M. E. Koukouli

Water vapor distributions measured with the Michelson Interferometer for Passive Atmospheric Sounding on board Envisat (MIPAS/Envisat)

[1]xa0We present water vapor profiles obtained from infrared limb emission measurements recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on board the European Environmental Satellite (Envisat). These retrievals are based on constrained nonlinear least squares fitting. The retrievals are very sensitive to the radiative signals of thin transparent clouds and measurements showing any signature of cloud contamination have been rigorously excluded. The vertical resolution of the retrieved water vapor profiles is 4.5 to 6.5 km up to an altitude of approximately 42 km. The resulting total error of the retrieved water vapor profiles, including measurement noise, systematic and random parameter uncertainties like interfering species or preretrieved temperature or spectroscopic data, is in the range of 6 to 9% in the stratosphere. Towards the tropopause, the error increases up to 30% due to the exponential gradient of the tropospheric water vapor profile, where small line of sight uncertainties lead to strong absolute variations in the water vapor profile below the hygropause. Averaged water vapor distributions obtained from measurements taken during 11 days in June, July, and August 2003 show the expected distributions with low water vapor volume mixing ratios (VMRs) above the tropopause, comparatively dry air inside the tropical stratospheric updraft region and indications for strong dehydration above the Antarctic continent inside the polar vortex. Additionally, in the transition from tropics to subtropics, a latitude band was observed where, in higher altitudes, large water vapor VMRs were measured compared to adjacent tropical and midlatitudinal regions. Especially over the Arabic peninsula, a moist region at 18 km altitude was observed, which was probably related to the South Asian monsoon circulation.

Global validation of empirically corrected EP-Total Ozone Mapping Spectrometer (TOMS) total ozone columns using Brewer and Dobson ground-based measurements

[1]xa0This article focuses on the global-scale validation of the empirically corrected Version 8 total ozone column data set acquired by the NASA Total Ozone Mapping Spectrometer (TOMS) during the period 1996–2004 when this instrument was flying aboard the Earth Probe (EP) satellite platform. This analysis is based on the use of spatially co-located, ground-based measurements from Dobson and Brewer spectrophotometers. The original EP-TOMS V8 total ozone column data set was also validated with these ground-based measurements to quantify the improvements made by the empirical correction that was necessary as a result of instrumental degradation issues occurring from the year 2000 onward that were uncorrectable by normal calibration techniques. EP-TOMS V8-corrected total ozone data present a remarkable improvement concerning the significant negative bias of around ∼3% detected in the original EP-TOMS V8 observations after the year 2000. Neither the original nor the corrected EP-TOMS satellite total ozone data sets show a significant dependence on latitude. In addition, both EP-TOMS satellite data sets overestimate the Brewer measurements for small solar zenith angles (SZA) and underestimate for large SZA, explaining a significant seasonality (∼1.5%) for cloud-free and cloudy conditions. Conversely, relative differences between EP-TOMS and Dobson present almost no dependence on SZA for cloud-free conditions and a strong dependence for cloudy conditions (from +2% for small SZA to −1% for high SZA). The dependence of the satellite ground-based relative differences on total ozone shows good agreement for column values above 250 Dobson units. Our main conclusion is that the upgrade to TOMS V8-corrected total ozone data presents a remarkable improvement. Nevertheless, despite its quality, the EP-TOMS data for the period 2000–2004 should not be used as a source for trend analysis since EP-TOMS ozone trends are empirically corrected using NOAA-16 and NOAA-17 solar backscatter ultraviolet/2 data as external references, and therefore, they are no longer considered as independent observations.

