The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clea...The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.展开更多
This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and wind...This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational （4DVAR） data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments--assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function--are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line-- including the surface warm inflow, cold pool, gust front, and low-level wind--are much closer to the observations after assimilating the surface data in VDRAS.展开更多
The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A（AMSU-A） on the track prediction of Typhoon Megi（2010） was studied using the Weather Research and Forecasting（WRF） model and a hybr...The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A（AMSU-A） on the track prediction of Typhoon Megi（2010） was studied using the Weather Research and Forecasting（WRF） model and a hybrid ensemble threedimensional variational（En3DVAR） data assimilation（DA） system.The influences of tuning the length scale and variance scale factors related to the static background error covariance（BEC） on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.展开更多
The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the acc...The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.展开更多
The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, o...The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.展开更多
In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RM...In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.展开更多
An optimal interpolation assimilation model for satellite altimetry data is developed based on Princeton Ocean Model （POM）, which is applied in a quasi-global domain, by the method of isotropic correlation between s...An optimal interpolation assimilation model for satellite altimetry data is developed based on Princeton Ocean Model （POM）, which is applied in a quasi-global domain, by the method of isotropic correlation between sea level anomaly （SLA） and sea temperature anomaly. The performance of this assimilation model is validated by the modeled results of SLA and the current patterns. Comparisons between modeling and satellite data show that both the magnitudes and distribution patterns of the sinmlated SLA are improved by assimilation. The most significant improvement is that meso-scale systems, e.g., eddies, are well reconstructed. The evolution of an eddy located in the northwest Pacific Ocean is traced by using the assimilation model. Model results show that during three months the eddy migrated southwestward for about 6 degrees before merging into the Kuroshio. The three dimensional structure of this eddy on 12 August 2001 is further analyzed. The strength of this warm, cyclonic eddy decreases with the increase of depth. The eddy shows different horizontal patterns at different layers, and the SLA and temperature fields agree with each other well. This study suggests that this kind of data assimilation is economic and reliable for eddy reconstruction, and can be used as a promising technique in further studies of ocean eddies as well as other fine circulation structures.展开更多
Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents th...Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).展开更多
In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite...In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite based on the Weather Research and Forecasting(WRF)model.In this LDA method,the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature.The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system.Meanwhile,the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged.This method considers the physical nature of the convective system,in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.The impact of this LDA method on short-term(≤6 h)forecasts was evaluated using two severe convective events in eastern China:a multi-region heavy rainfall event and a thunderstorm high-wind event.The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period,leading to a more reasonable storm environment.In the forecast fields,the simulations with LDA produced more realistic convective structures,resulting in an improvement in forecasts of precipitation and high winds.展开更多
The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo glo...The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.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
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
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
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
This study applies Ensemble Optimal Interpolation(EnOI)to assimilate individual spectral components derived from National Data Buoy Center(NDBC)buoy directional spectra into the WAVEWATCHⅢ(WW3)wave model during tropi...This study applies Ensemble Optimal Interpolation(EnOI)to assimilate individual spectral components derived from National Data Buoy Center(NDBC)buoy directional spectra into the WAVEWATCHⅢ(WW3)wave model during tropical cyclone(TC)Isaias(2020).The analysis provides a comprehensive evaluation of the assimilation’s impact on wave parameters,frequency spectra,and directional spectra.Two series of assimilation experiments—one based on spectral components(Exp^(*)-DaSpec)and the other on significant wave height(Exp^(*)-DaSWH)—are evaluated against a non-assimilated control run(Exp-NoDa).Particular focus is placed on six key parameters:SWH,mean wave period(MWP),mean wave direction(MWD),mean wave directional spread(MWS),dominant wave period(DWP),and dominant wave direction(DWD).Sensitivity analyses suggest 400 km and 0.30 as appropriate values for the localization radius and observation error variance,respectively,though no single setting is optimal across all wave parameters.Exp4-DaSWH and Exp4-DaSpec are therefore selected as representative experiments.In non-independent validation,Exp4-DaSpec generally outperforms Exp-NoDa across MWP,MWD,MWS,DWP,and DWD,demonstrating closer agreement with observed frequency spectra and directional patterns.In independent validation,Exp4-DaSpec maintains superior overall performance,whereas Exp4-DaSWH shows only limited improvement.Exp4-DaSWH may capture the spectral peak near 0.1 Hz,although its directional characteristics remain largely similar to those of Exp-NoDa.Importantly,the assimilation experiments significantly improve SWH in both non-independent and independent validations,with Exp4-DaSWH slightly outperforming Exp4-DaSpec overall.展开更多
