Meteorological Data Assimilation Research Articles

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

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

Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI)

The NASA Goddard Earth Observing System Composition Forecast system (GEOS-CF) provides global near-real-time analyses and forecasts of atmospheric composition. The current version of GEOS-CF builds on the GEOS general circulation model with Forward Processing assimilation of meteorological data (GEOS-FP) and includes detailed GEOS-Chem tropospheric and stratospheric chemistry. Here we add 3D variational data assimilation in GEOS-CF to assimilate satellite observations of ozone including MLS vertical profiles, OMI total columns, and AIRS and IASI hyperspectral 9.6 μm radiances. We focus our evaluations on the troposphere. We find that the detailed tropospheric chemistry in GEOS-CF significantly improves the simulated background ozone fields relative to previous versions of the GEOS model, allowing for specification of smaller background errors in assimilation and resulting in smaller assimilation increments to correct the simulated ozone. Assimilation increments are largest in the upper troposphere and are consistent between satellite data sets. The OMI and MLS ozone data generally provide more information than the AIRS and IASI radiances except at high latitudes where the radiances provide more information. Comparisons to independent ozonesonde and aircraft (ATom-4) observations for 2018 show significant GEOS-CF improvement from the assimilation, particularly in the extratropical upper troposphere.

Comparing Quality Control Procedures Based on Minimum Covariance Determinant and One-Class Support Vector Machine Methods of Aircraft Meteorological Data Relay Data Assimilation in a Binary Typhoon Forecasting Case

This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on the prediction of two typhoons, Nesat and Haitang (2017), using the Gridpoint Statistical Interpolation (GSI) assimilation system and the Weather Research and Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) and one-class Support Vector Machine (OCSVM), were employed to perform QC on the AMDAR observations before data assimilation. The QC results indicated that both methods significantly reduced kurtosis, skewness, and discrepancies between the AMDAR data and the reanalysis data. The data distribution after applying the MCD-QC method exhibited a closer resemblance to a Gaussian distribution. Four numerical experiments were conducted to assess the impact of different AMDAR data qualities on typhoon forecasting, including a control experiment without data assimilation (EXP-CNTL), assimilating all AMDAR observations (EXP-RAW), assimilating observations after applying MCD-QC (EXP-MCD), and assimilating observations after applying OCSVM-QC (EXP-SVM). The results demonstrated that using AMDAR data in assimilation improved the track and intensity prediction of the typhoons. Furthermore, utilizing QC before assimilation enhanced the performance of track forecasting prediction, with EXP-MCD showing the best performance. As for intensity prediction, the three assimilation experiments exhibited varying strengths and weaknesses at different times, with EXP-MCD showing smaller intensity forecast errors on average.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

Assessment of the impact of atmospheric aerosols and meteorological data assimilation on simulation of the weather over India during summer 2015

This study investigates the sensitivity of climate model-predicted surface radiation and surface meteorological parameters to atmospheric aerosols and meteorological data assimilation (meteorological initial and boundary conditions) utilizing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For this purpose, three sets of simulations including WRFChemCntrl (WRFChem without meteorological data assimilation), WRFChemDA (WRFChem with meteorological data assimilation) and WRFDA (WRF only with meteorological data assimilation) were performed over the South Asian domain. A 12-hourly cyclic 3-dimensional variational (3DVAR) meteorological data assimilation (DA) of in-situ meteorological observations was used to generate WRFChemDA and WRFDA reanalyses, during summer (March–May) 2015. The Carbon Bond Mechanism-Z (CBMZ) gas-phase chemistry with Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was selected for the chemistry simulations. A reduction in incoming shortwave radiation (∼10–60 W/m2) over the Indian landmass and a slight increase (∼10–20 W/m2) over the surrounding oceanic region due to the influence of aerosols in WRF-Chem simulations was observed, suggesting that aerosols effects, were simulated by the model successfully. These effects of aerosols were further evident in the model output of other parameters such as outgoing longwave radiation at the surface, 2-m temperature (T2), 2-m relative humidity (RH2), and planetary boundary layer height (PBLH). Our results also show that the inclusion of aerosols in the WRFDA simulations (i.e WRFChemDA) improved the prediction of incoming shortwave radiation but deteriorated some of the meteorological parameters. However, an improved agreement between model simulated radiation, T2, RH2 and PBLH and observations was evident in WRFChemDA output compared to WRFChemCntrl output, reinforcing DA induced improvements in meteorological parameters for a given model set-up.

