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Acta Meteorologica Sinica
Journal Prestige (SJR): 0.638
Citation Impact (citeScore): 1
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0894-0525
Published by Springer-Verlag Homepage  [2468 journals]
  • Optimized Vertical Layers for the Hybrid Terrain-Following Coordinate
           Minimizing Numerical Errors in a 2D Rising Bubble Experiment near Steep
           Terrain

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      Abstract: Abstract The basic terrain-following (BTF) coordinate simplifies the lower boundary conditions of a numerical model but leads to numerical error and instability on steep terrain. Hybrid terrain-following (HTF) coordinates with smooth slopes of vertical layers (slopeVL) generally overcome this difficulty. Therefore, the HTF coordinate becomes very desirable for atmospheric and oceanic numerical models. However, improper vertical layering in HTF coordinates may also increase the incidence of error. Except for the slopeVL of an HTF coordinate, this study further optimizes the HTF coordinate focusing on the thickness of vertical layers (thickVL). Four HTF coordinates (HTF1–HTF4) with similar slopeVL but different vertical transition methods of thickVL are designed, and the relationship between thickVL and numerical errors in each coordinate is compared in the classic idealized thermal convection [two-dimensional (2D) rising bubble] experiment over steep terrain. The errors of potential temperature θ and vertical velocity w are reduced most, by approximately 70% and 40%, respectively, in the HTF1 coordinate, with a monotonic increase in thickVL according to the increasing height; however, the errors of θ increased in all the other HTF coordinates, with nonmonotonic thickVLs. Furthermore, analyses of the errors of vertical pressure gradient force (VPGF) show that due to the interpolation errors of thickVL, the inflection points in the vertical transition of thickVL induce the initial VPGF errors; therefore, the HTF1 coordinate with a monotonic increase in thickVL has the smallest errors among all the coordinates. More importantly, the temporal evolution of VPGF errors manifests top-type VPGF errors that propagate upward gradually during the time integration. Only the HTF1 and HTF4 coordinates with a monotonic increase in thickVL near the top of the terrain can suppress this propagation. This optimized HTF coordinate (i.e., HTF1) can be a reference for designing a vertical thickVL in a numerical model.
      PubDate: 2023-12-01
       
  • Probabilistic Wind Gust Forecasting during the 2022 Beijing Winter
           Olympics

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      Abstract: Abstract The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs. Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy. Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics, The ECM-WF ensemble products were used to evaluate six post-processing methods. These methods include ensemble model output statistics (EMOS), backpropagation neural networks (BP), particle swarm optimization algorithms with back-propagation neural networks (PSO), truncated normal distributions, truncated logarithmic distributions, and generalized extreme value (GEV) distributions. The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared. All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products; however, the ability to correct the model forecast results varied significantly across different wind speed ranges. Specifically, the EMOS, truncated normal distribution, truncated logarithmic distribution, and GEV distribution demonstrated advantages in low wind-speed ranges, whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events. Furthermore, this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors. The application of probability integral transform (PIT) and quantile–quantile (QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution, thereby indicating the potential for further enhanced forecast accuracy. The results also underscore the significant utility of the PSO hybrid model, which amalgamates particle swarm optimization with a BP neural network, in the probabilistic forecasting of strong winds within the field of meteorology.
      PubDate: 2023-12-01
       
  • A Deep Learning Method for Statistical Downscaling of CLDAS Relative
           Humidity with Different Sources of Data: Sensitivity Analysis

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      Abstract: Abstract High-resolution relative humidity (RH) data are essential in studies of climate change and in numerical meteorological forecasting. However, because high-resolution meteorological grid data require a large number of stations, the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration’s Land Assimilation System (CLDAS) dataset. To retrieve high-quality and high-resolution RH data before 2008, we propose a statistical downscaling model (SDM) based on a generative adversarial network (GAN) to transform the original RH data from a resolution of 0.05° to 0.01°. The GAN-based SDM (GSDM) is trained with the RH of the CLDAS (0.05°) dataset after 2008 as its input, and the RH of the high-resolution CLDAS (HRCLDAS, 0.01°) dataset after 2008 as its target for training. The 2-m air temperature data from the HRCLDAS dataset are also included in the input, and the station observations of RH are incorporated in the target for training. To select the optimum data combination for the model, we compared three methods: (1) incorporating without auxiliary data (GSDM), (2) incorporating air temperature as an additional input (GSDM_T), and (3) incorporating air temperature as an additional input and the RH data at stations as an additional target for training (GSDMTO). Taking the Beijing–Tianjin–Hebei region as an example, we trained the GSDM by using data from 2018 and tested the model performance in 2019. The experimental results showed that the GSDMTO algorithm achieved the lowest root-mean-square error (3.85%), followed by the GSDM_T (4.01%) and GSDM (4.95%) algorithms. The proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear interpolation. In general, the GSDM_TO algorithm using a combination of different sources of data (air temperature and observed RH) achieved the best results among the various deep learning approaches, indicating that more auxiliary data and more accurate observations are beneficial in downscaling. This may be helpful for the statistical downscaling of other meteorological data.
      PubDate: 2023-12-01
       
