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We use data from the new China Ministry of Ecology and Environment (MEE) network to show that OMI satellite observations of tropospheric ozone can successfully map the distribution of surface ozone pollution in China and the frequency of high-ozone episodes. After subtracting the Pacific background, OMI ozone enhancements over China can quantify mean summer afternoon surface ozone with a precision of 10.7 ppb and a spatial correlation coefficient R=0.73. Day-to-day correlations between OMI and the MEE ozone data are statistically significant but limited by noise in the individual OMI retrievals. OMI shows significantly higher values on surface ozone episode days (>82 ppb). An extreme value model can successfully predict the probability of surface ozone episodes from the daily OMI data. The 2005-2017 OMI record shows a 0.67 ppb a-1 increase in mean summer afternoon ozone in eastern China and an increasing frequency of ozone pollution episodes particularly in the north. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (Level 2) to a regular grid (Level 3). When averaging Level 2 data over a long time period to a target Level 3 grid significantly finer than Level 2 pixels, this process is referred to as "oversampling'". An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous approaches. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOV) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid resolution, the ground size of FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for high resolution oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grid resolutions finer than ~16 km. The generalized 2-D super Gaussian function also enables smoothing of the Level 3 results by decreasing the shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO2 products and IASI NH3 products, showing substantially improved visualization of trace gas distribution and local gradients. [ABSTRACT FROM AUTHOR]

We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004-2008) and post-RA (2009-2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure <~50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ~550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6%, and the standard deviations (SDs) range from 5 to 10% for pressure <~50 hPa to less than 18% (27%) in the tropics (midlatitudes) for pressure >~50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2% with SDs of <5% and the MBs of the tropospheric ozone column (TOC) are within 6% with SDs of 15%. In the high latitudes, the profile MBs are within 10% with SDs of 5-15% for pressure <~50 hPa but increase to 30% with SDs as great as 40% for pressure >~50 hPa. The SOC MBs increase up to 3% with SDs as great as 6% and the TOC SDs increase up to 30%. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure>~100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2% in the stratosphere and 8% in the troposphere and up to 6% in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14-0.7%year-1 for pressure<~80 hPa, 0.7DUyear-1 in SOC, and -0.33DUyear-1 in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality. [ABSTRACT FROM AUTHOR]

We validate the Ozone Monitoring Instrument (OMI) ozone profile (PROFOZ) product including ozone profiles between 0.22-261hPa and Stratospheric Ozone Columns (SOCs) down to 100, 215, and 261hPa from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against the latest Microwave Limb Sound (MLS) v4.2x data. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the data set into before and after the occurrence of serious RA, i.e., pre-RA (2004-2008) and post-RA (2009-2014). During the pre-RA period, OMI ozone profiles agree very well with MLS data. Tthe global mean biases (MBs) are within 3% between 0.22-100 hPa and negative 3-9% for lower layers, and the standard deviations (SDs) are 3.5-5% from 1-40 hPa, 6-10% for upper layers and 5-20% for lower layers, after applying OMI averaging kernels to MLS data. OMI shows latitude and solar zenith angle (SZA) dependent biases, but MBs and SDs are mostly within 10% except for low/high altitudes of high latitudes/SZAs. During the post-RA period, OMI retrievals degrade slightly between 5-261hPa with MBs and SDs typically larger by 2-5%, and degrade much more, with larger MBs by up to 8% and SDs by up to 15% for pressure less than ~5hPa, where the MBs are larger by 10-15% south of 40 °N due to the blockage effect of RA and smaller by 15-20% north of 40 °N due to the solar contamination effect of RA. The much worse comparison at high altitudes indicates the UV-1 channel of pixels that are not flagged as RA is still affected by the RA. During the pre-RA period, OMI SOCs show very good agreement with MLS data with global mean MBs within 0.6% and SDs of 1.9% for SOCs down to 215 and 261hPa and of 2.30% for SOC down to 100hPa. Despite clearly worse ozone profile comparison during the post-RA period, OMI SOCs only slightly degrade, with SDs larger by 0.4-0.6% mostly due to looser spatial coincidence criterion as a result of missing data from RA and MBs larger by 0.4-0.7%. The retrieval comparison indicates significant bias trends, especially during the post-RA period. The spatiotemporal variation of the retrieval performance suggests the need to improve OMI's radiometric calibration to maintain the long-term stability and spatial consistency of the PROFOZ product. The good comparison with SOC down to 261hPa supports that MLS ozone at 261hPa, recommended for further evaluation by the MLS team, is suitable for scientific use. [ABSTRACT FROM AUTHOR]