Abstract. We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO 2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOSAura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NO x emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO 2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0-40 %), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO 2 profiles that are better mixed throughout the boundary layer. We evaluate the NO 2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO 2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are furCorrespondence to: Folkert Boersma (boersma@knmi.nl) ther updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (<5 %) on average over large polluted areas, but still causes significant changes locally. The main changes in the DOMINO v2.0 algorithm follow from the new LUT and the improved TM4 sampling that results in more NO 2 simulated aloft, where sensitivity to NO 2 is higher, and amount to reductions in tropospheric NO 2 columns of up to 20 % in winter, and 10 % in summer over extended polluted areas. We investigate the impact of aerosols on the NO 2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 % AMF reduction in summer and ±10 % changes in winter over the eastern United States.

Abstract. The GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) data processing for the Vaisala RS92 radiosonde was developed to meet the criteria for reference measurements. These criteria stipulate the collection of metadata, the use of well-documented correction algorithms, and estimates of the measurement uncertainty. An important and novel aspect of the GRUAN processing is that the uncertainty estimates are vertically resolved. This paper describes the algorithms that are applied in version 2 of the GRUAN processing to correct for systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, as well as how the uncertainties related to these error sources are derived. Currently, the RS92 is launched on a regular basis at 13 out of 15 GRUAN sites. An additional GRUAN requirement for performing reference measurements with the RS92 is that the manufacturerprescribed procedure for the radiosonde's preparation, i.e. heated reconditioning of the sensors and recalibration during ground check, is followed. In the GRUAN processing however, the recalibration of the humidity sensors that is applied during ground check is removed. For the dominant error source, solar radiation, laboratory experiments were performed to investigate and model its effect on the RS92's temperature and humidity measurements. GRUAN uncertainty estimates are 0.15 K for night-time temperature measurements and approximately 0.6 K at 25 km during daytime. The other uncertainty estimates are up to 6 % relative humidity for humidity, 10-50 m for geopotential height, 0.6 hPa for pressure, 0.4-1 m s −1 for wind speed, and 1 • for wind direction. Daytime temperature profiles for GRUAN and Vaisala processing are comparable and consistent within the estimated uncertainty. GRUAN daytime humidity profiles are up to 15 % moister than Vaisala processed profiles, of which two-thirds is due to the radiation dry bias correction and onethird is due to an additional calibration correction. Redundant measurements with frost point hygrometers (CFH and NOAA FPH) show that GRUAN-processed RS92 humidity profiles and frost point data agree within 15 % in the troposphere. No systematic biases occur, apart from a 5 % dry bias for GRUAN data around −40 • C at night.

[1] We examine the seasonal variation in lower tropospheric nitrogen oxides (NO x = NO + NO 2 ) at northern midlatitudes by evaluating tropospheric NO 2 columns observed from the Ozone Monitoring Instrument (OMI) satellite instrument with surface NO 2 measurements (SouthEastern Aerosol Research and Characterization and Air Quality System) and current bottom-up NO x emission inventories, using a global model of tropospheric chemistry (GEOS-Chem). The standard (SP) and DOMINO (DP) tropospheric NO 2 column products from OMI exhibit broadly similar spatial and seasonal variation, but differ substantially over continental source regions. A comparison of the two OMI tropospheric NO 2 products with in situ surface NO 2 concentrations and bottom-up NO x emissions over the southeast United States indicates that annual mean NO 2 columns from the DP are biased high by 21%-33% and those from the SP are biased high by 27%-43%. The bias in SP columns is highly seasonal, 67%-74% in summer compared with −6% to −1% in winter. Similar seasonal differences exist between top-down and bottom-up NO x emission inventories over North America, Europe, and East Asia. The air mass factor largely explains the observed seasonal difference between the DP and SP, and in turn the seasonal SP bias. We develop a third product (DP_GC) using averaging kernel information from the DP and NO 2 vertical profiles from GEOS-Chem. This product reduces to 5%-21% the annual mean bias over the southeast United States. We use the seasonal variation in the DP_GC to estimate the seasonal variation in the lifetime of lower tropospheric NO x against oxidation to HNO 3 over the eastern United States. The effective NO x lifetime at OMI overpass time (early afternoon) ranges from 7.6 h in summer to 17.8 h in winter, consistent within 3 h of the simulated lifetime. GEOS-Chem calculations reveal that the seasonal variation in OMI NO 2 columns largely reflects gas-phase oxidation of NO 2 in summer with an increasing role for heterogenous chemistry in winter.Citation: Lamsal, L. N., R. V. Martin, A. van Donkelaar, E. A. Celarier, E. J. Bucsela, K. F. Boersma, R. Dirksen, C. Luo, and Y. Wang (2010), Indirect validation of tropospheric nitrogen dioxide retrieved from the OMI satellite instrument: Insight into the seasonal variation of nitrogen oxides at northern midlatitudes,

