Wang Jingyu

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.

The principal modes of diurnal cycle of rainfall (DCR) over South China during the pre-summer rainy season are examined using 23-year satellite observations and reanalysis data. Three distinctly different DCR modes are identified via empirical orthogonal function analysis, i.e., the early-afternoon precipitation (EAP) mode, the late-afternoon precipitation (LAP) mode and the morning precipitation (MP) mode. Under the EAP mode, the rainfall starts to increase from midnight and reaches its peak in the early afternoon. The nocturnal to morning rainfall generally concentrates on the northeastern Pearl River Delta (PRD) and along the coastline. The coastal rainfall is initiated from the convergence zone induced by the strong onshore wind, and is further enhanced via the establishment of land breeze in the early morning. The northeastern PRD center is mainly attributed to the windward mechanical lifting associated with the strong low-level wind. The afternoon rainfall is pronounced over inland and exhibits significantly regional diversity. The eastern inland rainfall develops from the early-morning rainfall over the northeastern PRD, whereas the eastward propagating rainbelts associated with frontal activities are responsible for the formation of western inland rainfall. The LAP mode features a late-afternoon peak, which is triggered and developed locally with favorable thermal-dynamic conditions over the western inland South China. The MP mode exhibits a single early-morning peak. Nocturnal to morning rainfall is prominent on the northeastern PRD and near-offshore region. The near-offshore rainfall is basically induced by the convergence between the onshore wind and land breeze in the early morning, which further propagates far offshore in the morning due to effects of gravity wave.

On 15 February 2022, the city of Petrópolis in the highlands of the state of Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm), generated by a strongly invigorated mesoscale convective system. It resulted in flash floods and subsequent landslides that caused the deadliest landslide disaster recorded in Petrópolis, with 231 fatalities. In this paper, we analyzed the root causes and the key triggering factors of this landslide disaster by assessing the spatial relationship of landslide occurrence with various environmental factors. Rainfall data were retrieved from 1977 to 2022 (a combination of ground weather stations and the Climate Hazards Group InfraRed Precipitation – CHIRPS). Remotely sensed data were used to map the landslide scars, soil moisture, terrain attributes, line-of-sight displacement (land surface deformation), and urban sprawling (1985–2020). The results showed that the average monthly rainfall for February 2022 was 200 mm, the heaviest recorded in Petrópolis since 1932. Heavy rainfall was also recorded mostly in regions where the landslide occurred, according to analyses of the rainfall spatial distribution. As for terrain, 23 % of slopes between 45–60∘ had landslide occurrences and east-facing slopes appeared to be the most conducive for landslides as they recorded landslide occurrences of about 9 % to 11 %. Regarding the soil moisture, higher variability was found in the lower altitude (842 m) where the residential area is concentrated. Based on our land deformation assessment, the area is geologically stable, and the landslide occurred only in the thin layer at the surface. Out of the 1700 buildings found in the region of interest, 1021 are on the slope between 20 to 45∘ and about 60 houses were directly affected by the landslides. As such, we conclude that the heavy rainfall was not the only cause responsible for the catastrophic event of 15 February 2022; a combination of unplanned urban growth on slopes between 45–60∘, removal of vegetation, and the absence of inspection were also expressive driving forces of this disaster.

Satellite-based post-tornado assessments have been widely used for the detection of tornado tracks, which heavily relies on the identification of vegetation changes through observations at visible and near-infrared channels. During the deadly 10–11 December 2021 tornado outbreak, a series of violent tornadoes first touched down over northeastern Arkansas, an area dominated by cropland with rare vegetation coverage in winter. Through the examination of Moderate Resolution Imaging Spectroradiometer multi-spectral observations, this study reveals significant scars on shortwave infrared channels over this region, but none are captured by visible and near-infrared channels. The dominant soil type is aquert (one of vertisols), whose high clay content well preserves the severe changes in soil structure during the tornado passage, when the topmost soil layer was removed and underlying soil with higher moisture content was exposed to the air. This study suggests a quick post-tornado assessment method over less vegetated area by using shortwave infrared channels.

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 (<50 gm⁻²). 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.

The subseasonal features of the annual trends of runoff and other associated hydroclimatic variables in the upper Colorado River basin (UCRB) are examined using multiple data sets from in situ observations, reanalysis, and modeling for early spring (February, March, and April), given that about 58% of annual mean runoff decline from 1980 to 2018 stem from its decreases in the three months. Our analysis suggests that the strong annual trends of hydroclimatic variables in March are more statistically significant than other two months. While recent observational studies attribute the decline of runoff for either annual total or cool and warm seasons to regional warming and precipitation decrease, we suggested, for the first time, that a larger decreasing trend of the runoff in March is caused by the declining cloud optical depth which induces further decrease in precipitation and additional increase in temperature on top of climatic warming. The extra warming can reduce available water resource in the basin likely by enhancing evaporation in March. The recent cloud suppression likely results from stronger subsidence and larger moisture flux divergence over southwestern United States because of abnormal circulation patterns in varying climate, in turn leading to drier atmosphere which is unfavorable for cloud formation/development over the UCRB region. The cloud influence on the runoff in March in the UCRB revealed in this study implies the importance of understanding subseasonal variations of hydroclimate in the changing climate, as well as a need of future studies on the response of circulation patterns to climate change at subseasonal level and its implication on local hydroclimate.

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).

We study the air-sea interaction in a perspective of interannual timescale with wintertime observed sea surface temperature (SST), turbulent heat flux (THF) and sea level pressure (SLP) based on lead-lag correlation and regression. Being forced by the atmosphere, the ocean also has a response to the atmosphere, especially in the subarctic frontal zone (SAFZ) and the eastern North Pacific. The air-sea interactions in two regions are quite similar but associated with different patterns of atmospheric variability when SST leading SLP for one month. Since the SST changes in the eastern North Pacific and the equatorial east-central Pacific are synchronous in the dominant mode (EOF1) of the SST anomalies, the SLP responses to SST in the eastern North Pacific and central Pacific (CP) ENSO both show Pacific-North American (PNA) teleconnection pattern. The CP ENSO dominates the tropical influence on the air-sea interaction in the eastern North Pacific during November. Specifically, the response of Aleutian low to CP El Niño events leads to heat transport from ocean to atmosphere in the eastern North Pacific. In CP La Niña years, the air-sea heat exchange is affected by the combination of Aleutian low and lower troposphere westerlies.