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John S. Kain

Abstract

Numerous modifications to the Kain–Fritsch convective parameterization have been implemented over the last decade. These modifications are described, and the motivating factors for the changes are discussed. Most changes were inspired by feedback from users of the scheme (primarily numerical modelers) and interpreters of the model output (mainly operational forecasters). The specific formulation of the modifications evolved from an effort to produce desired effects in numerical weather prediction while also rendering the scheme more faithful to observations and cloud-resolving modeling studies.

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John S. Kain
and
J. Michael Fritsch

Abstract

An analysis of how parameterized convection interacts with hydrostatic, explicitly resolved precipitation processes to represent multiscale convective overturning in a mesoscale-resolution numerical simulation is presented. Critically important ingredients of the successful simulation are identified and the degree to which simulations are consistent with observations and theoretical considerations is examined. Of particular concern is how convective parameterization routines reconcile the deep moist absolutely unstable structures that form in mesoscale convective systems.

It is found that these structures arise primarily from resolvable-scale vertical motion and that the model responds to these structures not only by maintaining parameterized convection, but also by developing a hydrostatic manifestation of convective overturning on its smallest resolvable horizontal scales. The strongest and most distinctive mesoscale perturbations develop in regions where the mesoscale contribution to convective overturning rivals, and often exceeds, the parameterized contribution.

Because the internal features of mesoscale convective systems are poorly resolved by meso-β-scale grid elements in this simulation, their scale tends to be overestimated. However, the model results and observations suggest that models must account for multiscale convective overturning in order to properly characterize convective mass transports. Therefore, it is argued that the manner of representation of the convective process, wherein deep convection is allowed to occur partly as a parameterized subgrid-scale process and partly as a hydrostatic manifestation of convective overturning, is likely to give the optimal numerical solution for modeling systems with meso-β-scale resolution.

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Valliappa Lakshmanan
and
John S. Kain

Abstract

Verification methods for high-resolution forecasts have been based either on filtering or on objects created by thresholding the images. The filtering methods do not easily permit the use of deformation while identifying objects based on thresholds can be problematic. In this paper, a new approach is introduced in which the observed and forecast fields are broken down into a mixture of Gaussians, and the parameters of the Gaussian mixture model fit are examined to identify translation, rotation, and scaling errors. The advantages of this method are discussed in terms of the traditional filtering or object-based methods and the resulting scores are interpreted on a standard verification dataset.

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John S. Kain
and
J. Michael Fritsch

Abstract

A new one-dimensional cloud model, specifically designed for application in mesoscale convective parameterization schemes (CPSs), is introduced. The model is unique in its representation of environmental entrainment and updraft detrainment rates. In particular, the two-way exchange of mass between clouds and their environment is modulated at each vertical level by a buoyancy sorting mechanism at the interface of clear and cloudy air. The new entrainment/detrainment scheme allows vertical profiles of both updraft moisture detrainment and updraft vertical mass flux to vary in a physically realistic way as a function of the cloud-scale environment. These performance characteristics allow the parameterized vertical distribution of convective heating and drying to be much more responsive to environmental conditions than is possible with a traditional one-dimensional entraining plume model.

The sensitivities of the new model to variations in environmental convective available potential energy and vertical moisture distribution in idealized convective environments are demonstrated and its sensitivities to several key control parameters are examined. Finally, the performance of the new model in the Fritsch–Chappell CPS is evaluated. Parameterized heating and drying profiles are elucidated as they relate to the convective environment and to the type of cloud model used in the CPS.

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Michael E. Baldwin
and
John S. Kain

Abstract

The sensitivity of various accuracy measures to displacement error, bias, and event frequency is analyzed for a simple hypothetical forecasting situation. Each measure is found to be sensitive to displacement error and bias, but probability of detection and threat score do not change as a function of event frequency. On the other hand, equitable threat score, true skill statistic, and odds ratio skill score behaved differently with changing event frequency. A newly devised measure, here called the bias-adjusted threat score, does not change with varying event frequency and is relatively insensitive to bias. Numerous plots are presented to allow users of these accuracy measures to make quantitative estimates of sensitivities that are relevant to their particular application.

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Ryan A. Sobash
and
John S. Kain

Abstract

Eight years of daily, experimental, deterministic, convection-allowing model (CAM) forecasts, produced by the National Severe Storms Laboratory, were evaluated to assess their ability at predicting severe weather hazards over a diverse collection of seasons, regions, and environments. To do so, forecasts of severe weather hazards were produced and verified as in previous studies using CAM output, namely by thresholding the updraft helicity (UH) field, smoothing the resulting binary field to create surrogate severe probability forecasts (SSPFs), and verifying the SSPFs against observed storm reports. SSPFs were most skillful during the spring and fall, with a relative minimum in skill observed during the summer. SSPF skill during the winter months was more variable than during other seasons, partly due to the limited sample size of events, but was often less than that during the warm season. The seasonal behavior of SSPF skill was partly driven by the relationship between the UH threshold and the likelihood of obtaining severe storm reports. Varying UH thresholds by season and region produced SSPFs that were more skillful than using a fixed UH threshold to identify severe convection. Accounting for this variability was most important during the cool season, when a lower UH threshold produced larger SSPF skill compared to warm-season events, and during the summer, when large differences in skill occurred within different parts of the continental United States (CONUS), depending on the choice of UH threshold. This relationship between UH threshold and SSPF skill is discussed within the larger scope of generating skillful CAM-based guidance for hazardous convective weather and verifying CAM predictions.