  • Sensitivity of the Size of a TC to Sea Surface Temperatures in Its Outer
           Region

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      Abstract: Abstract We investigated the sensitivity of the size of a tropical cyclone (TC) to warming or cooling sea surface temperatures (SST) in its outer region by simulating the SST beyond a radius of 200 km from the TC center. Sensitivity experiments showed that an increased SST outside the core region of the TC had a negative effect on its size. Warming in the outer region contributed to the local enhancement of the latent heat flux from sea surface, which promoted the development of small-scale convection and warmed the lower and midtroposphere. This warming altered the local pressure gradient force in the upper and lower troposphere in such a way that it weakened the secondary circulation of the TC and led to suppression of the spiral rainbands outside the eyewall. Further analysis showed that the outward-propagating rainband structure favored an increase in the size of the TC. The diabatic heat released by the rainbands induced an inflow at lower levels, facilitating expansion of the TC. The greater the distance of the rainbands from the center of the TC, given the same amplitude of diabatic heating, the stronger the forced inflow, resulting in a faster increase in the size of the TC.
      PubDate: 2023-12-01
       
  • Calibration of Gridded Wind Speed Forecasts Based on Deep Learning

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      Abstract: Abstract The challenges of applying deep learning (DL) to correct deterministic numerical weather prediction (NWP) biases with non-Gaussian distributions are discussed in this paper. It is known that the DL UNet model is incapable of correcting the bias of strong winds with the traditional loss functions such as the MSE (mean square error), MAE (mean absolute error), and WMAE (weighted mean absolute error). To solve this, a new loss function embedded with a physical constraint called MAE_MR (miss ratio) is proposed. The performance of the UNet model with MAE_MR is compared to UNet traditional loss functions, and statistical post-processing methods like Kalman filter (KF) and the machine learning methods like random forest (RF) in correcting wind speed biases in gridded forecasts from the ECMWF high-resolution model (HRES) in East China for lead times of 1–7 days. In addition to MAE for full wind speed, wind force scales based on the Beaufort scale are derived and evaluated. Compared to raw HRES winds, the MAE of winds corrected by UNet (MAE_MR) improves by 22.8% on average at 24–168 h, while UNet (MAE), UNet (WMAE), UNet (MSE), RF, and KF improve by 18.9%, 18.9%, 17.9%, 13.8%, and 4.3%, respectively. UNet with MSE, MAE, and WMAE shows good correction for wind forces 1–3 and 4, but negative correction for 6 or higher. UNet (MAE_MR) overcomes this, improving accuracy for forces 1–3, 4, 5, and 6 or higher by 11.7%, 16.9%, 11.6%, and 6.4% over HRES. A case study of a strong wind event further shows UNet (MAE_MR) outperforms traditional post-processing in correcting strong wind biases.
      PubDate: 2023-12-01
       
  • Analysis of Pressure Forcings for the Vertical Turbulent Fluxes in the
           Convective Boundary Layer at Gray Zone Resolutions

