Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea

The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.

Reconstructing urban wind flows for urban air mobility using reduced-order data assimilation

Advancements in uncrewed aircrafts and communications technologies have led to a wave of interest and investment in unmanned aircraft systems(UASs)and urban air mobility(UAM)vehicles over the past decade.To support this emerging aviation application,concepts for UAS/UAM traffic management(UTM)systems have been explored.Accurately characterizing and predicting the microscale weather conditions,winds in particular,will be critical to safe and efficient operations of the small UASs/UAM aircrafts within the UTM.This study implements a reduced order data assimilation approach to reduce discrepancies between the predicted urban wind speed with computational fluid dynamics(CFD)Reynolds-averaged Navier Stokes(RANS)model with real-world,limited and sparse observations.The developed data assimilation system is UrbanDA.These observations are simulated using a large eddy simulation(LES).The data assimilation approach is based on the time-independent variational framework and uses space reduction to reduce the memory cost of the process.This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations and hence taking into account flow unsteadiness in a time when only the mean flow quantities are resolved.Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field.It is shown that near-wall locations,near turbulence generation areas with high wind speeds have the highest impact.Approximating the model error with its principal mode provides a better agreement with the truth and the hazardous areas for UAS navigation increases by more than 10%as wind hazards resulting from buildings wakes are better simulated through this process.

Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

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.

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.

Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting

Aircraft Meteorological Data Relay(AMDAR)observations have been widely used in numerical weather prediction(NWP)because of its high spatiotemporal resolution.The observational error of AMDAR is influenced by aircraft flight altitude and atmospheric condition.In this study,the wind speed and altitude dependent observational error of AMDAR is estimated.The statistical results show that the temperature and the observational error in wind speeds slightly decrease as altitude increases,and the observational error in wind speed increases as wind speed increases.Pseudo single AMDAR observation assimilation tests demonstrate that the wind speed and altitude dependent observational error can provide more reasonable analysis increment.Furthermore,to assess the performance of wind speed and altitude dependent observational error on data assimilation and forecasting,two-month 3-hourly cycling data assimilation and forecast experiments based on the Weather Research and Forecasting Model(WRF)and its Data Assimilation system(WRFDA)are performed for the period during 1 September-31 October,2017.The results of the two-month 3-hourly cycling experiments indicate that new observational error improves analysis and forecast of wind field and geo-potential height,and has slight improvements on temperature.The Fractions Skill Score(FSS)of the 6-h accumulated precipitation shows that new wind speed and altitude dependent observational error leads to better precipitation forecast skill than the default observational error in the WRFDA does.

Satellite All-sky Infrared Radiance Assimilation:Recent Progress and Future Perspectives

Satellite infrared(IR)sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction(NWP)models and atmospheric analysis/reanalysis.This paper reviews the development of satellite IR data assimilation in NWP in recent years,especially the assimilation of all-sky satellite IR observations.The major challenges and future directions are outlined and discussed.

A STUDY ON THE APPLICATION OF FY-2E CLOUD DRIFT WIND HEIGHT REASSIGNMENT IN NUMERICAL FORECAST OF TYPHOON CHANTHU(1003) TRACK

In this paper, we first analyzed cloud drift wind(CDW) data distribution in the vertical direction, and then reassigned the height of every CDW in the research domain in terms of background information, and finally, conducted contrast numerical experiments of assimilating the CDW data before and after reassignment to examine the impacts on the forecast of the track of Typhoon Chanthu(1003) from 00:00(Coordinated Universal Time) 21 July to 00:00 UTC23 July, 2010. The analysis results of the CDW data indicate that the number of CDWs is mainly distributed in the midand upper-troposphere above 500 h Pa, with the maximum number at about 300 h Pa. The height reassigning method mentioned in this work may update the height effectively, and the CDW data are distributed reasonably and no obvious contradiction occurs in the horizontal direction after height reassignment. After assimilating the height-reassigned CDW data, especially the water vapor CDW data, the initial wind field around Typhoon Chanthu(1003) became more reasonable, and then the steering current leading the typhoon to move to the correct location became stronger. As a result, the numerical track predictions are improved.

Blacklist Design of AMDAR Temperature Data and Their Application in the CMA-GFS

Blacklist methods are used in the CMA Global Forecasting System(CMA-GFS)to improve the application of aircraft temperature data to numerical weather prediction in the Northern Hemisphere and the tropics.In this paper,the ERA5 re-analysis data are used to analyze aircraft temperature observation errors of each aircraft and a blacklist is established using pre-quality controls and threshold methods.The blacklist-filtered and blacklisted aircraft temperature data are then applied to the four-dimensional variational assimilation system,respectively,and an assimilation cycle forecast for the period from September 1 to 30,2019 is carried out.The results show an uneven distribution in the global aircraft blacklist data.After the application of the blacklist methods,the RMSE of geopotential height and temperature analysis field decrease in the vertical direction by a maximum of~1.5 gpm at 200 hPa and~0.15 K at 250 hPa,respectively.Overall,the blacklist methods of aircraft temperature data improve the analysis and forecast in the CMA-GFS.

PREDICTION OF ANNUAL FREQUENCY OF AFFECTING TROPICAL CYCLONE USING THE PRODUCTS OF A HYBRID COUPLED AIR-SEA MODEL

Better correlation exists between the activity of tropical cyclones affecting East China and Shanghai and the concurrent signals of SSTA in tropical Pacific. In an attempt to justify this statistic finding, a four-dimensional variational data assimilation system is established to optimize the initial fields of a hybrid air-sea coupled model. The prediction skill of tropical SSTA is improved. Long-term statistical models for predicting annual TC frequency affecting East China area and Shanghai city are developed based on 37-year products of this model and the forecast trials have achieved satisfactory results in 1998 and 1999.

