Abstract

A comparison study of four cumulus parameterization schemes (CPSs), the Anthes–Kuo, Betts–Miller, Grell, and Kain–Fritsch schemes, is conducted using The Pennsylvania State University–National Center for Atmospheric Research mesoscale model. Performance of these CPSs is examined using six precipitation events over the continental United States for both cold and warm seasons. Grid resolutions of 36 and 12 km are chosen to represent current mesoscale research models and future operational models. The key parameters used to evaluate skill include precipitation, sea level pressure, wind, and temperature predictions. Precipitation is evaluated statistically using conventional skill scores (such as threat and bias scores) for different threshold values based on hourly rainfall observations. Rainfall and other mesoscale features are also evaluated by careful examination of analyzed and simulated fields, which are discussed in the context of timing, evolution, intensity, and structure of the precipitation systems.

It is found that the general 6-h precipitation forecast skill for these schemes is fairly good in predicting four out of six cases examined in this study, even for higher thresholds. The forecast skill is generally higher for cold-season events than for warm-season events. There is an increase in the forecast skill in the 12-km model, and the gain is most obvious in predicting heavier rainfall amounts. The model’s precipitation forecast skill is better in rainfall volume than in either the areal coverage or the peak amount. The scheme with the convective available potential energy–based closure assumption (Kain–Fritsch scheme) appears to perform better. Some systematic behaviors associated with various schemes are also noted wherever possible.

The partition of rainfall into subgrid scale and grid scale is sensitive to the particular parameterization scheme chosen, but relatively insensitive to either the model grid sizes or the convective environments.

The prediction of mesoscale surface features in warm-season cases, such as mesoscale pressure centers, wind-shift lines (gust fronts), and temperature fields, strongly suggests that the CPSs with moist downdrafts are able to predict these surface features more accurately.

Abstract

To quantitatively assess numerical predictive skill for synoptic and mesoscale features as a function of horizontal grid resolution, a series of experiments is conducted using the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model. For eight cases of continental cyclogenesis, 72-h integrations are examined using grids of 160, 80, and 26.7 km. First, we briefly examine error statistics for synoptic-scale cyclones and anticyclones. Next, a detailed analysis of model errors for mesoscale features is presented. A bandpass filtering technique, based on the Barnes objective analysis scheme, is used to separate perturbation quantities associated with the mesoscale features from the synoptic-scale fields. Error statistics are then compiled for various mesoscale features, including the intensity of mesolows, damming ridges, and postfrontal troughs, and the thermal gradients, propagation speed, and vertical velocity maxima associated with surface cold fronts. Finally, the accuracy of the predicted precipitation fields, produced using the Anthes-Kuo cumulus parameterization, is examined.

Objective verification reveals that forecast skill does not improve uniformly for all types of mesoscale features as horizontal grid resolution is increased, although the general trend is for reduced errors as expected. Improvements do occur on both the 80- and 27-km grids for all geographically related mesoscale features (such as orographic lee troughs). A similar improvement is seen for propagating mesoscale features (such as postfrontal troughs) and synoptic-scale cyclones as the grid length is reduced from 160 to 80 km. However, when the grid length is further reduced to 27 km, mean absolute errors and mean position errors actually increase for both classes of features. This greater variability in model performance suggests that as grid resolution is enhanced, other factors such as the accuracy of model physics and initial conditions become increasingly important.

The effect on precipitation bias and threat scores in these experiments is positive (reduced errors) when resolution is improved from 160 to 80 km but is generally insignificant or negative for the 27-km grid. Based on these results, the Anthes-Kuo convective parameterization used in these experiments is not recommended for application on grids of about 30 km or less.

Abstract

A four-dimensional data assimilation (FDDA) scheme based on Newtonian relaxation or “nudging” is tested using standard rawinsonde data in the Penn State/NCAR limited-area mesoscale model. It is imperative that we better understand these FDDA-generated datasets, which are widely used for model initialization and diagnostic analysis. The main hypothesis to be tested is that use of coarse-resolution rawinsonde observations throughout a model integration, rather than at only the initial time, can limit large-scale model error growth (amplitude and phase errors) while the model generates realistic mesoscale structures not resolved by the data.