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      Abstract: Abstract At kilometer and sub-kilometer resolutions, known as the numerical gray zone for boundary layer turbulence, the atmospheric boundary layer turbulence becomes partially resolved and partially subgrid-scale (SGS) in a numerical model, thus requiring scale-adaptive turbulence schemes. Such schemes are often built by modifying the existing parameterizations, either the planetary boundary layer (PBL) schemes or the large-eddy simulation (LES) closures, to produce the right SGS turbulent fluxes at gray zone resolutions. However, the underlying forcings responsible for the changes in the vertical turbulent fluxes are largely ignored in these approaches. This study follows the original approach of Wyngaard (2004) and analyzes the turbulent buoyancy and momentum flux budgets, to gain a better understanding of the variations of flux forcings at gray zone resolutions. The investigation focuses on the pressure covariance term, which is one of the most dominant terms in the budget equations. By using the coarse-grained LES of a dry convective boundary layer (CBL) case as reference, two widely-used pressure covariance models are evaluated and optimized across the gray zone resolution range. The optimized linear model is further evaluated a priori against another dry CBL case with a different bulk stability, and a shallow-cumulus-topped boundary layer case. The model applies well to both cases, and notably shows good performance for the cloud layer. Based on the analysis of the flux forcings and the optimized pressure model, a scale-adaptive turbulence model for the gray zone is derived from the steady-state flux budgets.
      PubDate: 2023-12-01
       
  • Improving Wind Forecasts Using a Gale-Aware Deep Attention Network

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      Abstract: Abstract Numerical weather prediction of wind speed requires statistical postprocessing of systematic errors to obtain reliable and accurate forecasts. However, use of postprocessing models is often undesirable for extreme weather events such as gales. Here, we propose a postprocessing algorithm based on a gale-aware deep attention network to simultaneously improve wind speed forecasts and gale area warnings. Specifically, the algorithm includes both a galeaware loss function that focuses the model on potential gale areas, and an observation station supervision strategy that alleviates the problem of missing extreme values caused by data gridding. The effectiveness of the proposed model was verified by using data from 235 wind speed observation stations. Experimental results show that our model can produce wind speed forecasts with a root-mean-square error of 1.1547 m s−1, and a Hanssen–Kuipers discriminant score of 0.517, performance that is superior to that of the other postprocessing algorithms considered.
      PubDate: 2023-12-01
       
  • Analysis of Boundary Layer Structure, Turbulence, and Flux Variations
           before and after the Passage of a Sea Breeze Front Using Meteorological
           Tower Data

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      Abstract: Abstract A detailed analysis of a sea breeze front (SBF) that penetrated inland in the Beijing–Tianjin–Hebei urban agglomeration of China was conducted. We focused on the boundary layer structure, turbulence intensity, and fluxes before and after the SBF passed through two meteorological towers in the urban areas of Tianjin and Beijing, respectively. Significant changes in temperature, humidity, winds, CO2, and aerosol concentrations were observed as the SBF passed. Differences in these changes at the two towers mainly resulted from their distances from the ocean, boundary layer conditions, and background turbulences. As the SBF approached, a strong updraft appeared in the boundary layer, carrying near-surface aerosols aloft and forming the SBF head. This was followed by a broad downdraft, which destroyed the near-surface inversion layer and temporarily increased the surface air temperature at night. The feeder flow after the thermodynamic front was characterized by low-level jets horizontally, and downdrafts and occasional up-drafts vertically. Turbulence increased significantly during the SBF’s passage, causing an increase in the standard deviation of wind components in speed. The increase in turbulence was more pronounced in a stable boundary layer compared to that in a convective boundary layer. The passage of the SBF generated more mechanical turbulences, as indicated by increased friction velocity and turbulent kinetic energy (TKE). The shear term in the TKE budget equation increased more significantly than the buoyancy term. The atmosphere shifted to a forced convective state after the SBF’s passage, with near isotropic turbulences and uniform mixing and diffusion of aerosols. Sensible heat fluxes (latent heat and CO2 fluxes) showed positive (negative) peaks after the SBF’s passage, primarily caused by horizontal and vertical transport of heat (water vapor and CO2) during its passage. This study enhances understanding of boundary layer changes, turbulences, and fluxes during the passage of SBFs over urban areas.
      PubDate: 2023-12-01
       
  • Latitudinal and Seasonal Variations in Tropical Cyclone-Induced Ocean
           Surface Cooling in the Tropical Western North Pacific

