The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States

Chen, D.; Liu, Z.; Schwartz, Craig S.; Lin, Hui-Chuan; Cetola, Jeffrey D.; Gu, Yu; Xue, Lulin

doi:10.5194/gmdd-7-3851-2014

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The official GSI version incorporated the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD in East Asia (Liu et al, 2011) and revealed the simultaneous DA effects of PM 2.5 and AOD in the continental United States (Schwartz et al, 2012). This GSI identified DA effects that weakened during the succeeding model's running as the model error grew (Jiang et al, 2013) and assessed the radiative forcing of the aerosols released by wildfires (Chen et al, 2014). This version was also utilized to improve air quality forecasts in China by assimilating a variety of satellite AOD data retrieved from the Geostationary Ocean Color Imager (Pang et al, 2018), Visible Infrared Imaging Radiometer Suite (Pang et al, 2018); Advanced Himawari-8 Imager (Xia et al, 2019a), and the Fengyun-3A/medium-resolution spectral imager (Bao et al, 2019;Xia et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China

Chang

Zhang

et al. 2021

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. The Gridpoint Statistical Interpolation data assimilation (DA) system was developed for the four size bin sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol mechanism in the Weather Research and Forecasting-Chemistry (WRF-Chem) model. The forward and tangent linear operators for the aerosol optical depth (AOD) analysis were derived from WRF-Chem aerosol optical code. We applied three-dimensional variational DA to assimilate the multi-wavelength AOD, ambient aerosol scattering coefficient, and aerosol absorption coefficient, measured by the sun–sky photometer, nephelometer, and aethalometer, respectively. These measurements were undertaken during a dust observation field campaign at Kashi in northwestern China in April 2019. The results showed that the DA analyses decreased the model aerosols' low biases; however, it had some deficiencies. Assimilating the surface particle concentration increased the coarse particles in the dust episodes, but AOD and the coefficients for aerosol scattering and absorption were still lower than those observed. Assimilating aerosol scattering coefficient separately from AOD improved the two optical quantities. However, it caused an overestimation of the particle concentrations at the surface. Assimilating the aerosol absorption coefficient yielded the highest positive bias in the surface particle concentration, aerosol scattering coefficient, and AOD. The positive biases in the DA analysis were caused by the forward operator underestimating aerosol mass scattering and absorption efficiency. As compensation, the DA system increased particle concentrations excessively to fit the observed optical values. The best overall improvements were obtained from the simultaneous assimilation of the surface particle concentration and AOD. The assimilation did not substantially change the aerosol chemical fractions. After DA, the clear-sky aerosol radiative forcing at Kashi was −10.4 W m−2 at the top of the atmosphere, which was 55 % higher than the radiative forcing value before DA.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China

Chang

Zhang

et al. 2021

Atmos. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…A typical example is the assimilation of aerosol optical depth (AOD; e.g. (Schutgens et al , ; Liu et al , ); (Saide et al , ); (); (Chen et al , ; Pagowski et al , ; Rubin and Collins, )) or aerosol backscattering measurements (e.g. Wang et al , ; ; (); (Pagowski et al , ); Zhang et al , ) from remote‐sensing instruments in an aerosol transport model.…”

Section: Introductionmentioning

confidence: 99%

Information constraints in variational data assimilation

Kahnert

2018

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Data assimilation of indirect observations from remote‐sensing instruments often leads to highly under‐determined inverse problems. Here a formulation of the variational method is discussed in which (a) the information content of the observations is systematically analysed by methods borrowed from retrieval theory; (b) the model space is transformed into a phase space in which one can partition the model variables into those that are related to the degrees of freedom for signal and noise, respectively; and (c) the minimization routine in the variational analysis is constrained to act on the signal‐related phase‐space variables only. This is done by truncating the dimension of the phase space. A first test of the method indicates that the constrained analysis speeds up computation time by about an order of magnitude compared with the formulation without information constraints.

show abstract

The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States

Cited by 2 publications

References 26 publications

Information constraints in variational data assimilation

Contact Info

Product

Resources

About