Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

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.

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System：Dual-Resolution Implementation and Testing Results

A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution（HR） deterministic background forecast with lower-resolution（LR） EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation（GSI） 3D variational（3DVar）analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT

The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar(CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting(WRF) model,the WRF model with a three-dimensional variational(3DVAR) data assimilation system and the WRF model with an ensemble square root filter(EnSRF) data assimilation system.In addition,the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze-Huaihe River Basin from July 4 to July 5,2003,through numerical simulation.The results show the following.(1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region,enhance the convective activities and reduce excessive simulated precipitation.(2) The 3DVAR assimilation method significantly adjusts the horizontal wind field.The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model.In addition,the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands.(3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model.The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers.In addition,the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands.(4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation,rain-band areal coverage and the center location and intensity of precipitation.

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.

A data assimilation-based forecast model of outer radiation belt electron fluxes

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting

A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.

IMPACT OF VERTICAL RESOLUTION, MODEL TOP AND DATA ASSIMILATION ON WEATHER FORECASTING——A CASE STUDY

The impacts of stratospheric initial conditions and vertical resolution on the stratosphere by raising the model top,refining the vertical resolution,and the assimilation of operationally available observations,including conventional and satellite observations,on continental U.S.winter short-range weather forecasting,were investigated in this study.The initial and predicted wind and temperature profiles were analyzed against conventional observations.Generally,the initial wind and temperature bias profiles were better adjusted when a higher model top and refined vertical resolution were used.Negative impacts were also observed in both the initial wind and temperature profiles,over the lower troposphere.Different from the results by only raising the model top,the assimilation of operationally available observations led to significant improvements in both the troposphere and stratosphere initial conditions when a higher top was used.Predictions made with the adjusted stratospheric initial conditions and refined vertical resolutions showed generally better forecasting skill.The major improvements caused by raising the model top with refined vertical resolution,as well as those caused by data assimilation,were in both cases located in the tropopause and lower stratosphere.Negative impacts were also observed in the predicted near surface wind and lower-tropospheric temperature.These negative impacts were related to the uncertainties caused by more stratospheric information,as well as to some physical processes.A case study shows that when we raise the model top,put more vertical layers in stratosphere and apply data assimilation,the precipitation scores can be slightly improved.However,more analysis is needed due to uncertainties brought by data assimilation.

Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting

An ensemble three-dimensional ensemble-variational(3DEnVar)data assimilation(E3DA)system was developed within the Weather Research and Forecasting model’s 3DVar framework to assimilate radar data to improve convective forecasting.In this system,ensemble perturbations are updated by an ensemble of 3DEnVar and the ensemble forecasts are used to generate the flow-dependent background error covariance.The performance of the E3DA system was first evaluated against one experiment without radar DA and one radar DA experiment with 3DVar,using a severe storm case over southeastern China on 5 June 2009.Results indicated that E3DA improved the quantitative forecast skills of reflectivity and precipitation,as well as their spatial distributions in terms of both intensity and coverage over 3DVar.The root-mean-square error of radial velocity from 3DVar was reduced by E3DA,with stronger low-level wind closer to observation.It was also found that E3DA improved the wind,temperature and water vapor mixing ratio,with the lowest errors at the surface and upper levels.3DVar showed moderate improvements in comparison with forecasts without radar DA.A diagnosis of the analysis revealed that E3DA increased vertical velocity,temperature,and humidity corresponding to the added reflectivity,while 3DVar failed to produce these adjustments,because of the lack of reasonable cross-variable correlations.The performance of E3DA was further verified using two convective cases over southern and southeastern China,and the reflectivity forecast skill was also improved over 3DVar.

APPLICATION EXPERIMENT OF ASSIMILATING RADAR-RETRIEVED WATER VAPOR IN SHORT-RANGE FORECAST OF RAINFALL IN THE ANNUALLY FIRST RAINY SEASON OVER SOUTH CHINA

A scheme of assimilating radar-retrieved water vapor is adopted to improve the quality of NWP initial field for improvement of the accuracy of short-range precipitation prediction. To reveal the impact of the assimilation of radar-retrieved water vapor on short-term precipitation forecast, three parallel experiments, cold start, hot start and hot start plus the assimilation of radar-retrieved water vapor, are designed to simulate the 31 days of May, 2013 with a fine numerical model for South China. Furthermore, a case of heavy rain that occurred from 8-9 May 2013 over the region from the southwest of Guangdong province to Pearl River Delta is analyzed in detail. Results show that the cold start experiment is not conducive to precipitation 12 hours ahead; the hot start experiment is able to reproduce well the first6 hours of precipitation, but badly for subsequent prediction; the experiment of assimilating radar-retrieved water vapor is not only able to simulate well the precipitation 6 hours ahead, but also able to correctly predict the evolution of rain bands from 6 to 12 hours in advance.