The main objective of this study is to determine what assimilation strategies and what meteorological fields (mass, wind or both) have the greatest positive impact via FDDA on the numerical simulators for two midlatitude, real-data cases using the full-physics version of a limited-area model. Seven experiments are performed for each case: one control experiment (no nudging), five experiments which nudge the model solution to analyses of observations, and a seventh experiment in which the actual rawinsonde observations are assimilated directly into the model. Subjective and statistical evaluation of the results include verification of the primitive variable fields, plus a detailed precipitation verification which is especially valuable since rainfall is the result of many complex physical processes and is usually characterized by small-scale variability, which makes it much more difficult to simulate accurately than the other variables.

The results show that the assimilation of both wind and thermal data throughout the model atmosphere had a consistently positive impact on the synoptic-scale and mesoscale mass and wind fields for both cases and for the precipitation simulations in the case dominated by large-scale forcing. However, in the other case for which small-scale convection was the dominant precipitation mechanism, the FDDA system using only rawinsonde data showed only a minor improvement in the rainfall. This may be attributed to 1) the fact that time scales of small convective systems am less than 12 h, the temporal resolution of the data used for FDDA, and 2) assimilation of 12-hourly temperature data near the surface may adversely affect the model's diurnal cycle and low-level stability, which are very important for convection.

Other results show that nudging vorticity or the rawinsonde-based mixing ratio analyses tended to seriously degrade the precipitation simulators for both cases and should be avoided. The transfer of information on the mesoscale from the wind (mass) fields to the mass (wind) fields was found to be significant: for shallow forcing (small equivalent depth), the winds were shown to adjust to the mass fields, while for large-scale forcing through the depth of the troposphere (large equivalent depth), wind data were generally more effective than mass data. The most accurate mass and wind fields in both cases, however, were produced by assimilating both wind and temperature information. Nudging the model' wind and temperature fields directly to the rawinsonde observations generally produced results comparable to nudging to the gridded analyses of these data.

Abstract

Four-dimensional data assimilation (FDDA) schemes capable of effectively analyzing asynoptic, near-continuous data streams art especially important on the mesobeta scale for both model initialization and dynamic analysis. A multiscale nudging approach that utilizes grid nesting is investigated for the generation of complete, dynamically consistent datasets for the mesobeta scale. These datasets are suitable for input into air quality models, but can also be used for other diagnostic purposes including model initialization. A multiscale nudging strategy is used here to simulate the wind flow for two cases over the Colorado Plateau and Grand Canyon region during the winter of 1990 when a special mesobeta-scale observing system was deployed in the region to study the canyon's visibility impairment problem. The special data included Doppler sodars, profilers rawinsondes, and surface stations. Combinations of these data and conventional mesoalpha-scale data were assimilated into a nested version of the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model to investigate the importance of wale interaction and scale separation during FDDA.

Mesoalpha-scale forcing was shown to be important for accurate simulation of the mesobeta-scale flow over the 48-h period of the simulators. Direct assimilation of mesoalpha-scale analyses on a finescale grid was shown to be potentially harmful to the simulation of mesobeta-scale features. Nudging to mesoalpha-scale analyses on the coarse grid enabled nudging to mesobeta-scale observations on the inner fine grid to be more effective. This grid-nesting multiscale FDDA strategy produced the most accurate simulation of the low-level wind fields. It is demonstrated that when designing an FDDA strategy, scale interactions of different flow regimes cannot be ignored, particularly for simulation periods of several days on the mesobeta scale.

Abstract

On 27–28 February 1982 cyclogenesis occurred along a Carolina coastal front. Despite the relatively weak low pressure center typical of many coastal storms, this case produced widespread hazardous conditions—within 12 h up to 30 cm of snow fell in the mountains of western Virginia and moderate icing persisted throughout 27 February in the Carolinas. The event contained many mesoscale and synoptic-scale phenomena such as cold-air damming, coastal frontogenesis, upper- and lower-tropospheric jet streaks, a thermally direct vertical-transverse ageostrophic circulation, and heavy mixed precipitation.

