Plants encounter dynamic light environments in natural field conditions,and species differ in their physiological and biochemical mechanisms for acclimating to fluctuating light(FL).The manner in which soybean(Glycine...Plants encounter dynamic light environments in natural field conditions,and species differ in their physiological and biochemical mechanisms for acclimating to fluctuating light(FL).The manner in which soybean(Glycine max(L.) Merr.) coordinates multiple physiological adjustments to FL remains poorly understood.This study assessed the effects of FL on soybean morphology and photosynthetic traits by examining changes in photosynthetic gas exchange parameters and chlorophyll(Chl) a fluorescence under alternating high-and low-light conditions.Results indicated that soybeans exposed to FL exhibited reduced dry matter accumulation,smaller and thinner leaves,and a lower Chl a/Chl b levels-characteristics typically associated with plants grown under continuous low-light.Despite these morphological similarities,their photosynthetic gas exchange rates and photosynthetic capacity were maintained at levels comparable to those under steady high light,unlike plants grown under constant low-light.Thus,acclimation to FL is distinct from adaptation to sustained low-light conditions.Correlation analyses revealed that the decline in carbon assimilation under FL primarily stemmed from two factors:the slow recovery of stomatal conductance upon transition to high light and the delayed relaxation of nonphotochemical quenching when light intensity decreased.Therefore,the reduction in carbon assimilation under FL cannot be attributed to low-light phase adjustments but rather reflects a lag in photosynthetic responsiveness to changing light conditions.展开更多
High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symme...High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.展开更多
A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
The role of brassinosteroids(BRs)in enabling plants to respond effectively to adverse conditions is well known,though the precise mechanism of action that helps plants cope with arsenic(As)toxicity is still difficult ...The role of brassinosteroids(BRs)in enabling plants to respond effectively to adverse conditions is well known,though the precise mechanism of action that helps plants cope with arsenic(As)toxicity is still difficult to interpret.Therefore we tested the effect of brassinolide(BL)spray(0,0.5,and 1 mg·L^(-1))on As(0,and 10 mg·L^(-1))stressed tomato defense responses As stress led to the induction of oxidative stress,impaired chlorophyll and nitrogen metabolism,and Fe uptake,in conjunction with a reduction in plant growth and biomass.BL spray,on the contrary,protected the photo synthetic system and helped plants grow better under As stress.This was achieved by controlling the metabolism of chlorophyll and proline and lowering the amounts of methylglyoxal and H_(2)O_(2) through glyoxalaseⅠandⅡand antioxidant enzyme s.BL decreased arsenic accumulation by directing As sequestration towards vacuoles and increased Fe amount in the leaves and roots by regulating the expression of As(Lsil and Lsi2)and Fe(IRT1,IRT2,NRAMP1,and NRAMP3)transporters in As-stressed tomatoes.Furthermore,BL boosted adaptability against As phytotoxicity,while reducing the damaging impacts on photosynthesis,nitrogen metabolism,sulfur asimilation,and Fe absorption.These results offer a solid framework for the development of exogenous BRs-based breeding strategies for safer agricultural development.展开更多
Based on the China Meteorological Administration’s Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS),the authors conducted a collaborative assimilation forecasting experiment utilizing both Beidou...Based on the China Meteorological Administration’s Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS),the authors conducted a collaborative assimilation forecasting experiment utilizing both Beidou radiosonde and drone-dropped(HAIYAN-I)radiosonde data in September 2023.Three assimilation experimental groups were designed as follows:Beidou radiosonde assimilation,drone-dropped radiosonde assimilation,and collaborative assimilation of Beidou and drone-dropped radiosonde data(hereinafter referred to as“Beidoudrop”).Additionally,a control group of operational forecasts without these data assimilations was set up.The results indicate that the operational forecast path in the control group deviated northward from the actual path.Besides,the Beidou-drop group showed the most significant improvement in terms of forecasting the typhoon path at 60 to 90 h lead times.Specifically,the 72 h and 90 h path errors were reduced by 66.8 and 82.4 km,respectively,resulting in a much more accurate forecast of Typhoon Haikui’s landing point,at the coastal junction of Fujian and Guangdong.Furthermore,the collaborative assimilation revealed a notable impact on improving the forecast of wind and rain associated with Haikui’s landfall,aligning more closely with the real case.A marked rise was also seen in the precipitation score of the Beidou-drop group,where the 50 mm TS(threat score)of the 72 h lead time increased from 0.33 in the control experiment to 0.75,and the 100 mm TS rose from 0.18 to 0.39.展开更多
