Abstract

The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m⁻² in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h⁻¹) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h⁻¹, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.

Abstract

Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D _mass and the normalized intercept parameter N_W , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm⁻¹ m⁻³), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D _mx–D _eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by N_W reduces the range in N_W , resulting in well-fitted power-law relationships between SWER/N_W and D _mass and between W/N_W and D _mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.

Abstract

The study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) when simulating an urban heat island (UHI) over the city of eThekwini, located along the southeast coast of South Africa. The CCAM is applied at a grid length of 1 km on the panel with eThekwini, in a stretched-grid mode. The CCAM is coupled to the urban climate model called the Australian Town Energy Budget (ATEB). The ATEB incorporates measured urban parameters including building characteristics, emissions, and albedo. The ATEB incorporates the land-cover boundary conditions obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The CCAM configuration applied realistically captured the orientation of the city and land-cover types. Simulations of meteorological variables such as temperatures and longwave radiation reproduced the spatial distribution and intensity of the UHI. Results show that the UHI is stronger during summer and weaker in all other seasons. The UHI developed because of natural factors (e.g., distribution of longwave radiation) and human factors (e.g., urban expansion, an increase in anthropogenic emissions, and additional heating). Because of the city’s location along the coast, the UHI simulation could be weakened by atmospheric circulations resulting from land and sea breezes. Mitigation methods such as applying reflective paints and revegetation of the city may increase albedo and latent heat fluxes but reduce the sensible heat fluxes and weaken the UHI. However, the UHI may not be completely eliminated since natural factors and emissions constantly influence its development.

Abstract

Much of the previous research on total and heavy precipitation trends across the Northeastern US (hereafter Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multi-duration (1, 2, 3, 7, 14, and 30 days), multi-return interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more frequent, shorter duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from Rotated Principal Component Analysis and k-means clustering analysis, which allowed for the main synoptic types present, as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return-intervals and included: coastal low (Nor’easters, tropical cyclones, and predecessor rain events), deep trough, east coast trough, zonal, and high pressure patterns.

Abstract

The new-generation multi-satellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products (GPM-IMERG) in different climatic and topographical regions of China for the 2014-2020 period. This study showed that (1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient (R) >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April-September) (RBias=7.41%) than during the dry season (RBias=13.65%). (2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. (3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.

Abstract

Tornado characteristics (e.g., frequency, intensity) are challenging to capture. Assessment of tornado characteristics typically requires damage as a proxy. The lack of validation in the Enhanced Fujita (EF) scale and the likelihood of rural tornadoes suggests that tornado characteristics are not accurately captured. This manuscript presents an approach to quantify the potential misclassification of tornado characteristics using Monte Carlo simulation for residential structures in rural areas. An analytical tornado wind field model coupled with fragility curves generates degrees of damage (i.e., DOD) from the EF scale in a wind speed to damage approach. The simulated DODs are then used to derive damage to wind speed relationships built from the National Weather Service Damage Assessment Toolkit (NWS DAT). Comparisons are then made between the simulated tornado characteristics and those derived from damage.

Results from the simulations show a substantial proportion of tornadoes were ‘missed’ and path width and path length on average are underestimated. An EF4 rating based on damage is favored for EF3 to EF5 simulated tornadoes. A linear regression was utilized and determined damagebased wind speeds of different percentiles, damage length, damage width and the number of structures rated at a particular DOD were important for prediction. The distribution of DODs was also used to predict wind speed and the associated intensity rating. These methods were tested on actual tornado cases. Tornadoes that have the same damage-based peak wind speed can be objectively assessed to determine differences in overall intensity. The results also raise questions about the level of confidence when assessing wind speed based on damage.

Abstract

Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign (LAFE) studied LLJs over the Central SGP site in Oklahoma, and found nearly equal occurrences of the usual southerly jets, and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the north-easterlies by more than 4 ms⁻¹ on average, reflecting a significantly higher frequency of winds stronger than 12 ms⁻¹.

The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly-wind nights, the winds blew stronger at the highest, westernmost sites by 2 ms⁻¹, whereas on the northeasterlyflow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016-2021 summertime data, revealed unusual wind and LLJ conditions.

Temporal hodographs using hourly-averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial-oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height.

