The MACC reanalysis: an 8-yr data set of atmospheric composition

Abstract

An eight-year long reanalysis of atmospheric composition data covering the period 2003–2010 was constructed as part of the FP7 funded Monitoring Atmospheric Composition and Climate project by assimilating satellite data into a global model and data assimilation system. This reanalysis provides fields of chemically reactive gases, namely carbon monoxide, ozone, nitrogen oxides, and formaldehyde, as well as aerosols and greenhouse gases globally at a resolution of about 80 km for both the troposphere and the stratosphere. This paper describes the assimilation system for the reactive gases and presents validation results for the reactive gases analysis fields to document the dataset and to give a first indication of its quality. <br><br> Tropospheric CO values from the MACC reanalysis are on average 10–20% lower than routine observations from commercial aircrafts over airports through most of the troposphere, and have larger negative biases in the boundary layer at urban sites affected by air pollution, possibly due to an underestimation of CO or precursor emissions. <br><br> Stratospheric ozone fields from the MACC reanalysis agree with ozone sondes and ACE-FTS data to within ±10% in most situations. In the troposphere the reanalysis shows biases of −5% to +10% with respect to ozone sondes and aircraft data in the extratropics, but has larger negative biases in the tropics. Area averaged total column ozone agrees with ozone fields from a multi sensor reanalysis data set to within a few percent. <br><br> NO<sub>2</sub> fields from the reanalysis show the right seasonality over polluted urban areas of the NH and over tropical biomass burning areas, but underestimate wintertime NO<sub>2</sub> maxima over anthropogenic pollution regions and overestimate NO<sub>2</sub> in Northern and Southern Africa during the tropical biomass burning seasons. <br><br> Tropospheric HCHO is well simulated in the MACC reanalysis even though no satellite data are assimilated. It shows good agreement with independent SCIAMACHY retrievals over regions dominated by biogenic emissions with some anthropogenic input, such as the Eastern US and China, and also over African regions influenced by biogenic sources and biomass burning

Impact of atmospheric aerosol loads on Concentrating Solar Power production in arid-desert sites

“…Therefore, daily values of Angstrom parameters (b and a) have been obtained from MACC reanalysis. MACC is a retrieval based on the ECMWF (European Centre for Mediumrange Weather Forecast) integrated forecasting system that provides information of many atmospheric compounds in both reanalysis and forecasting modes (Inness et al, 2012). In MACC reanalysis there is available worldwide gridded information of daily aerosol optical depth at k = 469, 550, 670, 865 and 1240 nm for the period 2003-2012 at a spatial resolution of 1.125°Â 1.125°.…”

Correcting satellite derived DNI with systematic and seasonal deviations: Application to India

“…Global horizontal irradiance is determined from the relationship between cloud index and clear sky index [11]. The solar radiation components under clear sky conditions are calculated with REST2 model which has been proven to have good performance when accurate atmospheric information is used as input [29,30]; the input for REST2 regarding aerosol optical depth and water vapor was obtained from the MACC reanalysis gridded dataset (http://www.gmes-atmosphere.eu/) that provides daily values of many atmospheric constituents and optical properties from 2003 at a spatial resolution of 1.125 Â 1.125 [31]. Daily values of aerosol optical depth (AOD) at several wavelengths (469, 550, 670, 865 and 1240 nm) and of water vapor content are collected from MACC.…”

Decision theory‐based detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash

“…Sessions et al , ). In this study we have formed a simple prior probability for dust for each calendar month using data from the ECMWF MACC‐Reanalysis dataset over a 10 year period (Inness et al , ). At location s i , the prior probability for dust is the proportion of 3 h intervals over the 10 year period that had a 550 nm optical depth greater than 0.5 in that location, which corresponds to a mass column loading of around 0.15 g m −2 (Linke et al , ).…”