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      Abstract: Abstract The passage of tropical cyclones induces ocean surface cooling through vertical mixing, upwelling, and surface heat loss. The dependence of tropical cyclone-induced ocean surface cooling on the intensity and translation speed of tropical cyclones has been documented in previous studies. The present study investigates the latitudinal and seasonal variations in tropical cyclone-induced ocean surface cooling in the tropical western North Pacific based on data for the 2001–2020 period. Our analysis focuses on the open ocean (0°–25°N, 130°E–180°) to reduce the interference of coastal topography so that the obtained results better represent the influences of the intensity and translation speed of tropical cyclones. Our analysis confirms the dependence on the intensity and translation speed of tropical cyclone-induced cooling. The new findings are as follows. First, the time to reach the maximum cooling increases with the magnitude of the maximum cooling. Second, the magnitude of ocean surface cooling increases with latitude in the tropical region for tropical cyclones with different intensities and translation speeds. Third, the ocean surface cooling is larger in summer and autumn than in spring for tropical cyclones with different intensities and translation speeds. Fourth, the dependence of ocean surface cooling on the translation speed is more obvious at higher latitudes in the tropics and less apparent in spring. These new findings add to the existing knowledge of the impacts of tropical cyclone intensity and translation speed on ocean surface cooling.
      PubDate: 2023-12-01
       
  • A Recombination Clustering Technique for Forecasting of Tropical Cyclone
           Tracks Based on the CMA-TRAMS Ensemble Prediction System

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      Abstract: Abstract Despite marked improvements in tropical cyclone (TC) track ensemble forecasting, forecasters still have difficulty in making quick decisions when facing multiple potential predictions, so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts, and one of such techniques is the cluster-based methods. To improve the effect and efficiency of the previous cluster-based methods, this study adopts recombination clustering (RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system (EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean (EM) and ensemble spread (ES). Finally, the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods. It is found that (1) for deterministic TC track forecasts, the RC-based TC track forecasts outperform all other methods at 12–72-h lead times; compared with the skillful EM (118.6 km), the improvements introduced by the use of RC reach up to 10.8% (8.1 km), 10.2% (13.7 km), and 8.7% (20.5 km) at forecast times of 24, 48, and 72 h, respectively. (2) For probabilistic TC track forecasts, RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts, by increasing the weight of the best cluster, with a decrease of 4.1% in brier score (BS) and an increase of 1.4% in area under the relative operating characteristic curve (AUC). (3) In particular, for cases with recurved tracks, such as typhoons Saudel (2017) and Bavi (2008), RC significantly reduces track errors relative to EM by 56.0% (125.5 km) and 77.7% (192.2 km), respectively. Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members, and is likely useful for feature construction in machine learning.
      PubDate: 2023-12-01
       
  • The 10 Most Influential Heavy Rain Events in China in 2022: Selection and
           Evaluation

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      Abstract: Abstract In 2022, a campaign to select and recognize 10 most influential heavy rain events (HREs) in China was initiated by the Chinese Meteorological Society and Wuhan Heavy Rain Research Institute of the China Meteorological Administration (CMA). A work flow involving both scientists and the general public for selecting major HREs over the Chinese mainland was implemented, and several evaluation indices that can represent HREs as well as associated causality and economic losses were established, based on which the top 10 most influential HREs in 2022 were recognized and announced to the public. The present paper introduces the selection and evaluation process and summarizes the main results. It is found that 38 major HREs occurred in South, North, and Northeast China in 2022, with the Pearl River basin and Songliao basin experiencing severe floods. A number of HREs occurred in Southwest China with high rainfall intensity, but small cumulative amount. Upper-level troughs, low vortices, low-level jets, low-level shear lines, the subtropical high, and typhoons are the main weather systems leading to the top 10 most influential HREs in 2022. Selection and evaluation of HREs form a quantitative record of major HREs, help concentrate limited research efforts on investigating the causes of major HREs, and promote the improvement of HRE forecasting skills.
      PubDate: 2023-12-01
       
  • Modulation of High-Latitude Tropical Cyclone Recurvature by Solar
           Radiation

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      Abstract: Abstract In this study, idealized simulations are conducted to investigate potential influences of solar radiation on the tropical cyclone (TC) recurvature at higher latitudes. Results indicate that TC track is sensitive to the seasonal variation of radiative forcing at higher latitudes. In the absence of a background flow, TCs at higher latitudes tend to recurve (remain northwestward) in the cold (warm) season. This feature is an additional aspect of the so-called intrinsic recurvature property of TC movement at high latitude. Physically, the greater meridional gradient of temperature in the cold season due to solar radiative forcing would induce a larger thermal wind, which affects the upper-level anticyclonic circulation and associated outflow. The structure changes of TC, mainly at upper-levels, modulate the steering flow for TC, leading to a higher probability of TCs at higher latitudes to recurve in the cold season than in the warm season.
      PubDate: 2023-12-01
       
