ALBERT

Description: We apply the NASA Goddard Trajectory Model to data from a series of ozonesondes to derive ozone loss rates in the lower stratosphere for the AASE-2/EASOE mission (January-March 1992) and for the SOLVE/THESEO 2000 mission (January-March 2000) in an approach similar to Match. Ozone loss rates are computed by comparing the ozone concentrations provided by ozonesondes launched at the beginning and end of the trajectories connecting the launches. We investigate the sensitivity of the Match results to the various parameters used to reject potential matches in the original Match technique. While these filters effectively eliminate from consideration 80% of the matched sonde pairs and 〉99% of matched observations in our study, we conclude that only a filter based on potential vorticity changes along the calculated back trajectories seems warranted. Our study also demonstrates that the ozone loss rates estimated in Match can vary by up to a factor of two depending upon the precise trajectory paths calculated for each trajectory. As a result, the statistical uncertainties published with previous Match results might need to be augmented by an additional systematic error. The sensitivity to the trajectory path is particularly pronounced in the month of January, for which the largest ozone loss rate discrepancies between photochemical models and Match are found. For most of the two study periods, our ozone loss rates agree with those previously published. Notable exceptions are found for January 1992 at 475K and late February/early March 2000 at 450K, both periods during which we generally find smaller loss rates than the previous Match studies. Integrated ozone loss rates estimated by Match in both of those years compare well with those found in numerous other studies and in a potential vorticity/potential temperature approach shown previously and in this paper. Finally, we suggest an alternate approach to Match using trajectory mapping. This approach uses information from all matched observations without filtering and uses a two-parameter fit to the data to produce robust ozone loss rate estimates. As compared to loss rates from our version of Match, the trajectory mapping approach produces generally smaller loss rates, frequently not statistically significantly different from zero, calling into question the efficacy of the Match approach.

Description: A global time series of total columnar water vapour from combined data of the Medium Resolution Imaging Spectrometer (MERIS) onboard ESA's Environmental Satellite (ENVISAT) and the Special Sensor Microwave/Imager (SSM/I) onboard the satellite series of the US Defense Meteorological Satellite Program (DMSP) is presented. The unique data set, generated in the framework of the ESA Data User Element (DUE) GlobVapour project, combines atmospheric water vapour observations over land and ocean, derived from measurements in the near-infrared and the microwave range, respectively. Daily composites and monthly means of total columnar water vapour are available as global maps on rectangular latitude–longitude grids with a spatial resolution of 0.05° × 0.05° over land and 0.5° × 0.5° over ocean for the years 2003 to 2008. The data are stored in NetCDF files and is fully compliant with the NetCDF Climate Forecast convention. Through the combination of high-quality microwave observations and near-infrared observations over ocean and land surfaces, respectively, the data set provides global coverage. The combination of both products is carried out such that the individual properties of the microwave and near-infrared products, in particular their uncertainties, are not modified by the merging process and are therefore well defined. Due to the global coverage and the provided uncertainty estimates this data set is potentially of high value for climate research. The SSM/I-MERIS TCWV data set is freely available via the GlobVapour project web page (www.globvapour.info) with associated doi:10.5676/DFE/WV_COMB/FP. In this paper, the details of the data set generation, i.e. the satellite data used, the retrieval techniques and merging approaches, are presented. The derived level 3 products are compared to global radiosonde data from the GCOS upper air network (GUAN), showing a high agreement with a root-mean-square deviation of roughly 4.4 kg m−2 and a small wet bias well below 1 kg m−2. Furthermore, the data set is shown to be free of seasonal biases. The consistency of the MERIS and SSM/I retrievals is demonstrated by applying the MERIS retrieval to sun glint areas over ocean.

Description: Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a Neural Network algorithms. An extended set of ozone sonde measurements at northern mid-latitudes has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

Description: We apply the NASA Goddard Trajectory Model with a series of ozonesondes to derive ozone loss rates in the lower stratosphere for the AASE-2/EASOE mission (January–March 1992) and for the SOLVE/THESEO 2000 mission (January–March 2000) in an approach similar to Match. Ozone loss rates are computed by comparing the ozone concentrations provided by ozonesondes launched at the beginning and end of the trajectories connecting the launches. We investigate the sensitivity of the Match results to the various parameters used to reject potential matches in the original Match technique. While these filters effectively eliminate from consideration ≥80% of the matched sonde pairs and 〉99% of matched observations, we conclude that only a filter based on potential vorticity changes along the calculated back trajectories seems warranted. Our study also demonstrates that the ozone loss rates estimated in Match can vary by up to a factor of two depending upon the precise trajectory paths calculated for each trajectory. As a result, the statistical uncertainties published with previous Match results might need to be augmented by an additional systematic error. The sensitivity to the trajectory path is particularly pronounced in the month of January, for which the largest ozone loss rate discrepancies between photochemical models and Match are found. For most of the two study periods, our ozone loss rates agree with those previously published. Notable exceptions are found for January 1992 at 475 K and late February/early March 2000 at 450 K, both periods during which we find less loss than the previous Match studies. Integrated ozone loss rates estimated by Match in both of those years compare well with those found in numerous other studies and in a potential vorticity/potential temperature approach shown previously and in this paper. Finally, we suggest an alternate approach to Match using trajectory mapping. This approach uses information from all matched observations without filtering to produce robust ozone loss rate estimates.

