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Heather Dawn Reeves, Nathan Lis, Guifu Zhang, and Andrew A. Rosenow

Abstract

New regulations are being issued by the Federal Aviation Administration (FAA) that require three-dimensional hydrometeor-phase diagnosis, including discrimination between freezing rain (FZRA) and freezing drizzle (FZDZ), for all commercial airports in the United States. Herein, a novel hydrometeor-phase algorithm, the spectral bin classifier (SBC), is adapted to meet these requirements. First, the SBC’s particle size distribution (PSD) is upgraded to be variable rather than fixed. This, along with some changes to the logic, allows for drizzle (DZ)/FZDZ to be diagnosed. Second, the SBC is modified to provide a low-altitude (LA), aboveground diagnosis (SBC-LA). Last, necessary changes to account for resolution issues in NWP thermal profiles are presented. Adding a dynamic-PSD capability improves the probability of detection (POD) by about 12%, but adapting the algorithm to include DZ/FZDZ worsens the PODs. This is due to potentially errant reports of rain (RA)/FZRA in environments that are more conducive to DZ/FZDZ. Assuming a diagnosis of DZ is a hit when RA is observed, and likewise for FZRA/FZDZ, increases the POD by between 35% and 60%. Although performance statistics for SBC-LA cannot be computed, about one-third of all RA and DZ soundings herein have an elevated layer of FZRA/FZDZ, underscoring the importance of an aboveground diagnosis for the aviation sector. The comparatively low vertical resolution of NWP profiles is found to degrade the SBC’s performance. Interpolating to a higher resolution helps to mitigate this problem. Several case studies of mixed phases at different airports are presented to highlight the enhanced decision support made possible by the above modifications.

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R. Yoshimura, K. Suzuki, J. Ito, R. Kikuchi, A. Yakeno, and S. Obayashi

Abstract

In this study, a clear-air turbulence event was reproduced using a high-resolution (250 m) large-eddy simulation in the Weather Research and Forecasting (WRF) Model, and the resulting wind field was used in a flight simulation to estimate the vertical acceleration changes experienced by an aircraft. Conditions were simulated for 16 December 2014 when many intense turbulence encounters (and one accident) associated with an extratropical cyclone were reported over the Tokyo area. Based on observations and the WRF simulation, the turbulence was attributed to shear-layer instability near the jet stream axis. Simulation results confirmed the existence of the instability, which led to horizontal vortices with an amplitude of vertical velocity from +20 to −12 m s−1. The maximum eddy dissipation rate was estimated to be over 0.7, which suggested that the model reproduced turbulence conditions likely to cause strong shaking in large-size aircraft. A flight simulator based on aircraft equations of motion estimated vertical acceleration changes of +1.57 to +0.08 G on a Boeing 777-class aircraft. Although the simulated amplitudes of the vertical acceleration changes were smaller than those reported in the accident (+1.8 to −0.88 G), the model successfully reproduced aircraft motion using a combination of atmospheric and flight simulations.

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Mohammad Ashrafi, Lloyd H. C. Chua, K. N. Irvine, and Peipei Yang

Abstract

The wind field over an urban lake may exhibit considerable variability resulting from wind-shielding effects from surrounding structures. Field measurements at an urban reservoir in Singapore were augmented by computational fluid dynamics (CFD) model results to develop a wind model over the reservoir surface via a data assimilation approach. The field measurements identified, depending on structure alignment with the prevailing wind direction, wind shielding that impacted wind direction and velocity over the reservoir surface. The wind model integrated the temporal response of the measurements and spatial distribution produced by the CFD modeling. The wind model was used to predict the spatiotemporal pattern of the wind field over the reservoir surface for a full year. The modeling results showed good agreement with measured wind data at three measurement locations on the reservoir surface. The wind model has been incorporated with a hydrodynamics and water quality model to provide the spatiotemporal wind forcing over the reservoir surface.

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Yu Shu, Jisong Sun, and Jin Chenlu