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Michael E. Baldwin
,
John S. Kain
, and
S. Lakshmivarahan

Abstract

An automated procedure for classifying rainfall systems (meso-α scale and larger) was developed using an operational analysis of hourly precipitation estimates from radar and rain gauge data. The development process followed two main phases: a training phase and a testing phase. First, 48 hand-selected cases were used to create a training dataset, from which a set of attributes related to morphological aspects of rainfall systems were extracted. A hierarchy of classes for rainfall systems, in which the systems are separated into general convective (heavy rain) and nonconvective (light rain) classes, was envisioned. At the next level of classification hierarchy, convective events are divided into linear and cellular subclasses, and nonconvective events belong to the stratiform subclass. Essential attributes of precipitating systems, related to the rainfall intensity and degree of linear organization, were determined during the training phase. The attributes related to the rainfall intensity were chosen to be the parameters of the gamma probability distribution fit to observed rainfall amount frequency distributions using the generalized method of moments. Attributes related to the degree of spatial continuity of each rainfall system were acquired from correlogram analysis. Rainfall systems were categorized using hierarchical cluster analysis experiments with various combinations of these attributes. The combination of attributes that resulted in the best match between cluster analysis results and an expert classification were used as the basis for an automated classification procedure.

The development process shifted into the testing phase, where automated procedures for identifying and classifying rainfall systems were used to analyze every rainfall system in the contiguous 48 states during 2002. To allow for a feasible validation, a testing dataset was extracted from the 2002 data. The testing dataset consisted of 100 randomly selected rainfall systems larger than 40 000 km2 as identified by an automated identification system. This subset was shown to be representative of the full 2002 dataset. Finally, the automated classification procedure classified the testing dataset into stratiform, linear, and cellular classes with 85% accuracy, as compared to an expert classification.

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Melissa S. Bukovsky
,
John S. Kain
, and
Michael E. Baldwin

Abstract

Bowing, propagating precipitation features that sometimes appear in NCEP's North American Mesoscale model (NAM; formerly called the Eta Model) forecasts are examined. These features are shown to be associated with an unusual convective heating profile generated by the Betts–Miller–Janjić convective parameterization in certain environments. A key component of this profile is a deep layer of cooling in the lower to middle troposphere. This strong cooling tendency induces circulations that favor expansion of parameterized convective activity into nearby grid columns, which can lead to growing, self-perpetuating mesoscale systems under certain conditions. The propagation characteristics of these systems are examined and three contributing mechanisms of propagation are identified. These include a mesoscale downdraft induced by the deep lower-to-middle tropospheric cooling, a convectively induced buoyancy bore, and a boundary layer cold pool that is indirectly produced by the convective scheme in this environment. Each of these mechanisms destabilizes the adjacent atmosphere and decreases convective inhibition in nearby grid columns, promoting new convective development, expansion, and propagation of the larger system. These systems appear to show a poor correspondence with observations of bow echoes on time and space scales that are relevant for regional weather prediction, but they may provide important clues about the propagation mechanisms of real convective systems.

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Christopher J. Anderson
,
Raymond W. Arritt
, and
John S. Kain

Abstract

The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.

The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.

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Aijun Deng
,
Nelson L. Seaman
, and
John S. Kain

Abstract

A shallow-convection parameterization suitable for both marine and continental regimes is developed for use in mesoscale models. The scheme is closely associated with boundary layer turbulence processes and can transition to either a deep-convection scheme in conditionally unstable environments or to an explicit (resolved scale) moisture scheme in moist stable environments. The shallow-convection mass-closure assumption uses a hybrid formulation based on boundary layer turbulent kinetic energy (TKE) and convective available potential energy (CAPE), while the convective trigger is primarily a function of boundary layer TKE. Secondary subgrid clouds having nearly neutral buoyancy can form as shallow-convective updrafts detrain mass to their environment. Called neutrally buoyant clouds (NBCs), these can be dissipated through lateral and vertical mixing, light precipitation, ice-crystal settling, and cloud-top entrainment instability (CTEI).

The shallow-convection scheme is developed and demonstrated in a 1D version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) mesoscale model (MM5) which includes a 1.5-order turbulence parameterization that predicts the TKE, an atmospheric radiation submodel, and an explicit moisture submodel. The radiation calculation includes the feedback effects of the subgrid NBCs predicted by the shallow-convection parameterization. Results from initial applications in both marine and continental environments are consistent with the observed characteristics of the mesoscale thermodynamic structures and local cloud-field parameters. A subsequent paper (Part II) presents more complete verifications in different environments and results of sensitivity experiments.

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