Meteorological Data Warehousing and Analysis for Supporting Air Navigation

Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data warehouse (DW) based on a new hybrid logical schema, concerning the assimilation and maintenance of historical meteorological data from all operating airports in Greece, along with data in the Greek Flight Information Region related to flight delays and cancellations. SQL is used for querying these data and makes them easily accessible and manageable. The data from the DW are collected and used as training data for the induction of predictive models. In this study, the prediction problem is cast as a classification task, and different decision tree induction techniques are applied to build accurate models that allow flexible scheduling and planning for the minimization of waiting time and inconvenience of passengers.

Investigation of the Sensitivity of Microwave Land Surface Emissivity to Soil Texture in MLEM

This study analyzes the spectral characteristics of desert surface emissivity according to soil classification and the influence of mineral materials and soil texture information using simulation results from the microwave land emissivity model (MLEM). It also aims at exploring the feasibility of reducing the simulation error in MLEM by refining the soil classification characteristic parameters (such as soil composition content, distribution of particle size, etc.). The surface emissivity of the Taklimakan Desert is derived, to our knowledge for the first time, from FY-3B/MWRI (FengYun-3B Microwave Radiation Imager), and then the spectral characteristics of the study area for different soil types are further analyzed according to soil classification. In addition, emissivity spectra of the four most widely mineral materials in the desert area are reproduced using an MLEM under different conditions. Results showed that microwave land emissivity is highly correlated with the soil type and changes are markedly affected by the soil water content, soil texture, mineral composition, and soil particle size. For the desert soil, the emissivity of horizontal/vertical polarization is affected by the frequency in those soils dominated by large-size particles. However, for those dominated by smaller particles, the surface emissivity is almost constant or appears to be somehow dependent on the frequency. Moreover, the season effect on emissivity characteristics is clear, especially for soils composed of small-size particles. The emissivity of horizontal polarization shows stronger seasonal variation than that of vertical polarization. The study findings also showed that refining soil texture information (soil component content, distribution of particle size) improves the simulation accuracy in desert areas. For example, for the soil dominated by clay and clay loam, the simulation error is reduced from 6–9% to less than 6%. The latter is evident, especially for soil types containing a large number of small particles, such as clay and clay loam, for which the simulation error is reduced. All in all, our study contributes to a better understanding of the influencing factors of soil texture and stratification of the near-surface soil, helping to improve microwave land surface emissivity prediction by the studied here model. As MLEM consists of an important part of the global meteorological data assimilation and weather forecast system, results can also help towards increasing the use of satellite data in desert areas and in improving the accuracy of numerical weather forecast models.

Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme

Abstract. The Weather Research and Forecasting model data assimilation (WRFDA) system, initially designed for meteorological data assimilation, is extended for aerosol data assimilation for the WRF model coupled with chemistry (WRF-Chem). An interface between WRF-Chem and WRFDA is built for the Regional Atmospheric Chemistry Mechanism (RACM) chemistry and the Modal Aerosol Dynamics Model for Europe (MADE) coupled with the Volatility Basis Set (VBS) aerosol schemes. This article describes the implementation of the new interface for assimilating PM2.5 and PM10 as well as four gas species (SO2, NO2, O3, and CO) on the ground. The effects of aerosol data assimilation are briefly examined through a month-long case study during the Korea–United States Air Quality (KORUS-AQ) period. It is demonstrated that the improved chemical initial conditions through the 3D-Var analysis can lead to consistent forecast improvements up to 26 %, reducing systematic bias errors in surface PM2.5 (PM10) concentrations to 0.0 (−1.9) µg m−3 over South Korea for 24 h.

