PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

2013 ◽  
Vol 49 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Eun-Hee Lee ◽  
Jong-Chul Ha ◽  
Sang-Sam Lee ◽  
Youngsin Chun
Keyword(s):  
Data Assimilation ◽  
South Korea ◽  
Interpolation Method ◽  
Asian Dust ◽  
Optimal Interpolation ◽  
Forecasting Model

scholarly journals Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

2014 ◽  
Vol 14 (7) ◽  
pp. 3511-3532 ◽  
Author(s):  
Y. Wang ◽  
K. N. Sartelet ◽  
M. Bocquet ◽  
P. Chazette
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Interpolation Method ◽  
Optimal Interpolation ◽  
Atmospheric Conditions ◽  
Model Aerosol ◽  
Paris Area ◽  
Lidar Observations

Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) Polair3D of the air quality modelling platform Polyphemus to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris on-board a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, Polyphemus well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM10 (particulate matter with diameter less than 10 μm) concentrations. Another analyses PM2.5 (particulate matter with diameter less than 2.5 μm) and PM2.5–10 (particulate matter with a diameter higher than 2.5 μm and lower than 10 μm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM10 than PM2.5, probably because PM2.5 is better modelled than PM10. Furthermore, the algorithm which analyses both PM2.5and PM2.5–10 provides the best scores for PM10. The averaged root-mean-square error (RMSE) of PM10 is 11.63 μg m−3 with DA (PM2.5 and PM2.5–10), compared to 13.69 μg m−3 with DA (PM10) and 17.74 μg m−3 without DA on 1 July 2009. The averaged RMSE of PM10 is 4.73 μg m−3 with DA (PM2.5 and PM2.5–10), against 6.08 μg m−3 with DA (PM10) and 6.67 μg m−3 without DA on 26 July 2009.

Global Wave Data Assimilation Model using Optimal Interpolation Method

2020 ◽  
Author(s):  
Kyeong Ok Kim ◽  
Hanna Kim ◽  
Kyung Tae Jung ◽  
Young Ho Kim
Keyword(s):  
Data Assimilation ◽  
Wave Energy ◽  
Interpolation Method ◽  
Energy Spectra ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Significant Wave ◽  
Wave Data ◽  
Wave Heights ◽  
Significant Wave Heights

<p>To construct a reanalyzed global ocean wave data set with improved accuracy, which is important for the better understanding and simulation of various near-surface ocean dynamics, a data assimilation method has been embedded to the global spectral wave model based on WW3. The major factors controlling the wave simulation accuracy are the wind condition and the parameterization on the wave energy development, dissipation and nonlinear processes between wave components. However, the atmospheric prediction accuracy is still not sufficient, and the parameterization cannot be generalized due to the local geographic conditions.</p><p>In detail, the data assimilation using the optimal interpolation method has been applied, verification through the comparison with satellite altimeters and buoy observations has been made with examination of the data assimilation effects. The significant wave heights computed by the integration of wave energy spectra are showed to be quite similar with observed results. However, the wave periods and directions related to the shape of wave energy spectra are not sufficiently comparable. Generally there have been difficulties in predicting the propagation of long period waves such as swells.</p><p>The wave energy spectra on wave number and direction domains was multiplied by optimal interpolation method with the ratio of observed significant wave heights on first guessed simulated results. The energy spectra was thereafter shifted by the difference between simulated and observed peak wave periods and directions. From then examination of the reanalysis simulation during 1 year, it could be seen that the accuracy of the model with the data assimilation shows better results than that without data assimilation.</p>

scholarly journals Seasonal Asian Dust Forecasting Using GloSea5-ADAM

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 526
Author(s):  
Sang-Boom Ryoo ◽  
Yun-Kyu Lim ◽  
Young-San Park
Keyword(s):  
South Korea ◽  
Dust Emission ◽  
Northeast Asia ◽  
Bare Soil ◽  
Asian Dust ◽  
Forecasting Model ◽  
Aerosol Model ◽  
Soil Fraction ◽  
Temporary Solution ◽  
Dust Events