The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)model...The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.展开更多
Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for du...Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.展开更多
To accurately predict the three-dimensional flow characteristics of the flow field inside a waterjet propulsion pump,data assimilation(DA)method based on unsteady ensemble Kalman filter(EnKF)is used for the reconstruc...To accurately predict the three-dimensional flow characteristics of the flow field inside a waterjet propulsion pump,data assimilation(DA)method based on unsteady ensemble Kalman filter(EnKF)is used for the reconstruction of the flow field of a pump at different flow rates Q/Q_(opt)=0.85,1,1.15,where Q_(opt)is optimal flow rate at the design point.As a compensation to the spatial limitation of planar particle image velocimetry(PIV)measurements,dynamic delayed detached-eddy simulation(DDES)results validated by the PIV data is used to provide the observational data at the optimized probe locations.In DA procedure,the shear stress transport(SST)model constants are optimized by the EnKF approach.The model constants are subsequently rescaled and fitted to form a variation with the flow rate,which is extended to the prediction of the flow field with other flow rates in the vicinity of the design condition.The results show that the SST model with recalibrated constants has improved the prediction of the internal flow field in the waterjet propulsion pump,especially the separation flow in the diffuser section.The modified model constants mainly reduce the eddy viscosity and significantly improve the fluctuation characteristics in the flow field.This study provides a reference for the fast and accurate prediction of the flow field information in the waterjet propulsion pump.展开更多
Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer ...Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer model,with its self-attention mechanism,effectively captures long-range dependencies,leading to a degradation of accuracy over time.Due to the non-linearity and uncertainty of physical processes,the transformer model encounters the problem of error accumulation,leading to a degradation of accuracy over time.To solve this problem,we combine the Data Assimilation(DA)technique with the transformer model and continuously modify the model state to make it closer to the actual observations.In this paper,we propose a deep learning model called TransNetDA,which integrates transformer,convolutional neural network and DA methods.By combining data-driven and DA methods for spatiotemporal prediction,TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy.The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and R2 metrics,showcasing its superior performance in predicting latent heat fluxes at the ocean surface.展开更多
For all-sky infrared radiance assimilation,the heteroscedasticity and non-Gaussian behavior of observation-minusbackground(OMB)departures are two major difficulties.The Geer–Bauer observation error inflation(GBOEI)sc...For all-sky infrared radiance assimilation,the heteroscedasticity and non-Gaussian behavior of observation-minusbackground(OMB)departures are two major difficulties.The Geer–Bauer observation error inflation(GBOEI)scheme is a universal way to handle the issues.However,it fails to take into account the consistency between model and observation,resulting in unreasonably large observation errors where the simulations agree with the observations.Thus,this study modifies the GBOEI scheme to rationalize the observation errors in such areas.With Advanced Himawari Imager water vapor channel data,the test results show that the normalized OMB with the new observation error approach leads to more Gaussian form than the GBOEI method and constant observation errors.Hence,the assimilation experiments with the new scheme produce better brightness temperature analysis than other methods,and also improve temperature and humidity analysis.Furthermore,a real case experiment of Typhoon Lekima(2019)with the new observation error scheme exhibits more accuracy,especially in track prediction,and substantial error reductions in wind,temperature,and humidity forecasts are also obtained.Meanwhile,5-day 6-hour cycling experiments in the real case of Typhoon Lekima(2019)with the new observation error scheme confirm that the new method does not introduce extra imbalance compared to the experiment with constant observation errors;plus,more accurate typhoon forecasts can also be obtained in both the analysis and forecast,especially in track prediction.展开更多
Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this st...Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this study,a Localized Error Subspace Transform Kalman Filter is employed in a coupled climate system model(the Flexible Global Ocean–Atmosphere–Land System Model,version f3-L(FGOALS-f3-L))to assimilate sea-ice concentration(SIC)and sea-ice thickness(SIT)data for melting-season ice predictions.The scheme is applied through the following steps:(1)initialization for generating initial ensembles;(2)analysis for assimilating observed data;(3)adoption for dividing ice states into five thickness categories;(4)forecast for evolving the model;(5)resampling for updating model uncertainties.Several experiments were conducted to examine its results and impacts.Compared with the control experiment,the continuous assimilation experiments(CTNs)indicate assimilations improve model SICs and SITs persistently and generate realistic initials.Assimilating SIC+SIT data better corrects overestimated model SITs spatially than when only assimilating SIC data.The continuous assimilation restart experiments indicate the initials from the CTNs correct the overestimated marginal SICs and overall SITs remarkably well,as well as the cold biases in the oceanic and atmospheric models.The initials with SIC+SIT assimilated show more reasonable spatial improvements.Nevertheless,the SICs in the central Arctic undergo abnormal summer reductions,which is probably because overestimated SITs are reduced in the initials but the strong seasonal cycle(summer melting)biases are unchanged.Therefore,since systematic biases are complicated in a coupled system,for FGOALS-f3-L to make better ice predictions,oceanic and atmospheric assimilations are expected required.展开更多
A record-breaking prolonged and extreme rainstorm occurred in Henan province,China during 18–23 July 2021.Global and regional numerical weather prediction(NWP)models consistently underpredicted both the 24-h accumula...A record-breaking prolonged and extreme rainstorm occurred in Henan province,China during 18–23 July 2021.Global and regional numerical weather prediction(NWP)models consistently underpredicted both the 24-h accumulated rainfall amount and the 1-h extreme precipitation in Zhengzhou city.This study examines the potential impacts of data assimilation(DA)of atmospheric vertical profiles based on the train-based mobile observation(MO)platforms on precipitation forecasts.The research involved assimilating virtual train-based air temperature(Ta),relative humidity(RH),U and V components of wind profile data based on the ERA5 reanalysis datasets into the Weather Research and Forecasting(WRF)model using three-dimensional variational(3DVar)method.Analysis confirms the reliability of Ta,RH,and wind speed(WS)profiles from ERA5 reanalysis datasets.The assimilation of virtual train-based moisture profiles enhanced the RH analysis field.Furthermore,the forecasts more accurately represented the coverage and intensity of the 6-hour and 24-hour accumulated precipitation,as well as areas with maximum rainfall durations exceeding 20 hours.The threat score(TS)and bias metrics for 6-h,12-h and 24-h accumulated precipitation forecasts showed marked improvement for heavy to torrential rain in Henan province,particularly in the Central and Northern regions(hereinafter referred to region CNH).The TS for 24-h accumulated precipitation forecasts at 50 and 100 mm rainfall levels increased by 0.17 and 0.18 in Henan province,and by 0.13 and 0.18 in region CNH.During the rainstorm period,water vapor content increased substantially,with enhanced moisture transport from south of Henan province to region CNH driven by southwesterly winds,accompanied by significantly strengthened updrafts.These improvement in water vapor and upward motion ultimately enhanced the forecasts of this extreme rainstorm event.展开更多
Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an exte...Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an extended skin temperature control variable(TSCV) approach is proposed in a variational assimilation framework,which also considers the background error correlation between skin temperature and atmospheric variables.A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS(Advanced Technology Microwave Sounder) microwave temperature-sounding channels over land.The results of the single observation tests show that by applying the TSCV approach,not only the direct analysis of skin temperature is realized,but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation process.The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis,which in turn reduces the RMSE of the surface variables and low-level air temperature forecasts.The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land,suggesting that the approach can facilitate the radiance simulation of these channels,thus contributing to the assimilation of window channels.展开更多
Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemic...Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.展开更多
