Browse

You are looking at 101 - 110 of 9,647 items for :

  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All
James M. Kurdzo, Emily F. Joback, Pierre-Emmanuel Kirstetter, and John Y. N. Cho

Abstract

The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation [R(A)] and reflectivity–differential reflectivity [R(Z, Z DR)] QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.

Restricted access
Duc Tran-Quang, Ha Pham-Thanh, The-Anh Vu, Chanh Kieu, and Tan Phan-Van

Abstract

This study examines the climatic shift of the tropical cyclone (TC) frequency affecting Vietnam’s coastal region during 1975–2014. By separating TC databases into two different 20-yr epochs, it is found that there is a consistent increase in both the number of strong TCs and the number of TC occurrences during the recent epoch (1995–2014) as compared with the reference epoch (1975–94) across different TC databases. This finding suggests that not only the number of strong TCs but also the lifetime of strong TCs affecting Vietnam’s coastal region has been recently increasing as compared with the reference epoch from 1975 to 1994. To understand the physical connection of these shifts in the TC frequency and duration, large-scale conditions obtained from reanalysis data are analyzed. Results show that meridional surface temperature gradient (STG) during the recent epoch is substantially larger than that during 1975–94. Such an increase in the meridional STG is important because it is potentially linked to the increase in large-scale vertical wind shear as well as the reduced intensity of summer monsoon in the South China Sea between the two epochs.

Restricted access
Anthony G. Barnston, Bradfield Lyon, Ethan D. Coffel, and Radley M. Horton

Abstract

The frequency of heat waves (defined as daily temperature exceeding the local 90th percentile for at least three consecutive days) during summer in the United States is examined for daily maximum and minimum temperature and maximum apparent temperature, in recent observations and in 10 CMIP5 models for recent past and future. The annual average percentage of days participating in a heat wave varied between approximately 2% and 10% in observations and in the model’s historical simulations during 1979–2005. Applying today’s temperature thresholds to future projections, heat-wave frequencies rise to more than 20% by 2035–40. However, given the models’ slight overestimation of frequencies and positive trend rates during 1979–2005, these projected heat-wave frequencies should be regarded cautiously. The models’ overestimations may be associated with their higher daily autocorrelation than is found in observations. Heat-wave frequencies defined using apparent temperature, reflecting both temperature and atmospheric moisture, are projected to increase at a slightly (and statistically significantly) faster rate than for temperature alone. Analyses show little or no changes in the day-to-day variability or persistence (autocorrelation) of extreme temperature between recent past and future, indicating that the future heat-wave frequency will be due predominantly to increases in standardized (using historical period statistics) mean temperature and moisture content, adjusted by the local climatological daily autocorrelation. Using nonparametric methods, the average level and spatial pattern of future heat-wave frequency is shown to be approximately predictable on the basis of only projected mean temperature increases and local autocorrelation. These model-projected changes, even if only approximate, would impact infrastructure, ecology, and human well-being.

Open access
F. Letson, T. J. Shepherd, R. J. Barthelmie, and S. C. Pryor

Abstract

Deep convection and the related occurrence of hail, intense precipitation, and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading-edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the U.S. southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) Model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modeling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements, and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates, and precipitation totals show good agreement with observations. The occurrence of precipitation during power-producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modeled with a mean proportion correct of 0.77 and an odds ratio of 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry, and there is evidence for positive model bias in cloud reflectivity.

Restricted access
Philip T. Bergmaier and Bart Geerts

Abstract

Modeling and observational studies stemming from the 2013–14 Ontario Winter Lake-Effect Systems (OWLeS) field campaign have yielded much insight into the structure and development of long-lake-axis-parallel (LLAP) lake-effect systems over Lake Ontario. This study uses airborne single- and dual-Doppler radar data obtained during two University of Wyoming King Air flights, as well as a high-resolution numerical model simulation, to examine and contrast two distinctly different LLAP band structures observed within a highly persistent lake-effect system on 7–9 January 2014. On 7 January, a very cold air mass accompanied by strong westerly winds and weak capping aloft resulted in a deep, intense LLAP band that produced heavy snowfall well inland. In contrast, weaker winds, weaker surface heat fluxes, and stronger capping aloft resulted in a weaker LLAP band on 9 January. This band was blocked along the downwind shore and produced only light snowfall closer to the shoreline. Although the two structures examined here represent opposite ends of a spectrum of LLAP bands, both cases reveal a well-organized mesoscale secondary circulation composed of two counterrotating horizontal vortices positioned on either side of a narrow updraft within the band. In both cases, this circulation traces back to a shallow, baroclinic land-breeze front originating along a bulge in the lake’s southern shoreline. As the band extends downstream and the low-level baroclinity weakens, buoyancy increases within the band—driven in part by cloud latent heating—leading to band intensification and a deeper, stronger, and more symmetric secondary circulation over the lake.

Restricted access
Trey McNeely, Ann B. Lee, Kimberly M. Wood, and Dorit Hammerling

Abstract

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes that drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine-learning algorithms have limited applicability on this front because of their “black box” structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for overocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed feature suite targets the global organization, radial structure, and bulk morphology (ORB) of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine-learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (vs absence) of such intensity-change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine-learning methods did not perform better than the linear logistic lasso model for current data.

Restricted access
Zhiduo Yan, Liang Pang, and Sheng Dong

Abstract

An increasing number of coastal and offshore structures have been built for coastal protection and marine development in recent years, and these marine structures need to be reasonably designed on the basis of wind speed. In this paper, extreme wind speed estimates are studied in detail by using the best-track datasets of northwestern Pacific Ocean tropical cyclones and ERA5 wind field data. The extreme wind speed fits by five distributions are compared using a blended sample of the wind fields from the ERA5 dataset and parametric wind data. The blend of wind fields improved the data accuracy and extreme value estimation reliability. In addition, the effects of the distribution model, data, threshold, and parameter estimation methods on the calculated results are discussed. The results show that the data had the greatest influences on probability prediction, followed by the distribution model and the parameter estimation method, with the threshold presenting the least influence. In this study, the reliability of the estimates was improved and the uncertainty of the results was analyzed, and the findings provide a wind speed design reference for the northern South China Sea.

Restricted access
Israel Lopez-Coto, Micheal Hicks, Anna Karion, Ricardo K. Sakai, Belay Demoz, Kuldeep Prasad, and James Whetstone

Abstract

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling.

Restricted access
Xue Yi, Deqin Li, Chunyu Zhao, Lidu Shen, and Xiaoyu Zhou

Abstract

High-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.

Restricted access
Rick Lader, Allison Bidlack, John E. Walsh, Uma S. Bhatt, and Peter A. Bieniek

Abstract

Warming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.

Restricted access