Application of the HYSPLIT model for birch pollen modelling in Poland

In this work, the HYSPLIT model was used to reproduce birch pollen concentrations in Poland for the years 2015 and 2016, where there was significant variation in terms of pollen concentrations and start/end dates of the pollen season. The analysis of pollen observations showed that the seasonal pollen integral (SPIn) was low in 2015 with a shorter season compared to 2016. In 2016, SPIn was unusually high. The HYSPLIT model simulation, with a one-hour temporal resolution, was conducted during the birch pollen season (from March to May) for 2015 and 2016. Meteorological data were obtained from the WRF model. The birch coverage map of the European Forest Institute was used. The emission, introduced to the model, covered Central Europe with a resolution of 0.3° × 0.3°. The results were compared to data from 11 observation stations in Poland. The measured birch pollen concentrations for 2015 were overestimated by HYSPLIT at 8 of the 11 stations (normalized mean bias/NMB from 0.13 to 2.53) and underestimated for three stations (NMB from − 0.44 to − 0.15). For 2016, the model highly underestimated the pollen concentrations, with NMB ranging from − 0.45 to − 0.93. In general, the results show that the model can resolve the main peaks of pollen concentrations, which is a step forward in the application of the HYSPLIT model for birch pollen forecasting over Poland. We suggest the application of methods that can reduce the bias of temperature such as meteorological data assimilation or bias correction, which could improve calculation of the start of emissions and consequently the start of the pollen season as well as pollen concentrations.

Evaluation and Uncertainty of Radioxenon Transport with a Mesoscale Model after the February 2013 Underground Test in North Korea

Abstract The transport of radioxenon released from the February 2013 underground nuclear weapons test in North Korea was analyzed at two receptors—one at the Comprehensive Nuclear Test Ban Treaty site Rn58 in Russia (400 km downwind) and a second at Rn38 in Japan (1000 km downwind). Transport was modeled with two ensembles of mesoscale simulations, one generated with varying initial and lateral boundary conditions taken from the Global Forecasting System uncertainty ensemble, and a second created from different parameterizations and surface conditions. The wind variability was similar for the two ensembles and consistent with observations at 925 mb (1 mb = 1 hPa) but not at the surface. Biases in calculated surface winds and the radioxenon concentration in the ensembles were attributed mainly to poor simulation of the sea breeze at both locations and mountain lee affects at Rn38 in Japan. These wind regimes affected the timing of the surface radioxenon plume at Rn58 and its duration at Rn38. Surface wind variability induced by terrain and land–sea contrast (the sea breeze) also had a significant effect on the surface winds and plume dynamics, including blocking of flow approaching elevated terrain near Vladivostok and the west side of Japan. Increased plume uncertainty was seen at night because of surface wind variability. Measured surface chemical variability was larger than found in the first European Tracer Experiment in central Europe. The study found that horizontal model resolution contributes to uncertainty but not as much as vertical resolution, boundary layer parameterizations, and assimilation of surface meteorological data near the receptor.

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.

Meteorological data assimilation for air quality modeling with WRF-Chem: Central Mexico case study

