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Kyle Ahern, Robert E. Hart, and Mark A. Bourassa

Abstract

Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL’s azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement.

Significance Statement

In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided.

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Robert G. Nystrom and Falko Judt

Abstract

In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd) and enthalpy (Ck) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design.

Significance Statement

The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.

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Yueya Wang, Xiaoming Shi, Lili Lei, and Jimmy Chi-Hung Fung

Abstract

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.

Significance Statement

Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

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Rong Kong, Ming Xue, Chengsi Liu, Alexandre O. Fierro, and Edward R. Mansell

Abstract

In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases.

Significance Statement

Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems.

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Quinton A. Lawton, Sharanya J. Majumdar, Krista Dotterer, Christopher Thorncroft, and Carl J. Schreck III

Abstract

While considerable attention has been given to how convectively coupled Kelvin waves (CCKWs) influence the genesis of tropical cyclones (TCs) in the Atlantic Ocean, less attention has been given to their direct influence on African easterly waves (AEWs). This study builds a climatology of AEW and CCKW passages from 1981 to 2019 using an AEW-following framework. Vertical and horizontal composites of these passages are developed and divided into categories based on AEW position and CCKW strength. Many of the relationships that have previously been found for TC genesis also hold true for non-developing AEWs. This includes an increase in convective coverage surrounding the AEW center in phase with the convectively enhanced (“active”) CCKW crest, as well as a buildup of relative vorticity from the lower to upper troposphere following this active crest. Additionally, a new finding is that CCKWs induce specific humidity anomalies around AEWs that are qualitatively similar to those of relative vorticity. These modifications to specific humidity are more pronounced when AEWs are at lower latitudes and interacting with stronger CCKWs. While the influence of CCKWs on AEWs is mostly transient and short lived, CCKWs do modify the AEW propagation speed and westward-filtered relative vorticity, indicating that they may have some longer-term influences on the AEW life cycle. Overall, this analysis provides a more comprehensive view of the AEW–CCKW relationship than has previously been established, and supports assertions by previous studies that CCKW-associated convection, specific humidity, and vorticity may modify the favorability of AEWs to TC genesis over the Atlantic.

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Jinhui Xie, Pang-Chi Hsu, Pallav Ray, Kuiping Li, and Weidong Yu

Abstract

As rainfed agriculture remains India’s critical source of livelihood, improving our understanding of rainy season onset timing in the region is of great importance for a better prediction. Using a new gridded dataset of rainy season characteristics, we found a clear phase relationship between the Madden–Julian oscillation (MJO) and the onset timing of the rainy season over the Indian subcontinent. A significantly high probability of rainy season onset is observed when the MJO convection stays over the western-central Indian Ocean. On the other hand, the rainy season onset is infrequent when the MJO is over the Maritime Continent and western Pacific. The MJO-associated convective instability with anomalous warm and moist air in the lower troposphere appears and grows during the period 10 days prior to the onset of rainy season, and drops substantially after the start of rainy season, suggesting its role as a trigger of rainy season onset. In contrast, the low-frequency background state (LFBS) with a period > 90 days favors a convectively unstable stratification even after the onset of the rainy season, supporting the succeeding precipitation during the entire rainy season. Based on the scale-decomposed moisture budget diagnosis, we further found that the key processes inducing the abrupt transition from a dry to a wet condition come mainly from two processes: 1) convergence of LFBS moisture by MJO-related circulation perturbations and 2) advection of MJO moisture anomalies by the background cross-equatorial flow toward the Indian subcontinent. The results may help provide a better and longer lead-time prediction of the rainy season onset over the Indian subcontinent.

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Ching-Yuang Huang, Sheng-Hao Sha, and Hung-Chi Kuo

Abstract

The global model FV3GFS is used to simulate Typhoon Lekima (2019), which exhibited track deflection when approaching west-northwestward toward Taiwan. The model successfully simulates the observed northward deflection and the track deflection is produced by topographically induced wavenumber-1 flow with a pair of vorticity gyres around the typhoon center. The gyres tend to rotate counterclockwise about the typhoon center and thus induce an earlier northward and then westward movement. Azimuthal-mean kinetic energy budget of the typhoon indicates that the effect of Taiwan terrain modifies the correlation between the recirculating flow and pressure gradient force east of Taiwan, leading to a slight weakening of the typhoon during the later track deflection. The northward cyclonic deflection in general will be induced for a cyclone to move toward the central to northern terrain such as Lekima. The curvature of the northward cyclonic deflection, however, is large (small) for a northwestbound (nearly westbound) vortex depending on the track-topography-impinging angle. The curvature difference can be explained with the concept of recirculating flow, which is the flow splitting due to topography and rejoins the vortex to produce the wavenumber-1 asymmetry. The cyclonic track curvature of the northwestbound Lekima is larger than that of the westbound Maria (2018) in the FV3GFS simulations. This adds robustness to the conclusion that minor to moderate terrain-related track deflections can be well simulated by the FV3GFS global model near Taiwan.