In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The propo...In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The proposed scheme is based on the WRFDA(Weather Research and Forecasting Data Assimilation)system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data.A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process.The scheme is characterized by its simplicity,fast implementation,and ease of maintenance.By determining an appropri-ate threshold for quality control,the percentage of outliers identified by the scheme remains highly stable over time.Moreover,the mean errors and standard deviations of the O-B(observation-minus-background)values are significantly reduced,improving the overall data quality.The main information and spatial distribution features of the data are pre-served effectively.After quality control,the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution.This adjustment is beneficial for the subsequent data assimilation process,contributing to more accurate numerical weather predictions.Thus,the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.展开更多
Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in ...Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in order to estimate biomass according to relationship between biomass and backscattering coefficients from SAR data. Based on cost function, parameters of growth model were optimized as per conjugate gradient method, minimizing the differences between estimated biomass and inversion values from SAR data. The results indicated that the simulated biomass using the revised growth model with SAR data was consistent with the measured one in time distribution and even higher in accuracy than that without SAR data. Hence, the key parameters of crop growth model could be revised by real-time growth information from SAR data and accuracy of the simulated biomass could be improved accordingly.展开更多
基金supported by the National Key Research and Development Program of China (2023YFF1001504)the National Natural Science Foundation of China (32071963)+2 种基金the National Key Research and Development Program of China (2022YFD2300902)the Guangxi Key Research and Development Program of China (GuikeAB23026107)the Chengdu Science and Technology Project,China (2023-YF08-00003-SN)。
文摘Plants encounter dynamic light environments in natural field conditions,and species differ in their physiological and biochemical mechanisms for acclimating to fluctuating light(FL).The manner in which soybean(Glycine max(L.) Merr.) coordinates multiple physiological adjustments to FL remains poorly understood.This study assessed the effects of FL on soybean morphology and photosynthetic traits by examining changes in photosynthetic gas exchange parameters and chlorophyll(Chl) a fluorescence under alternating high-and low-light conditions.Results indicated that soybeans exposed to FL exhibited reduced dry matter accumulation,smaller and thinner leaves,and a lower Chl a/Chl b levels-characteristics typically associated with plants grown under continuous low-light.Despite these morphological similarities,their photosynthetic gas exchange rates and photosynthetic capacity were maintained at levels comparable to those under steady high light,unlike plants grown under constant low-light.Thus,acclimation to FL is distinct from adaptation to sustained low-light conditions.Correlation analyses revealed that the decline in carbon assimilation under FL primarily stemmed from two factors:the slow recovery of stomatal conductance upon transition to high light and the delayed relaxation of nonphotochemical quenching when light intensity decreased.Therefore,the reduction in carbon assimilation under FL cannot be attributed to low-light phase adjustments but rather reflects a lag in photosynthetic responsiveness to changing light conditions.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080500)the National Natural Science Foundation of China(Grant Nos.U2142208,42475158,and 42105149)the High-Performance Computing Center of Nanjing University of Information Science&Technology for supporting this work。
文摘High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
基金financial support from the National Key Research and Development Program of China(Grant No.2023YFD220120302)supported by RUDN University Strategic Academic Leadership Program。
文摘The role of brassinosteroids(BRs)in enabling plants to respond effectively to adverse conditions is well known,though the precise mechanism of action that helps plants cope with arsenic(As)toxicity is still difficult to interpret.Therefore we tested the effect of brassinolide(BL)spray(0,0.5,and 1 mg·L^(-1))on As(0,and 10 mg·L^(-1))stressed tomato defense responses As stress led to the induction of oxidative stress,impaired chlorophyll and nitrogen metabolism,and Fe uptake,in conjunction with a reduction in plant growth and biomass.BL spray,on the contrary,protected the photo synthetic system and helped plants grow better under As stress.This was achieved by controlling the metabolism of chlorophyll and proline and lowering the amounts of methylglyoxal and H_(2)O_(2) through glyoxalaseⅠandⅡand antioxidant enzyme s.BL decreased arsenic accumulation by directing As sequestration towards vacuoles and increased Fe amount in the leaves and roots by regulating the expression of As(Lsil and Lsi2)and Fe(IRT1,IRT2,NRAMP1,and NRAMP3)transporters in As-stressed tomatoes.Furthermore,BL boosted adaptability against As phytotoxicity,while reducing the damaging impacts on photosynthesis,nitrogen metabolism,sulfur asimilation,and Fe absorption.These results offer a solid framework for the development of exogenous BRs-based breeding strategies for safer agricultural development.
