scholarly journals Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system

2020 ◽  
Author(s):  
Athanasios Tsikerdekis ◽  
Nick A. J. Schutgens ◽  
Otto P. Hasekamp
Keyword(s):  
Optical Properties ◽  
Data Assimilation ◽  
Optical Depth ◽  
Added Value ◽  
Mixing Ratio ◽  
Aerosol Model ◽  
Aerosol Absorption ◽  
Multiple Variables ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system for aerosol, based on an ensemble Kalman filter, has been developed for the global aerosol model ECHAM-HAM and applied to POLDER derived observations of optical properties. The advantages of this assimilation system is that the ECHAM-HAM aerosol modal scheme carries both aerosol particle numbers and mass which are both used in the data assimilation system as state vector, while POLDER retrievals in addition to Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) provide also information related to aerosol absorption like Aerosol Absorption Optical Depth (AAOD) and Single Scattering Albedo (SSA). The developed scheme can assimilate simultaneously combinations of multiple variables (e.g. AOD, AE, SSA), to optimally estimate mass mixing ratio and number mixing ratio of different aerosol species. We investigate the added value of assimilating AE, AAOD and SSA, in addition to the commonly used AOD, by conducting multiple experiments where different combinations of retrieved properties are assimilated. Results are evaluated with (independent) POLDER, MODIS Dark Target, MODIS Deep Blue and AERONET observations. The experiment where POLDER AOD, AE and SSA are assimilated shows systematic improvement in mean error, mean absolute error and correlation for AOD, AE, AAOD and SSA compared to the experiment where only AOD is assimilated. The same experiment reduces the global ME against AERONET from 0.072 to 0.001 for AOD, from 0.273 to 0.009 for AE and from -0.012 to 0.002 for AAOD. Additionally, sensitivity experiments reveal the benefits of assimilating AE over AOD at a second wavelength or SSA over AAOD, possibly due to a simpler observation covariance matrix in the present data assimilation framework. We conclude that the currently available AE and SSA do positively impact data assimilation.

scholarly journals Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system

2021 ◽  
Vol 21 (4) ◽  
pp. 2637-2674
Author(s):  
Athanasios Tsikerdekis ◽  
Nick A. J. Schutgens ◽  
Otto P. Hasekamp
Keyword(s):  
Optical Properties ◽  
Data Assimilation ◽  
Optical Depth ◽  
Added Value ◽  
Mixing Ratio ◽  
Aerosol Model ◽  
Aerosol Absorption ◽  
Multiple Variables ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system for aerosol, based on an ensemble Kalman filter, has been developed for the ECHAM – Hamburg Aerosol Model (ECHAM-HAM) global aerosol model and applied to POLarization and Directionality of the Earth's Reflectances (POLDER)-derived observations of optical properties. The advantages of this assimilation system is that the ECHAM-HAM aerosol modal scheme carries both aerosol particle numbers and mass which are both used in the data assimilation system as state vectors, while POLDER retrievals in addition to aerosol optical depth (AOD) and the Ångström exponent (AE) also provide information related to aerosol absorption like aerosol absorption optical depth (AAOD) and single scattering albedo (SSA). The developed scheme can simultaneously assimilate combinations of multiple variables (e.g., AOD, AE, SSA) to optimally estimate mass mixing ratio and number mixing ratio of different aerosol species. We investigate the added value of assimilating AE, AAOD and SSA, in addition to the commonly used AOD, by conducting multiple experiments where different combinations of retrieved properties are assimilated. Results are evaluated with (independent) POLDER, Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target, MODIS Deep Blue and Aerosol Robotic Network (AERONET) observations. The experiment where POLDER AOD, AE and SSA are assimilated shows systematic improvement in mean error, mean absolute error and correlation for AOD, AE, AAOD and SSA compared to the experiment where only AOD is assimilated. The same experiment reduces the global ME against AERONET from 0.072 to 0.001 for AOD, from 0.273 to 0.009 for AE and from −0.012 to 0.002 for AAOD. Additionally, sensitivity experiments reveal the benefits of assimilating AE over AOD at a second wavelength or SSA over AAOD, possibly due to a simpler observation covariance matrix in the present data assimilation framework. We conclude that the currently available AE and SSA do positively impact data assimilation.

scholarly journals A constraint method for supersaturation in a variational data assimilation system