  • Changes in Atmospheric Circulation during the Winter Regional Extreme Cold
           Events over China since 1960

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      Abstract: Abstract The number of days with occurrence of winter regional extreme cold events (RECEs) in China was found more during 1960/1961-1985/1986 (period 1), less during 1986/1987-2005/2006 (period 2), but more again during 2006/2007-2017/2018 (period 3). So far, the differences in the atmospheric circulation favoring RECEs among these three periods are unclear. In this paper, changes in atmospheric circulation during the RECEs over China are examined by using composite analysis based on the station observed temperature data and NCEP-NCAR reanalysis data in winters of 1960/1961-2017/2018. The results show: (1) the stratospheric polar vortex was more active and tended to split before the outbreak of RECEs in period 3 than that in other two periods. The shift of the stratospheric polar vortex to Eurasia helped the upper Arctic cold air to affect the lower latitudes. (2) The troposphere was characterized by a typical or significant three-wave pattern before the outbreak of RECEs in period 2, in contrast to a weakened three-wave pattern in period 1. Compared to periods 1 and 2, the Okhotsk blocking high was stronger in period 3, contributing to the inverted omega-shaped circulation pattern in East Asia-North Pacific section and a shift of global pattern from three-wave to two-wave. The weakened three-wave or two-wave circulation pattern was manifested by the stronger Ural/Okhotsk blocking high, conducive to the strengthening of the meridional circulation and the occurrence of RECEs in East Asia. (3) The Siberian high was the strongest in period 3, followed by period 1, and it was the weakest in period 2. Before the outbreak of RECEs, the Siberian high in period 3 began to intensify one week earlier than that in periods 1 and 2. Thus, the accumulation time of cold air mass in period 3 was the longest. In summary, the synergism of atmospheric circulation at high and low levels in periods 1 and 3 was more conducive to more and strong RECEs than that in period 2. Moreover, the split of the stratospheric polar vortex may have played an important role on the formation of tropospheric two-wave pattern in period 3. The results obtained herein may provide a better understanding of the mechanisms for occurrences of RECEs in China.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-3016-7
       
  • Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data

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      Abstract: Abstract Currently, there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex terrains. Using Fengyun-3B Visible and Infra-Red Radiometer (FY-3B VIRR) satellite data, we applied random forest (RF) methodology and selected 13 feature variables to obtain snow cover. A training set was generated, containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological Centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the FY-3B Multi-Sensor Synergy (MULSS), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product (MYD10A1), and the National Cryosphere Desert Data Center (NCDC). This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest cover. Then, two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to 2020. The results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow, understory snow, and snow on the Qinghai-Tibetan Plateau. The recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS, MYD10A1, and NCDC products. The overall accuracy of the RF product is 90.6%, and the recall rate is 93.8%. The omission and commission errors are 6.2% and 11.1%, respectively. Unlike other existing snow cover algorithms, the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data; thus, it is more easily popularized and operationally applicable to generating long-time series snow cover products.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-3003-z
       
  • Predictability and Risk of Extreme Winter PM2.5 Concentration in Beijing

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      Abstract: Abstract Air pollution remains a serious environmental and social problem in many big cities in the world. How to predict and estimate the risk of extreme air pollution is unsettled yet. This study tries to provide a solution to this challenge by examining the winter PM2.5 concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles (UNSEEN) method. The PM2.5 concentration observations in Beijing, Japanese 55-yr reanalysis data, and the Met Office near term climate prediction system (DePreSys3a) large ensemble simulations are used, and 10,000 proxy series are generated with the model fidelity test. It is found that in Beijing, the main meteorological driver of PM2.5 concentration is monthly 850-hPa meridional wind (V850). Although the skill in prediction of V850 is low on seasonal and longer timescales, based on the UNSEEN, we use large ensemble of initialized climate simulations of V850 to estimate the current chance and risk of unprecedented PM2.5 concentration in Beijing. We unravel that there is a 3% (2.1%–3.9%) chance of unprecedented low monthly V850 corresponding to high PM2.5 in each winter, within the 95% range, calculated by bootstrap resampling of the data. Moreover, we use the relationship between air quality and winds to remove the meridional wind influence from the observed record, and find that anthropogenic intervention appears to have reduced the risk of extreme PM2.5 in Beijing in recent years.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-3023-8
       
