Atmospheric Research最新文献

To improve forecast skill of extreme weather events, it is of primary importance to construct accurate initial conditions for a convective-scale ensemble prediction system (EPS). The traditional initial perturbation schemes, e.g., dynamic downscaling, fail to capture the amplitude or structure of convective-scale forecast errors accurately, especially near steep terrain or meso- and small-scale systems during weather system's rapid change. In this study, we developed a new initial perturbation optimizing technique, namely the cosine analysis constraint method. This method is then used to improve the downscaled initial perturbations by introducing smaller-scale information from the analysis increments, generated from data assimilation. We demonstrate the feasibility of the cosine analysis constraint method in the China Meteorological Administration (CMA) convective-scale EPS. Using the control experiment (CTRL) without any modification to the initial perturbation scheme as a reference, we designed the cosine analysis constraint experiment (CONS) and compared it with CTRL. We selected a case study of convective precipitation and two groups of one-month experiments were initialized to verify the feasibility of the new method and exclude case dependence. The results of the one-month test show that adopting the cosine analysis constraint method to optimize the initial perturbations can effectively enhance the consistency between the ensemble mean and the corresponding reanalysis field (regarded as observations). In the case study, the larger horizontal distribution of precipitation spread in CONS indicated the location of convective precipitation more effectively, which is important for operational weather forecasting. The significant effect of the moisture process was confirmed, especially in CONS. The verification results of the entire study domain were significantly improved after the initial perturbations were rescaled. Overall, the forecast skill of meteorological fields at different pressure levels and the extreme precipitation of smaller-scale convective systems were enhanced, which illustrated the potential of the cosine analysis constraint method to improve the quality of initial perturbations in CMA convective-scale EPS.

The potential climate-warming impact from aircraft contrails may be similar in magnitude to the direct effect from carbon dioxide emissions across all aviation. The warming impact may be mitigated through pre-tactical flight trajectory optimization to avoid ice supersaturation regions (ISSRs) while also considering aircraft performance and CO₂ emissions. The ability to perform such deviations depends on accurate predictions of water vapor in the upper troposphere and lower stratosphere (UTLS). Herein we evaluated the performance of two leading global numerical weather prediction (NWP) models: the Global Forecast System (GFS) developed in the USA and the Integrated Forecast System, (IFS) developed in Europe, and a research mesoscale model, Weather Research and Forecasting configured by SATAVIA (S-WRF) to predict UTLS moisture and ISSR. We compared humidity forecasts to observations from 383 aircraft flights and 3480 radiosonde profiles comprising approximately 1.5 million measurements over Europe and the Middle East for 10 months in 2022. Neither GFS nor IFS properly reproduced the observed distribution of relative humidity with respect to ice (RH_ice). Moreover, in addition to not being usable for prospective flight planning, the ERA5 reanalysis only slightly improved the outcome of the IFS. Only the S-WRF model with multi-moment cloud physics and high spatial resolution (5 km grid spacing) closely reproduced the observed relative frequency distribution of RH_ice. Furthermore, ISSR validation using near equal-area neighborhoods when computing Matthews Correlation Coefficient and F1 score showed that S-WRF scored higher (F1 = 0.66) than the IFS (F1 = 0.62), while the GFS had near zero score due to its near complete lack of predictions of RH_ice greater than 100 % in stark contrast to observations. In fact, S-WRF also correctly predicts 92 % of the time when conditions were not conducive to contrail formation. Ultimately, the S-WRF model could be used to alter flight plans to deviate above or below nearly certain contrail formation regions to reduce non-CO₂ climate impacts of aviation.