The Effect of Three Different Absorption Cross-Sections and their Temperature Dependence on Total Ozone Measured by a Mid-Latitude Brewer Spectrophotometer

Abstract The influence of variations in atmospheric temperature and ozone profiles on the total ozone column (TOC) derived from a Brewer MKII spectrophotometer operating in Thessaloniki, Greece, is investigated using three different sets of ozone absorption cross-sections. The standard Brewer total ozone retrieval algorithm uses the Bass and Paur (1985) cross-sections without accounting for the temperature dependence of the ozone cross-sections which produces a seasonally dependent bias in the measured TOC. The magnitude of this temperature effect depends on the altitude where the bulk of the ozone absorption occurs. Radiosonde measurements for the period 2000 to 2010 combined with climatological ozone profiles were used to calculate the effective temperature of ozone absorption and investigate its effect on the retrieved ozone column. Three different ozone absorption cross-section spectra convolved with the instruments slit function were used: those of Bass and Paur (hereafter BP), currently used in the standard Brewer retrieval algorithm; those of Brion, Daumont, and Malicet (Malicet et al., 1985; hereafter BDM); and the recently published set by Serdyuchenko et al. (2013 hereafter S13). The temperature dependence of the differential ozone absorption coefficient ranges between 0.09 and 0.13% per degree Celsius for BP, between −0.11 and −0.06% per degree Celsius for BDM, and between 0.018 to 0.022% per degree Celsius for S13, resulting in a seasonal bias in the derived TOC of up to 2%, 1.8%, and 0.4%, respectively. The temperature sensitivity of the differential ozone absorption coefficient for the Brewer spectrophotometer at Thessaloniki for the BP and BDM cross-sections is found to be within the range reported for other Brewer instruments in earlier studies, whereas the seasonal bias in TOC is minimized when using the new S13 cross-sections because of their small temperature dependence.

Longitudinal variations of temperature and ozone profiles observed by MIPAS during the Antarctic stratosphere sudden warming of 2002

[1]xa0The temperature and ozone volume mixing ratio (VMR) profiles measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT are used to study the unusual Antarctic major stratospheric warming of 2002. The observed zonal mean temperatures show rapid poleward increase and remarkable reversal of the latitudinal gradients at 35 km or below in several days. The highest temperature increase is of 50 K or more. The zonal mean ozone VMRs also increase poleward and have maximum values of 7 ppmv in a wide region between 20 and 40 km at latitudes south of 40°S. Temperature amplitudes of zonal wave number 1 to 3 exhibit a double-peaked structure with peaks near 25 km and 35 km. The ozone waves in the lower stratosphere are generally in phase with the corresponding temperature waves. At the onset of the warming, the wave 1 amplitudes drastically increase at 60°S–80°S, reaching maxima of ∼20 K for the temperature and ∼2 ppmv for the ozone VMR. Significant wave 3 amplitudes are also observed with maximum of 14–18 K and 1–1.5 ppmv for temperature and ozone VMR, respectively. The wave 3 amplitudes are larger than those of wave 2 by nearly a factor of 2 immediately before and after the polar vortex split. The large-amplitude wave 1 and 3 disturbances break down in 1 or 2 days, and the wave 2 variations are enhanced and attain amplitudes comparable to those of wave 1 and 3, resulting in an apparent wave 2 warming event. These results are consistent with other observations and suggest the importance of wave 3 forcing in the major warming.

NILU-UV multi-filter radiometer total ozone columns: Comparison with satellite observations over Thessaloniki, Greece

