Features of the new climate normal 1991-2020 and possible influences on climate monitoring and prediction in China

An update on the climate norms each decade is recommended by the World Meteorological Organization(WMO)partly to keep pace with conditions as climate changes over time.In accordance with such update,this study documents the features of the new climate normal defined for 1991-2020 and its impacts on climate monitoring and prediction in China.With on-site observation and model prediction datasets,our analysis reveals that the new normal of national average precipitation of China during winter and summer is respectively 3.0 and 10.8 mm higher than that of the period 1981-2010.As a result,precipitation observations during 1961-2020 consistently fall below the new normal.The adjustment of thresholds for precipitation extremes with new climate normals results in a decrease of extreme precipitation occurrence by 0.2-0.8 d on average over the winter and summer seasons during 1961-2020.Meanwhile,the application of new climate normals induces more pronounced negative temperature anomalies across most areas of China.The adjustments of extreme temperature thresholds have led to an increased occurrence of extremely cold days by 1-2 d on average over 1961-2020,while the frequency of extremely hot days decreases by more than 1.4 d.Furthermore,it is implied that with the development of global warming,the baselines for temperature and precipitation are rising.The application of the new climate normal may result in the omission of relative threshold based extreme events,promoting increased focus on climate risk reduction studies.Additionally,the average anomaly sign consistency rates(Pcs)of precipitation and temperature anomaly predictions,relative to the new normal and produced by the Beijing Climate Center,are consistently lower than those relative to the old normal.This decrease in Pcs implies new challenges for climate prediction,especially for temperature prediction.

COVID-19 transmission and control in land public transport:A literature review

Land public transport is an important link within and between cities,and how to control the transmission of COVID-19 in land public transport is a critical issue in our daily lives.However,there are still many inconsistent opinions and views about the spread of SARS-CoV-2 in land public transport,which limits our ability to implement effective interventions.The purpose of this review is to overview the literature on transmission characteristics and routes of the epidemic in land public transport,as well as to investigate factors affecting its spread and provide feasible measures to mitigate the infection risk of passengers.We obtained 898 papers by searching the Web of Science,Pubmed,and WHO global COVID database by keywords,and finally selected 45 papers that can address the purpose of this review.Land public transport is a high outbreak area for COVID-19 due to characteristics like crowding,inadequate ventilation,long exposure time,and environmental closure.Different from surface touch transmission and drop spray transmission,aerosol inhalation transmission can occur not only in short distances but also in long distances.Insufficient ventilation is the most important factor influencing long-distance aerosol transmission.Other transmission factors(e.g.,interpersonal distance,relative orientation,and ambient conditions)should be noticed as well,which have been summarized in this paper.To address various influencing factors,it is essential to suggest practical and efficient preventive measures.Among these,increased ventilation,particularly the fresh air(i.e.,natural ventilation),has proven to effectively reduce indoor infection risk.Many preventive measures are also effective,such as enlarging social distance,avoiding face-to-face orientation,setting up physical partitions,disinfection,avoiding talking,and so on.As research on the epidemic has intensified,people have broken down many perceived barriers,but more comprehensive studies on monitoring systems and prevention measures in land public transport are still needed.

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into ENSL.The results demonstrate that ENSL achieves better performance and generalization than individual models.The root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 48.2% and 28.5%,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

Assessing impact of intermittent window opening strategies on pathogen-laden droplet dispersion in a coach bus

Opening windows in coach buses is a practical approach to improving natural ventilation and mitigating infection risk(IR).Due to human behavior and weather conditions,the intermittent window opening strategy(IWOS)is a more common practice than keeping windows constantly open.Despite its prevalence,there are no studies exploring IWOS specifically in vehicles.We employed indoor-outdoor coupled CFD simulations to assess the effects of various IWOS on pathogen-laden droplet(PLD)dispersion and IR in a coach bus that occurred a COVID-19 outbreak in Hunan,China.Results reveal that after ventilating through two skylights for 600–1800 s,opening front and rear windows(FW+RW)or FW with a wind catcher(FW+WCH)for just 40 s can reduce PLD concentration(Cave)to 5%of its initial level and the intake fraction of the infector’s neighbor(IFn)drops by 95%.Upon closing FW+RW or FW+WCH,Cave and IFn take over 580 s to return to the pre-opening level.Moreover,intermittent FW opening halves Cave and IFn within 7 min,but leads to rapid increases upon window closure.Therefore,opening FW+RW and FW+WCH intermittently have pronounced impacts on indoor PLD concentration and are applicable approaches to control respiratory disease transmission in vehicles.According to the inhaled viral dose,it is recommended to open windows when driving time is over 12 minutes to reduce infection risk.In scenarios like epidemiological surveys and risk assessments,where assessing passenger infection risk is vital,some behaviors of opening windows cannot be overlooked and necessitate extra attention.