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
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
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
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
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
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
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
CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate ...CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.展开更多
Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interp...Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.展开更多
In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optim...In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization challenge.The enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware requirements.This paper employs the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation accuracy.By harnessing parallel computing principles,the paper introduces the Parallel Dual-Population Particle Swarm Optimization(PDPSO)Algorithm to reduce the algorithm processing time.Simulations were carried out using partial differential equations,and comparisons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm Algorithm(PSOCIWAC),and the TimeVarying Double Compression Factor Particle Swarm Algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO.Regarding processing time,PDPSO is 40%faster than PSOCIWAC and PSOTVCF and 70%faster than DPSO.展开更多
Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.B...Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.Based on the sequential data assimilation method and the concept of the Kalman gain matrix,this paper proposes a new nudging method with spatially dependent coefficients for tidal assimilation.The spatial-dependent nudging method not only retains the advantages of the traditional nudging method but also facilitates the direct determination of a more reasonable spatial distribution of nudging coefficients.Utilizing the M_(2)tidal constituent(the main lunar semidiurnal tide)as an illustration,we conducted assimilation experiments of sea-level data to the barotropic circulation and tide model to assess the global harmonic constants of the M_(2)constituent.The results demonstrate that the spatial-dependent nudging method successfully mitigates deviations of tidal phase lag.Following assimilation using the new method,the deviations of the M_(2)tidal amplitude and phase lag can be reduced by 47%and 18%compared to the traditional nudging method,respectively,while the respective values for the non-assimilated case are as much as 9%and 11%.We also applied the S-nudging method to realistic tidal simulations and noted a significantly enhanced effectiveness relative to traditional methods,making it highly valuable for modeling oceanic tidal circulations.展开更多
Glucosinolates(GSLs) are a group of nitrogen-and sulfur-containing secondary metabolites, synthesized primarily in members of the Brassicaceae family, that play an important role in food flavor, plant antimicrobial ac...Glucosinolates(GSLs) are a group of nitrogen-and sulfur-containing secondary metabolites, synthesized primarily in members of the Brassicaceae family, that play an important role in food flavor, plant antimicrobial activity, resistance to insect attack, stress tolerance, and human anti-cancer effects. As a sulfur-containing compound, glutathione has a strong connection with GSLs biosynthesis as a sulfur donor or redox system, and exists in reduced(glutathione;GSH) and oxidized(glutathione disulfide;GSSG) forms. However, the mechanism of GSH regulating GSLs biosynthesis remainds unclear. Hence, the exogenous therapy to pakchoi under normal growth condition and sulfur deficiency condition were conducted in this work to explore the relevant mechanism. The results showed that exogenous application of buthionine sulfoximine, an inhibitor of GSH synthesis, decreased the transcript levels of GSLs synthesis-related genes and transcription factors, as well as sulfur assimilation-related genes under the normal growth condition. Application of exogenous GSH inhibited the expression of GSLs synthesis-and sulfur assimilation-related genes under the normal condition, while the GSLs biosynthesis and the sulfur assimilation pathway were activated by exogenous application of GSH when the content of GSH in vivo of plants decreased owing to sulfur deficiency. Moreover,exogenous application of GSSG increased the transcript levels of GSLs synthesis-and sulfur assimilation-related genes under the normal growth condition and under sulfur deficiency. The present work provides new insights into the molecular mechanisms of GSLs biosynthesis underlying glutathione regulation.展开更多
Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China M...Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China Meteorological Administration Global Forecast System(CMA-GFS)has a model top near 0.1 hPa(60 km),the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa.In this study,channels 12–14 of the Advanced Microwave Sounding Unit A(AMSU-A)onboard five satellites of NOAA and METOP,whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS.It is shown that the new“Anchor”approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles.The bias growth rate of simulated upper-level channel observations is reduced to±0.001 K d^(–1),compared to–0.03 K d^(–1)derived from the current dynamic correction scheme.The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.展开更多
The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/op...The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.展开更多
An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix,which can be achieved by solving the advection-diffusion equation.Because of the directionality of the advection...An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix,which can be achieved by solving the advection-diffusion equation.Because of the directionality of the advection term,the discrete method needs to be chosen very carefully.The finite analytic method is an alternative scheme to solve the advection-diffusion equation.As a combination of analytical and numerical methods,it not only has high calculation accuracy but also holds the characteristic of the auto upwind.To demonstrate its ability,the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method.The more widely used upwind difference method is used as a control approach.The result indicates that the finite analytic method has higher accuracy than the upwind difference method.For the two-dimensional case,the finite analytic method still has a better performance.In the three-dimensional variational assimilation experiment,the finite analytic method can effectively improve analysis field accuracy,and its effect is significantly better than the upwind difference and the central difference method.Moreover,it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.展开更多
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.
