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Catherine M. Naud, Juan A. Crespo, and Derek J. Posselt

Abstract

Surface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low-latitude, CYGNSS surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts, and weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases, or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This impacts the changes in fluxes during cyclone intensification: the post-cold frontal surface heat flux maximum increases due to the increase in near surface winds. During cyclone dissipation, the fluxes in this sector decrease, due to the decrease in winds and in temperature and humidity contrast. The warm sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea-air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independently from changes in the cyclone characteristics. This suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal in addition to synoptic-scale processes.

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Bhupal Shrestha, J. A. Brotzge, J. Wang, N. Bain, C. D. Thorncroft, E. Joseph, J. Freedman, and S. Perez

Abstract

Vertical profiles of atmospheric temperature, moisture, wind, and aerosols are essential information for weather monitoring and prediction. Their availability, however, is limited in space and time due to the significant resources required to observe them. To fill this gap, the New York State Mesonet (NYSM) Profiler Network has been deployed as a national testbed to facilitate the research, development and evaluation of ground-based profiling technologies and applications. The testbed comprises 17 profiler stations across the state, forming a long-term regional observational network. Each Profiler station is comprised of a ground-based Doppler lidar, a microwave radiometer (MWR) and an environmental Sky Imaging Radiometer (eSIR). Thermodynamic profiles (temperature and humidity) from the MWR; wind and aerosol profiles from the Doppler lidar; and solar radiance and optical depth parameters from the eSIR are collected, processed, disseminated, and archived every 10 minutes. This paper introduces the NYSM Profiler Network and reviews the network design and siting, instrumentation, network operations and maintenance, data and products, and some example applications highlighting the benefits of the network. Some sample applications include improved situational awareness and monitoring of the sea/land breeze, long-range wildfire smoke transport, air quality (PM2.5 and AOD) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the nation’s observing system with the potential to improve analysis and prediction for many weather-sensitive sectors, such as aviation, ground transportation, health, and wind energy.

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Q. Huang, W.J. Jiang, and H.P. Hong

Abstract

Canada experiences a relatively large number of tornadoes, which can cause a significant amount of damage and fatalities. In the present study, a preferred prediction model for the spatially varying tornado occurrence rate is developed for Canada. The development takes into account the most commonly used spatial stochastic models and the underreporting due to low population density. It incorporates the annual average cloud-to-ground lightning flash (ACGLF) density and annual average thunderstorm days (ATD) as covariates in the prediction model. The model parameters estimation is carried out by using both the maximum likelihood method and the Bayesian inference.

The analysis results indicate that the negative binomial model is preferable to the zero-inflated Poisson model and the Poison model. The results show that the tornado occurrence in Canada is associated with large overdispersion. Also, the statistical analysis indicates that the prediction model for the tornado occurrence rate developed based on Bayesian inference is relatively insensitive to the assumed “non-informative” prior distributions. A prediction model is suggested for the spatially varying tornado occurrence rate based on the negative binomial model with the ACGLF density and ATD as covariates.

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James F. Booth, Veeshan Narinesingh, Katherine L. Towey, and Jeyavinoth Jeyaratnam

Abstract

Storm surge is a weather hazard that can generate dangerous flooding and is not fully understood in terms of timing and atmospheric forcing. Using observations along the Northeast United States, surge is sorted based on duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, while strong, short-duration events often involve negative surge in quick succession after the maximum. Using Lagrangian track information, the tropical and extratropical cyclones and atmospheric blocks that generate the surge events are identified. There is a linear correlation between surge duration and surge maximum, and the relationship is stronger for surge caused by extratropical cyclones as compared to those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, while the longest-duration events are most often caused by extratropical cyclones. At least half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, while strong, short-duration events are most often caused by cyclones in the absence of blocking. The dynamical influence of the blocks leads to slow-moving cyclones that take meandering paths. In contrast, for strong, short-duration events, cyclones travel faster and take a more meridional path. These unique dynamical scenarios provide better insight for interpreting the threat of surge in medium-range forecasts.

