A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations—a case study of Xuzhou, China

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.

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-2637-2021 ◽

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.

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