Abstract

Past microphysical investigations, including Part I of this study, have noted that the collection equation, when applied to the interaction between different hydrometeor species, can predict large mass transfer rates, even when an exact solution is used. The fractional depletion in a time step can even exceed unity for the collected species with plausible microphysical conditions and time steps, requiring “normalization” by a microphysical scheme. Although some of this problem can be alleviated through the use of more moment predictions and hydrometeor categories, the question as to why such “overdepletion” can be predicted in the first place remains insufficiently addressed. It is shown through both physical and conceptual arguments that the explicit time discretization of the bulk collection equation for any moment is not consistent with a quasi-stochastic view of collection. The result, under certain reasonable conditions, is a systematic overprediction of collection, which can become a serious error in the prediction of higher-order moments in a bulk scheme. The term numerical bounding is used to refer to the use of a quasi-stochastically consistent formula that prevents fractional collections exceeding unity for any moments. Through examples and analysis it is found that numerical bounding is typically important in mass moment prediction for time steps exceeding approximately 10 s. The Poisson-based numerical bounding scheme is shown to be simple in application and conceptualization; within a straightforward idealization it completely corrects overdepletion while even being immune to the rediagnosis error of the time-splitting method. The scheme’s range of applicability and utility are discussed.

Abstract

The collection equation is analyzed for the case of two spherical hydrometeors with collection efficiency unity and exponential size distributions. When the fall velocities are significantly different a more general form of the conventional Wisner approximation can be formulated. The accuracy of the new formula exceeds that of the Wisner approximation for all cases considered, except for the collection of a faster species by a slower species if the amount of the faster species is relatively small compared with that of the slower species. The exact solution of the collection equation is then rederived and cast into the form of a power series involving the ratio of the two characteristic fall velocities. It is shown that the new formulation is a first-order correction to the continuous collection equation for hydrometeors with finite diameters and fall velocities. Based on the analysis, the implications for the behavior of both the exact collection equation and its representation in numerical models are discussed.

Abstract

An idealized simulation of a supercell using the Regional Atmospheric Modeling System (RAMS) was able to produce a low-level mesocyclone near the intersection of the forward- and rear-flank downdrafts. The creation of the low-level mesocyclone is similar to previous studies. After 3600 s, the low-level mesocyclone underwent a period of rapid intensification, during which its form changed from an elongated patch to a compact center. This transition was also accompanied by a sudden decrease in pressure (to 12 mb below that of the neighboring flow), and was found to occur even in the absence of nested grids.

It is shown that the stage of strong intensification does not begin aloft, as in the dynamic pipe effect, and then descend to the surface. Rather, the vortex is initiated near the surface, and then builds upward. The process is completed in 5 min, and the final vortex can be clearly distinguished from the larger-scale mesocyclone at the cloud base. The reduction of pressure can be explained as a consequence of the evacuation of mass in the horizontal convergence equation. This is in contrast to axisymmetric models of vortex intensification, which generally rely on the evacuation of mass in the vertical divergence equation. In the latter cases a positive horizontal convergence tendency is what initiates the concentrated vortex. However, nondivergent models prove that vorticity concentration can occur in the absence of any horizontal convergence. Here the concentration is associated with a negative horizontal convergence tendency.

Abstract

Numerical weather prediction model skill is difficult to assess for transient, nonstationary, nondeterministic, or stochastic motions, like submeso and small meso-gamma motions. New approaches are needed to complement traditional methods and to quantify and evaluate the variability and the errors for these high-frequency, nondeterministic modes. A new verification technique that uses the wavelet transform as a bandpass filter to obtain scale-dependent frequency distributions of fluctuations is proposed for assessing model performance or accuracy. This new approach quantifies the nondeterministic variability independent of time while accounting for the time scale and amplitude of each fluctuation.

The efficacy of this wavelet decomposition technique for the verification of submeso and meso-gamma motions is first illustrated for a single case before the analysis is extended to six cases. The sensitivity of subkilometer grid-length Weather Research and Forecasting Model forecasts to the choice of three initialization strategies is assessed for both deterministic and stochastic motions using observations from a special network located at Rock Springs, Pennsylvania. It is demonstrated that the use of data assimilation in a preforecast period results in improved temperature and wind speed statistics for deterministic motions and for nondeterministic fluctuations with periods greater than ~20 min. As expected, there is little-to-no accuracy forecasting the occurrence of variability for temperature and wind in the smaller-submeso range and greater accuracy in the larger-submeso and meso-gamma ranges. Nonetheless, the model has some difficulty reproducing the observed variability with the correct amplitude. It underestimates the amplitude of observed fluctuations even for larger time scales, where better model performance could be expected.

Abstract

An idealized supercell simulation using the Regional Atmospheric Modeling System (RAMS) produced an elongated low-level mesocyclone that subsequently collapsed into a concentrated vortex. Though vorticity continually increased in the mesocyclone due to horizontal convergence, the collapse phase was additionally characterized by rapidly decreasing pressure, closed streamlines, and the creation of a compact vorticity center isolated from the remaining vorticity. It was shown in Part I of this study that the concentration phase was not initiated by an increase in horizontal convergence, suggesting that the proximate cause resided elsewhere.

In this study, the vortex concentration in Part I is examined from a vorticity dynamics perspective. It is shown that concentration occurs when inward radial velocity and vertical vorticity become more spatially correlated in the region surrounding the nascent vortex. It is also emphasized that the anisotropy of the horizontal convergence, which is nearly plane-convergent and of comparable magnitude to the mesocyclonic vorticity, is critical to an understanding of the process. The resultant evolution is intermediate between a state of purely two-dimensional nondivergent dynamics and one in which plane convergence confines vorticity to its axis of dilatation. This intermediate state produces a concentrated vortex more rapidly than either end state. The unsteady nature of the initial vorticity band also serves to increase the circulation and wind speed amplification of the final vortex. It is shown how conceptual models in the fluid dynamics literature can be applied to predicting the time and length scales of tornadic mesocyclone evolution.

Abstract

Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities.

To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

Abstract

To better understand the physical processes of the stable boundary layer and to quantify “submeso motions” in moderately complex terrain, exploratory case-study analyses were performed using observational field data supplemented by gridded North American Regional Reanalysis data and Pennsylvania State University real-time Weather Research and Forecasting Model output. Submeso motions are nominally defined as all motions between the largest turbulent scales and the smallest mesoscales. Seven nighttime cases from August and September of 2011 are chosen from a central Pennsylvania [“Rock Springs” (RS)] network of eight ground-based towers and two sound detection and ranging (sodar) systems . The observation network is located near Tussey Ridge, ~15 km southeast of the Allegheny Mountains. The seven cases are classified by the dominant synoptic-flow direction and proximity to terrain to assess the influence of synoptic conditions on the local submeso and mesogamma motions. It is found that synoptic winds with a large crossing angle over nearby Tussey Ridge can generate mesogamma wave motions and larger-magnitude submeso temperature and wind fluctuations in the RS network than do winds from the direction of the more distant Allegheny Mountains. Cases with synoptic winds that are nearly parallel to the topographic contours or are generally weak exhibit the smallest fluctuations. Changes in the magnitude of near-surface submeso temperature and wind fluctuations in response to local indicator variables are also analyzed. The observed submeso wind and temperature fluctuations are generally larger when the low-level wind speed and thermal stratification, respectively, are greater, but the synoptic flow and its relation to the terrain also play an important role.

Abstract

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.

Abstract

The Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim–Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.