The Yangtze River basin experienced extreme flooding during the summer of 2020, leading to widespread impact and significant economic losses. However, there is a lack of specific and quantitative anthropogenic attribution analysis. Here, we used the VIC model and the Risk Ratio framework to isolate the contributions of two anthropogenic (ANT) forcings, aka greenhouse gas (GHG) and aerosol (AER), to this event and the associated potential risk. We also assessed future risk by employing projections from four simulations under the SSP2–4.5 scenario in 2041–2100. Our findings reveal that ANT forcing reduces the probability by 74 %, while GHG forcing increase it by 6 %, and AER forcing decreases it by 92 % at the downstream Datong station. At the middle-stream Cuntan station, ANT forcing decreases the probability of extreme floodings similar to the 2020 event by 87 %, while GHG forcing decreases it by 82 %, and AER forcing decreases it by 95 %. In the future period under the SSP2–4.5 scenario, ANT forcing reduces the probability of extreme flood events like 2020 by 78 % and 82 % at the Cuntan and Datong stations, respectively. And The GHG and AER forcings contribute positively and negatively to the probability of flooding in the Yangtze River Basin, respectively, mainly through influencing the probability of extreme precipitation and potential evapotranspiration. Our study provides valuable insights for policymakers to comprehend the anthropogenic influence on extreme flooding and to guide effective risk management strategies.

Plain language summary

The Yangtze River basin experienced severe flooding in the summer of 2020, but there is no specific and quantitative anthropogenic attribution analysis yet. Therefore, we performed an attribution analysis using the hydrological model and global climate models to isolate the anthropogenic (ANT) contribution for the 1961–2020 and 2041–2100 periods. We find that ANT forcing decreases the risk of extreme flooding, similar to the 2020 event, at the middle-stream Cuntan station and the downstream Datong station. In the future, ANT forcing significantly reduces the probability of potential extreme flood events in both stations. Additionally, we discovered that the net effect of ANT forcing on extreme flood events is determined by the counterbalance of anthropogenic greenhouse gas and aerosol forcings. Our study aims to help policymakers gain a better understanding of the human impact on extreme flooding and develop effective risk management strategies.

Variations in rainfall patterns in the Northeast region of Brazil (NEB) are high and multiscalar, increasing susceptibility to extreme drought and/or flood events. The objective of this study was to characterize rainfall patterns and monthly dry-wet periods using the Standardized Precipitation Index (SPI) from 1990 to 2019 in the South Coast of NEB, utilizing geostatistical interpolation methods. The study was based on a climatological dataset from the coastal region of the state of Bahia, collected from 112 weather stations. A map projecting aquifer in the study area was established, and SPI was determined. The data were subjected to descriptive, multivariate, and geostatistical statistics. Hydrogeological and hydrochemical maps were prepared. The months of October to April are characterized as rainy months (>300 mm). The coefficient of variation showed low standards due to atmospheric circulation systems. The Gaussian and exponential models presented the best fits (R² > 0.86) for rainfall and SPI data. The quality of groundwater in the study area ranges from excellent to good, except for the north center part of the study area, where the groundwater quality is poor. An alert is issued for the southern region of the Bahian coast regarding the safety of the local population, including the risk of landslides resulting from rain and floods.

This study presents the first comprehensive analysis of the seasonal characteristics of raindrop size distribution (DSD) in Metro Manila, Philippines, using two years of measurements (2018–2020) from the PARSIVEL² disdrometer. Seasonal properties of DSD for both stratiform and convective rain types were examined during the Southwest Monsoon (SWM; June–September), Northeast Monsoon (NEM; October–February), and pre-SWM or Transition period (March–May). Key findings reveal the dominance of small raindrops (<1 mm) during the NEM period, while mid-sized (1–3 mm) to large raindrops (>3 mm) are more prevalent during the SWM and Transition periods. The study highlights notable seasonal differences in DSD at moderate rain rates (5–10 mm hr⁻¹), indicating variations in microphysical processes between stratiform and convective rain. Furthermore, the microphysical properties of convective rain in Metro Manila are found to be influenced by both oceanic and continental convective processes based on their mass-weighted mean diameter and generalized intercept parameter for all the monsoon periods. The DSD-derived dual-polarimetric radar variables are also shown to vary with the monsoon periods. Rainfall estimates using the DSD-derived dual-polarimetric relations statistically outperformed the empirical rainfall retrieval equation currently used by operational weather radars in the Philippines. Additionally, the Gamma shape parameter found in this study aligns with existing rainfall retrieval algorithm assumptions in space-borne radars. This similarity, along with the derived microphysical relations, could provide potential improvements in rainfall retrievals of ground-based and space-borne radars in tropical coastal environments like Metro Manila.