The springtime dust events in Northeast Asia pose many economic, social, and health-related risks. Statistical models in the forecasting of seasonal dust events do not fully account for environmental variations in dust sources due to climate change. The Korea Meteorological Administration (KMA) recently developed the GloSea5-ADAM, a numerically based seasonal dust forecasting model, by incorporating the Asian Dust and Aerosol Model (ADAM)’s emission algorithm into Global Seasonal Forecasting Model version 5 (GloSea5). The performance of GloSea5 and GloSea5-ADAM in forecasting seasonal Asian dust events in source (China) and leeward (South Korea) regions was compared. The GloSea5-ADAM solved the limitations of GloSea5, which were mainly attributable to GloSea5′s low bare-soil fraction, and successfully simulated 2017 springtime dust emissions over Northeast Asia. The results show that GloSea5-ADAM’s 2017 and 2018 forecasts were consistent with surface PM10 mass concentrations observed in China and South Korea, while there was a large gap in 2019. This study shows that the geographical distribution and physical properties of soil in dust source regions are important. The GloSea5-ADAM model is only a temporary solution and is limited in its applicability to Northeast Asia; therefore, a globally applicable dust emission algorithm that considers a wide variety of soil properties must be developed.

scholarly journals Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

2021 ◽  
Vol 11 (4) ◽  
pp. 1959
Author(s):  
Kyung M. Han ◽  
Chang H. Jung ◽  
Rae-Seol Park ◽  
Soon-Young Park ◽  
Sojin Lee ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Simulation ◽  
Interpolation Method ◽  
Correlation Coefficients ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Accurate Information ◽  
In Situ Observation ◽  
Particulate Matters ◽  
Pm10 And Pm2.5

In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

scholarly journals PM<sub>10</sub> data assimilation over Europe with the optimal interpolation method

2008 ◽  
Vol 8 (3) ◽  
pp. 9607-9640
Author(s):  
M. Tombette ◽  
V. Mallet ◽  
B. Sportisse
Keyword(s):  
Chemical Composition ◽  
Data Assimilation ◽  
Interpolation Method ◽  
Monitoring Network ◽  
Optimal Interpolation ◽  
Error Statistics ◽  
Operational Conditions ◽  
Base De Données ◽  
The One ◽  
Qualité De L’Air

Abstract. This paper presents experiments of PM10 data assimilation with the optimal interpolation method. The observations are provided by BDQA (Base de Données sur la Qualité de l'Air), whose monitoring network covers France. Two other databases (EMEP and AirBase) are used to evaluate the improvements in the analyzed state over one month (January, 2001) and for several outputs (PM10, PM2.5 and chemical composition). Then, the method is applied in operational conditions. The results show that the assimilation of PM10 observations significantly improves the one-day forecast for total mass (PM10 and PM2.5). The errors on aerosol chemical composition are not reduced and are sometimes amplified by the assimilation procedure, which shows the need for chemical data. As the observations cover a limited part of the domain (France versus Europe) and as the method used for assimilation is sequential, we focus on the horizontal and temporal impacts of assimilation in the last part of this paper. To conclude, we discuss the perspectives, especially the use of a variational method for assimilation or the investigation of the sensitivity to a few choices (e.g., the error statistics, etc.).

scholarly journals PM<sub>10</sub> data assimilation over Europe with the optimal interpolation method

2009 ◽  
Vol 9 (1) ◽  
pp. 57-70 ◽  
Author(s):  
M. Tombette ◽  
V. Mallet ◽  
B. Sportisse
Keyword(s):  
Chemical Composition ◽  
Data Assimilation ◽  
Total Mass ◽  
Interpolation Method ◽  
Monitoring Network ◽  
Optimal Interpolation ◽  
Operational Forecasts ◽  
Base De Données ◽  
The One ◽  
Qualité De L’Air

Abstract. This paper presents experiments of PM10 data assimilation with the optimal interpolation method. The observations are provided by BDQA (Base de Données sur la Qualité de l'Air), whose monitoring network covers France. Two other databases (EMEP and AirBase) are used to evaluate the improvements in the analyzed state over January 2001 and for several outputs (PM10, PM2.5 and chemical composition). The method is then applied in operational-forecast conditions. It is found that the assimilation of PM10 observations significantly improves the one-day forecast of total mass (PM10 and PM2.5), whereas the improvement is non significant for the two-day forecast. The errors on aerosol chemical composition are sometimes amplified by the assimilation procedure, which shows the need for chemical data. Since the observations cover a limited part of the domain (France versus Europe) and since the method used for assimilation is sequential, we focus on the horizontal and temporal impacts of the assimilation and we study how several parameters of the assimilation system modify these impacts. The strategy followed in this paper, with the optimal interpolation, could be useful for operational forecasts. Meanwhile, considering the weak temporal impact of the approach (about one day), the method has to be improved or other methods have to be considered.

