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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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David E. Rupp, Christopher Daly, Matt K. Doggett, Joseph I. Smith, and Ben Steinberg

Abstract

The exponential growth in solar radiation measuring stations across the conterminous United States permits the generation of gridded solar irradiance data that capture the spatio-temporal variability of solar irradiance far more accurately than previously possible from ground-based observations. Taking advantage of these observations, we generated a 30-year climatology (1991-2020) of mean monthly global irradiance at a resolution of 30 arcsec (∼800 m) on both a horizontal and sloped ground surface. This paper describes the methods used to generate the gridded data, which include extensive quality control of station data, spatial interpolation of effective cloud transmittance using the “PRISM” method, and simulation of the effects of elevation, shading, and reflection from nearby terrain on solar irradiance. A comparison of the new dataset to several other solar radiation products reveals some spatial features in solar radiation that are either lacking or under-resolved in some or all of the other datasets. Examples of these features include strong gradients near foggy coastlines and along mountain ranges where there is persistent orographically driven cloud formation. The workflow developed to create the long-term means will be used as a template for generating time series of monthly and daily solar radiation grids up to the present.

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Soubhik Biswas, Savin S. Chand, Andrew J. Dowdy, Wendy Wright, Cameron Foale, Xiaohui Zhao, and Anil Deo

Abstract

Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude compared with observations. Post-processing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias correction approaches based on quantile-quantile matching to correct the data from the 20th Century Reanalysis version 2c (20CRv2c) against observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, median-based approach, non-parametric linear method, spline-based method, log-normal, and Weibull distribution-based approaches. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the Forest Fire Danger Index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits compared to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.

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Melanie Schroers and Dr. Elinor Martin

Abstract

Long periods of extreme precipitation can cause costly damages to a region’s infrastructure, while also creating a higher risk for the region’s population. Planning for these periods would ideally begin at the subseasonal to seasonal (S2S) time scale, yet prediction of precipitation at this time scale has low skill. In this study we will use Jennrich et al. (2020)’s database of 14-day extreme precipitation events, to understand more about the synoptic connections and impacts of these extended extreme events. The synoptic connections of events were examined using the composites of event days 500hPa geopotential height and precipitable water anomalies. The combination of these two drivers leads to higher skill in the West Coast and Great Lakes than other regions, with an equitable threat score of 0.137 and 0.084 respectively and higher conditional probabilities of event occurrence. Therefore, the synoptic patterns associated with events, while significant, are not unique which poses prediction challenges. Historical impacts of these events, using NCEI storm reports, were assessed to benefit decision makers in future risk mitigation. A variety of reports were found during events, from winter weather reports in West Coast events, to tropical storm reports in Southeast events. Every region has significantly more flooding reports during events than in non-extreme 14-day periods, demonstrating the impacts of such extended events. Although there is still much to learn about extreme precipitation events, this study contributes to the foundational knowledge of synoptic drivers and impacts of events.

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Corey K. Potvin, Chris Broyles, Patrick S. Skinner, and Harold E. Brooks

Abstract

Many tornadoes are unreported due to lack of observers, or underrated in intensity, width, or track length due to lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors like studies of climate impacts on tornadoes and cost-benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length, and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately three times as many tornadoes capable of (E)F2+ damage than (E)F2+ tornadoes recorded. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, path length, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model.

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Chia-Ying Lee, Adam H. Sobel, Suzana J. Camargo, Michael K. Tippett, and Qidong Yang

Abstract

This study addresses hurricane hazard to the state of New York in past, present, and future, using synthetic storms generated by the Columbia HAZard model (CHAZ) and climate inputs from the fifth coupled model intercomparison project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951-2005) and the two future periods under the Representative Concentration Pathway 8.5 warming scenario: the near-future (2006-2040) and the late 21st century (2070-2099). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late 21st century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain due to a fundamental uncertainty in CHAZ projections of NYS hurricane frequency, in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

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Harish Baki, Sandeep Chinta, C. Balaji, and Balaji Srinivasan

Abstract

The prediction skill of a numerical model can be enhanced by calibrating the sensitive parameters that significantly influence the model forecast. The objective of the present study is to improve the prediction of surface wind speed and precipitation by calibrating the Weather Research and Forecasting (WRF) model parameters for the simulations of tropical cyclones over the Bay of Bengal region. Ten tropical cyclones across different intensity categories between 2011 and 2017 are selected for the calibration experiments. Eight sensitive model parameters are calibrated by minimizing the prediction error corresponding to 10m wind speed and precipitation, using a multiobjective adaptive surrogate model-based optimization (MO-ASMO) framework. The 10m wind speed and precipitation simulated by the default and calibrated parameter values across different aspects are compared. The results show that the calibrated parameters improved the prediction of 10m wind speed by 17.62% and precipitation by 8.20% compared to the default parameters. The effect of calibrated parameters on other model output variables, such as cyclone track and intensities, and 500 hPa wind fields, is investigated. Eight tropical cyclones across different categories between 2011 and 2018 are selected to corroborate the performance of the calibrated parameter values for other cyclone events. Finally, the robustness of the calibrated parameters across different boundary conditions and grid resolutions is also examined. These results will have significant implications for improving the predictability of tropical cyclone characteristics. This allows us to better plan the adaptation and mitigation strategies and thus help in reducing their adverse effects on society.

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Björn Maronga, Matthias Winkler, and Dan Li

Abstract

In this work we investigate the effect of area-wide building retrofitting on summer-time, street-level outdoor temperatures in an urban district in Berlin, Germany. We perform two building-resolving, week-long large-eddy simulations: one with non-retrofitted buildings and the other with retrofitted buildings in the entire domain to meet today’s energy efficiency standards. The comparison of the two simulations reveals that the mean outdoor temperatures are higher with retrofitted buildings during daytime conditions. This behavior is caused by the much smaller inertia of the outermost roof/wall layer in the retrofitting case, which is thermally decoupled from the inner roof/wall layers by an insulation layer. As a result, the outermost layer heats up more rigorously during the daytime, leading to increased sensible heat fluxes into the atmosphere. During the nighttime, the outermost layer’s temperature drops down faster, resulting in cooling of the atmosphere. However, as the simulation progresses the cooling effect becomes smaller and the warming effect becomes larger. After one week we find the mean temperatures to be 4 K higher during the daytime, while the cooling effects become negligible.

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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.

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