文摘Based on the China Meteorological Administration’s Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS),the authors conducted a collaborative assimilation forecasting experiment utilizing both Beidou radiosonde and drone-dropped(HAIYAN-I)radiosonde data in September 2023.Three assimilation experimental groups were designed as follows:Beidou radiosonde assimilation,drone-dropped radiosonde assimilation,and collaborative assimilation of Beidou and drone-dropped radiosonde data(hereinafter referred to as“Beidoudrop”).Additionally,a control group of operational forecasts without these data assimilations was set up.The results indicate that the operational forecast path in the control group deviated northward from the actual path.Besides,the Beidou-drop group showed the most significant improvement in terms of forecasting the typhoon path at 60 to 90 h lead times.Specifically,the 72 h and 90 h path errors were reduced by 66.8 and 82.4 km,respectively,resulting in a much more accurate forecast of Typhoon Haikui’s landing point,at the coastal junction of Fujian and Guangdong.Furthermore,the collaborative assimilation revealed a notable impact on improving the forecast of wind and rain associated with Haikui’s landfall,aligning more closely with the real case.A marked rise was also seen in the precipitation score of the Beidou-drop group,where the 50 mm TS(threat score)of the 72 h lead time increased from 0.33 in the control experiment to 0.75,and the 100 mm TS rose from 0.18 to 0.39.
基金Major Key Project of PCL(PCL2025A10)Open Research Project of the China Meteorological Administration Hydro-Meteorology Key Laboratory(23SWQXM036)+2 种基金National Natural Science Foundation of China(42375160)Project of the Key Laboratory of Atmospheric Sounding of China Meteorological Administration(2022KLAS06M)Science and Technology Research Project of the Guangdong Provincial Meteorological Bureau(GRMC2024M04)。
文摘The assimilation of dual-polarization(dual-pol)radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction(NWP)models.However,existing dual-pol radar data assimilation(DA)methods exhibit limitations in terms of computational efficiency and data utilization.In this study,a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data.The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data,yielding a reasonable range of root mean square errors(RMSEs).On this basis,the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update(IAU)scheme,establishing a complete dual-pol radar DA framework for the CMA-MESO model.To evaluate the efficacy of this DA scheme,comparative simulation experiments were conducted for Typhoon Lekima(2019).Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores(TSs)for 3-hour accumulated precipitation during medium-and heavy-rainfall events.Additionally,the 24-hour accumulated rainfall TSs for the medium-,heavy-,and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment.The DA method also yields superior predictions of the spatial distribution of extremerainfall events.These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.