Meteorological data assimilation from surface observations (PREPBUFR) and satellite radiance (BUFR) provided by the National Centers for Environmental Prediction (NCEP) is carried out to determine their possible influence on chemical variables concentrations such as ozone (O 3 ), obtained from air quality modeling over central Mexico using the photochemical Weather Research and Forecasting Model with Chemistry (WRF-Chem) during a bad-pollution event due to high O 3 concentrations in the Mexico City Metropolitan Area on May 1-4, 2013. For this, the Weather Research and Forecasting Data Assimilation (WRFDA) module was adapted to run with WRF-Chem, and the 3DVAR assimilation technique (which is implemented in the WRFDA) was selected. Six study cases were defined taking into account the combination of the data source type with the assimilation process start times (00:00 and 12:00 UTC). Results indicate that independently of these factors, data assimilation modifies in general the meteorological variables (temperature and wind) initial conditions to obtain a better agreement between model simulations and observations, although statistics results are even higher when the process starts at 12:00 UTC using a combination of PREPBUFR and BUFR data (PB+RD cases). It was also verified that there is an influence on O 3 concentrations since the statistical metrics obtained for the different experiments carried out are modified; however, it is insufficient to considerably improve the chemical variable model performance.

Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China

Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.

Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

Abstract. Atmospheric inversions inform us about the magnitude and variations of greenhouse gas (GHG) sources and sinks from global to local scales. Deployment of observing systems such as spaceborne sensors and ground-based instruments distributed around the globe has started to offer an unprecedented amount of information to estimate surface exchanges of GHG at finer spatial and temporal scales. However, all inversion methods still rely on imperfect atmospheric transport models whose error structures directly affect the inverse estimates of GHG fluxes. The impact of spatial error structures on the retrieved fluxes increase concurrently with the density of the available measurements. In this study, we diagnose the spatial structures due to transport model errors affecting modeled in situ carbon dioxide (CO2) mole fractions and total-column dry air mole fractions of CO2 (XCO2). We implement a cost-effective filtering technique recently developed in the meteorological data assimilation community to describe spatial error structures using a small-size ensemble. This technique can enable ensemble-based error analysis for multiyear inversions of sources and sinks. The removal of noisy structures due to sampling errors in our small-size ensembles is evaluated by comparison with larger-size ensembles. A second filtering approach for error covariances is proposed (Wiener filter), producing similar results over the 1-month simulation period compared to a Schur filter. Based on a comparison to a reference 25-member calibrated ensemble, we demonstrate that error variances and spatial error correlation structures are recoverable from small-size ensembles of about 8 to 10 members, improving the representation of transport errors in mesoscale inversions of CO2 fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO2 mole fractions differ significantly from total-column XCO2 error variances. We conclude that error variances for remote-sensing observations need to be quantified independently of in situ CO2 mole fractions due to the complexity of spatial error structures at different altitudes. However, we show the potential use of meteorological error structures such as the mean horizontal wind speed, directly available from ensemble prediction systems, to approximate spatial error correlations of in situ CO2 mole fractions, with similarities in seasonal variations and characteristic error length scales.

Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies

In this paper, the latest progress, major achievements and future plans of Chinese meteorological satellites and the core data processing techniques are discussed. First, the latest three FengYun (FY) meteorological satellites (FY-2H, FY-3D, and FY-4A) and their primary objectives are introduced. Second, the core image navigation techniques and accuracies of the FY meteorological satellites are elaborated, including the latest geostationary (FY-2/4) and polar-orbit (FY-3) satellites. Third, the radiometric calibration techniques and accuracies of reflective solar bands, thermal infrared bands, and passive microwave bands for FY meteorological satellites are discussed. It also illustrates the latest progress of real-time calibration with the onboard calibration system and validation with different methods, including the vicarious China radiance calibration site calibration, pseudo invariant calibration site calibration, deep convective clouds calibration, and lunar calibration. Fourth, recent progress of meteorological satellite data assimilation applications and quantitative science produce are summarized at length. The main progress is in meteorological satellite data assimilation by using microwave and hyper-spectral infrared sensors in global and regional numerical weather prediction models. Lastly, the latest progress in radiative transfer, absorption and scattering calculations for satellite remote sensing is summarized, and some important research using a new radiative transfer model are illustrated.