This study aims to construct and validate a neural network (NN) model for the production of high frequency (~1min) ground-based estimates of total ozone column (TOC) at a mid-latitude UV and ozone monitoring station in the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki (LAP/AUTh) for the years 2005-2014. In the first stage of model development, ~30,000 records of coincident solar UV spectral irradiance measurements from a Norsk Institutt for Luftforskning (NILU)-UV multi-filter radiometer and TOC measurements from a co-located Brewer spectroradiometer are used to train a NN to learn the nonlinear functional relation between the irradiances and TOC. The model is then subjected to sensitivity analysis and validation. Close agreement is obtained (R2=0.94, RMSE=8.21 DU and bias=-0.15 DU relative to the Brewer) for the training data in the correlation of NN estimates on Brewer derived TOC with 95% of the coincident data differing by less than 13 DU. In the second stage of development, a long time series (≥1 million records) of high frequency (~1min) NILU-UV ground-based measurements are presented as inputs to the NN model to generate high frequency TOC estimates. The advantage of the NN model is that it is not site dependent and is applicable to any NILU input data lying within the range of the training data. GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura and GOME2/MetOp-A TOC records are then used to perform a precise cross-validation analysis and comparison with the NILU TOC estimates over Thessaloniki. All 4 satellite TOC dataset are retrieved using the GOME Direct Fitting algorithm, version 3 (GODFIT_v3), for reasons of consistency. The NILU TOC estimates within ±30min of the overpass times agree well with the satellite TOC retrievals with coefficient of determination in the range 0.88≤R2≤0.90 for all sky conditions and 0.95≤R2≤0.96 for clear sky conditions. The mean fractional differences are found to be -0.67%±2.15%, -1.44%±2.25%, -2.09%±2.06% and -0.85%±2.19% for GOME, SCIAMACHY, OMI and GOME2 respectively for the clear sky cases. The near constant standard deviation (~±2.2%) across the array of sensors testifies directly to the stability of both the GODFIT_v3 algorithm and the NN model for providing coherent and robust TOC records. Furthermore, the high Pearson product moment correlation coefficients (0.94<R<0.98) testify to the strength of the linear relationship between the satellite algorithm retrievals of TOC and ground-based estimates, while biases of less than 5 DU suggest that systematic errors are low. This novel methodology contributes to the ongoing assessment of the quality and consistency of ground and space-based measurements of total ozone columns.

Top-Down SO 2 Emissions Over China; A Satellite Approach

With the rapid development of the Chinese economy since 2000, sulfur dioxide, SO2, emissions from China have been of increasing global concern. There have been indications that the emission growth rate slowed around 2005 and that emissions began to decrease after 2006, mainly due to the wide application of flue-gas desulfurization devices in power plants. However due to the differences in growth rate among the many Chinese Provinces, it remains to be seen whether this decreasing trend can be verified for the entire domain. In this study we use space-based observations of SO2 columns from the Ozone Monitoring Instrument on board the Aura satellite, OMI/Aura, to derive monthly top-down SO2 emissions over China via inverse modeling with the CHIMERE chemical transport model. The driving SO2 emissions have been provided by the Multi-resolution Emission Inventory for China (MEIC) model. In MEIC, the SO2 sources are further partitioned among industry, power and residential provenances. This study aims at providing updated SO2 emissions, based on satellite observations, which may later be used to update existing chemical transport model results.

A Robust Seasonality Detector for Geophysical Time Series: Application to Satellite SO2 Observations Over China

We have developed a robust seasonality detector that uses singular spectrum analysis (SSA) and a chi-squared red noise test to extract statistically-significant frequencies from smoothed spectra computed with the discrete Fourier transform (DFT). SSA is found to provide a useful time-series decomposition into a low frequency trend, the total noise and periodicity, but is unable to extract individual cyclical components. We show that it is possible to identify these cycles in the frequency domain by applying a statistical-significance test to the smoothed spectrum such that: (i) spectral estimates at peak frequencies account for the largest proportion of the total variance and (ii) that the peaks are distinct from an equivalent auto-regression AR(1) red noise continuum. We apply this seasonality detector to 141 noisy and often fairly discontinuous time series of monthly mean anthropogenic SO2 loads over major cities and power plants in China extracted from ten years of OMI/Aura satellite observations between 2005 and 2015. We routinely observed the presence of an annual cycle (99 cases) but also a bi-annual cycle (60 cases) in the satellite data. This strong annual and inter-annual variability observed from space is also detected in co-located ground-based SO2 concentrations at the Xinglong observational station in Hebei Province, China.