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 the General Project of Top-Design of Multi-Scale Nature-Social ModelsData Support and Decision Support System for NSFC Carbon Neutrality Major Project(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.
基金supported by NOAA JTTI award via Grant #NA21OAR4590165, NOAA GOESR Program funding via Grant #NA16OAR4320115provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative Agreement #NA11OAR4320072, U.S. Department of Commercesupported by the National Oceanic and Atmospheric Administration (NOAA) of the U.S. Department of Commerce via Grant #NA18NWS4680063。
文摘Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.
基金Supported by Hubei Provincial Department of Education Teaching Research Project(2016294,2017320)Hubei Provincial Humanities and Social Science Research Project(17D033)+2 种基金College Students Innovation and Entrepreneurship Training Program(National)(20191050013)Hubei Province Natural Science Foundation General Project(2021CFB584)2023 College Student Innovation and Entrepreneurship Training Program Project(202310500047,202310500049)。
文摘In recent years,numerical weather forecasting has been increasingly emphasized.Variational data assimilation furnishes precise initial values for numerical forecasting models,constituting an inherently nonlinear optimization challenge.The enormity of the dataset under consideration gives rise to substantial computational burdens,complex modeling,and high hardware requirements.This paper employs the Dual-Population Particle Swarm Optimization(DPSO)algorithm in variational data assimilation to enhance assimilation accuracy.By harnessing parallel computing principles,the paper introduces the Parallel Dual-Population Particle Swarm Optimization(PDPSO)Algorithm to reduce the algorithm processing time.Simulations were carried out using partial differential equations,and comparisons in terms of time and accuracy were made against DPSO,the Dynamic Weight Particle Swarm Algorithm(PSOCIWAC),and the TimeVarying Double Compression Factor Particle Swarm Algorithm(PSOTVCF).Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO.Regarding processing time,PDPSO is 40%faster than PSOCIWAC and PSOTVCF and 70%faster than DPSO.
基金supported by the National Natural Science Foundation of China(Grant No.42276011 and Grant No.U23A2032).
文摘Tides represent a crucial dynamic process in the ocean and play a vital role in both marine and atmospheric studies;thus,accurate simulation of tidal processes is of the utmost importance in tidal circulation models.Based on the sequential data assimilation method and the concept of the Kalman gain matrix,this paper proposes a new nudging method with spatially dependent coefficients for tidal assimilation.The spatial-dependent nudging method not only retains the advantages of the traditional nudging method but also facilitates the direct determination of a more reasonable spatial distribution of nudging coefficients.Utilizing the M_(2)tidal constituent(the main lunar semidiurnal tide)as an illustration,we conducted assimilation experiments of sea-level data to the barotropic circulation and tide model to assess the global harmonic constants of the M_(2)constituent.The results demonstrate that the spatial-dependent nudging method successfully mitigates deviations of tidal phase lag.Following assimilation using the new method,the deviations of the M_(2)tidal amplitude and phase lag can be reduced by 47%and 18%compared to the traditional nudging method,respectively,while the respective values for the non-assimilated case are as much as 9%and 11%.We also applied the S-nudging method to realistic tidal simulations and noted a significantly enhanced effectiveness relative to traditional methods,making it highly valuable for modeling oceanic tidal circulations.
