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Wang, Jingyu

Publications and source records attributed to Wang, Jingyu.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Climatological occurrences of hail and tornadoes associated with mesoscale convective systems in the United States

Hail and tornadoes are hazardous weather events responsible for significant property damage and economic loss worldwide. The most devastating occurrences of hail and tornadoes are commonly produced by supercells in the United States. However, these supercells may also grow upscale into mesoscale convective systems (MCSs) or be embedded within them. The relationship between hail and tornado occurrences with MCSs in the long-term climatology has not been thoroughly examined. In this study, radar features associated with MCSs are extracted from a 14-year MCS tracking database across the contiguous United States, and hazard reports are mapped to these MCS features. We investigate the characteristics of hail and tornadoes in relation to MCSs, considering seasonal and regional variabilities. On average, 8 %–17 % of hail events and 17 %–32 % of tornado events are associated with MCSs, depending on the criteria used to define MCSs. The highest total and MCS-associated hazard events occur from March to May, while the highest MCS-associated portion (23 % for hail and 45 % for tornadoes) is observed in winter (December–February) due to the dominance of MCSs caused by strong synoptic forcing. As hailstone size increases, the fraction associated with MCS decreases, but there is an increasing trend for tornado severity from EF0 to EF3 (Enhanced Fujita Scale). Violent tornadoes at EF4 and EF5 associated with MCSs were also observed, which are generated by supercells embedded within MCSs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Characterization of the aerosol vertical distributions and their impacts on warm clouds based on multi-year ARM observations

Aerosol vertical distribution plays a crucial role in cloud development and thus precipitation since both aerosol indirect and semi-direct effects significantly depend on the relative position of aerosol layer in reference to cloud, but its precise influence on cloud remains unclear. In this study, we integrated multi-year Raman Lidar measurements of aerosol vertical profiles from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) facility with available Value-Added Products of cloud features to characterize aerosol vertical distributions and their impacts on warm clouds over the continental and marine ARM atmospheric observatories, i.e., Southern Great Plains (SGP) and Eastern North Atlantic (ENA). A unimodal seasonal distribution of aerosol optical depths (AODs) with a peak in summer is found at upper boundary layer over SGP, while a bimodal distribution is observed at ENA for the AODs at lower levels with a major winter-spring maximum. The diurnal mean of upper-level AOD at SGP shows a maximum in the early evening. According to the relative positions of aerosol layers to clouds we further identify three primary types of aerosol vertical distribution, including Random, Decreasing, and Bottom. It is found that the impacts of aerosols on cloud may or may not vary with aerosol vertical distribution depending on environmental conditions, as reflected by the wide variations of the relations between AOD and cloud properties. For example, as AOD increases, the liquid water paths (LWPs) tend to be reduced at SGP but enhanced at ENA. The relations of cloud droplet effective radius with AOD largely depend on aerosol vertical distributions, particularly showing positive values in the Random type under low-LWP condition (<50gm -2 ). In conclusion, the distinct features of aerosol-cloud interactions in relation to aerosol vertical distribution are likely attributed to the continental-marine contrast in thermodynamic environments and aerosol conditions between SGP and ENA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Urbanization may enhance tornado potential: A single case report

Tornadoes pose a risk of catastrophic economic loss and casualty in the United States. Modification of land use by urbanization alters the meteorological conditions that may impact tornado formation and intensification processes. Here we explored the simulated impact of Kansas City urbanization on the tornado potential of a supercell storm. In this studied case, we found that urbanization might enhance tornado potential by a) strengthening the low-level streamwise vorticity in the storm inflow region, thus forming stronger rotating updrafts; and b) intensifying near-surface horizontal vorticity near the boundary of the forward-flank cold pool which increases the ingestion, tilting, and stretching of streamwise horizontal vorticity into vertical vorticity. The former results from the stronger east-to-west pressure perturbation gradient due to the faster, stronger outflow boundary, and the latter is mainly a result of stronger cold fronts and a better alignment of storm-relative inflow with the horizontal vorticity vector. We emphasize that our conclusions only represent one possibility of how urbanization would affect tornado potential, and a more systematic examination is needed to achieve a more general conclusion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Contrasting Responses of Hailstorms to Anthropogenic Climate Change in Different Synoptic Weather Systems

Hailstones and extreme precipitation generate substantial economic losses across the United States (US) and the globe. Their strong association with short-lived, intense convective storms poses a great challenge in predicting their future changes. Here, we conducted model simulations at 1.2 km grid spacing for severe convective storms with large hail and heavy precipitation that occurred in two typical types of synoptic-scale environments in spring seasons over the central US under both current and future climate conditions. We find that the responses of large hail (diameters >2.5 cm) to anthropogenic climate change (ACC) are markedly different between the hailstorms developed in the two types of synoptic-scale environments, with over 110% increase in large hail occurrences for the frontal systems, whereas less than 30% increase for the Great Plains low-level jet (GPLLJ) systems. This is explained by the larger increase in convective intensity and updraft width and a smaller increase in warm cloud depth in the frontal storms compared with the GPLLJ storms. Interestingly, the occurrences and intensity of heavy precipitation (rain rate >20 mm hr-1) in both types of systems are similarly sensitive to ACC (e.g., 40% and 33% increases in the occurrences for the frontal and GPLLJ systems, respectively). These results advance our knowledge of hail projection and have important implications for managing risks for future hail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1

Abstract. Realistic simulation of the Earth's mean-state climate remains a major challenge, and yet it is crucial for predicting the climate system in transition. Deficiencies in models' process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections and incorrect attribution of the physical mechanisms governing past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and long-standing biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when it is run as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback, as well as the fidelity of the simulated base climate state, are important for constraining the climate in the past and future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between −11 and −23 C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near approx. 400 microm in randomly oriented maximum dimension (Dmax). The ice mass at −23 C is primarily in a closely collocated mode, whereas a mass mode near Dmax approx. 1000 microns becomes dominant with decreasing elevation to the −11 C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak Dmax by a factor of 3-5 and underpredict ice number concentration by a factor of 4-10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

Fridlind, Ann M.↗
NASA NTRS2017

Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between -11 and -23 degree C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near 400 micrometer in randomly oriented maximum dimension (D[superscript max] ). The ice mass at -23 degree C is primarily in a closely collocated mode, whereas a mass mode near D[superscript max] -1000 micrometer becomes dominant with decreasing elevation to the -11 degree C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak D[superscript max] by a factor of 3-5 and underpredict ice number concentration by a factor of 4-10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

Fridlind, Ann M.↗