A nested version of the PSU–NCAR three-dimensional mesoscale model with 35-km resolution successfully reproduced most principal synoptic and mesoscale feature associated with the event. This study presents a series of numerical experiments designed to examine the role of several physical processes on the evolution of and interaction between atmospheric phenomena having dithering scales, each of which contributed to the development of the storm. In particular, the physical processes studied include: 1) the role of diabatic heating associated with convective and grid-scale precipitation, 2) the role of a thermally direct transverse circulation about the entrance region of a strong polar jet streak and 3) modification of the marine planetary boundary layer by fluxes of heat and moisture over the Gulf Stream.

Of the three mechanisms investigated, the diabatic heating associated with precipitation is found to have the most significant impact on storm development. Without latent heating, cyclogenesis does not occur along the Carolina coastal front despite the presence of strong low-level baroclinicity and cyclonic vorticity. A less dramatic but still important relationship is found between storm formation and the other two physical mechanisms. The experiments indicate that the timing of storm development is delayed and the intensity weakened by reducing the strength of both the polar jet streak and fluxes over the Gulf Stream. In particular, weakening these processes disrupts the positive phase relationship between upper- and lower-tropospheric forcing in the last 12 h of the study. The three basic mechanisms are shown to affect the cyclogenesis by altering many of the important mesoscale features and processes that contribute to storm development, including the intensity of the vertical-transverse circulation around the jet streak, the location of the upward branch of the circulation, precipitation intensity, buoyancy of parcels advected over the coastal front, low-level and upper-level height falls associated with latent heating, and the southeasterly low-level jet over the coastal front.

Abstract

Next-Generation Radar (NEXRAD) velocity azimuth display (VAD) winds were available at 10 sites in the northeastern United States during intensive observing periods of the North American Research Strategy on Tropospheric Ozone-Northeast field study conducted in the summer of 1995. These VAD winds represent a potentially valuable routine source of upper-air data suitable for mesoscale four-dimensional data assimilation and other mesometeorological applications. The objectives of this paper are to develop appropriate quality-checking methods for these data during a period with weak dynamic forcing and to learn if their assimilation into a nonhydrostatic mesoscale model, the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), can reduce wind errors in lengthy numerical integrations.

Two types of quality checking were applied: 1) a standard internal vertical and temporal consistency check, and 2) a new filter that uses bias-corrected model predictions as a first guess. After unreliable data were removed, the VAD winds were assimilated into MM5. Experiment evaluation using independent data demonstrated that the VAD winds significantly reduced model wind errors, especially below 2.0 km, where the wind data are most numerous in this case. Independent verification also indicated that the filter presented in this paper contributed to the improvement of the data-assimilated model results. Although the application in this case is designed to generate wind fields to drive an air quality model, the techniques developed, with some generalization and testing, also should be adaptable for forecast-initialization applications.

Abstract

An objective analysis scheme has been developed which combines use of different weighting functions, two of which are anisotropic (elliptical and banana-shaped). The “effective” distance between a grid point and an observation point used for the anisotropic functions may be applied in any objective analysis scheme which uses distance to calculate weights or correlations, but a successive-correlation scheme is used here as a vehicle for testing. This relatively simple and computationally inexpensive scheme produces wind and moisture analyses in which along-flow autocorrelation is accentuated, especially in regions of curved flow, and thus simulates conventional subjective analysis procedures. Sample analyses from a case study are presented which demonstrate the improvement which may result from using this scheme rather than one with the circular weighting function alone.

In tests with an analytically defined, curving jet stream, the multiple weighting function scheme with the “banana” function was superior to schemes using the circular function either alone or with an elliptical function for all of the error statistics considered, including a 30% reduction in rms vector error.

This objective analysis scheme also includes an alternative method for calculating corrections at individual grid points which is designed to eliminate discontinuities which may occur when more common correction methods are applied. Additional analytical tests and sample analyses confirm that the new correction method decreases noise in gradients (e.g., vorticity, divergence) of analyzed fields which result with the use of other correction methods in data-sparse regions or over the entire domain when the ratio between the grid space and the mean station separation is small (5–10%). The analytical tests also indicate that the new correction method performs slightly better than other methods for the analyzed variable itself (as well as the gradient) regardless of the scale.