基金funded by the National Natural Science Foundation of China (Grant Nos.31972394 and 31501748)。
文摘Glucosinolates(GSLs) are a group of nitrogen-and sulfur-containing secondary metabolites, synthesized primarily in members of the Brassicaceae family, that play an important role in food flavor, plant antimicrobial activity, resistance to insect attack, stress tolerance, and human anti-cancer effects. As a sulfur-containing compound, glutathione has a strong connection with GSLs biosynthesis as a sulfur donor or redox system, and exists in reduced(glutathione;GSH) and oxidized(glutathione disulfide;GSSG) forms. However, the mechanism of GSH regulating GSLs biosynthesis remainds unclear. Hence, the exogenous therapy to pakchoi under normal growth condition and sulfur deficiency condition were conducted in this work to explore the relevant mechanism. The results showed that exogenous application of buthionine sulfoximine, an inhibitor of GSH synthesis, decreased the transcript levels of GSLs synthesis-related genes and transcription factors, as well as sulfur assimilation-related genes under the normal growth condition. Application of exogenous GSH inhibited the expression of GSLs synthesis-and sulfur assimilation-related genes under the normal condition, while the GSLs biosynthesis and the sulfur assimilation pathway were activated by exogenous application of GSH when the content of GSH in vivo of plants decreased owing to sulfur deficiency. Moreover,exogenous application of GSSG increased the transcript levels of GSLs synthesis-and sulfur assimilation-related genes under the normal growth condition and under sulfur deficiency. The present work provides new insights into the molecular mechanisms of GSLs biosynthesis underlying glutathione regulation.
基金supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2021JC0009)the Natural Science Foundation of China(Grant Nos.U2142212 and 42105136)。
文摘Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China Meteorological Administration Global Forecast System(CMA-GFS)has a model top near 0.1 hPa(60 km),the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa.In this study,channels 12–14 of the Advanced Microwave Sounding Unit A(AMSU-A)onboard five satellites of NOAA and METOP,whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS.It is shown that the new“Anchor”approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles.The bias growth rate of simulated upper-level channel observations is reduced to±0.001 K d^(–1),compared to–0.03 K d^(–1)derived from the current dynamic correction scheme.The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.
文摘The facies distribution of a reservoir is one of the biggest concerns for geologists,geophysicists,reservoir modelers,and reservoir engineers due to its high importance in the setting of any reliable decisionmaking/optimization of field development planning.The approach for parameterizing the facies distribution as a random variable comes naturally through using the probability fields.Since the prior probability fields of facies come either from a seismic inversion or from other sources of geologic information,they are not conditioned to the data observed from the cores extracted from the wells.This paper presents a regularized element-free Galerkin(R-EFG)method for conditioning facies probability fields to facies observation.The conditioned probability fields respect all the conditions of the probability theory(i.e.all the values are between 0 and 1,and the sum of all fields is a uniform field of 1).This property achieves by an optimization procedure under equality and inequality constraints with the gradient projection method.The conditioned probability fields are further used as the input in the adaptive pluri-Gaussian simulation(APS)methodology and coupled with the ensemble smoother with multiple data assimilation(ES-MDA)for estimation and uncertainty quantification of the facies distribution.The history-matching of the facies models shows a good estimation and uncertainty quantification of facies distribution,a good data match and prediction capabilities.
基金The National Key Research and Development Program of China under contract Nos 2022YFC3104804,2021YFC3101501,and 2017YFC1404103the National Programme on Global Change and Air-Sea Interaction of China under contract No.GASI-IPOVAI-04the National Natural Science Foundation of China under contract Nos 41876014,41606039,and 11801402.
文摘An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix,which can be achieved by solving the advection-diffusion equation.Because of the directionality of the advection term,the discrete method needs to be chosen very carefully.The finite analytic method is an alternative scheme to solve the advection-diffusion equation.As a combination of analytical and numerical methods,it not only has high calculation accuracy but also holds the characteristic of the auto upwind.To demonstrate its ability,the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method.The more widely used upwind difference method is used as a control approach.The result indicates that the finite analytic method has higher accuracy than the upwind difference method.For the two-dimensional case,the finite analytic method still has a better performance.In the three-dimensional variational assimilation experiment,the finite analytic method can effectively improve analysis field accuracy,and its effect is significantly better than the upwind difference and the central difference method.Moreover,it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.