In this study, we investigate the atmospheric circulation anomalies related to the PM_2.5 mass concentration rapid decline cases in Beijing during winter when the PM_2.5 concentration is the highest. From 2014 to 2021, 66 PM_2.5 concentration rapid decline days (RDDs) are identified by considering the 90 % thresholds of the difference of PM_2.5 mass concentration between two adjacent days. The composite evolution of PM_2.5 concentration for the rapid decline cases features a slow increase on the accumulation phase but a rapid decline on RDDs with the average daily PM_2.5 concentration decreases by 69.1 %. The composite evolution of atmospheric circulation anomalies in the lower troposphere related to these PM_2.5 concentration rapid decline cases exhibits the eastward shift of cyclonic/anticyclonic geopotential height anomalies from the Mongolian Plateau/Northeast Asia to the Japan Sea/western Pacific, coinciding with a southerly-to-northerly reverse of wind anomalies in Beijing. The anomalous northerly on RDDs enhances the wind speed and therefore corresponds to the rapid decline of PM_2.5 concentration. Further analysis suggests that the above process is responsible for 72.7 % of RDDs, and other about one-third of RDDs display anomalous southerly in Beijing, which also result in comparable PM_2.5 reduction amplitude to that resulted from the northerly RDDs. The rapid decline of PM_2.5 concentration for the Southerly RDDs cases is related to the near-surface southeasterly, which extends from 125°E to the west of Beijing, bringing the clean air masses from the Bohai Sea to the Beijing region.

To analyze the physicochemical mechanisms affecting the variation in fine suspended particulate matter (PM_2.5) concentrations in Taichung City, the largest city in central Taiwan (the second largest city in Taiwan), during a high-pollution event from November 3 to 6, 2021, we applied the sulfur tracking method (STM) and integrated source apportionment method (ISAM) of the WRF/CMAQ model to simulate the impacts of various emission sources. The sources of pollution in Taichung City are very similar, which shows that the impacts of point, line, and area sources should not be neglected in addition to the boundary conditions. SO₄²⁻ is mainly generated from point emissions and the production of H₂O₂, Fe, Mn, and O₃. NO₃⁻ is also mainly generated from point sources in Taichung City, with HNO₃ being the main source at noon and ANO₃ at other times of the day. NH₄⁺ is mainly generated from area sources in Taichung City. OM is more complex, mainly originating from line sources in Taichung City and other sources, such as point/area emissions in Taichung City and other emissions from Changhua County. The most important mechanism is low-volatility/semivolatile oxidized combustion of OC at noon, followed by low-volatility/semivolatile POA, which is produced in the morning or evening. EC mainly originates from line sources in Taichung City and Changhua County. In other nearby counties, EC is dominated by local emission sources. In addition, when the concentration of PM_2.5 is high, the Neutralization Ratio (NR) is high and PM_2.5 is relatively neutral or slightly alkaline. On the contrary, when the concentration of PM_2.5 is low, the NR is lower than 1 and the aerosol is acidic. Besides, this study used positive matrix factorization (PMF), which indicates that the PM_2.5 at the UAPRS originated from eight kinds of pollution, namely, windblown dust, oil cracking, iron and steel industry, sea salt, transport, Cl⁻ containing exhaust, biomass burning, fossil combustion containing abundant SO₄²⁻ and the heavy oil refining and coal combustion industry. The direction of the source of pollution can be traced by a conditional bivariate probability function (CBPF). Overall, fossil fuel combustion, mainly involving sulfate, is the largest source of pollution, with the heavy oil refining and coal combustion industry contributing less, and the remaining factors contribute relatively evenly.

Precipitable Water Vapor (PWV) is a crucial parameter in climate research. However, obtaining high precision PWV data remains a problem. Discrepancies in water vapor elevation data from diverse observation sources pose challenges, emphasizing the need for precise vertical correction. In this paper, we proposed the precipitable water vapor model with systematic difference correction by using ERA5 (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) data and GNSS (Global Navigation Satellite System) data. In this proposed method, two vertical correction models, employing cubic polynomial and exponential functions, were developed. The exponential function demonstrated superior overall performance with the RMS error of 0.96 mm, effectively capturing vertical characteristics across diverse regions, while the cubic polynomial function had the RMS (Root Mean Square) error of 2.77 mm validated by ERA5 PWV. The cubic polynomial function was found more suitable for low-elevation regions, while the exponential function excelled in high-elevation regions. Validated by radiosonde PWV, the cubic polynomial function and the exponential function show a strong correlation with latitude. Both functions exhibit smaller RMS values at higher latitudes and larger RMS values at lower latitudes. Validated by GNSS PWV, the cubic polynomial function exhibits superior accuracy, with the RMS of 2.39 mm, compared to the exponential function, particularly in specific regions. Analyzing six years of data reveals significant systematic differences between ERA5 and GNSS PWV. This discrepancy exhibits a noticeable annual periodic variation. The global GNSS stations were organized into grids, with stations within the same region grouped together for correction. Models considering systematic differences exhibited substantial RMS reductions and better accuracy. These findings can provide reference in atmospheric correction and the study of extreme weather events.