The adjoint-based Two Oceans One Sea State Estimate(TOOSSE)

An eddy-resolving four-dimensional variational(adjoint)data assimilation and state estimate was constructed for the low-to mid-latitude Pacifi c,Indian Oceans,and South China Sea based on the framework of“Estimating the Circulation and Climate of the Oceans(ECCO)”.It is named as the Two Oceans One Sea State Estimate(TOOSSE).It fi ts a model to a number of modern observations of 2015-2016,including the Argo fl oat temperature and salinity,satellite altimetric sea surface anomalies,by adjusting initial temperature and salinity,sea surface boundary conditions,and background diapycnal diff usivities.In total,~50%of the original model-data misfi ts have been eliminated,and the estimated state agreed well with a variety of independent observations at meso-to large scales,and on the intra-seasonal to interannual timescales.Mesoscale variability is systematically strengthened in TOOSSE and closer to observations than that without data assimilation,which is especially evidenced by the improved simulation of the mesoscale tropical instability waves(TIWs).Adjustments to ocean surface forcing parameters exhibit both large and frontal/mesoscale structures,and the magnitude reach 20%-100%of the fi rst guesses;the adjustments to diapycnal diff usivity exhibit an obvious elevation(decrement)in(below)the thermocline in the equatorial band.The results indicate that TOOSSE represents a dynamically and thermodynamically consistent ocean state estimate of the 2015-2016 Indo-Pacifi c Ocean,and can be widely utilized for regional process studies.

Effect of 2-m Temperature Data Assimilation in the CMA-MESO 3DVAR System

Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.

Application of Lightning Data Assimilation to Numerical Forecast of Super Typhoon Haiyan (2013)

Previous observations from World Wide Lightning Location Network(WWLLN) and satellites have shown that typhoon-related lightning data have a potential to improve the forecast of typhoon intensity. The current study was aimed at investigating whether assimilating TC lightning data in numerical models can play such a role. For the case of Super Typhoon Haiyan in 2013, the lightning data assimilation(LDA) was realized in the Weather Research and Forecasting(WRF) model, and the impact of LDA on numerical prediction of Haiyan’s intensity was evaluated.Lightning data from WWLLN were used to adjust the model’s relative humidity(RH) based on the method developed by Dixon et al.(2016). The adjusted RH was output as a pseudo sounding observation, which was then assimilated into the WRF system by using the three-dimensional variational(3DVAR) method in the cycling mode at 1-h intervals. Sensitivity experiments showed that, for Super Typhoon Haiyan(2013), which was characterized by a high proportion of the inner-core(within 100 km from the typhoon center) lightning, assimilation of the inner-core lightning data significantly improved its intensity forecast, while assimilation of the lightning data in the rainbands(100–500 km from the typhoon center) led to no obvious improvement. The improvement became more evident with the increase in LDA cycles, and at least three or four LDA cycles were needed to achieve obvious intensity forecast improvement. Overall, the improvement in the intensity forecast by assimilation of the inner-core lightning data could be maintained for about 48 h. However, it should be noted that the LDA method in this study may have a negative effect when the simulated typhoon is stronger than the observed, since the LDA method cannot suppress the spurious convection.

Assimilation of All-Sky Radiance from the FY-3 MWHS-2 with the Yinhe 4D-Var System

Compared with traditional microwave humidity sounding capabilities at 183 GHz,new channels at 118 GHz have been mounted on the second generation of the Microwave Humidity Sounder(MWHS-2)onboard the Chinese FY-3C and FY-3D polar orbit meteorological satellites,which helps to perform moisture sounding.In this study,as the allsky approach can manage non-linear and non-Gaussian behavior in cloud-and precipitation-affected satellite radiances,the MWHS-2 radiances in all-sky conditions were first assimilated in the Yinhe four-dimensional variational data assimilation(YH4DVAR)system.The data quality from MWHS-2 was evaluated based on observation minus background statistics.It is found that the MWHS-2 data of both FY-3C and FY-3D are of good quality in general.Six months of MWHS-2 radiances in all-sky conditions were then assimilated in the YH4DVAR system.Based on the forecast scores and observation fits,we conclude that the all-sky assimilation of the MWHS-2 at 118-and 183-GHz channels on FY-3C/D is beneficial to the analysis and forecast fields of the temperature and humidity,and the impact on the forecast skill scores is neutral to positive.Additionally,we compared the impacts of assimilating the 118-GHz channels and the equivalent Advanced Microwave Sounding Unit-A(AMSUA)channels on global forecast accuracy in the absence of other satellite observations.Overall,the impact of the 118-GHz channels on the forecast accuracy is not as large as that for the equivalent AMSUA channels.Nevertheless,all-sky radiance assimilation of MWHS-2 in the YH4DVAR system has indeed benefited from the 118-GHz channels.

Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method——A Case Study of a Mesoscale Convective System

Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.

Accounting for CO2 Variability over East Asia with a Regional Joint Inversion System and Its Preliminary Evaluation

A regional surface carbon dioxide(CO_2) flux inversion system, the Tan-Tracker-Region, was developed by incorporating an assimilation scheme into the Community Multiscale Air Quality(CMAQ) regional chemical transport model to resolve fine-scale CO_2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach(POD-4 DVar) is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO_2 concentrations and surface CO_2 fluxes are applied to help reduce the uncertainty in initial CO_2 concentrations. A persistence dynamical model was developed to describe the evolution of the surface CO_2 fluxes and help avoid the "signal-to-noise" problem; thus, CO_2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments(OSSEs). The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the performance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation network in different CO_2 flux situations. The results indicate that more observation sites would be useful to systematically improve the estimation of CO_2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO_2 flux variability over East Asia could be performed with the regional inversion system.