Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Polymeric nanocomposites for electrocaloric refrigeration

Electrocaloric refrigeration represents an alternative solid-state cooling technology that has the potential to reach the ultimate goal of achieving zero-global-warming potential,highly efficient refrigeration,and heat pumps.To date,both polymeric and inorganic oxides have demonstrated giant electrocaloric effect as well as respective cooling devices.Although both polymeric and inorganic oxides have been identified as promising cooling methods that are distinguishable from the traditional ones,they still pose many challenges to more practical applications.From an electrocaloric material point of view,electrocaloric nanocomposites may provide a solution to combine the beneficial effects of both organic and inorganic electrocaloric materials.This article reviews the recent advancements in polymer-based electrocaloric composites and the state-of-the-art cooling devices operating these nanocomposites.From a device point of view,it discusses the existing challenges and potential opportunities of electrocaloric nanocomposites.

Establishing a non-hydrostatic global atmospheric modeling system at3-km horizontal resolution with aerosol feedbacks on the Sunway supercomputer of China

During the era of global warming and highly urbanized development,extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property.Despite the vast development of atmospheric models,there still exist substantial numerical forecast biases objectively.To accurately predict extreme weather,severe air pollution,and abrupt climate change,numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution.Global integrated atmospheric simulation at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output(I/O)requirement.Through multi-dimension-parallelism structuring,aggressive and finer-grained optimizing,manual vectorizing,and parallelized I/O fragmenting,an integrated Atmospheric Model Across Scales(iAMAS)was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost.The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour(SDPH)with routine I/O,which enabled us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts.The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol feedbacks may significantly improve the global weather forecast.