Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts

Abstract

Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM 2.5 predictions reduced at over 90 % of the stations and AOD errors reduced at 100 % of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

Online coupled regional meteorology chemistry models in Europe: current status and prospects

“…The use of the NWP system for data assimilation allows the use of the existing infra-structure for satellite data handling and the MACC system is able to assimilate more than one data set from a large array of satellite instruments (GOME, MIPAS, MLS, OMI, SBUV, SCIAMACHY, MOPITT, IASI, TANSO, AIRS) for O 3 , CO, NO 2 , SO 2 , HCHO, CH 4 , CO 2 and AOD. In the USA, CDA is conducted in WRF-Chem using both 3DVAR (Pagowski et al, 2010;Liu et al, 2011;Schwartz et al, 2012;Saide et al, 2012Saide et al, , 2013 and EnKF (Pagowski and Grell, 2012); there is an on-going project to assimilate surface PM 2.5 data as well as AOD using a hybrid approach that employs both EnKF and 3DVAR. Furthermore, the adjoint of WRF-Chem is currently under development with the objective of performing sensitivity analysis with a variational method in the near future and possibly CDA with inverse modelling of parameter fields later.…”

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…We present two case studies, which correspond to the use of AOD (Saide et al, 2013) and cloud number droplet satellite retrievals (N d ) (Saide et al, 2012a). The WRF-Chem configuration is based on Saide et al (2012b).…”

“…Figures in the left column assimilate only MODIS 550 nm AOD, while the ones in the right column assimilate MODIS 550, 660, 870, and 1240 nm over the ocean and only 550 nm over land. Modified from Saide et al (2013). the significant initial disagreement between the SILAM and MODIS AOD.…”

“…The important result in that work was the ability to improve aerosol simulations using the assimilation of cloud droplet number concentration data, which is only possible due to the coupled nature of WRF-Chem that integrates aerosol indirect effects into the forecasts. Saide et al (2013) also used a modified GSI 3D-Var to assimilate MODIS AOD data into WRF-Chem for a sectional aerosol treatment and using the adjoint of the Mie computation for the AOD from aerosol concentrations. Improvements in aerosol concentrations were obtained at most locations when compared to measurements at surface monitoring sites in California and Nevada.…”

Estimation of Satellite-Based SO42− and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model

“…Considering emission inventories and precursor gases as input, CTMs have also been capable of simulating various chemical components of PM 2.5 . For example, inspired by AOD assimilation methods [20], Sajeev et al [15] combined satellite-derived AOD data with global modeling of the coincident aerosol vertical profile and composition to produce a global long-term mean ambient outdoor satellite-model PM 2.5 composition dataset at a spatial resolution of 0.1 × 0.1 • . Following [15], Geng et al estimated the chemical composition of PM 2.5 over China.…”