal variability in lightning flash characteristics. Specifically, we analyze three severe storms that developed in environments with low-to-moderate instability and strong 0–6-km wind shear in northern Alabama during the 2016–17 VORTEX-Southeast field campaign. In these three cases (a tornadic supercell embedded in stratiform precipitation, a nontornadic supercell, and a supercell embedded within a quasi-linear convective system), we find that the volume of the K DP columns exhibits a stronger correlation with the total flash rate. The higher covariability of the K DP column volume with the total flash rate suggests that the overall electrification and precipitation microphysics were dominated by cold cloud processes. The lower covariability with the Z DR column volume indicates the presence of nonsteady updrafts or a less prominent role of warm rain processes in graupel growth and subsequent electrification. Furthermore, we observe that the majority of cloud-to-ground (CG) lightning strikes a carried negative charge to the ground. In contrast to findings from a tornadic supercell over the Great Plains, lightning flash initiations in the Alabama storms primarily occurred outside the footprint of the Z DR and K DP column objects.

Significance Statement

This study quantifies the correlation between mixed-phase updraft intensity and total lightning flash rate in three severe storms in northern Alabama. In the absence of direct updraft velocity measurements, we use polarimetric signatures, such as Z DR and K DP columns, as proxies for updraft strength. Our analysis of polarimetric radar and lightning mapping array data reveals that the lightning flash rate is more highly correlated with the K DP column volume than with the Z DR column volume in all three storms examined. This contrasts with previous findings in storms over the central Great Plains, where the Z DR column volume showed higher covariability with flash rate. Interestingly, lightning initiation in the Alabama storms mainly occurred outside the Z DR and K DP column areas, contrary to previous findings.

Open access
Daniel J. Lloveras
and
Dale R. Durran

Abstract

We present an improved approach to generating moist baroclinically unstable background states for f-plane-channel simulations via potential vorticity (PV) inversion. Previous studies specified PV distributions with constant values in the troposphere and the stratosphere, but this produces unrealistic static-stability profiles that decrease sharply with height in the troposphere. Adding moisture to such environments can yield unrealistically large values of convective available potential energy (CAPE) even for reasonable relative humidity (RH) distributions. In our modified approach, we specify a PV distribution that increases with height in the troposphere and the stratosphere, yielding background states with more realistic values of static stability and CAPE. This modification produces environments that are better suited for representing moist processes, namely, deep convection, in idealized extratropical-cyclone simulations. Also, we present a method for introducing moisture that preserves a specified RH distribution while maintaining hydrostatic balance. Our approach allows for a large degree of control over the initial conditions, as background states with different jet strengths and shapes, average temperatures, moisture contents, or horizontal shears can easily be obtained without changing the underlying PV formula and inadvertently producing unreasonable values of static stability or CAPE. We demonstrate the characteristics of idealized extratropical cyclones developing in our background states by adding localized perturbations that represent an upper-level trough passing over a low-level frontal zone. In particular, we illustrate the impacts of horizontal shear, moisture, and grid spacing on baroclinic-wave development.

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Matthew T. Bray
and
Steven M. Cavallo

Abstract

Arctic cyclones (ACs) are a primary driver of surface weather in the Arctic, contributing to heat and moisture transport and forcing short-term sea ice variability. Still, our understanding of the processes that drive ACs, particularly their large scales and long lifetimes, is limited. ACs are commonly associated with one or more cyclonic tropopause polar vortices (TPVs), potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere that may spur baroclinic development in the surface system, though the exact processes that link the two have yet to be fully explored. In this study, we investigate physical links between TPVs, especially their mesoscale structure and moisture profiles, and ACs with idealized observing system simulation experiments (OSSEs). Starting with a nature run, we simulate different types of dropsonde observations over a TPV during the nascent phase of a nearby AC. The Model for Prediction Across Scales (MPAS) and the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter are then used to run experiments to test the impact of these detailed TPV observations. In addition to a control, five main experiments are conducted, assimilating new observations of temperature and humidity. All experiments reduce forecast errors at the surface and throughout the troposphere. Additional humidity observations alter vertical PV distributions, which in turn impact the development of the AC. Experiments with additional temperature observations exhibit improvements in TPV structure and surrounding PV features and produce stronger surface cyclones with skillful TPV forecasts for up to 36 h longer than the control.