基金sponsored by the National Natural Science Foundation of China(U2442601 and U2442218)the High Performance Computing Platform of Nanjing University of Information Science&Technology(NUIST)for their support of this work。
文摘Numerical models play an important role in convective-scale forecasting,and dual-polarization radar observations can provide detailed microphysical data.In this study,we implement a direct assimilation operator for dual-polarization radar data using the hydrometeor background error covariance(HBEC)in the China Meteorological Administration MESO-scale weather forecasting system(CMA-MESO,formerly GRAPES-MESO)and conducted assimilation and forecasting experiments with X-band and S-band dual-polarization radar data on two cases.The results indicate that the direct assimilation of dual-polarization radar data enhanced the microphysical fields and the thermodynamic structure of convective systems to some extent based on the HBEC,thereby improving precipitation forecasts.Among the sensitivity tests of microphysical parameterization schemes,including the LIUMA scheme,the THOMPSON scheme,and the WSM6scheme(WRF Single-Moment 6-class),we find that the greatest improvement in the equivalent potential temperature,relative humidity,wind,and accumulated precipitation forecasts occurred in the experiment using the WSM6 scheme,as the distribution of solid precipitation particles was closer to the hydrometeor classification algorithm from the dualpolarization radar observations in our cases.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272231 and 12227803).
文摘To accurately predict the three-dimensional flow characteristics of the flow field inside a waterjet propulsion pump,data assimilation(DA)method based on unsteady ensemble Kalman filter(EnKF)is used for the reconstruction of the flow field of a pump at different flow rates Q/Q_(opt)=0.85,1,1.15,where Q_(opt)is optimal flow rate at the design point.As a compensation to the spatial limitation of planar particle image velocimetry(PIV)measurements,dynamic delayed detached-eddy simulation(DDES)results validated by the PIV data is used to provide the observational data at the optimized probe locations.In DA procedure,the shear stress transport(SST)model constants are optimized by the EnKF approach.The model constants are subsequently rescaled and fitted to form a variation with the flow rate,which is extended to the prediction of the flow field with other flow rates in the vicinity of the design condition.The results show that the SST model with recalibrated constants has improved the prediction of the internal flow field in the waterjet propulsion pump,especially the separation flow in the diffuser section.The modified model constants mainly reduce the eddy viscosity and significantly improve the fluctuation characteristics in the flow field.This study provides a reference for the fast and accurate prediction of the flow field information in the waterjet propulsion pump.
基金The National Natural Science Foundation of China under contract Nos 42176011 and 61931025the Fundamental Research Funds for the Central Universities of China under contract No.24CX03001A.
文摘Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer model,with its self-attention mechanism,effectively captures long-range dependencies,leading to a degradation of accuracy over time.Due to the non-linearity and uncertainty of physical processes,the transformer model encounters the problem of error accumulation,leading to a degradation of accuracy over time.To solve this problem,we combine the Data Assimilation(DA)technique with the transformer model and continuously modify the model state to make it closer to the actual observations.In this paper,we propose a deep learning model called TransNetDA,which integrates transformer,convolutional neural network and DA methods.By combining data-driven and DA methods for spatiotemporal prediction,TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy.The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and R2 metrics,showcasing its superior performance in predicting latent heat fluxes at the ocean surface.
基金funded by the National Natural Science Foundation of China(Grant Nos.42192553 and 41805071)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_1413)the High Performance Computing Center of Nanjing University of Information Science&Technology for their support of this work。
文摘For all-sky infrared radiance assimilation,the heteroscedasticity and non-Gaussian behavior of observation-minusbackground(OMB)departures are two major difficulties.The Geer–Bauer observation error inflation(GBOEI)scheme is a universal way to handle the issues.However,it fails to take into account the consistency between model and observation,resulting in unreasonably large observation errors where the simulations agree with the observations.Thus,this study modifies the GBOEI scheme to rationalize the observation errors in such areas.With Advanced Himawari Imager water vapor channel data,the test results show that the normalized OMB with the new observation error approach leads to more Gaussian form than the GBOEI method and constant observation errors.Hence,the assimilation experiments with the new scheme produce better brightness temperature analysis than other methods,and also improve temperature and humidity analysis.Furthermore,a real case experiment of Typhoon Lekima(2019)with the new observation error scheme exhibits more accuracy,especially in track prediction,and substantial error reductions in wind,temperature,and humidity forecasts are also obtained.Meanwhile,5-day 6-hour cycling experiments in the real case of Typhoon Lekima(2019)with the new observation error scheme confirm that the new method does not introduce extra imbalance compared to the experiment with constant observation errors;plus,more accurate typhoon forecasts can also be obtained in both the analysis and forecast,especially in track prediction.
