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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 because of 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 comprises a ground-based Doppler lidar, a microwave radiometer (MWR), and an environmental Sky Imager–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 min. 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 that highlight 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 aerosol optical depth) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the U.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.

Significance Statement

The New York State Mesonet (NYSM) Profiler Network enables routine measurement of aboveground weather data and products to monitor weather and air quality across the state at high resolutions. The NYSM Profiler Network provides real-time vertical profile information to users across the emergency management, aviation, utility, and public health sectors, including NOAA and NASA, for operations and research, filling a critical gap in monitoring the low-level atmosphere. These data have been used to improve situational awareness and monitor boundary layer dynamics, sea-land breeze development, precipitation type, and air quality. Most important, the NYSM Profiler Network provides a national testbed for the creation and evaluation of new ground-based profiling instrumentation and products.

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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 modeling. 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 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. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and subdaily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.

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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 have relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification that is 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 events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a 4-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 streak spacing of 200–400 m and roll sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

Open access
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 northeastern United States, surge is sorted on the basis of duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, whereas 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 with those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, whereas the longest-duration events are most often caused by extratropical cyclones. At least one-half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, whereas 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.

Open access
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.

Full access
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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Ying-Hui Jia
,
Fang-Fang Li
,
Kun Fang
,
Guang-Qian Wang
, and
Jun Qiu

Abstract

Recently, strong sound wave was proposed to enhance precipitation. The theoretical basis of this proposal has not been effectively studied either experimentally or theoretically. On the basis of the microscopic parameters of atmospheric cloud physics, this paper solved the complex nonlinear differential equation to show the movement characteristics of cloud droplets under the action of sound waves. The motion process of an individual cloud droplet in a cloud layer in the acoustic field is discussed as well as the relative motion between two cloud droplets. The effects of different particle sizes and sound field characteristics on particle motion and collision are studied to analyze the dynamic effects of thunder-level sound waves on cloud droplets. The amplitude of velocity variation has positive correlation with sound pressure level (SPL) and negative correlation with the frequency of the surrounding sound field. Under the action of low-frequency sound waves with sufficient intensity, individual cloud droplets could be forced to oscillate significantly. A droplet smaller than 40 μm can be easily driven by sound waves of 50 Hz and 123.4 dB. The calculation of the collision process of two droplets reveals that the disorder of motion for polydisperse droplets is intensified, resulting in the broadening of the collision time range and spatial range. When the acoustic frequency is less than 100 Hz (at 123.4 dB) or the SPL is greater than 117.4 dB (at 50 Hz), the sound wave can affect the collision of cloud droplets significantly. This study provides a theoretical perspective of the acoustic effect on the microphysics of atmospheric clouds.

Full access
Hilde Haakenstad
,
Øyvind Breivik
,
Birgitte R. Furevik
,
Magnar Reistad
,
Patrik Bohlinger
, and
Ole Johan Aarnes

Abstract

The 3-km Norwegian Reanalysis (NORA3) is a 15-yr mesoscale-permitting atmospheric hindcast of the North Sea, the Norwegian Sea, and the Barents Sea. With a horizontal resolution of 3 km, the nonhydrostatic numerical weather prediction model HARMONIE–AROME runs explicitly resolved deep convection and yields hindcast fields that realistically downscale the ERA5 reanalysis. The wind field is much improved relative to its host analysis, in particular in mountainous areas and along the improved grid-resolving coastlines. NORA3 also performs much better than the earlier hydrostatic 10-km Norwegian Hindcast Archive (NORA10) in complex terrain. NORA3 recreates the detailed structures of mesoscale cyclones with sharp gradients in wind and with clear frontal structures, which are particularly important when modeling polar lows. In extratropical windstorms, NORA3 exhibits significantly higher maximum wind speeds and compares much better to observed maximum wind than do NORA10 and ERA5. The activity of the model is much more realistic than that of NORA10 and ERA5, both over the ocean and in complex terrain.

Open access
Domingo Muñoz-Esparza
,
Hyeyum Hailey Shin
,
Teddie L. Keller
,
Kyoko Ikeda
,
Robert D. Sharman
,
Matthias Steiner
,
Jeff Rawdon
, and
Gary Pokodner

Abstract

Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this situation, the Federal Aviation Administration has developed a precipitous terrain classification as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current precipitous point value (PPV) algorithm relies on the terrain characteristics within a local area of 2 n mi (3.7 km) in radius and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely, turbulence, altimeter-setting errors, and density-altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 2° × 2° region over the Rocky Mountains in Colorado containing three airports that are surrounded by precipitous terrain. Both available turbulence reports and model’s turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter-setting errors and density-altitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter-setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, whereas density-altitude effects are on the contrary predominantly present during the spring and summer months and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain.

Open access