We compare tropospheric NO 2 column measurements from the Ozone Monitoring Instrument (OMI) aboard the EOS Aura satellite with coincident in situ aircraft measurements on vertical spirals over the southern United States, Mexico, and the Gulf of Mexico during the INTEX-B campaign in March 2006. Good correlation with no significant bias (r 2 ¼ 0:67, slope ¼ 0:99 AE 0:17, n ¼ 12) is found for the ensemble of comparisons when the aircraft could spiral sufficiently low to sample most of the NO 2 column. Urban spirals where large extrapolations were needed below the aircraft floor (1000 ft) showed poorer agreement. We use the OMI observations together with a global chemical transport model (GEOS-Chem) to estimate emissions of nitrogen oxides over the eastern United States and Mexico in March 2006. Comparison to EPA's National Emissions Inventory 1999 (NEI99) calls for a decrease in power plant emissions and an increase in on-road vehicle emissions relative to that inventory. The rise in vehicular emissions is offsetting the reduction in power plant and industry emissions. These findings are consistent with independent assessments. Our OMI-derived emission estimates for Mexico are higher by a factor of 2:0 AE 0:5 than bottom-up emissions, similar to a comparison between the recently released Mexican NEI99 inventory and the bottom-up showing that the Mexican NEI99 inventory is 1.6-1.8Â higher.

Abstract. We compare a full-year (2006) record of surface air NO 2 concentrations measured in Israeli cities to coinciding retrievals of tropospheric NO 2 columns from satellite sensors (SCIAMACHY aboard ENVISAT and OMI aboard Aura). This provides a large statistical data set for validation of NO 2 satellite measurements in urban air, where validation is difficult yet crucial for using these measurements to infer NO x emissions by inverse modeling. Assuming that NO 2 is well-mixed throughout the boundary layer (BL), and using observed average seasonal boundary layer heights, near-surface NO 2 concentrations are converted into BL NO 2 columns. The agreement between OMI and (13:45) BL NO 2 columns (slope=0.93, n=542), and the comparable results at 10:00 h for SCIAMACHY, allow a validation of the seasonal, weekly, and diurnal cycles in satellite-derived NO 2 . OMI and BL NO 2 columns show consistent seasonal cycles (winter NO 2 1.6-2.7× higher than summer). BL and coinciding OMI columns both show a strong weekly cycle with 45-50% smaller NO 2 columns on Saturday relative to the weekday mean, reflecting the reduced weekend activity, and validating the weekly cycle observed from space. The diurnal difference between SCIAMACHY (10:00) and OMI (13:45) NO 2 is maximum in summer when SCIAMACHY is up to 40% higher than OMI, and minimum in winter when OMI slightly exceeds SCIAMACHY. A similar seasonal variation in the diurnal difference is found in the source region of Cairo. The surface measurements in Israel cities confirm this seasonal variation in the diurnal cycle. Using simulations from Correspondence to: F. Boersma (boersma@knmi.nl) a global 3-D chemical transport model (GEOS-Chem), we show that this seasonal cycle can be explained by a much stronger photochemical loss of NO 2 in summer than in winter.