Comparison of Ground-Based Tropospheric NO 2 Columns with OMI/Aura Products in the Greater Area of Thessaloniki by Means of an Air Quality Modeling Tool

Phaethon is a ground-based MAX-DOAS system, easily deployed at different locations to address specific air quality problems and support satellite validation studies. Three Phaethon systems have been deployed at different sites in the greater area of Thessaloniki, characterized by diverse local pollution levels representing urban, suburban and rural conditions, aiming at linking tropospheric trace-gas modeling with satellite products. Tropospheric NO2 columns derived at these sites located within an area of about 15 by 30 km, comparable to the size of OMI/Aura pixel, are compared with the satellite retrievals. The OMI/Aura products underestimate the NO2 in the city centre, representing the average pollution levels in the sub-satellite pixel area which, in the case of Thessaloniki, corresponds mostly to rural conditions. In order to minimize the collocation differences in spatial distribution between satellite and ground-based measurements, the former are adjusted by factors that are calculated by means of a high resolution air quality modeling tool, consisting of WRF meteorological model and CAMx air quality model. This approach shows significant improvement in the comparisons between ground-based and satellite-derived observations.

MAX-DOAS NO 2 observations over Guangzhou, China; ground-based and satellite comparisons

In this study, the tropospheric NO2 vertical column density (VCD) over an urban site in Guangzhou megacity in China is investigated by means of MAX-DOAS measurements during a campaign from late March 2015 to mid-March 2016. A MAX-DOAS system was deployed at the Guangzhou Institute of Geochemistry of the Chinese Academy of Sciences and operated there for about 1 year, during the spring and summer months. The tropospheric NO2 VCDs retrieved by the MAX-DOAS are presented and compared with space-borne observations from GOME-2/MetOpA, GOME-2/MetOp-B and OMI/Aura satellite sensors. The comparisons reveal good agreement between satellite and MAX-DOAS observations over Guangzhou, with correlation coefficients ranging between 0.795 for GOME-2B and 0.996 for OMI. However, the tropospheric NO2 loadings are underestimated by the satellite sensors on average by 25.1, 10.3 and 5.7 %, respectively, for OMI, GOME-2A and GOME2B. Our results indicate that GOME-2B retrievals are closer to those of the MAX-DOAS instrument due to the lower tropospheric NO2 concentrations during the days with valid GOME-2B observations. In addition, the effect of the main coincidence criteria is investigated, namely the cloud fraction (CF), the distance (d) between the satellite pixel center and the ground-based measurement site, as well as the time period within which the MAX-DOAS data are averaged around the satellite overpass time. The effect of CF and time window criteria is more profound on the selection of OMI overpass data, probably due to its smaller pixel size. The available data pairs are reduced to half and about one-third for CF≤ 0.3 and CF≤ 0.2, respectively, while, compared to larger CF thresholds, the correlation coefficient is improved to 0.996 from about 0.86, the slope value is very close to unity (∼ 0.98) and the mean satellite underestimation is reduced to about half (from ∼ 7 to ∼ 3.5× 1015 molecules cm−2). On the other hand, the distance criterion affects mostly GOME-2B data selection, because GOME-2B pixels are quite evenly distributed among the different radii used in the sensitivity test. More specifically, the number of collocations is notably reduced when stricter radius limits are applied, the r value is improved from 0.795 (d ≤ 50 km) to 0.953 (d ≤ 20 km), and the absolute mean bias decreases about 6 times for d ≤ 30 km compared to the reference case (d ≤ 50 km).

Compilation of a NOx Emission Inventory for the Balkan Region Using Satellite Tropospheric NO2 Columns

The important improvements in the quality of space-born tropospheric trace gas estimates have permitted their use, in combination with inverse atmospheric modelling, to obtain evolved top-down pollutant emission estimates. In this study, inverse modeling is applied to the case of tropospheric nitrogen dioxide (NO2) columns as seen by the OMI/Aura instrument and estimated by the Comprehensive Air Quality Model with extensions (CAMx). The main idea is to use the a priori information from the bottom up emission inventory used in the CAMx model, the tropospheric NO2 quantities estimated by the CAMx runs and the tropospheric NO2 columns deduced by the satellite observations to create an a posteriori NOx emission inventory. This new inventory, constrained in the top-down manner by the satellite estimates, can be used anew in the CAMx model to produce a new modeled NOx product. This work has identified biases in the original emission inventory for instance due to missing emission sources or over-estimation of the spread of emission sources and has proved an improved bottom-up emissions inventory.