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 On the assimilation of ice velocity and concentration data into large-scale sea ice models

Ocean Science ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 321-335 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimal Interpolation ◽  
Ice Thickness ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is first step towards real data assimilation into NEMO-LIM, a global sea ice-ocean 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.

scholarly journals Mapping altimetry in the forthcoming SWOT era by back-and-forth nudging a one-layer quasi-geostrophic model

2020 ◽  
Author(s):  
Florian Le Guillou ◽  
Sammy Metref ◽  
Emmanuel Cosme ◽  
Julien Le Sommer ◽  
Clément Ubelmann ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Model Simulation ◽  
Interpolation Method ◽  
Ocean Dynamics ◽  
Optimal Interpolation ◽  
Small Scale ◽  
Altimetric Data ◽  
The Impact ◽  
Mean Square Errors

AbstractDuring the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields which are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standardDUACS products are created with an optimal interpolation method which is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution.The present paper makes the case that this temporal-spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging, to interpolate altimetric data while respecting quasigeostrophic dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadirs data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

scholarly journals Assimilating summer sea-ice concentration into a coupled ice–ocean model using a LSEIK filter

2015 ◽  
Vol 56 (69) ◽  
pp. 38-44 ◽  
Author(s):  
Qinghua Yang ◽  
Svetlana N. Losa ◽  
Martin Losch ◽  
Jiping Liu ◽  
Zhanhai Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
General Circulation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
In Situ Observation ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

AbstractThe decrease in summer sea-ice extent in the Arctic Ocean opens shipping routes and creates potential for many marine operations. For these activities accurate predictions of sea-ice conditions are required to maintain marine safety. In an attempt at Arctic sea-ice prediction, the summer of 2010 is selected to implement an Arctic sea-ice data assimilation (DA) study. The DA system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter to assimilate Special Sensor Microwave Imager/Sounder (SSMIS) sea-ice concentration operational products from the US National Snow and Ice Data Center (NSIDC). Based on comparisons with both the assimilated NSIDC SSMIS concentration and concentration data from the Ocean and Sea Ice Satellite Application Facility, the forecasted sea-ice edge and concentration improve upon simulations without data assimilation. By the nature of the assimilation algorithm with multivariate covariance between ice concentration and thickness, sea-ice thickness fields are also updated, and the evaluation with in situ observation shows some improvement compared to the forecast without data assimilation.

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 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.).

Recent development of a supermodel - an interactive multi-model ensemble

2021 ◽  
Author(s):  
Shuo Wang ◽  
Francois Counillon ◽  
Shunya Koseki ◽  
Noel Keenlyside ◽  
Alok Kumar Gupta ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Model Simulation ◽  
Optimal Interpolation ◽  
Model Ensemble ◽  
Sst Bias ◽  
Dynamical Regimes ◽  
The Individual ◽  
Spatially Varying ◽  
Earth System Models

&lt;p&gt;An interactive multi-model ensemble (named as supermodel) based on three state-of-the-art earth system models (i.e., NorESM, MPIESM and CESM) is developed. The models are synchronized every month by data assimilation. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which&amp;#160;the covariance matrix is constructed from a historical ensemble. The assimilated data is a weighted combination of the monthly output sea surface temperature (SST) of these individual models, but the full ocean state is constrained by the covariance matrix. The synchronization of the models during the model simulation makes this approach&amp;#160;different from the traditional multi-model ensemble&amp;#160;approach in which model outputs are combined a-posteriori.&lt;/p&gt;&lt;p&gt;We compare the different approaches to estimate the supermodel weights: equal weights, spatially varying weights based on the minimisation of the bias. The performance of these supermodels is compared to that of the individual models, and multi-model ensemble for the period 1980 to 2006. SST synchronisation is achieved&amp;#160;in most oceans and in dynamical regimes such as ENSO. The supermodel with spatially varying weights overperforms the supermodel with equal weights. It reduces the SST bias by over 30% compare to the multi-model ensemble. The temporal variability of the supermodel is slightly on the low side but improved compared to the multi-model ensemble. The simulations are being extended to 2100 to assess the simulation of climate variability and climate change. Performing prediction experiments with the supermodel is the main perspective in the next step. &amp;#160;&lt;/p&gt;

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.

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