Description: Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a neural network algorithm. An extended set of ozone sonde measurements at northern mid-latitudes for the years 2004–2008 has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

Description: We analyze three different methods to validate and intercompare satellite measurements of atmospheric composition, and apply them to tropospheric ozone retrievals from the Tropospheric Emission Spectrometer (TES) and the Ozone Monitoring Instrument (OMI). The first method (in situ method) uses in situ vertical profiles for absolute instrument validation; it is limited by the sparseness of in situ data. The second method (CTM method) uses a chemical transport model (CTM) as an intercomparison platform; it provides a globally complete intercomparison with relatively small noise added by model error. The third method (averaging kernel smoothing method) involves smoothing the retrieved profile from one instrument with the averaging kernel matrix of the other; it also provides a global intercomparison but dampens the actual difference between instruments and adds noise from the a priori. Application to a full year (2006) of TES and OMI data shows mean positive biases of 5.3 parts per billion volume (ppbv) (10%) for TES and 2.8 ppbv (5%) for OMI at 500 hPa relative to in situ data from ozonesondes. We show that the CTM method (using the GEOS-Chem CTM) closely approximates results from the in situ method while providing global coverage. It reveals that differences between TES and OMI are generally less than 10 ppbv (18%), except at northern mid-latitudes in summer and over tropical continents. The CTM method allows for well-constrained CTM evaluation in places where the satellite observations are consistent. We thus find that GEOS-Chem underestimates tropospheric ozone in the tropics, reflecting a combination of possible factors, and overestimates ozone in the northern subtropics and southern mid-latitudes, likely because of excessive stratospheric influx.

Description: During the winter of 1999-2000, the Sage III Ozone Loss and Validation Experiment (SOLVE) field experiment took place in Kiruna, Sweden. The purpose of SOLVE was to examine ozone depletion mechanisms in the Arctic stratosphere (from about 10 to 50 km altitude) during the winter and early spring, when a band of strong winds (the 'polar vortex') circle the pole. Measurements of stratospheric ozone were made by several different kinds of instruments in different meteorological situations. We analyzed these data using the 'quasi-conservative coordinate mapping' technique, in which the measurements are analyzed in terms of meteorological properties ('potential temperature' and 'potential vorticity') which tend not to change very much over a few days. This technique reduces or removes the changes that are associated with the polar vortex moving around. Over longer time periods, potential temperature and potential vorticity change as air cools and descends within the polar vortex. We account for these changes by calculating the trajectories of air parcels, and this enables us to extend the analysis over a ten-week period from January 10 to March 17, 2000. Using data from the NASA ER-2 aircraft, from the DIAL and AROTEL laser sounders on the NASA DC-8 aircraft, and balloon-borne ozonesondes, our analysis reveals changes in ozone which, because we have removed the effects of polar vortex motion and the descending air, indicate chemical destruction of ozone in early 2000. We find a peak decline rate of approximately 0.03 ppmv/day near 470 K of potential temperature (near 20 km) in mid-January which sinks in altitude to around 440 K (near 18 km) in mid-March.

Description: The Envisat chemistry instruments provide an opportunity to continue the ozone data sets produced by TOMS, SBUV, HALOE, and SAGE. These data sets have already been extensively validated and are assured to be of high accuracy. Continuity and consistency of data sets among the various satellite instruments as well as ground networks is essential for detecting an ozone recovery and climate change. Our objective, as part of the Envisat CaWal program, is to validate SCIAMACHY, MIPAS, and GOMOS ozone products using these heritage instruments. In addition we plan to validate SCIAMACHY Level 1 radiance using heritage satellite data and ground observations using a radiative transfer model. Ground based data included US ozonesonde profiles and a double monochromator Brewer instrument located at Goddard Space Flight Center. To date we have tested our intercomparisons algorithms using available Envisat data compared with SBW/2 and the ground based observations. We analyzed the comparisons with respect to ozone amounts and latitude and found the differences that were expected based on the initial release of Envisat data, which had known deficiencies. We will present our latest results based on the most current release of Envisat ozone data products.

Description: Validation of satellite data remains a high priority for the construction of climate data sets. Traditionally ground based measurements have provided the primary comparison data for validation. For some atmospheric parameters such as ozone, a thoroughly validated satellite data record can be used to validate a new instrument s data product in addition to using ground based data. Comparing validated data with new satellite data has several advantages; availability of much more data, which will improve precision, larger geographical coverage, and the footprints are closer in size, which removes uncertainty due to different observed atmospheric volumes. To demonstrate the applicability and some limitations of this technique, observations from the newly launched SCIAMACHY instrument were compared with the NOM-16 SBW/2 and ERS-2 GOME instruments. The SBW/2 data had all ready undergone validation by comparing to the total ozone ground network. Overall the SCIAMACHY data were found to low by 3% with respect to satellite data and 1% low with respect to ground station data. There appears to be seasonal and or solar zenith angle dependences in the comparisons with SBW/2 where differences increase with higher solar zenith angles. It is known that accuracies in both satellite and ground based total ozone algorithms decrease at high solar zenith angles. There is a strong need for more accurate measurement from and the ground under these conditions. At the present time SCIAMACHY data are limited and longer data set with more coverage in both hemispheres is needed to unravel the cause of these differences.