  • Changes in Persistent Precipitation in Northwest China and Related
           Large-Scale Circulation Features

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      Abstract: Abstract Based on China’s daily precipitation data of 2415 stations and ERA5 hourly reanalysis data from 1961 to 2019, the station-based and regional precipitation events over Northwest China (NWC) are identified and sorted into persistent precipitation (PP, duration & 2 days) events and non-persistent precipitation (NPP, duration = 1 day) events; and then changes in the persistence structure of the PP and NPP events over NWC and the long-term mean adjustment of the related large-scale circulation configuration are analyzed. The results show that PP and NPP both witness an increasing trend over most parts of NWC. In terms of the total precipitation at most stations and the regional mean, contributions from PP have been increasing, while those from NPP have been decreasing. This demonstrates that the wetting trend in NWC is mainly caused by the increase in PP. Through analyzing the large-scale circulation corresponding to regional PP events at several representative levels, we found that the westerly jet at 200 hPa, the ridge/trough systems at 500 hPa, and the Mongolian low at sea level are the key circulation systems responsible for regional PP events over NWC. As for long-term mean changes after and before 1990 (a shifting point recognized by previous studies), it is found that the extent of the South Asian high becomes larger and the westerly jet shifts northward by approximately 1.5 degrees in the upper troposphere. The ridge near the Ural Mountains and the ridge downstream of NWC strengthen by approximately 10–30 hPa at 500 hPa. Furthermore, the difference between the Mongolian low trough and its surrounding high pressure increases by approximately 2 hPa at the sea level. The combinations of circulation changes from upper to lower levels facilitate the strengthening of ascending motions. These adjustments in circulations create more favorable conditions for PP to occur over NWC in the last three decades.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-3030-9
       
  • Operational Plan, Effect Verification, and Key Technical Settings for a
           Stadium-Scale Artificial Rain Reduction Experiment

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      Abstract: Abstract To explore the key technologies of artificial weather modification for specific targets (e.g., a stadium) and improve the efficiency of artificial rainfall modification for major events, this study conducts an artificial rainfall reduction experiment for the closing ceremony of Nanjing Youth Olympic Games on 28 August 2014. Satellite retrievals, radar observations, sounding data, and other sources of information as well as Cloud and Precipitation Accurate Analysis System (CPAS) are used in this study. The main conclusions are as follows. (1) On 28 August 2014, a large-scale cumulus cloud system with mixed-phase stratocumulus and stratus precipitation was observed. This system was influenced by the weak shear of a low-level trough and the precipitation was dominated by cold clouds with dry layers between clouds. Thereby, we adopted the crystal-priming over-catalytic hypothesis and conducted a rocket-catalytic rain abatement operation at a certain distance (100–25 km) from the stadium. Rocket shootings of different intensities were implemented for two echoes that affected the stadium successively (two rounds of 15 rocket shootings within 15 min for an isolated weak echo IA; multiple rounds of 156 rocket shootings within 80 min for a strong echo IB). Amazingly, after the shootings with the catalysis in the air, reflectivity of the two echoes was reduced at all altitudes with the most significant reduction at the 2-km altitude, and the time needed for the obvious reduction was 40 min. The most obvious reduction of the two echoes then maintained for 60 and 53 min, respectively, and the operation time needed for the echo zone to recover after the stop of rocket shooting was 108 min for echo IA and 90 min for echo IB. The two echoes moving across the stadium during the time period of the closing ceremony (2000–2130 local time) were at their minimal strengths, with almost no echo over the target stadium. This demonstrates that the rocket shooting strategy of over-crystallization catalysis is effective, and the shooting site, time, and dose are reasonable. The following technical parameters were used during this experiment. At about 80–25 km away from the target stadium in the west, the rocket shooting lasted for 15–80 min and the doses were not less than 1 shot min−1 (1 shot min−1 for echo IA, 2.25 shots min−1 for echo IB). The attenuation rate was 0.21 dBZ min−1 for the average 15 dBZ of echo IA. For the average 25 dBZ of echo IB, the attenuation rate was 0.27 dBZ min−1. The above technical settings helped achieve the goal of reducing rain over the stadium to almost zero for nearly 1-h period during the critical time of the event.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-2112-z
       
  • Variation of Dust in Northern China and Its Reproduction in BCC-ESM1 since
           1980