基金jointly funded by the National Natural Science Foundation of China(NSFC)[grant number 42130608]the China Postdoctoral Science Foundation[grant number 2024M753169]。
文摘Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this study,a Localized Error Subspace Transform Kalman Filter is employed in a coupled climate system model(the Flexible Global Ocean–Atmosphere–Land System Model,version f3-L(FGOALS-f3-L))to assimilate sea-ice concentration(SIC)and sea-ice thickness(SIT)data for melting-season ice predictions.The scheme is applied through the following steps:(1)initialization for generating initial ensembles;(2)analysis for assimilating observed data;(3)adoption for dividing ice states into five thickness categories;(4)forecast for evolving the model;(5)resampling for updating model uncertainties.Several experiments were conducted to examine its results and impacts.Compared with the control experiment,the continuous assimilation experiments(CTNs)indicate assimilations improve model SICs and SITs persistently and generate realistic initials.Assimilating SIC+SIT data better corrects overestimated model SITs spatially than when only assimilating SIC data.The continuous assimilation restart experiments indicate the initials from the CTNs correct the overestimated marginal SICs and overall SITs remarkably well,as well as the cold biases in the oceanic and atmospheric models.The initials with SIC+SIT assimilated show more reasonable spatial improvements.Nevertheless,the SICs in the central Arctic undergo abnormal summer reductions,which is probably because overestimated SITs are reduced in the initials but the strong seasonal cycle(summer melting)biases are unchanged.Therefore,since systematic biases are complicated in a coupled system,for FGOALS-f3-L to make better ice predictions,oceanic and atmospheric assimilations are expected required.
基金R&D major projects from China State Railway Group Co.,Ltd.(K2022G039)Tibet Autonomous Region Science and Technology Program Project(XZ202402ZD0006-06)+1 种基金Open bidding project for selecting the best candidates from China Meteorological Administration(CMAJBGS202303)The Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0105)。
文摘A record-breaking prolonged and extreme rainstorm occurred in Henan province,China during 18–23 July 2021.Global and regional numerical weather prediction(NWP)models consistently underpredicted both the 24-h accumulated rainfall amount and the 1-h extreme precipitation in Zhengzhou city.This study examines the potential impacts of data assimilation(DA)of atmospheric vertical profiles based on the train-based mobile observation(MO)platforms on precipitation forecasts.The research involved assimilating virtual train-based air temperature(Ta),relative humidity(RH),U and V components of wind profile data based on the ERA5 reanalysis datasets into the Weather Research and Forecasting(WRF)model using three-dimensional variational(3DVar)method.Analysis confirms the reliability of Ta,RH,and wind speed(WS)profiles from ERA5 reanalysis datasets.The assimilation of virtual train-based moisture profiles enhanced the RH analysis field.Furthermore,the forecasts more accurately represented the coverage and intensity of the 6-hour and 24-hour accumulated precipitation,as well as areas with maximum rainfall durations exceeding 20 hours.The threat score(TS)and bias metrics for 6-h,12-h and 24-h accumulated precipitation forecasts showed marked improvement for heavy to torrential rain in Henan province,particularly in the Central and Northern regions(hereinafter referred to region CNH).The TS for 24-h accumulated precipitation forecasts at 50 and 100 mm rainfall levels increased by 0.17 and 0.18 in Henan province,and by 0.13 and 0.18 in region CNH.During the rainstorm period,water vapor content increased substantially,with enhanced moisture transport from south of Henan province to region CNH driven by southwesterly winds,accompanied by significantly strengthened updrafts.These improvement in water vapor and upward motion ultimately enhanced the forecasts of this extreme rainstorm event.