Significance Statement

Arctic cyclones (ACs) are a weather feature that can produce high winds, precipitation, and changes to sea ice cover in the Arctic. As a result, forecasting these storms accurately is important for human and economic interests in the region; however, there are currently gaps in our understanding of how ACs strengthen and persist. In this study, we explore potential links between ACs and weather features higher up in the atmosphere called tropopause polar vortices (TPVs) using computer modeling experiments. This study shows that there are important connections between the characteristics of TPVs and the development of ACs. These findings will be useful for making more accurate forecasts of future events and advancing our knowledge of how sea ice changes relate to the atmosphere.

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Pu Liu
,
Markus Dabernig
,
Aitor Atencia
,
Yong Wang
, and
Yuchu Zhao

Abstract

Accurate temperature forecasts are critical for various industries and sectors. We propose a probabilistic neural network (PNN), an extension of the distributional regression network (DRN), for 2-m temperature forecasts, consisting of three variants with different inputs and target variables. The first variant, standardized anomaly probabilistic neural network (SAPNN), employs a two-step approach involving standardized anomalies and global PNN modeling to effectively capture underlying features and anomalies. The second variant, PNN with geographical predictors (PNNGE), incorporates raw and static geographical predictors to enhance predictive performance. The third variant, PNN with station one-hot encoding (PNNEN), utilizes raw with station one-hot encoding predictors to represent geographical information effectively. We compare three PNN variants with two benchmarks: 1) standardized anomaly model output statistics (SAMOS) and 2) three DRN variants identical to those applied to PNN. These evaluations utilize ECMWF data from 2019 to 2020 at 6-h intervals up to 72 h over Hebei, China. Results show that SAPNN and PNNGE are better than SAMOS, while PNNEN notably exhibits a significant 14% improvement in the continuous ranked probability skill score (CRPSS). Moreover, the PNN variants exhibit comparable or superior performance to DRN regarding forecast accuracy, CRPSS, and reliability, showcasing a better-calibrated spread–error relationship. This study highlights the value of the proposed PNN variants with a distribution output in capturing nonlinear relationships within different sources of predictors and improving temperature forecast skills.

Significance Statement

This study aims to improve the accuracy, skills, and reliability of 2-m temperature forecasts, which are crucial in agriculture and energy management. To achieve this, we extend a popular artificial intelligence framework and explore four data sources with two schemes to systematically compare the predictive performances in making temperature forecasts. The findings of this research are vital as they offer novel ways to improve forecast skills. Imagine having a weather app that is significantly more accurate, enabling you to plan your day better. This study is about discovering innovative approaches to enhance forecast skills and reliability, which could benefit various aspects of our daily lives. One of the new methods even exhibits a 14% improvement in forecast skills.

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Aaron Johnson
and
Xuguang Wang

Abstract

A series of convection-allowing 36-h ensemble forecasts during the 2018 spring season are used to better understand the impacts of ensemble configuration and blending different sources of initial condition (IC) perturbation. Ten- and forty-member ensemble configurations are initialized with the multiscale IC perturbations generated as a product of convective-scale data assimilation (MULTI) and initialized with the MULTI IC perturbations blended with IC perturbations downscaled from coarser-resolution ensembles (BLEND). The forecast performance of both precipitation and nonprecipitation variables is consistently improved by the larger ensemble size. The benefit of the larger ensemble is largely, but not entirely, due to compensating for underdispersion in the fixed-physics ensemble configuration. A consistent improvement in precipitation forecast skill results from blending in the 10-member ensemble configuration, corresponding to a reduction in the ensemble calibration error (i.e., reliability component of Brier score). In the 40-member ensemble configuration, the advantage of blending is limited to the ∼18–22-h lead times at all precipitation thresholds and the ∼35–36-h lead times at the lowest threshold, both corresponding to an improved resolution component of the Brier score. The advantage of blending in the 40-member ensemble during the diurnal convection maximum of ∼18–22-h lead times is primarily due to cases with relatively weak synoptic-scale forcing, while advantages at later lead times beyond ∼30-h lead time are most prominent on cases with relatively strong synoptic-scale forcing. The impacts of blending and ensemble configuration on forecasts of nonprecipitation variables are generally consistent with the impacts on the precipitation forecasts.

