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Temple R. Lee, Michael Buban, and Tilden P. Meyers

Abstract

Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Rib) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Rib parameterizations to stable regimes and developed parameterizations for the friction velocity (u *), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Rib parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Rib parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Rib parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.

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Johannes M. L. Dahl

Abstract

About 140 years ago, Lord Kelvin derived the equations describing waves that travel along the axis of concentrated vortices such as tornadoes. Although Kelvin’s vortex waves, also known as centrifugal waves, feature prominently in the engineering and fluid dynamics literature, they have not attracted as much attention in the field of atmospheric science. To remedy this circumstance, Kelvin’s elegant derivation is retraced, and slightly generalized, to obtain solutions for a hierarchy of vortex flows that model basic features of tornado-like vortices. This treatment seeks to draw attention to the important work that Lord Kelvin did in this field, and reveal the remarkably rich structure and dynamics of these waves. Kelvin’s solutions help explain the vortex breakdown phenomenon routinely observed in modeled tornadoes, and it is shown that his work is compatible with the widely used criticality condition put forth by Benjamin in 1962. Moreover, it is demonstrated that Kelvin’s treatment, with the slight generalization, includes unstable wave solutions that have been invoked to explain some aspects of the formation of multiple-vortex tornadoes. The analysis of the unstable solutions also forms the basis for determining whether, for example, an axisymmetric or a spiral vortex breakdown occurs. Kelvin’s work thus helps explain some of the visible features of tornado-like vortices.

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Thomas M. Hamill

Abstract

Common methods for the postprocessing of deterministic 2-m temperature (T 2m) forecasts over the United States were evaluated from +12- to +120-h lead. Forecast data were extracted from the Global Ensemble Forecast System (GEFS) v12 reforecast dataset and thinned to a ½° grid. Analyzed data from the European Centre/Copernicus reanalysis (ERA5) were used for training and validation. Data from the 2000–18 period were used for training, and 2019 forecasts were validated. The postprocessing methods compared were the raw forecast guidance, a decaying-average bias correction (DAV), quantile mapping (QM), a univariate model output statistics (uMOS) algorithm, and a multivariate (mvMOS) algorithm. The mvMOS algorithm used the raw forecast temperature, the DAV adjustment, and the QM adjustment as predictors. Forecasts from all the postprocessing methods reduced the root-mean-square error (RMSE) and bias relative to the raw guidance. QM produced forecasts with slightly higher error than DAV. DAV estimates were the most consistent from day to day. The uMOS and mvMOS algorithms produced statistically significant lower RMSEs than DAV at forecast leads longer than 1 day, with mvMOS exhibiting the lowest error. Taylor diagrams showed that the MOS methods reduced the variability of the forecasts while improving forecast-analyzed correlations. QM and DAV modified the distribution of forecasts to more closely exhibit those of the analyzed data. A main conclusion is that the judicious statistical combination of guidance from multiple postprocessing methods is capable of producing forecasts with improved error statistics relative to any one individual technique. As each method applied here is algorithmically relatively simple, this suggests that operational deterministic postprocessing combining multiple correction methods could produce improved T 2m guidance.

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Yaping Wang, Nusrat Yussouf, Edward R. Mansell, Brian C. Matilla, Rong Kong, Ming Xue, and Vanna C. Chmielewski

Abstract

The Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM-derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the ensemble Kalman filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events: one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case it degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.

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Stanley B. Trier, Glen S. Romine, David A. Ahijevych, Ryan A. Sobash, and Manda B. Chasteen

Abstract

A 50-member convection-allowing ensemble was used to examine environmental factors influencing afternoon convection initiation (CI) and subsequent severe weather on 5 April 2017 during intensive observing period (IOP) 3b of the Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast (VORTEX-SE). This case produced several weak tornadoes (rated EF1 or less), and numerous reports of significant hail (diameter ≥ 2 in.; ≥~5 cm), ahead of an eastward-moving surface cold front over eastern Alabama and southern Tennessee. Both observed and simulated CI was facilitated by mesoscale lower-tropospheric ascent maximized several tens of kilometers ahead of the cold-frontal position, and the simulated mesoscale ascent was linked to surface frontogenesis in the ensemble mean. Simulated maximum 2–5 km AGL updraft helicity (UHmax) was used as a proxy for severe-weather-producing mesocyclones, and considerable variability in UHmax occurred among the ensemble members. Ensemble members with UHmax > 100 m2 s−2 had stronger mesoscale ascent than in members with UHmax < 75 m2 s−2, which facilitated timelier CI by producing greater adiabatic cooling and moisture increases above the PBL. After CI, storms in the larger UHmax members moved northeastward toward a mesoscale region with larger convective available potential energy (CAPE) than in smaller UHmax members. The CAPE differences among members were influenced by differences in the location of an antecedent mesoscale convective system, which had a thermodynamically stabilizing influence on the environment toward which storms were moving. Despite providing good overall guidance, the model ensemble overpredicted severe weather likelihoods in northeastern Alabama, where comparisons with VORTEX-SE soundings revealed a positive CAPE bias.