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Ioannis Cheliotis, Elsa Dieudonné, Hervé Delbarre, Anton Sokolov, Egor Dmitriev, Patrick Augustin, Marc Fourmentin, François Ravetta, and Jacques Pelon

Abstract

The studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas) and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75 m tower for a 2-month period (September-October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets (nllj) events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a four-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streaks spacing of 200 to 400 m, and rolls sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, and John W. Pomeroy

Abstract

Gridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modelling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first order conservative, bilinear, and distance weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 mm and 0.13 mm are noted in high (0.95) and low (0.05) quantiles respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. Whilst regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and sub-daily), as it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.

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Stephen Jewson

Abstract

Knutson et al. (2020) recently published a meta-study that gives multi-model projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cylone basins. The changes were presented as the change that would occur with 2 °C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

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Roger Edwards, Harold E. Brooks, and Hannah Cohn

Abstract

U.S. tornado records form the basis for a variety of meteorological, climatological, and disaster-risk analyses, but how reliable are they in light of changing standards for rating, as with the 2007 transition of Fujita (F) to enhanced Fujita (EF) damage scales? To what extent are recorded tornado metrics subject to such influences that may be nonmeteorological in nature? While addressing these questions with utmost thoroughness is too large of a task for any one study, and may not be possible given the many variables and uncertainties involved, some variables that are recorded in large samples are ripe for new examination. We assess basic tornado-path characteristics—damage rating, length, width, and occurrence time, as well as some combined and derived measures—for a 24-yr period of constant path-width recording standard that also coincides with National Weather Service modernization and the WSR-88D deployment era. The middle of that period (in both time and approximate tornado counts) crosses the official switch from F to EF. At least minor shifts in all assessed path variables are associated directly with that change, contrary to the intent of EF implementation. Major and essentially stepwise expansion of tornadic path widths occurred immediately upon EF usage, and widths have expanded still farther within the EF era. We also document lesser increases in pathlengths and in tornadoes rated at least EF1 in comparison with EF0. These apparently secular changes in the tornado data can impact research dependent on bulk tornado-path characteristics and damage-assessment results.

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Hans Van de Vyver, Bert Van Schaeybroeck, Rozemien De Troch, Lesley De Cruz, Rafiq Hamdi, Cecille Villanueva-Birriel, Philippe Marbaix, Jean-Pascal van Ypersele, Hendrik Wouters, Sam Vanden Broucke, Nicole P. M. van Lipzig, Sébastien Doutreloup, Coraline Wyard, Chloé Scholzen, Xavier Fettweis, Steven Caluwaerts, and Piet Termonia

Abstract

Subdaily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated subdaily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. The aim of this paper is as follows: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a microensemble of convection-permitting RCM simulations over Belgium against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e., hourly or 2 hourly), but not on 6–24-h time scales. Some metrics such as the diurnal cycle or the Clausius–Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears to be robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications.

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Xiantong Liu, Huiqi Li, Sheng Hu, Qilin Wan, Hui Xiao, Tengfei Zheng, Minghua Li, Langming Ye, Zheyong Guo, Yao Wang, and Zhaochao Yan

Abstract

According to the high-accuracy linear shape–slope (μ–Λ) relationship observed by several two-dimensional video disdrometers (2DVD) in South China, a high-precision and fast solution method of the gamma (Γ) raindrop size distribution (RSD) function based on the zeroth-order moment (M 0) and the third-order moment (M 3) of RSD has been proposed. The 0-moment M 0 and 3-moment M 3 of RSD can be easily calculated from rain mass mixing ratio Q r and total number concentration N tr simulated by the two-moment (2M) microphysical scheme, respectively. Three typical heavy-rainfall processes and all RSD samples observed during 2019 in South China were selected to verify the accuracy of the method. Relative to the current widely used exponential RSD with a fixed shape parameter of zero in the 2M microphysical scheme, the Γ RSD function using the linear constrained gamma (C-G) method agreed better with the Γ-fit RSD from 2DVD observations. The characteristic precipitation parameters (e.g., rain rate, M 2, M 6, and M 9) obtained by the proposed method are generally consistent with the parameters calculated by Γ-fit RSD from 2DVD observations. The proposed method has effectively solved the problem that the shape parameter in the 2M microphysical scheme is set to a constant, and therefore the Γ RSD functions are closer to the observations and have obviously smaller errors. This method has a good potential to be applied to 2M microphysical schemes to improve the simulation of heavy precipitation in South China, but it also paves the way for in-depth applications of radar data in numerical weather prediction models.

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