scholarly journals Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

2021 ◽  
pp. 1-52
Author(s):  
Wenjun Cui ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Zhe Feng
Keyword(s):  
United States ◽  
Great Plains ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Propagation Speed ◽  
Extreme Rainfall ◽  
The United States ◽  
Convective System ◽  
Large Variability ◽  
Mesoscale Convective

AbstractThis study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

scholarly journals A Radar-Based Climatology of Mesoscale Convective Systems in the United States

2019 ◽  
Vol 32 (5) ◽  
pp. 1591-1606 ◽  
Author(s):  
Alex M. Haberlie ◽  
Walker S. Ashley
Keyword(s):  
United States ◽  
Agricultural Land ◽  
Gulf Coast ◽  
Warm Season ◽  
The United States ◽  
Corn Belt ◽  
High Plains ◽  
Convective System ◽  
Aquifer Recharge ◽  
Mesoscale Convective

Abstract This research applies an automated mesoscale convective system (MCS) segmentation, classification, and tracking approach to composite radar reflectivity mosaic images that cover the contiguous United States (CONUS) and span a relatively long study period of 22 years (1996–2017). These data afford a novel assessment of the seasonal and interannual variability of MCSs. Additionally, hourly precipitation data from 16 of those years (2002–17) are used to systematically examine rainfall associated with radar-derived MCS events. The attributes and occurrence of MCSs that pass over portions of the CONUS east of the Continental Divide (ECONUS), as well as five author-defined subregions—North Plains, High Plains, Corn Belt, Northeast, and Mid-South—are also examined. The results illustrate two preferred regions for MCS activity in the ECONUS: 1) the Mid-South and Gulf Coast and 2) the Central Plains and Midwest. MCS occurrence and MCS rainfall display a marked seasonal cycle, with most of the regions experiencing these events primarily during the warm season (May–August). Additionally, MCS rainfall was responsible for over 50% of annual and seasonal rainfall for many locations in the ECONUS. Of particular importance, the majority of warm-season rainfall for regions with high agricultural land use (Corn Belt) and important aquifer recharge properties (High Plains) is attributable to MCSs. These results reaffirm that MCSs are a significant aspect of the ECONUS hydroclimate.

Characteristics of U.S. Extreme Rain Events during 1999–2003

2006 ◽  
Vol 21 (1) ◽  
pp. 69-85 ◽  
Author(s):  
Russ S. Schumacher ◽  
Richard H. Johnson
Keyword(s):  
United States ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Organizational Structures ◽  
The United States ◽  
Convective System ◽  
Precipitation Total ◽  
Extreme Rain ◽  
Rain Events ◽  
Reflectivity Data

Abstract This study examines the characteristics of a large number of extreme rain events over the eastern two-thirds of the United States. Over a 5-yr period, 184 events are identified where the 24-h precipitation total at one or more stations exceeds the 50-yr recurrence amount for that location. Over the entire region of study, these events are most common in July. In the northern United States, extreme rain events are confined almost exclusively to the warm season; in the southern part of the country, these events are distributed more evenly throughout the year. National composite radar reflectivity data are used to classify each event as a mesoscale convective system (MCS), a synoptic system, or a tropical system, and then to classify the MCS and synoptic events into subclassifications based on their organizational structures. This analysis shows that 66% of all the events and 74% of the warm-season events are associated with MCSs; nearly all of the cool-season events are caused by storms with strong synoptic forcing. Similarly, nearly all of the extreme rain events in the northern part of the country are caused by MCSs; synoptic and tropical systems play a larger role in the South and East. MCS-related events are found to most commonly begin at around 1800 local standard time (LST), produce their peak rainfall between 2100 and 2300 LST, and dissipate or move out of the affected area by 0300 LST.

scholarly journals Evaluation of Cumulus and Microphysics Parameterizations in WRF across the Convective Gray Zone

2019 ◽  
Vol 34 (4) ◽  
pp. 1097-1115 ◽  
Author(s):  
Julia Jeworrek ◽  
Gregory West ◽  
Roland Stull
Keyword(s):  
Great Plains ◽  
Wrf Model ◽  
Mesoscale Convective System ◽  
The United States ◽  
Smooth Transition ◽  
Cumulus Parameterization ◽  
Convective System ◽  
Gray Zone ◽  
Cumulus Scheme ◽  
Mesoscale Convective