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      Abstract: Abstract In this paper, we explore the possible causes and mechanisms for the variation of dust in northern China from 1980 to 2014 using the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data, observational data, and BCC-ESM1 (Beijing Climate Center Earth System Model version 1) simulation data. Two important dust centers are identified in China: one in the Taklamakan Desert in southern Xinjiang Region and the other in the Badain Jaran Desert in western Inner Mongolia Plateau. Both centers display distinct seasonal variations, with high dust concentration in spring and summer and low in autumn and winter. BCC-ESM1 is able to generally capture the main spatial and temporal characteristics of dust in northern China. Both the MERRA-2 reanalysis data and BCC-ESM1 simulation data show a decreasing trend in spring dust, which is evident during 1980–2000 and 2001–2014. The analysis based on daily mean dust loads and wind fields from MERRA-2 and BCC-ESM1 indicates that dusty weather in North China may be mainly caused by transport of the dust, especially that from the central and western Inner Mongolia Plateau during the prior 0–2 days, through the westerly winds from the upstream “dust core” region (38°–45°N, 90°–105°E). This is one of the important paths for dust to move into North China. The weakened westerly wind in the lower troposphere in this “dust core” region may be responsible for the reduction of spring dust in North China.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-2195-6
       
  • Cloud Microphysical Processes and Atmospheric Water Budget during the 20
           July 2021 Extreme Precipitation Event in Zhengzhou, China

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      Abstract: Abstract This study investigated the cloud microphysical processes and atmospheric water budget during the extreme precipitation event on 20 July 2021 in Zhengzhou of Henan Province, China, based on observations, reanalysis data, and the results from the high-resolution large-eddy simulation nested in the Weather Research and Forecasting (WRF) model with assimilation of satellite and radar observations. The results show that the abundant and persistent southeasterly supply of water vapor, induced by Typhoons In-Fa and Cempaka, under a particular synoptic pattern featured with abnormal northwestward displacement of the western Pacific subtropical high, was conducive to warm rain processes through a high vapor condensation rate of cloud water and an efficient collision–coalescence process of cloud water to rainwater. Such conditions were favorable for the formation and maintenance of the quasi-stationary warm-sector heavy rainfall. Precipitation formation through the collision–coalescence process of cloud water to rainwater accounted for approximately 70% of the total, while the melting of snow and graupel accounted for only approximately 30%, indicating that warm cloud processes played a dominant role in this extreme rainfall event. However, enhancement of cold cloud processes promoted by latent heat release also exerted positive effect on rainfall during the period of most intense hourly rainfall. It was also found that rainwater advection from outside of Zhengzhou City played an important role in maintaining the extreme precipitation event.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-2166-y
       
  • Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG
           Data and Terrain Decomposition Using the BEMD Algorithm

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      Abstract: Abstract Continuous high spatial-resolution 10-day precipitation data are essential for crop growth services and phenological research. In this study, we first use the bidimensional empirical mode decomposition (BEMD) algorithm to decompose the digital elevation model (DEM) data and obtain high-frequency (OR3), intermediate-frequency (OR5), and low-frequency (OR8) margin terrains. Then, we propose a refined precipitation spatialization model, which uses ground-based meteorological observation data, integrated multi-satellite retrievals for global precipitation measurement (GPM IMERG) satellite precipitation products, DEM data, terrain decomposition data, prevailing precipitation direction (PPD) data, and other multisource data, to construct China’ s high-resolution 10-day precipitation data from 2001 to 2018. The decomposition results show mountainous terrain from fine to coarse scales; and the influences of altitude, slope, and aspect on precipitation are better represented in the model after topography is decomposed. Moreover, terrain decomposition data can be added to the model simulation to improve the quality of the simulation product; the simulation quality of the model in summer is better than that in spring and autumn, and is relatively poor in winter; and OR5 and OR8 can be improved in the simulation, with better OR5 and OR8 dynamically selected. In addition, preprocessing the data before precipitation spatialization is particularly important. For example, adding 0.01 to the 0 value of precipitation, multiplying the small value of precipitation less than 1 by 10, and performing the normal distributions transform (e.g., Yeo–Johnson) on the data can improve the simulation quality.
      PubDate: 2023-10-01
      DOI: 10.1007/s13351-023-2171-1
       
 
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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Tel: +00 44 (0)131 4513762
 


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