Site index(SI)is determined from the top height development and is a proxy for forest productivity,defined as the expected top height for a given species at a certain index age.In Norway,an index age of 40 years is us...Site index(SI)is determined from the top height development and is a proxy for forest productivity,defined as the expected top height for a given species at a certain index age.In Norway,an index age of 40 years is used.By using bi-temporal airborne laser scanning(ALS)data,SI can be determined using models estimated from SI observed on field plots(the direct approach)or from predicted top heights at two points in time(the height differential approach).Time series of ALS data may enhance SI determination compared to conventional methods used in operational forest inventory by providing more detailed information about the top height development.We used longitudinal data comprising spatially consistent field and ALS data collected from training plots in 1999,2010,and 2022 to determine SI using the direct and height differential approaches using all combinations of years and performed an external validation.We also evaluated the use of data assimilation.Values of root mean square error obtained from external validation were in the ranges of 16.3%–21.4%and 12.8%–20.6%of the mean fieldregistered SI for the direct approach and the height differential approach,respectively.There were no statistically significant effects of time series length or the number of points in time on the obtained accuracies.Data assimilation did not result in any substantial improvement in the obtained accuracies.Although a time series of ALS data did not yield greater accuracies compared to using only two points in time,a larger proportion of the study area could be used in ALS-based determination of SI when a time series was available.This was because areas that were unsuitable for SI determination between two points in time could be subject to SI determination based on data from another part of the time series.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
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
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
基金sponsored by the National Natural Science Foundation of China(Grant No.42275171)the Basic Research Operating Expenses of the Institute of Meteorological Sciences,CMA(Grant No.2023Z019)+3 种基金the National Key Research and Development Program of China(Grant No.2022YFF0801304)the China Meteorological Administration Youth Innovation Team Fund(Grant No.CMA2024QN05)a Liaoning Provincial Meteorological Bureau Project(Grant No.D202201)Shenyang Institute of Atmospheric Environment Projects(Grant Nos.2022SYIAEJY13 and 2018SYIAEZD5).
文摘The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.
基金primarily supported by the National Fundamental Research 973 Program of China(Grant No.2013CB430101)the National Natural Science Foundation of China(Grant Nos.41275031,41322032 and 41475015)+1 种基金the Social Commonwealth Research Program(Grant Nos.GYHY201506004 and GYHY201006007)the Program for New Century Excellent Talents in Universities of China
文摘This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational （4DVAR） data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments--assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function--are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line-- including the surface warm inflow, cold pool, gust front, and low-level wind--are much closer to the observations after assimilating the surface data in VDRAS.
基金supported by the National Fundamental 973 Research Program of China(Grant No.OPPAC-2013CB430102)Natural Science Foundation of China(41375025)the Priority Academic Program Development(PAPD) of Jiangsu Higher Education Institutions
文摘The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A（AMSU-A） on the track prediction of Typhoon Megi（2010） was studied using the Weather Research and Forecasting（WRF） model and a hybrid ensemble threedimensional variational（En3DVAR） data assimilation（DA） system.The influences of tuning the length scale and variance scale factors related to the static background error covariance（BEC） on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.
基金The Ocean Public Welfare Industry Research Special of China under contract No.201105009the Fundamental Research Funds for Central Universities of China under contract No.2013B20714+1 种基金the National Natural Science Foundation of China under contract Nos 41222038 and 41206023the National Basic Research Program of China(973 Program)under contract No.2011CB403606
文摘The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.
基金The National Natural Science Foundation of China under contract Nos 41030854,41106005,41176003,and 41206178the National Science and Technology Support Program of China under contract No.2011BAC03B02-01-04
文摘The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.
基金Supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(grant no.2019QZKK0105)the National Key Research and Development Program of China(2018YFC1506603).
文摘In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.
基金The Key Project of National Natural Science Foundation Basic Research Program of China (Argo973, Grant No. 2007CB816002)special fund for fundamental scientific research under contract No. 2008G08the advanced programs of ministry of personnel for returness
文摘An optimal interpolation assimilation model for satellite altimetry data is developed based on Princeton Ocean Model （POM）, which is applied in a quasi-global domain, by the method of isotropic correlation between sea level anomaly （SLA） and sea temperature anomaly. The performance of this assimilation model is validated by the modeled results of SLA and the current patterns. Comparisons between modeling and satellite data show that both the magnitudes and distribution patterns of the sinmlated SLA are improved by assimilation. The most significant improvement is that meso-scale systems, e.g., eddies, are well reconstructed. The evolution of an eddy located in the northwest Pacific Ocean is traced by using the assimilation model. Model results show that during three months the eddy migrated southwestward for about 6 degrees before merging into the Kuroshio. The three dimensional structure of this eddy on 12 August 2001 is further analyzed. The strength of this warm, cyclonic eddy decreases with the increase of depth. The eddy shows different horizontal patterns at different layers, and the SLA and temperature fields agree with each other well. This study suggests that this kind of data assimilation is economic and reliable for eddy reconstruction, and can be used as a promising technique in further studies of ocean eddies as well as other fine circulation structures.
