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M. K. Yau, Yubao Liu, Da-Lin Zhang, and Yongsheng Chen

Abstract

The objectives of Part VI of this series of papers are to (a) simulate the finescale features of Hurricane Andrew (1992) using a cloud-resolving grid length of 2 km, (b) diagnose the formation of small-scale wind streaks, and (c) perform sensitivity experiments of varying surface fluxes on changes in storm inner-core structures and intensity.

As compared to observations and a previous 6-km model run, the results show that a higher-resolution explicit simulation could produce significant improvements in the structures and evolution of the inner-core eyewall and spiral rainbands, and in the organization of convection. The eyewall becomes much more compact and symmetric with its width decreased by half, and the radius of maximum wind is reduced by ∼10 to 20 km. A zone of deep and intense potential vorticity (PV) is formed at the edge of the eye. A ring of maximum PV is collocated in regions of maximum upward motion in the eyewall and interacts strongly with the eyewall convection. The convective cores in the eyewall are associated with small-scale wind streaks.

The formation of the wind streaks is diagnosed from an azimuthal momentum budget. The results reveal small-scale Lagrangian acceleration of the azimuthal flow. It is found that at the lowest model level of 40 m, the main contributor to the Lagrangian azimuthal wind tendency is the radial advection of angular momentum per unit radius. At an altitude of 1.24 km, vertical advection of the azimuthal wind, in addition to the radial advection of angular momentum per unit radius, plays important roles.

Results of a series of sensitivity tests, performed to examine the impact of several critical factors in the surface and boundary layer processes on the inner-core structures and the evolution of the hurricane intensity, are presented.

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Yujue Liu, Yubao Liu, Domingo Muñoz-Esparza, Fei Hu, Chao Yan, and Shiguang Miao

Abstract

A multiscale modeling study of a real case has been conducted to explore the capability of the large-eddy simulation version of the Weather Research and Forecasting Model (WRF-LES) over Xiaohaituo Mountain (a game zone for the Beijing, China, 2022 Winter Olympic Games). In comparing WRF-LES results with observations collected during the Mountain Terrain Atmospheric Observations and Modeling (MOUNTAOM) field campaign, it is found that at 37-m resolution with LES settings, the model can reasonably capture both large-scale events and microscale atmospheric circulation characteristics. Employing the Shuttle Radar Topography Mission 1 arc s dataset (SRTM1; ~30 m) high-resolution topographic dataset instead of the traditional USGS_30s (~900 m) dataset effectively improves the model capability for reproducing fluctuations and turbulent features of surface winds. Five sensitivity experiments are conducted to investigate the impact of different PBL treatments, including YSU/Shin and Hong (SH) PBL schemes and LES with 1.5-order turbulence kinetic energy closure model (1.5TKE), Smagorinsky (SMAG), and nonlinear backscatter and anisotropy (NBA) subgrid-scale (SGS) stress models. In this case, at gray-zone scales, differences between YSU and SH are negligible. LES outperform two PBL schemes that generate smaller turbulence kinetic energy and increase the model errors for mean wind speed, energy spectra, and probability density functions of velocity. Another key finding is that wind field features in the boundary layer over complex terrain are more sensitive to the choice of SGS models than above the boundary layer. With the increase of model resolution, the effects of the SGS model become more significant, especially for the statistical characteristics of turbulence. Among these three SGS models, NBA has the best performance. Overall, this study demonstrates that WRF-LES is a promising tool for simulating real weather flows over complex terrain.

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Yong Liu, Huopo Chen, Huijun Wang, Jianqi Sun, Hua Li, and Yubao Qiu

Abstract

Lake ice phenology, as an indicator for climate variability and change, exerts a great influence on regional climate and hydrometeorology. In this study, the changing characteristics of lake ice phenology at Lake Qinghai (LQH) are investigated using retrieved historical datasets during 1979–2016. The results show that the variation of the lake freeze-up date over LQH is characterized by a strong interannual variability. Further analysis has revealed that November sea ice concentration (SIC) variation in the Kara Sea can exert a great impact on the freeze-up date at LQH. During the low sea ice years, the open sea serves as a strong diabatic heating source, largely contributing to the enhanced Arctic Eliassen–Palmer flux, which then results in the deceleration of zonal wind in the middle and high latitudes. In addition to this, accompanied with the decreasing Kara SIC, the enhanced stationary Rossby wave flux propagating along the high-latitude regions may further exert remarkable influences in deepening the East Asian trough, which provides a favorable atmospheric circulation pattern for cold air intrusion from the Arctic and Siberian regions to mainland China. The decreased surface air temperature would thus advance the freezing date over LQH. Furthermore, the close relationship between atmospheric circulation anomalies and Kara SIC variations is validated by a large ensemble of simulations from the Community Earth System Model, and the atmospheric circulation patterns induced by the SIC anomalies are reproduced to some extent. Therefore, the November Kara Sea ice anomaly might be an important predictor for the variation in the freeze-up date at LQH.

