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Comparison of a Spectral Bin and Two Multi-Moment Bulk Microphysics Schemes for Supercell Simulation:Investigation into Key Processes Responsible for Hydrometeor Distributions and Precipitation

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Advances in Atmospheric Sciences 2024年第5期41卷 784-800页

作者： Marcus JOHNSON Ming XUE Youngsun JUNG Center for Analysis and Prediction of Storms University of OklahomaNormanOK 73072USA School of Meteorology University of OklahomaNormanOK 73072USA office of Science and Technology Integration National Weather ServiceSilver SpringMD 20910USA

There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design.

关键词： precipitation spectral bin microphysics bulk microphysics parameterization microphysics processes WRF model supercell storm

Bone metastasis is a late-onset and unfavorable event in survivors of gastric cancer after radical gastrectomy:Results from a clinical observational cohort

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Cancer Pathogenesis and Therapy 2024年第1期2卷 50-57页

作者： Cheng Zhang Xiaopeng Zhang Chong Feng Yahui Yang Minmin Xie Ying Feng Zhijun Wu Hui Xu Changhao Wu Tai Ma Department of Oncology The First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui 230022China anhui Provincial Cancer Institute/anhui Provincial office for Cancer Prevention and Control HefeiAnhui 230022China Department of Noncommunicable Diseases and Health Education Hefei Center for Disease Control and PreventionHefeiAnhui 230091China Department of Oncology The Second Affiliated Hospital of Xuzhou Medical UniversityXuzhouJiangsu 221006China Department of Oncology Ma’anshan Municipal People’s HospitalMa’anshanAnhui 243099China Department of Biochemistry and Physiology Faculty of Health and Medical SciencesUniversity of SurreyGuildford GU27HXUnited Kingdom

Background The timing and incidence of recurrent bone metastasis(BM)after radical gastrectomy in patients with gastric cancer(GC)as well as the survival of these patients were not fully understood.The aim of this study was to analyze the data of an observational GC cohort and identify patients who underwent curative gastrectomy and had recurrent BM to describe and clarify the pattern and profile of BM evolution after surgery.Methods Data were retrieved from a hospital-based GC cohort,and patients who underwent upfront radical gastrectomy were selected.The time points of specific organ metastatic events were recorded,and the person-year incidence rate of metastatic events was calculated.The latency period of BM events after gastrectomy was measured and compared with that of the other two most common metastatic events,liver metastasis(LM)and distant lymph node metastasis(LNM),using analysis of variance.Propensity score matching and subgroup analysis were used for sensitivity analysis.Results A total of 1324 GC cases underwent radical gastrectomy between January 2011 and December 2021.Of these,67 BM,218 LM,and 248 LNM occurred before the last follow-up.The incidence of BM events was 1.7/100 person-years,which was approximately 3-fold lower than that of LM and distant LNM events(5.5 and 6.3 per 100 person-years,respectively).BM events had a significantly longer latency(median time,16.5 months)than LM and LNM events(11.1 and 12.0 months,respectively).Recurrent BM led to a worse prognosis(median survival,4.5 months)than those of LM and LNM events(median survival,7.7 and 7.1 months,respectively).However,no difference in overall survival after gastrectomy was observed among the groups.Conclusions Compared with other common metastatic events,BM in GC after gastrectomy is a late-onset event indicating poor survival.

关键词： Stomach neoplasms Neoplasm metastasis Incidence Survival analysis Propensity score

Crossroads:Pathogenic role and therapeutic targets of neutrophil extracellular traps in rheumatoid arthritis

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BIOCELL 2024年第1期48卷 9-19页

作者： YANG LI JIAN LIU YUEDI HU CHENGZHI CONG YIMING CHEN QIAO ZHOU Department of Rheumatology The First Affiliated Hospital of Anhui University of Chinese MedicineHefei230031China First Clinical Medical School Anhui University of Chinese MedicineHefei230031China Institute of Rheumatology Anhui Academy of Chinese MedicineHefei230031China

Rheumatoid arthritis(RA)is a prevalent autoimmune disease whose main features include chronic synovial inflammation,bone destruction,and joint degeneration.Neutrophils are often considered to be the first responders to inflammation and are a key presence in the inflammatory milieu of RA.Neutrophil extracellular traps(NETs),a meshwork of DNA-histone complexes and proteins released by activated neutrophils,are widely involved in the pathophysiology of autoimmune diseases,especially RA,in addition to playing a key role in the neutrophil innate immune response.NETs have been found to be an important source of citrullinated autoantigen antibodies and inflammatory factor release,which can activate RA synovial fibroblasts(FLS)and cause joint damage.This article reviews the role of NETs in the pathophysiology of RA,demonstrating the application of multiple molecules with various therapies,with a view to informing the discovery and development of novel biomarkers and therapeutic targets for RA.