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Robert A. Warren, Harald Richter, and Richard L. Thompson

Abstract

Proximity soundings have long been used to explore how the vertical structure of temperature, humidity, and winds influence convective storms and their associated hazards. In severe thunderstorm research and forecasting, convective parameters are often used to summarize certain characteristics of the sounding. While extremely useful, these parameters are unable to describe the rich complexity that is readily apparent in hodographs and skew T–logp diagrams. Motivated by a desire to retain more of these details, the present study uses self-organizing maps (SOMs) to group soundings based on their full vertical structure. The analysis makes use of a sample of more than 10 000 model proximity soundings for right-moving supercells associated with tornadoes and significant severe hail and straight-line winds in the contiguous United States (CONUS). Separate SOMs are developed for the wind and thermodynamic profiles, each with 3 × 3 nodes, resulting in a set of nine hodographs and nine skew T–logp diagrams that broadly represent the spectrum of near-storm environments for significant severe right-moving supercells in the CONUS. Both SOMs are shown to provide a good representation of the variability in key convective parameters, although, for the thermodynamic SOM, variations in LCL heights and midlevel lapse rates are somewhat limited. Based on the soundings assigned to them, the SOM nodes are characterized in terms of their associated hazards, their relationship with storm mode and mesocyclone strength, and their spatial and temporal variability. Potential applications of the SOMs in severe weather forecasting and idealized numerical simulations are also highlighted.

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Zhan Zhang, Jun A. Zhang, Ghassan J. Alaka Jr., Keqin Wu, Avichal Mehra, and Vijay Tallapragada

Abstract

A statistical analysis is performed on the high-frequency (3⅓ s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) Model for all tropical cyclones (TCs) in the North Atlantic Ocean basin over a 3-yr period (2017–19). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax, with average values of 6–8 km. The Vmax fluctuations were as high as 20 kt (10.3 m s−1) in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt (2.8 m s−1) for category-2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running-mean windows of 3–9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

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Chanil Park, Seok-Woo Son, Joowan Kim, Eun-Chul Chang, Jung-Hoon Kim, Enoch Jo, Dong-Hyun Cha, and Sujong Jeong

Abstract

This study identifies diverse synoptic weather patterns of warm-season heavy rainfall events (HREs) in South Korea. The HREs not directly connected to tropical cyclones (TCs) (81.1%) are typically associated with a midlatitude cyclone from eastern China, the expanded North Pacific high and strong southwesterly moisture transport in between. They are frequent both in the first (early summer) and second rainy periods (late summer) with impacts on the south coast and west of the mountainous region. In contrast, the HREs resulting from TCs (18.9%) are caused by the synergetic interaction between the TC and meandering midlatitude flow, especially in the second rainy period. The strong south-southeasterly moisture transport makes the southern and eastern coastal regions prone to the TC-driven HREs. By applying a self-organizing map algorithm to the non-TC HREs, their surface weather patterns are further classified into six clusters. Clusters 1 and 3 exhibit frontal boundary between the low and high with differing relative strengths. Clusters 2 and 5 feature an extratropical cyclone migrating from eastern China under different background sea-level pressure patterns. Cluster 4 is characterized by the expanded North Pacific high with no organized negative sea-level pressure anomaly, and cluster 6 displays a development of a moisture pathway between the continental and oceanic highs. Each cluster exhibits a distinct spatio-temporal occurrence distribution. The result provides useful guidance for predicting the HREs by depicting important factors to be differently considered depending on their synoptic categorization.

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Kevin Gray and Jeffrey Frame

Abstract

Despite an increased understanding of environments favorable for tornadic supercells, it is still sometimes unknown why one favorable environment produces many long-tracked tornadic supercells and another seemingly equally-favorable environment produces only short-lived supercells. One relatively unexplored environmental parameter that may differ between such environments is the degree of backing or veering of the midlevel shear vector, especially considering that such variations may not be captured by traditional supercell or tornado forecast parameters. We investigate the impact of the 3-6 km shear vector orientation on simulated supercell evolution by systematically varying it across a suite of idealized simulations. We found that the orientation of the 3-6 km shear vector dictates where precipitation loading is maximized in the storms, and thus alters the storm-relative location of downdrafts and outflow surges. When the shear vector is backed, outflow surges generally occur northwest of an updraft, produce greater convergence beneath the updraft, and do not disrupt inflow, meaning that the storm is more likely to persist and produce more tornado-like vortices (TLVs). When the shear vector is veered, outflow surges generally occur north of an updraft, produce less convergence beneath the updraft, and sometimes undercut it with outflow, causing it to tilt at low levels, sometimes leading to storm dissipation. These storms are shorter lived and thus also produce fewer TLVs. Our simulations indicate that the relative orientation of the 3-6 km shear vector may impact supercell longevity and hence the time period over which tornadoes may form.

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Peyton K. Capute and Ryan D. Torn

Abstract

Arctic cyclones (ACs) are synoptic scale features that can be associated with strong, intense winds over the Arctic region for long periods of time, potentially leading to rapid declines of sea ice during the summer. As a consequence, sea ice predictions may rely on the predictability of cyclone-related wind speed and direction, which critically depends on the cyclone track and intensity. Despite this, there are relatively few studies that have documented the predictability of ACs during the summer, beyond a few case studies, nor has there been an extensive comparison of whether these cyclones are more or less predictable relative to comparable midlatitude cyclones, which have been studied in greater detail. The goal of this study is to document the practical predictability of AC position and intensity forecasts over 100 cases and compare it to 89 Atlantic basin midlatitude cyclones using the Global Ensemble Forecast System (GEFS) Reforecast V2. This dataset contains 11-member ensemble forecasts initialized daily from 1985-present using a fixed model. In this study, 1 and 3 day forecast hours are compared, where predictability is defined as the ensemble mean root mean square error and ensemble standard deviation (SD). Although Atlantic basin cyclone tracks are characterized by higher predictability relative to comparable ACs, intensity predictability is higher for ACs. In addition, storms characterized by low ensemble SD and predictability are found in regions of higher baroclinic instability than storms characterized by high predictability. There appears to be little, if any, relationship between latent heat release and precipitable water and predictability.

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