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Rod Frehlich, Robert Sharman, Francois Vandenberghe, Wei Yu, Yubao Liu, Jason Knievel, and George Jumper

Abstract

Area-averaged estimates of Cn 2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering effects of the underlying NWP model. The key assumptions are the existence of a universal statistical description of small-scale turbulence and a locally universal spatial filter for the NWP model variables. Under these assumptions, spatial structure functions of the NWP model variables can be related to the structure functions of the atmospheric variables and extended to the smaller underresolved scales. The shape of the universal spatial filter is determined by comparisons of model structure functions with the climatological spatial structure function determined from an archive of aircraft data collected in the upper troposphere and lower stratosphere. This method of computing Cn 2 has an important advantage over more traditional methods that are based on vertical differences because the structure function–based estimates avoid reference to the turbulence outer length scale. To evaluate the technique, NWP model–derived structure-function estimates of Cn 2 are compared with nighttime profiles of Cn 2 derived from temperature structure-function sensors attached to a rawinsonde (thermosonde) near Holloman Air Force Base in the United States.

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Susanne Grossman-Clarke, Joseph A. Zehnder, William L. Stefanov, Yubao Liu, and Michael A. Zoldak

Abstract

A refined land cover classification for the arid Phoenix (Arizona) metropolitan area and some simple modifications to the surface energetics were introduced in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The single urban category in the existing 24-category U.S. Geological Survey land cover classification used in MM5 was divided into three classes to account for heterogeneity of urban land cover. Updated land cover data were derived from 1998 Landsat Thematic Mapper satellite images. The composition of the urban land use classes in terms of typical fractions of vegetation and anthropogenic surfaces was determined from ground-truth information, allowing a variety of moisture availability for evaporation by land cover class. Bulk approaches for characteristics of the urban surface energy budget, such as heat storage, the production of anthropogenic heat, and radiation trapping, were introduced in MM5’s Medium Range Forecast boundary layer scheme and slab land surface model. A 72-h simulation was performed with MM5 on a 2 km × 2 km grid during June 1998. The new land cover classification had a significant impact on the turbulent heat fluxes and the evolution of the boundary layer and improved the capability of MM5 to simulate the daytime part of the diurnal temperature cycle in the urban area. The nighttime near-surface air temperatures were improved significantly by adding radiation trapping, heat storage, and anthropogenic heating to the model.

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Daran L. Rife, Christopher A. Davis, Yubao Liu, and Thomas T. Warner

Abstract

This study describes the verification of model-based, low-level wind forecasts for the area of the Salt Lake valley and surrounding mountains during the 2002 Salt Lake City, Utah, Winter Olympics. Standard verification statistics (such as bias and mean absolute error) for wind direction and speed were compared for four models: the Eta, Rapid Update Cycle (RUC-2), and Global Forecast System of the National Centers for Environmental Prediction, and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Even though these models had horizontal grid increments that ranged over almost two orders of magnitude, the highest-resolution MM5 with a 1.33-km grid increment exhibited a forecast performance similar to that of the other models in terms of grid-average, conventional verification metrics. This is in spite of the fact that the MM5 is the only model capable of reasonably representing the complex terrain of the Salt Lake City region that exerts a strong influence on the local circulation patterns. The purpose of this study is to investigate why the standard verification measures did not better discriminate among the models and to describe alternative measures that might better represent the ability of high-horizontal-resolution models to forecast locally forced mesogamma-scale circulations. The spatial variability of the strength of the diurnal forcing was quantified by spectrally transforming the time series of wind-component data for each observation location. The amount of spectral power in the band with approximately a diurnal period varied greatly from place to place, as did the amount of power in the bands with periods longer (superdiurnal) and shorter (subdiurnal) than the diurnal. It is reasonable that the superdiurnal power is largely in the synoptic-scale motions, and thus can be reasonably predicted by all the models. In contrast, the subdiurnal power is mainly in nondiurnally forced small-scale fluctuations that are generally unpredictable with any horizontal resolution because they are unobserved in three dimensions by the observation network.

A strong positive relationship is demonstrated between the strength of the local forcing at each observation location, as measured by the spectral power in the diurnal band of the wind component time series, and forecast skill, as reflected by an alternative verification metric, a measure of anomaly correlation. However, the mean-absolute error showed no relationship to the power in the diurnal band. Two other measures of comparison among the low-level wind forecasts, the direction climatology and the spatial variance, showed a positive correlation between forecast quality and horizontal resolution.