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Hongpei Yang
and
Yu Du

Abstract

During the development of squall lines, low-frequency gravity waves exhibit contrasting behaviors behind and ahead of the system, corresponding to its low-level upshear and downshear sides, respectively. This study employed idealized numerical simulations to investigate how low-level shear and tilted convective heating influence waves during two distinct stages of squall-line evolution. In the initial stage, low-level shear speeds up upshear waves, while it has contrasting effects on the amplitudes of different wave modes, distinguishing it from the Doppler effect. Downshear deep tropospheric downdraft (n = 1 wave) exhibits larger amplitudes, resulting in strengthened low-level inflow and upper-level outflow. However, n = 2 wave with low-level ascent and high-level descent has higher amplitude upshear and exhibits a higher altitude of peak w values downshear, leading to the development of a more extensive upshear low-level cloud deck and a higher altitude of downshear cloud deck. In the mature stage, as the convective updraft greatly tilts rearward (upshear), stronger n = 1 waves occur behind the system, while downshear-propagating n = 2 waves exhibit larger amplitudes. These varying wave behaviors subsequently contribute to the storm-relative circulation pattern. Ahead of the squall line, stronger n = 2 waves and weaker n = 1 waves produce intense outflow concentrated at higher altitudes, along with moderate midlevel inflow and weak low-level inflow. Conversely, behind the system, the remarkable high pressure in the upper troposphere and wake low are attributed to more intense n = 1 waves. Additionally, the cloud anvil features greater width and depth rearward and is situated at higher altitudes ahead of the system due to the joint effects of n = 1 and n = 2 waves.

Significance Statement

Squall lines are a significant source of high-impact weather events, and their development has been partially explained through linear wave dynamics. While the recurrent generation of waves during squall-line evolution has been found, the differentiation of wave behavior behind and ahead of the system, as well as its implications for storm circulation, has remained unclear. This study employs idealized simulations to reveal that during different stages of convection, low-level shear and the tilting of convective heating exert contrasting effects on wave behaviors. Moreover, various wave modes exhibit distinct responses to specific factors, and their combined effect elucidates the structural discrepancies observed both rearward and forward of the convective updraft. These findings could allow a step toward a better understanding of the intricate interaction between waves and convections.

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Francesco De Martin
,
Silvio Davolio
,
Mario Marcello Miglietta
, and
Vincenzo Levizzani

Abstract

The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an outflow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical profiles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a flat terrain and over a region with complex terrain are discussed.

Significance Statement

The Po Valley is a highly populated area where some of the most violent tornadoes in Europe have developed. We investigated a tornado outbreak that occurred on 19 September 2021 in this region, in order to identify its main environmental characteristics. High-resolution numerical simulations revealed that values of instability and wind shear were compatible with the development of several tornadoes only in a narrow area close to the intersection of two surface boundaries (a triple point). Moreover, the atmospheric environment during the tornado outbreak was strongly influenced by the presence of mountain ridges surrounding the plain. We have summarized our results in a conceptual model that can potentially be used for forecasting applications.

Open access
Yu-Chieng Liou
,
Tzu-Jui Chou
,
Yu-Ting Cheng
, and
Yung-Lin Teng

Abstract

This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Open access
Yaodeng Chen
,
Hong Zheng
,
Tao Sun
,
Deming Meng
,
Luyao Qin
, and
Jinfang Yin

Abstract

On 20–21 July 2021, a record-breaking rainfall event occurred in Henan Province, China, and a maximum hourly accumulated precipitation of 201.9 mm was recorded at Zhengzhou Meteorological Station. To improve the prediction of such extreme rainfall and to better understand the impacts of the radar reflectivity assimilation on forecasting, we assimilated radar reflectivity data using the hydrometeor background error covariance (HBEC) that includes vertical and multivariate correlations and then diagnosed the dynamic, thermal, and microphysical forecasts of this event. The results show that the radar reflectivity assimilation based on the HBEC properly transferred the observed radar reflectivity to the analysis of hydrometeors and other model states, and clearly improved the heavy rainfall forecast. The diagnosis of the dynamic and thermal forecasts indicated that the reflectivity assimilation based on the HBEC improved the convective environments of the precipitation systems, with stronger cold pools near the surface and deeper and wetter updrafts near Zhengzhou station, when compared with the experiment that did not assimilate radar reflectivity and the experiment that assimilated radar reflectivity without using the HBEC. The diagnosis of the microphysical forecasts further shows that assimilating reflectivity data using HBEC contributed to higher conversion rates of water vapor and cloud water to graupel and higher conversion rates of graupel and cloud water to rainwater, when compared with the other experiments. These improvements of both convective environments and microphysical processes within the convections ultimately enhanced the forecasts of this extreme rainfall event.

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