Abstract This study evaluates the grid-length dependency of the Weather Research and Forecasting (WRF) Model precipitation performance for two cases in the Southern Great Plains of the United States. The aim is to investigate the ability of different cumulus and microphysics parameterization schemes to represent precipitation processes throughout the transition between parameterized and resolved convective scales (e.g., the gray zone). The cases include the following: 1) a mesoscale convective system causing intense local precipitation, and 2) a frontal passage with light but continuous rainfall. The choice of cumulus parameterization appears to be a crucial differentiator in convective development and resulting precipitation patterns in the WRF simulations. Different microphysics schemes produce very similar outcomes, yet some of the more sophisticated schemes have substantially longer run times. This suggests that this additional computational expense does not necessarily provide meaningful forecast improvements, and those looking to run such schemes should perform their own evaluation to determine if this expense is warranted for their application. The best performing cumulus scheme overall for the two cases studies here was the scale-aware Grell–Freitas cumulus scheme. It was able to reproduce a smooth transition from subgrid- (cumulus) to resolved-scale (microphysics) precipitation with increasing resolution. It also produced the smallest errors for the convective event, outperforming the other cumulus schemes in predicting the timing and intensity of the precipitation.

Mesoscale Processes Contributing to Extreme Rainfall in a Midlatitude Warm-Season Flash Flood

2008 ◽  
Vol 136 (10) ◽  
pp. 3964-3986 ◽  
Author(s):  
Russ S. Schumacher ◽  
Richard H. Johnson
Keyword(s):  
Gravity Wave ◽  
Flash Flood ◽  
Warm Season ◽  
Mesoscale Convective System ◽  
Extreme Rainfall ◽  
Cold Pool ◽  
Convective System ◽  
Low Level ◽  
Mesoscale Convective ◽  
Moist Environment

Observations and numerical simulations are used to investigate the atmospheric processes that led to extreme rainfall and resultant destructive flash flooding in eastern Missouri on 6–7 May 2000. In this event, a quasi-stationary mesoscale convective system (MCS) developed near a preexisting mesoscale convective vortex (MCV) in a very moist environment that included a strong low-level jet (LLJ). This nocturnal MCS produced in excess of 300 mm of rain in a small area to the southwest of St. Louis, Missouri. Operational model forecasts and simulations using a convective parameterization scheme failed to produce the observed rainfall totals for this event. However, convection-permitting simulations using the Weather Research and Forecasting Model were successful in reproducing the quasi-stationary organization and evolution of this MCS. In both observations and simulations, scattered elevated convective cells were repeatedly initiated 50–75 km upstream before merging into the mature MCS and contributing to the heavy rainfall. Lifting provided by the interaction between the LLJ and the MCV assisted in initiating and maintaining the convection. Simulations indicate that the MCS was long lived despite the lack of a convectively generated cold pool at the surface. Instead, a nearly stationary low-level gravity wave helped to organize the convection into a quasi-linear system that was conducive to extreme local rainfall amounts. Idealized simulations of convection in a similar environment show that such a low-level gravity wave is a response to diabatic heating and that the vertical wind profile featuring a strong reversal of the wind shear with height is responsible for keeping the wave nearly stationary. In addition, the convective system acted to reintensify the midlevel MCV and also caused a distinct surface low pressure center to develop in both the observed and simulated system.

scholarly journals Evaluating Smartphone Pressure Observations for Mesoscale Analyses and Forecasts

2017 ◽  
Vol 32 (2) ◽  
pp. 511-531 ◽  
Author(s):  
Luke E. Madaus ◽  
Clifford F. Mass
Keyword(s):  
United States ◽  
Rural Areas ◽  
Mesoscale Convective System ◽  
Convective System ◽  
Active Period ◽  
Eastern United States ◽  
Short Term ◽  
Mesoscale Convective ◽  
Dynamic Structures ◽  
Further Development

Abstract Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.

scholarly journals Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
John R. Lawson ◽  
William A. Gallus ◽  
Corey K. Potvin
Keyword(s):  
Mesoscale Convective System ◽  
Real Data ◽  
The United States ◽  
Convective System ◽  
Grid Spacing ◽  
Structure Amplitude ◽  
Cold Pools ◽  
Bow Echo ◽  
Mesoscale Convective ◽  
Horizontal Grid