Open access
Joël Stein and Fabien Stoop

Abstract

The neighborhood-based ensemble evaluation using the continuous ranked probability score is based on the pooling of the cumulative density function (CDF) for all the points inside a neighborhood. This methodology can be applied to the forecast CDF for measuring the predictive input of neighboring points in the center of the neighborhood. It can also be applied at the same time to forecast CDF and observed CDF so as to quantify the quality of the pooled ensemble forecast at the scale of the neighborhood. Fair versions of these two neighborhood scores are also defined in order to reduce their dependencies on the size of ensemble forecasts. The borderline case of deterministic forecasts is also explored so as to be able to compare them with ensemble forecasts. The information of these new scores is analyzed on idealized and real cases of rain accumulated during 3 h and of 2-m temperature forecast by four deterministic and probabilistic forecasting systems operational at Météo-France.

Open access
Kelly Lombardo and Matthew R. Kumjian

Abstract

During the early morning hours of 5 November 2018, a mature mesoscale convective system (MCS) propagated discretely over the second-most populous province of Argentina, Córdoba Province, during the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations–Cloud, Aerosol, and Complex Terrain Interactions (RELAMPAGO–CACTI) joint field campaigns. Storm behavior was modified by the Sierras de Córdoba, a north–south-oriented regional mountain chain located in the western side of the province. Here, we present observational evidence of the discrete propagation event and the impact of the mountains on the associated physical processes. As the mature MCS moved northeastward and approached the windward side of the mountains, isolated convective cells developed downstream in the mountain lee, 20–50 km ahead of the main convective line. Cells were initiated by an undular bore, which formed as the MCS cold pool moved over the mountain ridge and perturbed the leeside nocturnal, low-level stable layer. The field of isolated cells organized into a new MCS, which continued to move northeastward, while the parent storm decayed as it traversed the mountains. Only the southern portion of the storm propagated discretely, due to variability in mountain height along the chain. In the north, taller mountain peaks prevented the MCS cold pool from moving over the terrain and perturbing the stable layer. Consequently, no bore was generated, and no discrete propagation occurred in this region. To the south, the MCS cold pool was able to traverse the lower-relief mountains, and the discrete propagation was successful.

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Thomas M. Gowan, W. James Steenburgh, and Justin R. Minder

Abstract

Landfalling lake- and sea-effect (hereafter lake-effect) systems often interact with orography, altering the distribution and intensity of precipitation, which frequently falls as snow. In this study, we examine the influence of orography on two modes of lake-effect systems: long-lake-axis-parallel (LLAP) bands and broad-coverage, open-cell convection. Specifically, we generate idealized large-eddy simulations of a LLAP band produced by an oval lake and broad-coverage, open-cell convection produced by an open lake (i.e., without flanking shorelines) with a downstream coastal plain, 500-m peak, and 2000-m ridge. Without terrain, the LLAP band intersects a coastal baroclinic zone over which ascent and hydrometeor mass growth are maximized, with transport and fallout producing an inland precipitation maximum. The 500-m peak does not significantly alter this structure, but slightly enhances precipitation due to orographic ascent, increased hydrometeor mass growth, and reduced subcloud sublimation. In contrast, a 2000-m ridge disrupts the band by blocking the continental flow that flanks the coastlines. This, combined with differential surface heating between the lake and land, leads to low-level flow reversal, shifting the coastal baroclinic zone and precipitation maximum offshore. In contrast, the flow moves over the terrain in open lake, open-cell simulations. Over the 500-m peak, this yields an increase in the frequency of weaker (<1 m s−1) updrafts and weak precipitation enhancement, although stronger updrafts decline. Over the 2000-m ridge, however, buoyancy and convective vigor increase dramatically, contributing to an eightfold increase in precipitation. Overall, these results highlight differences in the influence of orography on two common lake-effect modes.

Significance Statement

Landfalling lake- and sea-effect snowstorms frequently interact with hills, mountains, and upland regions, altering the distribution and intensity of snowfall. Using high-resolution numerical modeling with simplified lake shapes and terrain features, we illustrate how terrain features affect two common types of lake-effect storms and why long-lake-axis-parallel (LLAP) bands can feature high precipitation rates but weaker orographic enhancement than broad-coverage, open-cell convection.

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