Abstract

The mesoscale vortex (MV) is an important rain-producing system. In this study, the reanalysis data and satellite precipitation products are used to classify MVs into three categories: mesoscale convective vortex (MCV), mesoscale stratiform vortex (MSV), and mesoscale dry vortex (MDV). Then, these three categories of midlevel MVs in China from 2007 to 2016 are investigated. A total of 21 053 MVs are obtained. Most MVs form in the northwest of parent convection, and 45% of MVs generate secondary convection. The Tibetan Plateau is the main MV source region. Steered by the westerlies, MVs mainly move eastward. MCV is active in summer, MDV in winter, and MSV in spring and autumn. MCV diurnal variations are closely related to local topography, and MDVs mainly form around midnight. Composite analyses show that MCVs form near the high-value center of convective available potential energy at the development stage of parent convection. The composite MCV forms near the low pressure trough and the thermal ridge at 500 hPa, and a low-level jet exists to the south of the MCV center. At the initiation and maturity stages of MCV, strong convergence and divergence respectively exist at low levels and 400 hPa. The vortex circulation mainly locates near 500 hPa. Above the vortex is a warm core associated with the latent heat release, and below is a cold anomaly related to the cold pool. In the downshear region, there is strong low-level convergence and ascending motion, higher humidity, and greater latent heat release, which favor the formation of secondary convection.

Open access
David Werth and Robert Buckley

Abstract

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions.

Significance Statement

We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

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Zihan Chen and Amanda H. Lynch

Abstract

We present a tracking algorithm for synoptic to meso-α-scale Arctic cyclones that differentiates between cold- and warm-core systems. The algorithm is applied to the ERA5 reanalysis north of 60°N from 1950 to 2019. In this dataset, over one-half of the cyclones that meet minimum intensity and duration thresholds can be classified as cold-core systems. Systems that undergo transition, typically from cold to warm core, make up 27.2% of cyclones and are longer lived. The relatively infrequent warm-core cyclones are more intense and are most common in winter. The Arctic-wide occurrence of maritime cyclones has increased from 1979 to 2019 when compared with the period from 1950 to 1978, but the trends have high interannual variability. This shift has ramifications for transportation, fisheries, and extractive industries, as well as impacts on communities across the Arctic.

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Yehui Zhang, Birong Zhang, and Na Yang

Abstract

The Global Climate Observing System Reference Upper-Air Network (GRUAN) with high-vertical-resolution radiosonde data at three Arctic stations and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5) were used to investigate the characteristics of multiple temperature inversions (TI) and humidity inversions (HI) in this study. It is found that surface-based inversion (SBI) at two coastal stations exists throughout the whole year, mainly due to the surface cooling in cold months, advection warm months, and the orography of the stations. The seasonal variation of surfaced-based HI (SBHI) frequency is similar to that of SBI, and its intensity is greater in summer because of the larger air moisture content. The frequency of the first elevated TI (EI1) and HI (EHI1) are both higher than that of the surface-based ones. The second elevated TI/HI layer (EI2/EHI2) is shallower and weaker than that of the EI1/EHI1. At two coastal stations, EI1 caused by warm advection is thicker and stronger than that caused by subsidence. At the station farther from the coast, EI1 caused by subsidence is higher, thinner, and stronger. The top height and depth of the EHI2 both show seasonal variations, with larger values in the cold months. EHI1 tends to be formed by the TI, whereas EHI2 is dominant by humidity advection at all studied stations. HI under the influence of TI is usually thicker and stronger than that formed by humidity advection. The coexistence of EI and EHI is the most frequent inversion structure at these stations.

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Albeht Rodríguez Vega, Juan Carlos Antuña-Marrero, David Barriopedro, Ricardo García-Herrera, Victoria E. Cachorro Revilla, Ángel de Frutos Baraja, and Juan Carlos Antuña-Sánchez

Abstract

We present a climatological study of aerosols in four representative Caribbean Sea islands that is based on daily mean values of aerosol optical properties for the period 2008–16, using the aerosol optical depth (AOD) and Ångström exponent (AE) to classify the dominant aerosol type. A climatological assessment of the spatiotemporal distribution of the main aerosol types, their links with synoptic patterns, and the transport from different sources is provided. Maximum values of AOD occur in the rainy season, coinciding with the minimum in AE and an increased occurrence of dust, whereas the minimum of AOD occurs in the dry season, due to the predominance of marine aerosols. Marine and dust aerosol are more frequent in the easternmost islands and decrease westward because of an increase of continental and mixture dust aerosols. Therefore, the westernmost station displays the most heterogeneous composition of aerosols. Using a weather-type classification, we identify a quantifiable influence of the atmospheric circulation in the distribution of Caribbean aerosols. However, they can occur under relatively weak and/or diverse synoptic patterns, typically involving transient systems and specific configurations of the Azores high that depend on the considered station. Backward trajectories indicate that dry-season marine aerosols and rainy-season dust are transported by air parcels traveling within the tropical easterly winds. The main source region for both types of aerosols is the subtropical eastern Atlantic Ocean, except for Cuba, where the largest contributor to dry-season marine aerosols is the subtropical western Atlantic. Different aerosol types follow similar pathways, suggesting a key role of emission sources in determining the spatiotemporal distribution of Caribbean aerosols.

