Correcting photolysis rates on the basis of satellite observed clouds

Abstract

[1] Clouds can significantly affect photochemical activities in the boundary layer by altering radiation intensity, and therefore their correct specification in the air quality models is of outmost importance. In this study we introduce a technique for using the satellite observed clouds to correct photolysis rates in photochemical models. This technique was implemented in EPA's Community Multiscale Air Quality modeling system (CMAQ) and was tested over a 10 day period in August 2000 that coincided with the Texas Air Quality Study (TexAQS). The simulations were performed at 4 and 12 km grid size domains over Texas, extending east to Mississippi, for the period of 24 to 31 August 2000. The results clearly indicate that inaccurate cloud prediction in the model can significantly alter the predicted atmospheric chemical composition within the boundary layer and exaggerate or underpredict ozone concentration. Cloud impact is acute and more pronounced over the emission source regions and can lead to large errors in the model predictions of ozone and its by-products. At some locations the errors in ozone concentration reached as high as 60 ppb which was mostly corrected by the use of our technique. Clouds also increased the lifetime of ozone precursors leading to their transport out of the source regions and causing further ozone production down-wind. Longer lifetime for nitrogen oxides (NO x = NO + NO 2 ) and its transport over regions high in biogenic hydrocarbon emissions (in the eastern part of the domain) led to increased ozone production that was missing in the control simulation. Over Houston-Galveston Bay area, the presence of clouds altered the chemical composition of the atmosphere and reduced the net surface removal of reactive nitrogen compounds. Use of satellite observed clouds significantly improved model predictions in areas impacted by clouds. Errors arising from an inconsistency in the cloud fields can impact the performance of photochemical models used for case studies as well as for air quality forecasting. Air quality forecast models often use the model results from the previous forecast (or some adjusted form of it) for the initialization of the new forecast. Therefore such errors can propagate into the future forecasts, and the use of observed clouds in the preparation of initial concentrations for air quality forecasting could be beneficial.

Modeling of photolysis rates over Europe: impact on chemical gaseous species and aerosols

“…A better representation of clouds by assimilation of cloud data from satellite for example will have a higher impact on surface O 3 (Pour-Biazar et al, 2007).…”

Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7

“…Two new options were included in CMAQv4.7 for computing photochemical rate constants. One option utilizes satellite-derived cloud information to adjust photolysis rates (Pour-Biazar et al, 2007). Predicting the location and amount of cloud cover has historically been one of the most difficult problems in numerical weather prediction and air quality modeling.…”

“…In addition, the Asymmetric Convective Model version 2 (ACM2) for the planetary boundary layer (PBL) addresses atmospheric issues that are particularly important for nearsurface chemical transport modeling (Pleim, 2007), and it is used in both the meteorological and chemical transport models to maximize physical consistency. Also, the Pleim-Xiu land-surface model (PX LSM) (Xiu and Pleim, 2001;Pleim and Xiu, 2003;Pleim and Gilliam, 2009) was developed to accurately model surface heat and moisture fluxes from soil and vegetation and provide key parameters for chemical dry deposition. Nudging, the ACM2, and the PX LSM have all been available in MM5 for several years and were deemed critical for CMAQ simulations.…”

Simulation of GOES-R ABI aerosol radiances using WRF-CMAQ: a case study approach

“…3); although, there is a need for improved cloud representation in dynamical models (Pour-Biazar et al, 2007). The clouds often differ from their positions in the simulated images due to difficulty in simulating the observed clouds from WRF.…”