태풍사전방재모델의 예측 정확도 향상을 위한 연구 Part I: 고해상도 해수면 온도 적용과 기상 자료동화에 대한 WRF 민감도 분석

태풍사전방재모델의 예측 정확도 향상을 위한 연구 Part I: 고해상도 해수면 온도 적용과 기상 자료동화에 대한 WRF 민감도 분석

Results of adaptation and verification of the coupled numerical models set for predicting the variation of oceanographic features in the North-Western part of the Black Sea

In 2014 Ukraine lost the Ukrainian National Automated System of Marine Forecasting for the Black Sea that was created and operated at the premises of Marine Hydrophysical Institute of the National Academy of Sciences of Ukraine located in the Crimea. Within the framework of research works aimed at establishing a new marine forecasting system a possibility of employing the internationally acclaimed set of coupled numerical models Delft3D-FLOW + SWAN (the Simulating WAves Nearshore) for operational forecasting of the short-term (5 to 10 days) spatio-temporal variability of oceanographic features in the Ukrainian part of the Sea of Azov and the Black Sea Basin is considered. To ensure operation of the models set in the forecasting mode it was suggested to use a prediction of variability of meteorological characteristics at the air-sea interface obtained with the help of the numerical weather forecast model GFS (Global Forecast System). This paper presents the results of verification of Delft3D-FLOW and SWAN numerical models which were adapted to the conditions of the North-Western part of the Black Sea and its Odesa area in the version of meteorological data (fields of wind speed and direction, atmospheric pressure) assimilation from the GFS forecast archive. A technique of telescoping the spatial curvilinear computational grids with different resolution capacity was used in the process of models set adaptation to the conditions of the prognostic area. The models were verified by comparing modelling results with observational data on sea level variability in the ports of Odesa area of the North-Western part of the Black Sea (Chornomorsk, Odesa, Yuzhnyi), as well as with data on wind speed and direction, drift currents and characteristics of wind-induced waves recorded over the studied periods by the gauges of stationary hydrometeorological buoy which was mounted in the Bay of Odessa. Based on the analysis of the results of verification of coupled numerical models Delft3D-FLOW + SWAN set it was concluded that the set of coupled models has good prospects of being used in the system of operational forecasting of the variability of oceanographic parameters of the sea environment in the Ukrainian part of the Sea of Azov and the Black Sea Basin in the version of assimilation of meteorological information obtained from the GFS global forecast model.

Numerical Dust Storm Simulation Using Modified Geographical Domain and Data Assimilation: 3DVAR and 4DVAR (WRF-Chem/WRFDA)

Dust storms have severe environmental, economic, health, weather, and climate change impacts. A severe dust storm that hit Egypt on 22 January 2004 was selected as a case study to establish an accurate numerical model to simulate dust storms over Egypt using the Weather Research Forecast with a chemistry module (WRF-Chem). Two simulation setups using WRF-Chem were conducted using two geographic domains: the first exclusively included dust sources within Egypt, while the second included an external dust source, the Bodele Depression in southwestern Chad. The first simulation was only able to capture the core of the dust plume from the internal dust source Egyptian Qattara Depression, but the second was able to capture the spatial dust distribution from both the Qattara Depression and the Bodele Depression. Moreover, the results from our second simulation model had less errors than comparable results using moderate resolution imaging spectroradiometer (MODIS). We then investigated the impact of meteorological data assimilation methods using both three-dimensional and four-dimensional variational assimilation algorithms to simulate the aerosol optical depth of the dust storm using weather research forecast data assimilation (WRFDA) framework.