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Lulin Xue, Courtney Weeks, Sisi Chen, Sarah A. Tessendorf, Roy M. Rasmussen, Kyoko Ikeda, Branko Kosovic, Dalton Behringer, Jeffery R. French, Katja Friedrich, Troy J. Zaremba, Robert M. Rauber, Christian P. Lackner, Bart Geerts, Derek Blestrud, Melvin Kunkel, Nick Dawson, and Shaun Parkinson

Abstract

A dry-air intrusion induced by the tropopause folding split the deep cloud into two layers resulting in a shallow orographic cloud with a supercooled liquid cloud top at around −15°C and an ice cloud above it on 19 January 2017 during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The airborne AgI seeding of this case was simulated by the WRF Weather Modification (WRF-WxMod) Model with different configurations. Simulations at different grid spacing, driven by different reanalysis data, using different model physics were conducted to explore the ability of WRF-WxMod to capture the properties of natural and seeded clouds. The detailed model–observation comparisons show that the simulation driven by ERA5 data, using Thompson–Eidhammer microphysics with 30% of the CCN climatology, best captured the observed cloud structure and supercooled liquid water properties. The ability of the model to correctly capture the wind field was critical for successful simulation of the seeding plume locations. The seeding plume features and ice number concentrations within them from the large-eddy simulations (LES) are in better agreement with observations than non-LES runs mostly due to weaker AgI dispersion associated with the finer grid spacing. Seeding effects on precipitation amount and impacted areas from LES seeding simulations agreed well with radar-derived values. This study shows that WRF-WxMod is able to simulate and quantify observed features of natural and seeded clouds given that critical observations are available to validate the model. Observation-constrained seeding ensemble simulations are proposed to quantify the AgI seeding impacts on wintertime orographic clouds.

Significance Statement

Recent observational work has demonstrated that the impact of airborne glaciogenic seeding of orographic supercooled liquid clouds is detectable and can be quantified in terms of the extra ground precipitation. This study aims, for the first time, to simulate this seeding impact for one well-observed case. The stakes are high: if the model performs well in this case, then seasonal simulations can be conducted with appropriate configurations after validations against observations, to determine the impact of a seeding program on the seasonal mountain snowpack and runoff, with more fidelity than ever. High–resolution weather simulations inherently carry uncertainty. Within the envelope of this uncertainty, the model compares very well to the field observations.

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John A. Callahan, Daniel J. Leathers, and Christina L. Callahan

Abstract

Coastal flooding is one of the most costly and deadly natural hazards facing the U.S. mid-Atlantic region today. Impacts in this heavily populated and economically significant region are caused by a combination of the location’s exposure and natural forcing from storms and sea level rise. Tropical cyclones (TCs) and midlatitude (ML) weather systems each have caused extreme coastal flooding in the region. Skew surge was computed over each tidal cycle for the past 40 years (1980–2019) at several tide gauges in the Delaware and Chesapeake Bays to compare the meteorological component of surge for each weather type. Although TCs cause higher mean surges, ML weather systems can produce surges just as severe and occur much more frequently, peaking in the cold season (November–March). Of the top 10 largest surge events, TCs account for 30%–45% in the Delaware and upper Chesapeake Bays and 40%–45% in the lower Chesapeake Bay. This percentage drops to 10%–15% for larger numbers of events in all regions. Mean sea level pressure and 500-hPa geopotential height (GPH) fields of the top 10 surge events from ML weather systems show a low pressure center west-southwest of “Delmarva” and a semistationary high pressure center to the northeast prior to maximum surge, producing strong easterly winds. Low pressure centers intensify under upper-level divergence as they travel eastward, and the high pressure centers are near the GPH ridges. During lower-bay events, the low pressure centers develop farther south, intensifying over warmer coastal waters, with a south-shifted GPH pattern relative to upper-bay events.

Significance Statement

Severe coastal flooding is a year-round threat in the U.S. mid-Atlantic region, and impacts are projected to increase in magnitude and frequency. Research into the meteorological contribution to storm surge, separate from mean sea level and tidal phase, will increase the scientific understanding and monitoring of changing atmospheric conditions. Tropical cyclones and midlatitude weather systems both significantly impact the mid-Atlantic region during different times of year. However, climate change may alter the future behavior of these systems differently. Understanding the synoptic environment and quantifying the surge response and subbay geographic variability of each weather system in this region will aid in public awareness, near-term emergency preparation, and long-term planning for coastal storms.

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