Evaluation of Radar and Automatic Weather Station Data Assimilation for a Heavy Rainfall Event in Southern China

To improve the accuracy of short-term （0-12 h） forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System （HAPS）, has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting （WRF-ARW） model and the Advanced Regional Prediction System （ARPS） three-dimensional variational data assimilation （3DVAR） pack- age. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station （AWS） data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting （QPF） by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to 9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6-9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score （FSS） and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.

CORRECTION OF ASYMMETRIC STRENGTHENING OF QUIKSCAT WIND FIELD AND ASSIMILATION APPLICATION IN TYPHOON SIMULATION

As an approach to the technological problem that the wind data of QuikSCAT scatterometer cannot accurately describe the zone of typhoon-level strong wind speed, some objective factors such as the typhoon moving speed, direction and friction are introduced in this study to construct the asymmetric strengthening of the QuikSCAT wind field. Then by adopting a technology of four-dimensional data assimilation, an experiment that includes both the assimilation and forecasting phases is designed to simulate Typhoon Rananim numerically. The results show that with model constraints and adjustment, this technology can incorporate the QuikSCAT wind data to the entire column of the model atmosphere, improve greatly the simulating effects of the whole-column wind, pressure field and the track as well as the simulated typhoon intensity covered by the forecast phase, and work positively for the forecasting of landfall locations.

Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012)

We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar)method in data assimilation and prediction experiments for Typhoon Haikui(2012)using the Weather Research and Forecasting(WRF)model.Observation data included radial velocity(Vr)and reflectivity(Z)data from a single Doppler radar,quality controlled prior to assimilation.Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods.Compared with a forecast that began with NCEP analysis data,our radar data assimilation results were clearly improved in terms of structure,intensity,track,and precipitation prediction for Typhoon Haikui(2012).The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient.The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity,track,and precipitation;however,the impacts of Vr data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.

Impact of assimilating FY-3C MWHS2 data in the RMAPS-ST forecast system on its rainfall forecasts

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.

ASSIMILATION OF OBSERVED SURFACE WIND WITH GRAPES

With the advances of numerical weather simulation and reduced data assimilation updating cycle, surface observation data assimilation becomes more and more important in data assimilation systems It is widely accepted that a better data assimilation system should contain the restriction of thermod^amic processes in the surface layer. Therefore, in this paper, a new surface wind observation operator is utilized in Global and Regional Assimilation PrEdiction System_3D-Variance （GRAPES_3D-Var）, with the restriction of thermodynamic process in the planetary boundary layer （PBL）. In order to research the ability of this new surface wind observation operator in assimilation and forecasting, a series of experiments are operated by using the GRAPES model. The main results indicate that this new method of surface wind observation operator has positive impact on the forecast with the GRAPES model.

Preliminary Meteorological Results of a Four-Dimensional Data Assimilation Technique in Southern Italy

A four-dimensional data assimilation (FDDA) scheme based on a Newtonian relaxation (or “nudging”) was tested using observational asynoptic data collected at a coastal site in the Central Mediterranean peninsula of Calabria, southern Italy. The study is referred to an experimental campaign carried out in summer 2008. For this period a wind profiler, a sodar and two surface meteorological stations were considered. The collected measurements were used for the FDDA scheme, and the technique was incorporated into a tailored version of the Regional Atmospheric Modeling System (RAMS). All instruments are installed and operated routinely at the experimental field of the CRATI-ISAC/CNR located at 600 m from the Tyrrhenian coastline. Several simulations were performed, and the results show that the assimilation of wind and/or temperature data, both throughout the simulation time (continuous FDDA) and for a 12 h time window (forecasting configuration), produces improvements of the model performance. Considering a whole single day, improvements are sub-stantial in the case of continuous FDDA while they are smaller in the case of forecasting configuration. En-hancements, during the first six hours of each run, are generally higher. The resulting meteorological fields are finalised as input into air quality and agro-meteorological models, for short-term predictions of renew-able energy production forecast, and for atmospheric model initialization.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula

The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality forecasting.Meteorological data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the CCMM.In this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula.The nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA.When the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain.This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological predictions.Compared to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA.The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the case.Therefore,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.