基金sponsored by the National Natural Science Foundation of China(Grant No.42075148)the High-Performance Computing Center of Nanjing University of Information Science & Technology for supporting this work。
文摘Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an extended skin temperature control variable(TSCV) approach is proposed in a variational assimilation framework,which also considers the background error correlation between skin temperature and atmospheric variables.A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS(Advanced Technology Microwave Sounder) microwave temperature-sounding channels over land.The results of the single observation tests show that by applying the TSCV approach,not only the direct analysis of skin temperature is realized,but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation process.The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis,which in turn reduces the RMSE of the surface variables and low-level air temperature forecasts.The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land,suggesting that the approach can facilitate the radiance simulation of these channels,thus contributing to the assimilation of window channels.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program of Ministry of Science and Technology of the People's Republic of China[grant number 2022QZKK0101]the Science and Technology Department of the Tibet Program[grant number XZ202301ZY0035G]。
文摘Since meteorological conditions are the main factor driving the transport and dispersion of air pollutants,an accurate simulation of the meteorological field will directly affect the accuracy of the atmospheric chemical transport model in simulating PM_(2.5).Based on the NASM joint chemical data assimilation system,the authors quantified the impacts of different meteorological fields on the pollutant simulations as well as revealed the role of meteorological conditions in the accumulation,maintenance,and dissipation of heavy haze pollution.During the two heavy pollution processes from 10 to 24 November 2018,the meteorological fields were obtained using NCEP FNL and ERA5 reanalysis data,each used to drive the WRF model,to analyze the differences in the simulated PM_(2.5) concentration.The results show that the meteorological field has a strong influence on the concentration levels and spatial distribution of the pollution simulations.The ERA5 group had relatively small simulation errors,and more accurate PM_(2.5) simulation results could be obtained.The RMSE was 11.86𝜇g m^(-3)lower than that of the FNL group before assimilation,and 5.77𝜇g m^(-3)lower after joint assimilation.The authors used the PM_(2.5) simulation results obtained by ERA5 data to discuss the role of the wind field and circulation situation on the pollution process,to analyze the correlation between wind speed,temperature,relative humidity,and boundary layer height and pollutant concentrations,and to further clarify the key formation mechanism of this pollution process.
基金funded by Beijige Fund of Nanjing Joint Institute for Atmospheric Sciences(BJG202501)the Joint Research Project for Meteorological Capacity Improvement(22NLTSY009)+2 种基金Key Scientific Research Projects of Jiangsu Provincial Meteorological Bureau(KZ202203)China Meteorological Administration projects(CMAJBGS202316)the Guiding Research Projects of Jiangsu Provincial Meteorological Bureau(ZD202404,ZD202419).
文摘In order to further enhance the numerical application of weather radar radial velocity,this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation.The proposed scheme is based on the WRFDA(Weather Research and Forecasting Data Assimilation)system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data.A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process.The scheme is characterized by its simplicity,fast implementation,and ease of maintenance.By determining an appropri-ate threshold for quality control,the percentage of outliers identified by the scheme remains highly stable over time.Moreover,the mean errors and standard deviations of the O-B(observation-minus-background)values are significantly reduced,improving the overall data quality.The main information and spatial distribution features of the data are pre-served effectively.After quality control,the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution.This adjustment is beneficial for the subsequent data assimilation process,contributing to more accurate numerical weather predictions.Thus,the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.
基金Supported by National High-tech R & D Program of China (863 Program)(2007AA12Z174)~~
文摘Biomass from SAR data was assimilated into crop growth model to describe relationship between crop biomass and crop growth time to improve estimation accuracy of biomass. In addition, inverse model was established in order to estimate biomass according to relationship between biomass and backscattering coefficients from SAR data. Based on cost function, parameters of growth model were optimized as per conjugate gradient method, minimizing the differences between estimated biomass and inversion values from SAR data. The results indicated that the simulated biomass using the revised growth model with SAR data was consistent with the measured one in time distribution and even higher in accuracy than that without SAR data. Hence, the key parameters of crop growth model could be revised by real-time growth information from SAR data and accuracy of the simulated biomass could be improved accordingly.