Based on the CLoud, Albedo and RAdiation dataset, AVHRR-based, version 2 (CLARA-A2), Tropical Rainfall Measuring Mission 3B43 (TRMM-3B43), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) reanalysis data, the potential cloud precipitation capacity (PCPA) of typical regions in China is compared, and the relationship between impact factors and PCPA is discussed. Results have suggested that the Tarim Basin (TB) has scarce cloud water resources, while cloud water path (CWP) values are higher in South China (SC) and Sichuan Basin (SB) under the influence of the East Asian monsoon. Moreover, different typical regions of China exhibit varying dependencies on the ice water path (IWP) and liquid water path (LWP). There is a strong correlation between the IWP and precipitation in the Tibet Plateau (TP), Northeast China (NE), SC, and SB. The precipitation in TB demonstrates a more pronounced correlation with the LWP. Through a comparison of the correlation between PCPA and influencing factors in different typical regions of China, it is found that convective available potential energy (CAPE), surface latent heat flux (SLHF), surface sensible heat flux (SSHF), and 0–3 km relative humidity (RH) exhibit stronger correlation with PCPA than 2 m temperature (T2m) and 2–5 km vertical wind shear (SHEAR). Further investigation revealed that the joint effect of CAPE, RH, and SLHF has a pronounced effect on PCPA, particularly during spring and autumn. Additionally, the PCPA of TP exhibits significant dependency on the joint effect of these three influential factors. Furthermore, the ratio of LWP to IWP (RLI) also affects PCPA. In spring and autumn, the PCPA of TB and NC exhibits a positive correlation with RLI, whereas the PCPA of TP, SC, NE, and SB shows a negative correlation with RLI. In summer, the PCPA of TB and SC exhibits a notably negative correlation with RLI. This study deepens the understanding of the formation mechanism of cloud precipitation in typical regions of China, provides the basis for climate forecast and improves the accuracy of weather forecast.

On June 25, 2020, an atypical bow echo (ATBE) accompanied by a meso-γ-scale vortex (MV) incurred the strongest gust (41.4 m s⁻¹) recorded since 1957 in the North China Plain. In this article, we investigate the evolution characteristics of MV and its mechanism for the extreme wind by using the observation data of Doppler weather radar and automatic weather stations as well as the four-dimensional Variational Doppler Radar Analysis System (VDRAS). The results show that the MV in this process was initially born at a height of 0.9–2.4 km, and the contracting and stretching vertical vortex rapidly descended to surface from the 2.0 km height with the rotation speed increasing rapidly. Six minutes later, the extreme wind occurred. The generation mechanism of the MV is that, under the strong vertical wind shear and downdraft in the lower troposphere, the vertical vortex tube of MV was transformed from the horizontal vortex tube generated in the zone of perturbation temperature gradient. The influence mechanism of MV on the extreme wind is that, as the MV continued to intensify and extend downward, a negative perturbation low-pressure center formed in the lower troposphere near the MV. Meanwhile, the descending rear-inflow jet (RIJ) was blocked by the prefrontal upward motion and a positive perturbation high-pressure center formed at around 1 km height. The violent positive-negative couplets of perturbation pressure gradient produced a downward nonhydrostatic perturbation pressure gradient force (NPPGF) that further intensified the vertical downward velocity. In addition to the strong downdraft velocity, the Coriolis force also enhanced the formation of the extreme wind through the action of ageostrophic winds. Besides, this paper also compares the mechanism of this process with that of a typical bow echo MV and the extreme wind it affected.