scholarly journals Recent developments in the data assimilation of AROME/HU numerical weather prediction model

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 521-553
Author(s):  
Helga Tóth ◽  
Viktória Homonnai ◽  
Máté Mile ◽  
Anikó Várkonyi ◽  
Zsófia Kocsis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Interpolation Method ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Numerical Weather Prediction Model ◽  
Optimal Interpolation ◽  
Surface Data ◽  
Recent Developments ◽  
The Impact

A local three-dimensional variational data assimilation (DA) system was implemented operationally in AROME/HU (Application of Research to Operations at Mesoscale) non-hydrostatic mesoscale model at the Hungarian Meteorological Service (OMSZ) in 2013. In the first version, rapid update cycling (RUC) approach was employed with 3-hour frequency in local upper-air DA using conventional observations only. Optimal interpolation method was adopted for the surface data assimilation later in 2016. This paper describes the current developments showing the impact of more conventional and remote-sensing observations assimilated in this system, which reveals the benefit of additional local high-resolution observations. Furthermore, it is shown that an hourly assimilation-forecast cycle outperforms the 3-hourly updated system in our DA. Besides the upper-air assimilation developments, a simplified extended Kalman filter (SEKF) was also tested for surface data assimilation, showing promising performance on both the analyses and the forecasts of AROME/HU system.

An application of a data assimilation method based on the diffusion stochastic process theory using altimetry data in Atlantic

2016 ◽  
Vol 31 (3) ◽  
Author(s):  
Konstantin P. Belyaev ◽  
Andrey A. Kuleshov ◽  
Clemente A. S. Tanajura
Keyword(s):  
Stochastic Process ◽  
Data Assimilation ◽  
Interpolation Method ◽  
Planck Equation ◽  
Ocean Model ◽  
Sea Surface ◽  
Process Theory ◽  
Optimal Interpolation ◽  
Computationally Efficient ◽  
The Impact

AbstractA data assimilation (DA) method based on the application of the diffusion stochastic process theory, particularly, of the Fokker-Planck equation, is considered. The method was introduced in the previous works; however, it is substantially modified and extended to the multivariate case in the current study. For the first time, the method is here applied to the assimilation of sea surface height anomalies (SSHA) into the Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean. The impact of assimilation of SSHA is investigated and compared with the assimilation by an Ensemble Optimal Interpolation method (EnOI). The time series of the analyses produced by both assimilation methods are evaluated against the results from a free model run without assimilation. This study shows that the proposed assimilation technique has some advantages in comparison with EnOI analysis. Particularly, it is shown that it provides slightly smaller error and is computationally efficient. The method may be applied to assimilate other data such as observed sea surface temperature and vertical profiles of temperature and salinity.

Research about Influencing Factor on Interpolation Accuracy in Data Exchange of Fluid-Structure Interaction

2013 ◽  
Vol 318 ◽  
pp. 100-107
Author(s):  
Zhen Shen ◽  
Biao Wang ◽  
Hui Yang ◽  
Yun Zheng
Keyword(s):  
Data Exchange ◽  
Shape Function ◽  
Interpolation Method ◽  
Matching Condition ◽  
Optimal Interpolation ◽  
Complex Interpolation ◽  
Interpolation Function ◽  
Interpolation Methods ◽  
Interpolation Accuracy ◽  
Lower Accuracy

Six kinds of interpolation methods, including projection-shape function method, three-dimensional linear interpolation method, optimal interpolation method, constant volume transformation method and so on, were adoped in the study of interpolation accuracy. From the point of view about the characterization of matching condition of two different grids and interpolation function, the infuencing factor on the interpolation accuracy was studied. The results revealed that different interpolation methods had different interpolation accuracy. The projection-shape function interpolation method had the best effect and the more complex interpolation function had lower accuracy. In many cases, the matching condition of two grids had much greater impact on the interpolation accuracy than the method itself. The error of interpolation method is inevitable, but the error caused by the grid quality could be reduced through efforts.

Sign in / Sign up

Export Citation Format

Share Document