Abstract

Observations and numerical model fields were analyzed to study the meteorological structures contributing to high concentrations of lower-tropospheric ozone over the northeastern United States on 14–15 July 1995. It was found that the episode is characteristic of high-ozone events associated with the Bermuda high, having light winds, high temperatures, few clouds, and sparse rain over the entire region. The specific distribution of ozone at the peak of the episode on 14 July is of particular interest, however, since only the area from the urban corridor to the Atlantic Coast experienced ozone exceedances of the National Ambient Air Quality Standard. The analyses showed that an Appalachian lee trough (APLT) played a vital role in this pattern. Mesoscale structures associated with the APLT that affected ozone formation and distribution included 1) south-southwesterly winds east of the trough, which favored accumulation of emissions in an airstream that passed directly along the urban corridor; 2) west to northwesterly winds behind the APLT, which led to lower accumulation of emissions in that sector; 3) mixing depth contrasts across the APLT, which favored less dilution of primary and secondary pollutants to the east of the trough; and 4) low-level convergence and upward vertical velocities at the APLT, which led to the development of an elevated mixed layer over the planetary boundary layer on the east side of the trough, where pollutants could be trapped and transported for long distances by a low-level jet.

Abstract

On 27–28 February 1982 cyclogenesis occurred along a Carolina coastal front. As the relatively weak low pressure center developed and moved northeastward along the front, up to 30 cm of snow fell in 12 hours in the mountains of western Virginia and moderate icing persisted throughout 27 February in the Carolinas. On 28 February the mesoscale cyclone intensified more rapidly, turned east-northeast after passing Cape Hatteras, and gradually became a typical synoptic scale oceanic storm.

A nested version of the Penn State/NCAR mesoscale model with 35-km fine-mesh resolution is used to simulate the prestorm environment and subsequent cyclogenesis during a 36-h period. Evaluation of the numerical results indicates that the model successfully reproduced most principal synoptic and mesoscale features associated with this complex east coast cyclogenesis case, including storm path and intensification, coastal front structure, cold-air damming, circulations induced by a polar jet streak, low-level jets, and precipitation. In particular this study 1) provides an in-depth numerical examination of a case of east coast cyclogenesis in which entrance region jet streak dynamics provides the dominant upper-level support white only a weak baroclinic wave was approaching from the west, 2) reveals the existence of two moist airstreams fed by onshore flow from the marine boundary layer east of the coastal front (a southeasterly low-level jet and a rapidly rising “feeder” supporting the ascending branch of the polar jet streak's entrance circulation), 3) explores the origin, history, and significance of these moist airstreams to cyclogenesis (the southeasterly low-level jet supports inland precipitation while the rapidly ascending airstream contributes to heavy precipitation and failling pressure along the coast), and 4) demonstrates that both airstreams are well developed very early during the cyclogenesis before the midlevel baroclinic wave reaches the coast.

A mesoscale modeling system is being applied on an experimental basis at The Pennsylvania State University (Penn State) for production of real-time, high resolution, numerical weather forecasts for the northeastern United States. The initial model experimentation is being supported by Penn State. It is believed to be the first time that a real-time, three-dimensional mesoscale model has been run routinely at an American university, although mesoscale models have been run in real time in government laboratories. A version of the Penn State/NCAR mesoscale model is employed, using a two-way interacting nested grid with a fine-grid increment of 30 km, a coarse-grid increment of 90 km, and 15 computational levels. The forecast cycle is initiated automatically by the Department of Meteorology's Digital Equipment Corporation VAX 8350 system when all the required 0000 UTC surface and upper-air National Weather Service (NWS) data have been received, quality checked, and archived. Lateral boundary conditions are extracted from the current or previous NWS nested-grid model forecast. The dataset constructed on the VAX system is then transmitted by a fiber-optic data network to an IBM 3090 located on the Penn State campus, where the model is initialized and run for a 24- to 36-h forecast. By about 0600 UTC, well before the beginning of the work day, a short-range mesoscale forecast is available in the Meteorology Department's weather station. These forecasts can be performed routinely on a daily basis, or they can be initiated when large-scale numerical guidance from the NWS indicates the possible development of significant mesoscale disturbances. Regular inspection of the fine-mesh model forecasts is serving as a catalyst for further improvements in the model and is stimulating the development of techniques for evaluation of mesoscale-model forecast skill and/or utilization of mesoscale numerical guidance in an operational setting. We are also finding that this real-time forecast capability is making significant contributions to the mesoscale-meteorology research program as well as to the teaching and public-service responsibilities of the Department of Meteorology at Penn State.