On adjoint method based atmospheric emission source tracing

The utmost important task for air pollution prevention and control is to identify the source of pollution such that targeted management and governance can be greatly enhanced. In this paper, we have develoepd an air quality forecasting system called “the national air quality high-resolution forecast and pollution control decision support system (NARS)”, which integrates atmospheric environment monitoring and analysis, meteorological numerical forecasting, emission source inversion, air quality forecasting, meteorological and atmospheric chemical data assimilation, emission sources tracing, profit and loss assessment together with dynamic optimal control. The system could be applied to perform the closed-loop prevention and control for atmospheric pollution by monitoring, forecasting, assimilation, traceability, emission source inversion and optimal control, and the system stands a great promise of providing a solution for current air pollution prevention and control strategy. In the NARS, a CAMx based adjoint model was developed, and was further demonstrated to perform dynamic inversion and grid-based quantitative emission source tracing. Our results show that the system can trace quickly and quantitatively both spatial and temporal distributions of emission sources and their contribution rates resulting from severe air pollution for certain target area for the coming 7 days. As an application of the NARS, the emission inversion, meteorological field simulation, air quality forecasting and grid traceability were studied for the time period from September 2016 to March 2017 in Beijing. Compared with the observation of national control station in Beijing-Tianjin-Hebei region (BTH), the results showed that the forecast accuracies of the pollution processes, the pollution level and the pollutant concentration are close to 100%, 88.8% and 84.7%, respectively, and the correlation coefficient between the modeling and observation is 0.81. The results of grid traceability showed that the emissions of PM2.5 pollution in the main urban area of Beijing are mainly from the air pollutant transmission channel in the southwest of. The emission contributions of BTH plus the surrounding areas are respectively 66%, 29% and 5%. The 19% PM2.5 emissions of BTH have resulted in 80% Beijing PM2.5 concentration in heavily polluted and severely polluted days. Among them, 9% Beijing local emissions account for 63% PM2.5, and 10% Hebei emissions accounted for 17%. The 26% BTH emissions have resulted in 80% PM2.5 in Beijing’s main city for slightly or heavily polluted days. Furthermore, Beijing emissions accounting for 9% of BTH emissions have contributed 61%; meanwhile, Hebei emissions accounting for 15% of BTH emissions have contributed the 18% of the pollution; finally, Tianjin 2% accouts for the leaving 1%. The emissions leading to the PM2.5 pollution in Beijing main city mainly distribute in the city zone and the sourthern of Beijing, some districts or counties of Baoding and Shijiazhuang. The top six districts or counties accounting for 48% pollutants are located in Beijing, while the top 20 districts or counties contribute 73%. Here, using the the adjoint model developed, we are able to analyze synchronously the emisison apportionment of the modeling results through combining the dynamic inversion and the monitoring emission inventories. Using both profit-loss assessment and natural cybernetics, the NARS is able to locate the emisison sources for optimal emisison control during a heavy air pollution episode.

Improvement of the Real-time PM2.5 Forecast over the Beijing-Tianjin-Hebei Region using an Optimal Interpolation Data Assimilation Method

A routine air quality data assimilation (DA) system was established at the China National Environmental Monitoring Center (CNEMC) based on the optimal interpolation (OI) method. The surface observations from more than 1,400 stations across China were assimilated into a real-time air quality forecast system with three nested domains. The initial conditions of NO2, SO2 and PM2.5 in the three domains were optimized by the data assimilation system. The impact of the data assimilation on the real-time PM2.5 forecast over the Beijing-Tianjin-Hebei (BTH) Region during the heavy haze season of 2015 was evaluated. The results show that the DA can significantly improve real-time PM2.5 forecasts, reducing the root mean square error (RMSE) by 23%, 8.2% and 4.8% in the forecasts of the first, second and third day, respectively. The mean fractional bias and the mean fractional error of the forecast were reduced from 50.9% and 70.67% to 40% and 62.3%, respectively, and the performance changed from “criteria” to approaching “goal” (as defined by Boylan and Russell, 2006). Additionally, increasing the assimilation frequency can improve the DA system performance for real-time forecasts. As can be seen from the various cases studied here, the improvement in data assimilation is more significant when the bias of the model is higher and there is still much room for correction. The results also show a rapid decay of the DA effects on the PM2.5 forecast, which highlights the limitations of the current routine data assimilation system in which only initial conditions are optimized. Further improvements in the data assimilation system with meteorological data assimilation and chemical parameter optimization are needed.