(PEGSs),. Definitions, basic characteristics, and examples are provided for each. In addition, the development of each of the systems was analyzed using 2-km national composite radar reflectivity data. A three-level classification process describing MCS development is identified. These levels include determining the presence of stratiform precipitation, arrangement of convective cells, and interaction of convective clusters. Each level of the classification process is described and compared in detail, along with examples.The environment in which each MCS developed was determined from the standard National V\Teather Service upper-air network. Additionally, the severe weather iii reports were logged for each system. The findings of these data are discussed in context of the present classification schemes. Furthermore, composite analyses of the satellite lifecycle and precipitation lifecycle are presented.

No abstract

Air pollution generated in industrial and urban areas can act to suppress precipitation by creating a narrow cloud droplet spectrum, which inhibits the collision and coalescence process. In fact, precipitation ratios of elevated sites to upwind coastal urban areas have decreased during the twentieth century for locations in California and Israel while pollution emissions have increased. Precipitation suppression by pollution should also be evident in other areas of the world where shallow, orographic clouds produce precipitation. This study investigates the precipitation trends for sites along the Front Range of the Rocky Mountains to determine the effect of air pollution on precipitation in this region. The examination of precipitation trends reveals that the ratio of upslope precipitation for elevated sites west of Denver and Colorado Springs, Colorado, to upwind urban sites has decreased by approximately 30% over the past half-century. Similar precipitation trends were not found for more pristine sites in the region, providing evidence of precipitation suppression by air pollution.

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Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.

OBSERVATIONAL ANALYSIS OF THE PREDICTABILITY OF MESOSCALE CONVECTIVE SYSTEMSMesoscale convective systems (MCSs) have a large influence on the weather over the central United States during the warm season by generating essential rainfall and severe weather. To gain insight into the predictability of these systems, the precursor environment of several hundred MCSs were thoroughly studied across the U.S. during the warm seasons of 1996-98. Surface analyses were used to identify triggering mechanisms for each system, and North American Regional Reanalyses (NARR) were used to examine dozens of parameters prior to MCS development. Statistical and composite analyses of these parameters were performed to extract valuable information about the environments in which MCSs form. Similarly, environments that are unable to support organized convective systems were also carefully investigated for comparison with MCS precursor environments.The analysis of these distinct environmental conditions led to the discovery of significant differences between environments that support MCS development and those that do not support convective organization. MCSs were most commonly initiated by frontal boundaries; however, such features that enhance convective initiation are often not sufficient for MCS development, as the environment needs to lend additional support for the development and organization oflong-lived convective systems. Low-level warm air advection, low-level vertical wind shear, and convective instability were found to be III the most important parameters in determining whether concentrated convection would undergo upscale growth into a MCS.Based on these results, an index was developed for use in forecasting MCSs. The MCS index is comprised of conditional terms to ensure that the index is only defined in regions where convective initiation and development are possible. The MCS index assigns a likelihood of MCS development based on three terms: 700 mb temperature advection, 0-3 Ian vertical wind shear, and the lifted index eLI).Each of these parameters promotes convective development and organization through the enhancement of vertical lifting. An analysis of the MCS index showed that it exhibits similar diurnal, episodic, and seasonal characteristics to MCSs. In addition, an objective evaluation of the MCS index revealed that it possesses significant skill in forecasting MCSs, especiallygiven that convective initiation has occurred, offering the possibility of usefulness in operational forecasting.iv

For practical purposes, the convection initiation forecasting challenge should be franned in terms of the initiation of mesoscale convective events rather than the formation^^, and Hrov\/th of individual cumulonimbus clouds.

Southeast U.S. cold season severe weather events can be difficult to predict because of the marginality of the supporting thermodynamic instability in this regime. The sensitivity of this environment to prognoses of instability encourages additional research on ways in which mesoscale models represent turbulent processes within the lower atmosphere that directly influence thermodynamic profiles and forecasts of instability. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling and proceeds with a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. It is demonstrated that simultaneous representation of both nonlocal and local mixing in the Asymmetric Convective Model, version 2 (ACM2), scheme has the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense for the low-level lapse rate and depth of the PBL, on average. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing.