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  • Author or Editor: Israel L. Jirak x
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Israel L. Jirak
and
William R. Cotton

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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Andrew R. Wade
and
Israel L. Jirak

Abstract

This study explored how forecasters can best use the two main forms of operational convection-allowing model guidance: the High-Resolution Ensemble Forecast (HREF) system and the hourly High-Resolution Rapid Refresh (HRRR). The former represents a wider range of possible outcomes, but the latter updates much more frequently and incorporates newer observations. HREF and time-lagged High-Resolution Rapid Refresh (HRRR-TL) probabilistic forecasts of reflectivity and updraft helicity, as well as two methods of combining HREF and HRRR into hourly updating blended guidance, were evaluated for the 2021 Spring Forecasting Experiment (SFE) period. In both objective skill and the subjective ratings of SFE participants, the 1200 UTC HREF proved difficult to outperform over this sample of events, even when incorporating HRRR initializations as late as 1800 UTC. It was usually better to use either of the experimental blending techniques than to simply discard the older HREF in favor of newer HRRR solutions. The greater model diversity and dispersion of solutions within the HREF is likely primarily responsible for this result. A possible bias in diurnal convection initiation timing and coverage in the newly upgraded HRRRv4 was also investigated, including on subdomains targeted to weakly forced diurnal initiation, and was found to have little or no systematic effect on HRRRv4’s operational utility.

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Nathan A. Wendt
and
Israel L. Jirak

Abstract

The Multi-Radar Multi-Sensor (MRMS) system generates an operational suite of derived products in the National Weather Service useful for real-time monitoring of severe convective weather. One such product generated by MRMS is the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0°C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥25.4 mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous United States from 2012 to 2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence also shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density.

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Israel L. Jirak
and
William R. Cotton
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Israel L. Jirak
and
William R. Cotton

Abstract

Mesoscale 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 environments of several hundred MCSs across the United States were reviewed during the warm seasons of 1996–98. Surface analyses were used to identify initiating mechanisms for each system, and North American Regional Reanalysis (NARR) data were used to examine the environment prior to MCS development. Similarly, environments unable to support organized convective systems were also investigated for comparison with MCS precursor environments. Significant differences were found between environments that support MCS development and those that do not support convective organization. MCSs were most commonly initiated by frontal boundaries; however, features that enhance convective initiation are often not sufficient for MCS development, as the environment needs also to be supportive for the development and organization of long-lived convective systems. Low-level warm air advection, low-level vertical wind shear, and convective instability were found to be the most important parameters in determining whether concentrated convection would undergo upscale growth into an MCS. Based on these results, an index was developed for use in forecasting MCSs. The MCS index assigns a likelihood of MCS development based on three terms: 700-hPa temperature advection, 0–3-km vertical wind shear, and the lifted index. An evaluation of the MCS index revealed that it exhibits features consistent with common MCS characteristics and is reasonably accurate in forecasting MCSs, especially given that convective initiation has occurred, offering the possibility of usefulness in operational forecasting.

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Israel L. Jirak
,
William R. Cotton
, and
Ray L. McAnelly

Abstract

An investigation of several hundred mesoscale convective systems (MCSs) during the warm seasons (April–August) of 1996–98 is presented. Circular and elongated MCSs on both the large and small scales were classified and analyzed in this study using satellite and radar data. The satellite classification scheme used for this study includes two previously defined categories and two new categories: mesoscale convective complexes (MCCs), persistent elongated convective systems (PECSs), meso-β circular convective systems (MβCCSs), and meso-β elongated convective systems (MβECSs). Around two-thirds of the MCSs in the study fell into the larger satellite-defined categories (MCCs and PECSs). These larger systems produced more severe weather, generated much more precipitation, and reached a peak frequency earlier in the convective season than the smaller, meso-β systems. Overall, PECSs were found to be the dominant satellite-defined MCS, as they were the largest, most common, most severe, and most prolific precipitation-producing systems.

In addition, 2-km national composite radar reflectivity data were used to analyze the development of each of the systems. A three-level radar classification scheme describing MCS development is introduced. The classification scheme is based on the following elements: presence of stratiform precipitation, arrangement of convective cells, and interaction of convective clusters. Considerable differences were found among the systems when categorized by these features. Grouping systems by the interaction of their convective clusters revealed that more than 70% of the MCSs evolved from the merger of multiple convective clusters, which resulted in larger systems than those that developed from a single cluster. The most significant difference occurred when classifying systems by their arrangement of convective cells. In particular, if the initial convection were linearly arranged, the mature MCSs were larger, longer-lived, more severe, and more effective at producing precipitation than MCSs that developed from areally arranged convection.

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Aaron J. Hill
,
Russ S. Schumacher
, and
Israel L. Jirak

Abstract

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ∼9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time lagging. Validated RF models are tested with ∼1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

Significance Statement

Medium-range severe weather forecasts generated from statistical models are explored here alongside operational forecasts from the Storm Prediction Center (SPC). Human forecasters at the SPC rely on traditional numerical weather prediction model output to make medium-range outlooks and statistical products that mimic operational forecasts can be used as guidance tools for forecasters. The statistical models relate simulated severe weather environments from a global weather model to historical records of severe weather and perform noticeably better than human-generated outlooks at shorter lead times (e.g., day 4 and 5) and are capable of capturing the general location of severe weather events 8 days in advance. The results highlight the value in these data-driven methods in supporting operational forecasting.

Free access
Brian Joseph Squitieri
,
Andrew R. Wade
, and
Israel L. Jirak

Abstract

Over the course of the century, much research has been done to understand how derechos form, what environments support derecho development, and how forecasts of derechos can be improved. Following the definition, climatology, and societal impacts of derechos in Part I of this manuscript, Part II offers a thorough review on the parent MCS structures that produce derechos, which synoptic setups support derecho-producing MCSs, and the successes and failures of forecasting derechos. This manuscript reviews the 3D structure of MCSs that support derecho development, as well as both the strongly and weakly forced synoptic environments where derechos frequently occur. In addition, successes and failures common among most derecho numerical forecast studies are discussed to suggest where the greatest improvements in derecho forecasting may be made.

Open access
Brian Joseph Squitieri
,
Andrew R. Wade
, and
Israel L. Jirak

Abstract

Research efforts from the last several decades to the present have aimed to better understand when and where derechos occur across the United States and other parts of the world, and what impacts derechos have on society. While the scientific community agrees that derechos are widespread wind storms associated with extratropical mesoscale convective systems, varying quantitative thresholds of what constitutes a derecho exist among peer-reviewed journal articles, introducing ambiguity throughout the literature of what is classified as a derecho, and where derechos most frequently occur. The scientific community would benefit from a summary on the more crucial aspects of derechos and where ambiguities or inconsistencies exist in the literature. Part I of this derecho historical overview discusses the history of derecho identification, and how differences in derecho identification strategies affect our understanding of their spatial climatology across the United States and Europe. Impacts to human life and commerce are also summarized.

Open access
Andrew R. Wade
,
Israel L. Jirak
, and
Anthony W. Lyza

Abstract

This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.

Significance Statement

The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.

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