关键词： Rheumatoid arthritis Neutrophil extracellular traps Review

Predicting PM_(2.5) Concentration in the Yangtze River Delta Region Using Climate System Monitoring Indices and Machine Learning

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Journal of Meteorological Research 2024年第2期38卷 249-261页

作者： Jinghui MA Shiquan WAN Shasha XU Chanjuan WANG Danni QIU Shanghai Typhoon Institute Shanghai Meteorological ServiceShanghai 200030 Key Laboratory of Polar Atmosphere-Ocean-Iice System for weather and Climate Ministry of EducationDepartment of Atmospheric and Oceanic SciencesFudan UniversityShanghai 200438 Shanghai Key Laboratory of Meteorology and Health Shanghai Meteorological ServiceShanghai 200030 Yangzhou Meteorological office Yangzhou 225001 Department of Physics Yangzhou UniversityYangzhou 225003

Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta(YRD)region.External climatic factors,such as sea surface temperature and sea ice,together with the atmospheric circulation,directly affect meteorological conditions in the YRD region,thereby modulating the variation in atmospheric PM_(2.5) concentration.This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM_(2.5) concentration over 0-12 months in the YRD region.After calculating the contribution ratios and lagged correlation coefficients of all indices over the previous 12 months,the top 36 indices were selected for model training.Then,the nine indices that contributed most to the PM_(2.5) concentration in the YRD region,including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean,were selected for physical mechanism analysis.An evolutionary model was developed to forecast the average PM_(2.5) concentration in major cities of the YRD in autumn and winter,with a correlation coefficient of 0.91.In model testing,the correlation coefficient between the predicted and observed PM_(2.5) concentrations was in the range of 0.73-0.83 and the root-mean-square error was in the range of 9.5-11.6μg m-3,indicating high predictive accuracy.The model performed exceptionally well in capturing abnormal changes in PM_(2.5) concentration in the YRD region up to 50 days in advance.

关键词： PM_(2.5)concentration machine learning evolutionary modeling seasonal prediction

QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

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Journal of Meteorological Research 2022年第1期36卷 93-106页

作者： Anyuan XIONG Na LIU Yujia LIU Shulin ZHI Linlin WU Yongjian XIN Yan SHI Yunjian ZHAN National Meteorological Information Center China Meteorological AdministrationBeijing 100081 Jiangxi Meteorological Observatory Nanchang 330096 anhui weather modification office Hefei 230061

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

关键词： machine learning benchmark dataset quantitative precipitation estimation precipitation forecast

Shape Classification of Cloud Particles Recorded by the 2D-S Imaging Probe Using a Convolutional Neural Network

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Journal of Meteorological Research 2023年第4期37卷 521-535页

作者： Rong ZHANG Haixia XIAO Yang GAO Haizhou SU Dongnan LI Lei WEI Junxia LI Hongyu LI Cloud–Precipitation Physics and weather modification Key Laboratory(CPML) China Meteorological AdministrationBeijing 100081 CMA weather modification Centre China Meteorological Administration(CMA)Beijing 100081 Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041 Heilongjiang weather modification office Harbin 150030

The airborne two-dimensional stereo(2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classification tools,our ability to extract meaningful morphological information related to cloud microphysical processes is limited. To solve this issue, we propose a novel classification algorithm for 2D-S cloud particle images based on a convolutional neural network(CNN), named CNN-2DS. A 2D-S cloud particle shape dataset was established by using the 2D-S cloud particle images observed from 13 aircraft detection flights in 6 regions of China(Northeast, Northwest, North,East, Central, and South China). This dataset contains 33,300 cloud particle images with 8 types of cloud particle shape(linear, sphere, dendrite, aggregate, graupel, plate, donut, and irregular). The CNN-2DS model was trained and tested based on the established 2D-S dataset. Experimental results show that the CNN-2DS model can accurately identify cloud particles with an average classification accuracy of 97%. Compared with other common classification models [e.g., Vision Transformer(ViT) and Residual Neural Network(ResNet)], the CNN-2DS model is lightweight(few parameters) and fast in calculations, and has the highest classification accuracy. In a word, the proposed CNN-2DS model is effective and reliable for the classification of cloud particles detected by the 2D-S probe.

关键词： cloud particles particle shape 2D-S probe shape classification convolutional neural network

Seasonal to sub-seasonal variations of the Asian Tropopause Aerosols Layer affected by the deep convection,surface pollutants and precipitation

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Journal of Environmental Sciences 2022年第4期34卷 53-65页

作者： Dongyou Wu Tenglong Shi Xiaoying Niu Ziqi Chen Jiecan Cui Yang Chen Xueying Zhang Jun Liu Mingxia Ji Xin Wang Wei Pu College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000China Jilin weather modification office Changchun 130000China School of Earth System Science Tianjin UniversityTianjin 300072China