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Teddie L. Keller, Stanley B. Trier, William D. Hall, Robert D. Sharman, Mei Xu, and Yubao Liu

Abstract

At 1818 mountain standard time 20 December 2008, a Boeing 737 jetliner encountered significant crosswinds while accelerating for takeoff at the Denver International Airport (DIA), ran off the side of the runway, and burst into flames. Passengers and crew were able to evacuate quickly, and, although there were injuries, there were no fatalities. Winds around the time of the accident were predominantly from the west, with substantial spatial and temporal speed variability across the airport property. Embedded in this mostly westerly flow were intermittent gusts that created strong crosswinds for the north–south runways. According to the report from the National Transportation Safety Board, it was one of these strong gusts that initiated the events that led to the runway excursion and subsequent crash of the aircraft. Numerous aircraft reported significant mountain-wave activity and turbulence over Colorado on the day of the accident. To determine whether wave activity may have contributed to the strong, intermittent gustiness at DIA, a high-resolution multinested numerical simulation was performed using the Clark–Hall model, with a horizontal grid spacing of 250 m in the inner domain. Results from this simulation suggest that the surface gustiness at DIA was associated with undulations in a train of lee waves in a midtropospheric stable layer above the airport, creating regions of higher-velocity air descending toward the surface. In contrast, a simulation with horizontal grid spacing that was similar to that of a state-of-the-art operational forecast model (3 km) did not predict strong winds at DIA.

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Susanne Grossman-Clarke, Yubao Liu, Joseph A. Zehnder, and Jerome D. Fast

Abstract

A modified version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) was applied to the arid Phoenix, Arizona, metropolitan region. The ability of the model to simulate characteristics of the summertime urban planetary boundary layer (PBL) was tested by comparing model results with observations from two field campaigns conducted in May/June 1998 and June 2001. The modified MM5 included a refined land use/cover classification and updated land use data for Phoenix and bulk approaches of characteristics of the urban surface energy balance. PBL processes were simulated by a version of MM5’s Medium-Range Forecast Model (MRF) scheme that was enhanced by new surface flux and nonlocal mixing approaches. Simulated potential temperature profiles were tested against radiosonde data, indicating that the modified MRF scheme was able to simulate vertical mixing and the evolution and height of the PBL with good accuracy and better than the original MRF scheme except in the late afternoon. During both simulation periods, it is demonstrated that the modified MM5 simulated near-surface air temperatures and wind speeds in the urban area consistently and considerably better than the standard MM5 and that wind direction simulations were improved slightly.

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Luca Delle Monache, Thomas Nipen, Yubao Liu, Gregory Roux, and Roland Stull

Abstract

Two new postprocessing methods are proposed to reduce numerical weather prediction’s systematic and random errors. The first method consists of running a postprocessing algorithm inspired by the Kalman filter (KF) through an ordered set of analog forecasts rather than a sequence of forecasts in time (ANKF). The analog of a forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast. The second method is the weighted average of the observations that verified when the 10 best analogs were valid (AN). ANKF and AN are tested for 10-m wind speed predictions from the Weather Research and Forecasting (WRF) model, with observations from 400 surface stations over the western United States for a 6-month period. Both AN and ANKF predict drastic changes in forecast error (e.g., associated with rapid weather regime changes), a feature lacking in KF and a 7-day running-mean correction (7-Day). The AN almost eliminates the bias of the raw prediction (Raw), while ANKF drastically reduces it with values slightly worse than KF. Both analog-based methods are also able to reduce random errors, therefore improving the predictive skill of Raw. The AN is consistently the best, with average improvements of 10%, 20%, 25%, and 35% with respect to ANKF, KF, 7-Day, and Raw, as measured by centered root-mean-square error, and of 5%, 20%, 25%, and 40%, as measured by rank correlation. Moreover, being a prediction based solely on observations, AN results in an efficient downscaling procedure that eliminates representativeness discrepancies between observations and predictions.

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Andrea N. Hahmann, Dorita Rostkier-Edelstein, Thomas T. Warner, Francois Vandenberghe, Yubao Liu, Richard Babarsky, and Scott P. Swerdlin

Abstract

The use of a mesoscale model–based four-dimensional data assimilation (FDDA) system for generating mesoscale climatographies is demonstrated. This dynamical downscaling method utilizes the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), wherein Newtonian relaxation terms in the prognostic equations continually nudge the model solution toward surface and upper-air observations. When applied to a mesoscale climatography, the system is called Climate-FDDA (CFDDA). Here, the CFDDA system is used for downscaling eastern Mediterranean climatographies for January and July. The downscaling method performance is verified by using independent observations of monthly rainfall, Quick Scatterometer (QuikSCAT) ocean-surface winds, gauge rainfall, and hourly winds from near-coastal towers. The focus is on the CFDDA system’s ability to represent the frequency distributions of atmospheric states in addition to time means. The verification of the monthly rainfall climatography shows that CFDDA captures most of the observed spatial and interannual variability, although the model tends to underestimate rainfall amounts over the sea. The frequency distributions of daily rainfall are also accurately diagnosed for various regions of the Levant, except that very light rainfall days and heavy precipitation amounts are overestimated over Lebanon. The verification of the CFDDA against QuikSCAT ocean winds illustrates an excellent general correspondence between observed and modeled winds, although the CFDDA speeds are slightly lower than those observed. Over land, CFDDA- and the ECMWF-derived wind climatographies when compared with mast observations show similar errors related to their inability to properly represent the local orography and coastline. However, the diurnal variability of the winds is better estimated by CFDDA because of its higher horizontal resolution.

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