2021 ◽  
Author(s):  
Ken SAWADA ◽  
Yuki HONDA
Keyword(s):  
Data Assimilation ◽  
Penalty Function ◽  
Function Method ◽  
Weather Prediction ◽  
Penalty Function Method ◽  
Rain Event ◽  
Mixing Ratio ◽  
Early Stages ◽  
Data Assimilation System ◽  
Assimilation System

AbstractThe reproducibility of precipitation in the early stages of forecasts, often called a spin-down or spin-up problem, has been a significant issue in numerical weather prediction. This problem is caused by moisture imbalance in the analysis data, and in the case of the Japan Meteorological Agency’s (JMA’s) mesoscale data assimilation system JNoVA, we found that the imbalance stems from the existence of unrealistic supersaturated states in the minimal solution of the cost function in JNoVA. Based on the theory of constrained optimization problems, we implemented an exterior penalty function method for the mixing ratio within JNoVA to suppress unrealistic supersaturated states. The advantage of this method is the simplicity of its theory and implementation. The results of twin data assimilation cycle experiments conducted for the Heavy Rain Event of July 2018 over Japan show that—with the new method—unrealistic supersaturated states are reduced successfully, negative temperature bias to the observations is alleviated, and a sharper distribution of the mixing ratio is obtained. These changes help to initiate the development of convection at the proper location and improve the fractions skill score (FSS) of precipitation in the early stages of the forecast. From these results, we conclude that the initial shock caused by moisture imbalance is mitigated by implementing the penalty function method, and the new moisture balance has a positive impact on the reproducibility of precipitation in the early stages of forecasts.

scholarly journals JRAero: the Japanese Reanalysis for Aerosol v1.0

2017 ◽  
Vol 10 (9) ◽  
pp. 3225-3253 ◽  
Author(s):  
Keiya Yumimoto ◽  
Taichu Y. Tanaka ◽  
Naga Oshima ◽  
Takashi Maki
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Japan Meteorological Agency ◽  
Observation Data ◽  
Aerosol Transport ◽  
Aerosol Model ◽  
Meteorological Research Institute ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1°  ×  1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE  =  0.08, R = 0. 90, MFE  =  28.1 %, MFB  =  0.6 %, and IOA  =  0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

scholarly journals JRAero: the Japanese Reanalysis for Aerosol v1.0

2017 ◽  
Author(s):  
Keiya Yumimoto ◽  
Taichu Y. Tanaka ◽  
Naga Oshima ◽  
Takashi Maki
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Japan Meteorological Agency ◽  
Observation Data ◽  
Aerosol Transport ◽  
Aerosol Model ◽  
Meteorological Research Institute ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a 2-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard Terra and Aqua satellites every 6 hours and has a TL159 horizontal resolution (approximately 1.1° × 1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the set-up of the reanalysis and examines its quality. Comparisons with MODIS AODs showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) = 0.05, a correlation coefficient (R) = 0.96, a mean fractional error (MFE) = 23.7 %, a mean fractional bias (MFB) = 2.8 %, and an index of agreement (IOA) = 0.98. The better agreement of the first guess, compared with the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0.90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was  0.90 at 40.7 % of the sites, and IOA was > 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

scholarly journals Data assimilation of CALIPSO aerosol observations

2010 ◽  
Vol 10 (1) ◽  
pp. 39-49 ◽  
Author(s):  
T. T. Sekiyama ◽  
T. Y. Tanaka ◽  
A. Shimizu ◽  
T. Miyoshi
Keyword(s):  
Data Assimilation ◽  
Grid Point ◽  
Dust Emission ◽  
Aerosol Model ◽  
Source Regions ◽  
Independent Observations ◽  
Ground Based Lidar ◽  
Dust Events ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We have developed an advanced data assimilation system for a global aerosol model with a four-dimensional ensemble Kalman filter in which the Level 1B data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were successfully assimilated for the first time, to the best of the authors' knowledge. A one-month data assimilation cycle experiment for dust, sulfate, and sea-salt aerosols was performed in May 2007. The results were validated via two independent observations: 1) the ground-based lidar network in East Asia, managed by the National Institute for Environmental Studies of Japan, and 2) weather reports of aeolian dust events in Japan. Detailed four-dimensional structures of aerosol outflows from source regions over oceans and continents for various particle types and sizes were well reproduced. The intensity of dust emission at each grid point was also corrected by this data assimilation system. These results are valuable for the comprehensive analysis of aerosol behavior as well as aerosol forecasting.

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