The Asian Tropopause Aerosols Layer(ATAL)refers to an accumulation of aerosols in the upper troposphere and lower stratosphere during boreal summer over Asia,which has a fundamental impact on the monsoon system and climate change.In this study,we primarily analyze the seasonal to sub-seasonal variations of the ATAL and the factors potentially influencing those variations based on MERRA2 reanalysis.The ability of the reanalysis to reproduce the ATAL is well validated by CALIPSO observations from May to October 2016.The results reveal that the ATAL has a synchronous spatiotemporal pattern with the development and movement of the Asian Summer Monsoon.Significant enhancement of ATAL intensity is found during the prevailing monsoon period of July-August,with two maxima centered over South Asia and the Arabian Peninsula.Owing to the fluctuations of deep convection,the ATAL shows an episodic variation on a timescale of 7-12 days.Attribution analysis indicates that deep convection dominates the variability of the ATAL with a contribution of 62.7%,followed by a contribution of 36.6%from surface pollutants.The impact of precipitation is limited.The ATAL further shows a clear diurnal variation:the peak of ATAL intensity occurs from 17:30 to 23:30 local time(LT),when the deep convection becomes strongest;the minimum ATAL intensity occurs around 8:30 LT owing to the weakened deep convection and photochemical reactions in clouds.The aerosol components of the ATAL show different spatiotemporal patterns and imply that black carbon and organic carbon come mainly from India,whereas sulfate comes mainly from China during the prevailing monsoon period.

关键词： ATAL Seasonal to sub-seasonal variations Deep convection Asian Summer Monsoon

Macro-and Micro-physical Characteristics of Different Parts of Mixed Convective-stratiform Clouds and Differences in Their Responses to Seeding

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Advances in Atmospheric Sciences 2022年第12期39卷 2040-2055页

作者： Dejun LI Chuanfeng ZHAO Peiren LI Cao Liu Dianli GONG Siyao LIU Zhengteng YUAN Yingying CHEN Hubei Meteorological Service Center Wuhan 430205China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China weather modification office of Shanxi Province Taiyuan 030032China Wuhan Central Meteorological Observatory Wuhan 430074China weather modification office of Shandong Province Jinan 250031China

This study investigates the cloud macro-and micro-physical characteristics in the convective and stratiform regions and their different responses to the seeding for mixed convective-stratiform clouds that occurred in Shandong province on 21 May 2018,based on the observations from the aircraft,the Suomi National Polar-Orbiting Partnership(NPP)satellite,and the high-resolution Himawari-8(H8)satellite.The aircraft observations show that convection was deeper and radar echoes were significantly enhanced with higher tops in response to seeding in the convective region.This is linked with the conversion of supercooled liquid droplets to ice crystals with released latent heat,resulting in strengthened updrafts,enhanced radar echoes,higher cloud tops,and more and larger precipitation particles.In contrast,in the stratiform cloud region,after the Silver Iodide(AgI)seeding,the radar echoes become significantly weaker at heights close to the seeding layer,with the echo tops lowered by 1.4–1.7 km.In addition,a hollow structure appears at the height of 6.2–7.8 km with a depth of about 1.6 km and a diameter of about 5.5 km,and features such as icing seeding tracks appear.These suggest that the transformation between droplets and ice particles was accelerated by the seeding in the stratiform part.The NPP and H8 satellites also show that convective activity was stronger in the convective region after seeding;while in the stratiform region,a cloud seeding track with a width of 1–3 km appears 10 km downstream of the seeding layer 15 minutes after the AgI seeding,which moves along the wind direction as width increases.

关键词： airborne Ka-band Precipitation Radar(KPR) mixed convective-stratiform clouds convective region stratiform region cloud seeding cloud microphysical properties

Observations of Cloud Condensation Nuclei in North China

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Acta meteorologica Sinica 2008年第1期22卷 97-106页

作者：石立新段英 weather modification office of Hebei Province

Using a DMT （Droplet Measurement Technologies） continuous flow streamwise thermal gradient cloud condensation nuclei （CCN） counter mounted on a Cheyenne IIIA aircraft, about 20 flights for aircraft mea- surements of CCN over North China were conducted in the autumn of 2005 and spring of 2006. According to the design for aircraft observation, the method of spiral ascent or descent in the troposphere was used for the vertical measurement of CCN, and some certain levels were chosen for horizontal measurement. The vertical distributions of CCN concentrations show that most CCN particles are concentrated in the low level of troposphere and CCN concentration decreased with height increasing. It suggests that the main source of CCN is from the surface. This result is consistent with former studies during 1983-1985 in China with a static thermal gradient CCN counter. The comparison of vertical observations between polluted rural area near Shijiazhuang and non-polluted rural area near Zhangjiakou shows that there is about five times difference in CCN concentration. But over two polluted cities, Shijiazhuang and Handan, there is no notable difference in CCN concentration. The horizontal flight measurements for penetrating the cumulus clouds experiment show the apparent decrease of CCN in clouds. It confirms that cloud has a definite consumptive effect on CCN particles because some CCN particles can form cloud droplets. The surface measurements of CCN in Shijiazhuang City were made during June-August 2005. The statistical CCN data show the great difference in concentration at the same supersaturation （S） in Shijiazhuang summertime. The minimum CCN concentrations were 584, 808, and 2431 cm-3, and the maximum concentrations were 9495, 16332, and 21812 cm-3 at S=0.1%, 0.3%, and 0.5%, respectively. CCN has a diurnal variation cycle. From 0600 BT, the concentration began to increase and reached the maximum at about noon. Then it generally decreased throughout the afternoon. The reason maybe is related to the onset of emissions

关键词： North China cloud condensation nuclei （CCN） observation