The bow echo, a mesoscale convective system (MCS) responsible for much hail and wind damage across the United States, is associated with poor skill in convection-allowing numerical model forecasts. Given the decrease in convection-allowing grid spacings within many operational forecasting systems, we investigate the effect of finer resolution on the character of bowing-MCS development in a real-data numerical simulation. Two ensembles were generated: one with a single domain of 3-km horizontal grid spacing, and another nesting a 1-km domain with two-way feedback. Ensemble members were generated from their control member with a stochastic kinetic-energy backscatter scheme, with identical initial and lateral-boundary conditions. Results suggest that resolution reduces hindcast skill of this MCS, as measured with an adaptation of the object-based Structure–Amplitude–Location method. The nested 1-km ensemble produces a faster system than in both the 3-km ensemble and observations. The nested 1-km simulation also produced stronger cold pools, which could be enhanced by the increased (fractal) cloud surface area with higher resolution, allowing more entrainment of dry air and hence increased evaporative cooling.

Tracking Mesoscale Convective Systems that are Potential Candidates for Tropical Cyclogenesis

2020 ◽  
Vol 148 (2) ◽  
pp. 655-669 ◽  
Author(s):  
Kelly M. Núñez Ocasio ◽  
Jenni L. Evans ◽  
George S. Young
Keyword(s):  
Propagation Speed ◽  
Extreme Rainfall ◽  
Life Cycles ◽  
Mesoscale Convective Systems ◽  
Tropical Cyclogenesis ◽  
Convective System ◽  
Background Flow ◽  
Extreme Rainfall Events ◽  
Convective Systems ◽  
Mesoscale Convective

Abstract This study introduces the development of the Tracking Algorithm for Mesoscale Convective Systems (TAMS), an algorithm that allows for the identifying, tracking, classifying, and assigning of rainfall to mesoscale convective systems (MCSs). TAMS combines area-overlapping and projected-cloud-edge tracking techniques to maximize the probability of detecting the progression of a convective system through time, accounting for splits and mergers. The combination of projection on area overlapping is equivalent to setting the background flow in which MCSs are moving on. Sensitivity tests show that area-overlapping technique with no projection (thus, no background flow) underestimates the real propagation speed of MCSs over Africa. The MCS life cycles and propagation derived using TAMS are consistent with climatology. The rainfall assignment is also more reliable than with previous methods as it utilizes a combination of regridding through linear interpolation with high temporal and spatial resolution data. This makes possible the identification of extreme rainfall events associated with intense MCSs more effectively. TAMS will be utilized in future work to build an AEW–MCS dataset to study tropical cyclogenesis.

Comparison of Ground-Based Radar and Geosynchronous Satellite Climatologies of Warm-Season Precipitation over the United States

2008 ◽  
Vol 47 (12) ◽  
pp. 3264-3270 ◽  
Author(s):  
John D. Tuttle ◽  
Richard E. Carbone ◽  
Phillip A. Arkin
Keyword(s):  
United States ◽  
Brightness Temperature ◽  
Satellite Data ◽  
Warm Season ◽  
Propagation Speed ◽  
Radar Data ◽  
The United States ◽  
Temperature Threshold ◽  
Brightness Temperatures ◽  
The Difference

Abstract Studies in the past several years have documented the climatology of warm-season precipitation-episode statistics (propagation speed, span, and duration) over the United States using a national composited radar dataset. These climatological studies have recently been extended to other continents, including Asia, Africa, and Australia. However, continental regions outside the United States have insufficient radar coverage, and the newer studies have had to rely on geostationary satellite data at infrared (IR) frequencies as a proxy for rainfall. It is well known that the use of IR brightness temperatures to infer rainfall is subject to large errors. In this study, the statistics of warm-season precipitation episodes derived from radar and satellite IR measurements over the United States are compared and biases introduced by the satellite data are evaluated. It is found that the satellite span and duration statistics are highly dependent upon the brightness temperature threshold used but with the appropriate choices of thresholds can be brought into good agreement with those based upon radar data. The propagation-speed statistics of satellite events are on average ∼4 m s−1 faster than radar events and are relatively insensitive to the brightness temperature threshold. A simple correction procedure based upon the difference between the steering winds for the precipitation core and the winds at the level of maximum anvil outflow is developed.

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