Abstract

This study examined the statistics of aviation turbulence that occurred in Japan between 2006 and 2018 by analyzing pilot reports (PIREP). In total, 81 639 turbulence events, with moderate or greater intensity, were reported over this period. The monthly number of turbulence cases has an annual periodic variation as observed in different regions by previous studies. The number of turbulence cases is high from March to June and low in July and August. Higher numbers of turbulence cases are experienced along the major flight routes in Japan, especially around Tokyo, for the active period between 0900 and 2000 local time. The number of cases of turbulence peaks when the flight reaches an altitude of 33 000 ft (FL330; 1000 ft ≈ 300 m), whereas it decreases when the flight altitude is above FL380 and below FL280. The statistical features are not largely different among the four seasons; however, there are some exceptions. For instance, the number of turbulence cases is large in high altitudes in summer and small in low altitudes in winter. Considering the number of flights, it is evident that the frequency of turbulence is higher in altitudes between FL200 and FL350, although the number of flights is low in this altitude region. The number of convectively induced turbulence cases is relatively large during the daytime in summer in comparison with the other seasons. A large amount of mountain-wave turbulence is observed around the mountainous region in autumn and winter when the jet stream flows over Japan.

Abstract

Using hail records at national meteorological stations for 2014–18, ERA-Interim reanalysis data, and Doppler weather radar data, the spatiotemporal distribution of hail events (HEs) in the Beijing–Tianjin–Hebei region is revealed, and the environmental conditions and hailstorm structures corresponding to large hail (diameter ≥ 20 mm) events (LHEs) and small hail (2 ≤ diameter < 20 mm) events (SHEs) are compared. It is found that, although HEs may be more frequent in mountainous areas, most LHEs occur in the plains and near the foot of the mountains. The HE frequency peaks in June, and the average hailstone size is larger during May and June. According to daytime records, the HEs predominantly occur in the afternoon and evening, whereas LHE tends to be more in the evening. Comparison of environmental parameters suggests that, relative to SHEs, LHEs tend to correspond to higher 2-m temperature, a wetter lower layer, a larger difference in relative humidity between 925 and 500 hPa, greater unstable energy, and stronger wind shear. Hailstorms associated with LHEs tend to feature greater mesoscale rotation velocity than those associated with SHEs. Hailstorms usually show rapid increase (RI) in vertically integrated liquid (VIL) before hailstones are observed. A significant difference between the hailstorms associated with LHEs and SHEs is that the former has an obviously longer time interval between the end of VIL RI and the occurrence of hailfall, indicating that the large hail size benefits from the constant supply of liquid water and the hail can be lifted by updrafts for a long time.

Abstract

In this study, high particulate matter (PM_2.5) pollution episodes were examined in Seoul, the capital city of South Korea, which, based on the episode characteristics, were influenced by a distinct meteorological mode, long-range transport (LRT), from two-level meteorological observations: surface and 850-500 hPa level. We performed two-step statistical analysis including principal component (PC) analysis of meteorological variables based on the observation data, followed by multiple linear regression (MLR). The meteorological variables included surface temperature (T _sfc), wind speed (WS _sfc), and the east–west (u _sfc) and north– south (v _sfc) components of wind speed, as well as wind components at 850 hPa geopotential height (u ₈₅₀ and v ₈₅₀, respectively) and the vertical temperature gradient between 850 and 500 hPa. Our two-step analysis of data collected during 2018–2019 revealed that the dominant factors influencing high-PM_2.5 days in Seoul (129 days) were upper wind characteristics in winter, including positive u ₈₅₀ and negative v ₈₅₀, that were controlled by the presence of continental anticyclones that increased the likelihood of LRT of PM_2.5 pollutants. Regional-scale meteorological variables, including surface and upper meteorological variables on normal and high-PM_2.5 days, showed distinct covariation over Seoul, a megacity in the eastern part of northeast Asia with large anthropogenic emissions. Although this study examined only two atmospheric layers (surface and 500-850 hPa), our results clearly detected high-PM_2.5 episodes with LRT characteristics, suggesting the importance of considering both geographical distinctiveness and seasonal meteorological covariability when scaling down continental-to-local response to emission reduction.