基金supported by the National Natural Science Foundation of China (Grant No. 41305066)the Special Funds for Public Welfare of China (Grant No. GYHY201306045)the National Basic Research Program of China (Grant Nos. 2010CB951101 and 2010CB428403)
文摘Information on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil(VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter(LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment(OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index(LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the potential to provide more accurate estimations of the net primary productivity(NPP) and carbon flux to atmosphere(CFta).
基金supported by the National Key Research and Development Program of China(2017YFC1501902)the Natural Science Foundation of Shanghai Science and Technology Committee(21ZR1457700).
文摘In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite based on the Weather Research and Forecasting(WRF)model.In this LDA method,the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature.The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system.Meanwhile,the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged.This method considers the physical nature of the convective system,in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.The impact of this LDA method on short-term(≤6 h)forecasts was evaluated using two severe convective events in eastern China:a multi-region heavy rainfall event and a thunderstorm high-wind event.The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period,leading to a more reasonable storm environment.In the forecast fields,the simulations with LDA produced more realistic convective structures,resulting in an improvement in forecasts of precipitation and high winds.
基金National Program on Key Basic Research Project of China(2012CB955203,2013CB430202)National Natural Science Foundation of China(40231014,41175065)+1 种基金China Meteorological Administration R&D Special Fund for Public Welfare(meteorology)(GYHY201306021)National High Technology Research and Development Program of China(2010AA012404)
文摘The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.
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The Youth Talent Support Program for Doctoral Students of the China Association for Science and Technologythe Key Laboratory of Space Ocean Remote Sensing and Application at Ministry of Natural Resources under contract No.2023CFO005+1 种基金the National Natural Science Foundation of China under contract No.42176011the Fundamental Research Funds for the Central Universities under contract No.24CX03001A.
文摘This study applies Ensemble Optimal Interpolation(EnOI)to assimilate individual spectral components derived from National Data Buoy Center(NDBC)buoy directional spectra into the WAVEWATCHⅢ(WW3)wave model during tropical cyclone(TC)Isaias(2020).The analysis provides a comprehensive evaluation of the assimilation’s impact on wave parameters,frequency spectra,and directional spectra.Two series of assimilation experiments—one based on spectral components(Exp^(*)-DaSpec)and the other on significant wave height(Exp^(*)-DaSWH)—are evaluated against a non-assimilated control run(Exp-NoDa).Particular focus is placed on six key parameters:SWH,mean wave period(MWP),mean wave direction(MWD),mean wave directional spread(MWS),dominant wave period(DWP),and dominant wave direction(DWD).Sensitivity analyses suggest 400 km and 0.30 as appropriate values for the localization radius and observation error variance,respectively,though no single setting is optimal across all wave parameters.Exp4-DaSWH and Exp4-DaSpec are therefore selected as representative experiments.In non-independent validation,Exp4-DaSpec generally outperforms Exp-NoDa across MWP,MWD,MWS,DWP,and DWD,demonstrating closer agreement with observed frequency spectra and directional patterns.In independent validation,Exp4-DaSpec maintains superior overall performance,whereas Exp4-DaSWH shows only limited improvement.Exp4-DaSWH may capture the spectral peak near 0.1 Hz,although its directional characteristics remain largely similar to those of Exp-NoDa.Importantly,the assimilation experiments significantly improve SWH in both non-independent and independent validations,with Exp4-DaSWH slightly outperforming Exp4-DaSpec overall.
基金part of the Centre for Research-based Innovation SmartForest:Bringing Industry 4.0 to the Norwegian forest sector(NFR SFI project no.309671,smartforest.no)。
文摘Site index(SI)is determined from the top height development and is a proxy for forest productivity,defined as the expected top height for a given species at a certain index age.In Norway,an index age of 40 years is used.By using bi-temporal airborne laser scanning(ALS)data,SI can be determined using models estimated from SI observed on field plots(the direct approach)or from predicted top heights at two points in time(the height differential approach).Time series of ALS data may enhance SI determination compared to conventional methods used in operational forest inventory by providing more detailed information about the top height development.We used longitudinal data comprising spatially consistent field and ALS data collected from training plots in 1999,2010,and 2022 to determine SI using the direct and height differential approaches using all combinations of years and performed an external validation.We also evaluated the use of data assimilation.Values of root mean square error obtained from external validation were in the ranges of 16.3%–21.4%and 12.8%–20.6%of the mean fieldregistered SI for the direct approach and the height differential approach,respectively.There were no statistically significant effects of time series length or the number of points in time on the obtained accuracies.Data assimilation did not result in any substantial improvement in the obtained accuracies.Although a time series of ALS data did not yield greater accuracies compared to using only two points in time,a larger proportion of the study area could be used in ALS-based determination of SI when a time series was available.This was because areas that were unsuitable for SI determination between two points in time could be subject to SI determination based on data from another part of the time series.
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.
