John C. Gille

The SPARC Data Initiative: A comparison of ozone climatologies from international satellite limb sounders

A comprehensive quality assessment of the ozone products from 18 limb-viewing satellite instruments is provided by means of a detailed intercomparison. The ozone climatologies in form of monthly zonal mean time series covering the upper troposphere to lower mesosphere are obtained from LIMS, SAGE I/II/III, UARS-MLS, HALOE, POAM II/III, SMR, OSIRIS, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, and SMILES within 1978–2010. The intercomparisons focus on mean biases of annual zonal mean fields, interannual variability, and seasonal cycles. Additionally, the physical consistency of the data is tested through diagnostics of the quasi-biennial oscillation and Antarctic ozone hole. The comprehensive evaluations reveal that the uncertainty in our knowledge of the atmospheric ozone mean state is smallest in the tropical and midlatitude middle stratosphere with a 1σ multi-instrument spread of less than ±5%. While the overall agreement among the climatological data sets is very good for large parts of the stratosphere, individual discrepancies have been identified, including unrealistic month-to-month fluctuations, large biases in particular atmospheric regions, or inconsistencies in the seasonal cycle. Notable differences between the data sets exist in the tropical lower stratosphere (with a spread of ±30%) and at high latitudes (±15%). In particular, large relative differences are identified in the Antarctic during the time of the ozone hole, with a spread between the monthly zonal mean fields of ±50%. The evaluations provide guidance on what data sets are the most reliable for applications such as studies of ozone variability, model-measurement comparisons, detection of long-term trends, and data-merging activities.

Improvement of the global surface emissivity from MOPITT measurements and its impacts on the retrievals of tropospheric carbon monoxide profiles

Carbon monoxide (CO) is an important tropospheric trace species and can serve as a useful tracer of atmospheric transport. The Measurements of Pollution In The Troposphere (MOPITT) instrument uses the 4.7 μm CO band to measure the spatial and temporal variation of the CO profile and total column amount in the troposphere from space. Launched in 1999 on board the NASA Terra satellite, the MOPITT views the earth with a pixel size 22 km by 22 km and a cross-track swath that measures a near-global distribution of CO every 3 days. In the operational MOPITT CO retrieval algorithm (V3; Version 3), surface skin temperature (Ts) and emissivity (E) are retrieved simultaneously with the CO profile. The accuracy of E and Ts is crucial for obtaining the CO retrieval within the 10% accuracy from the MOPITT measurements. However, because both Ts and E are retrieved from the same piece of information from the MOPITT measurements, the accuracy of both valuables may be limited. Extra surface skin temperature information is needed to determine surface emissivity, and vice versa. In this study, we use MODIS Ts within the MOPITT FOVs, in conjunction with those MOPITT signals most sensitive to the background scene, to compute the surface emissivity through an iterative retrieval algorithm. A monthly 1degree grid averaged 4.7 μm surface emissivity map is generated. The evaluation of the accuracy of this monthly 1 degree grid averaged 4.7 μm surface emissivity map is presented and its impacts on the retrievals of tropospheric CO profiles from the MOPITT measurements are also discussed.

Validation of the retrieval of surface skin temperature and surface emissivity from MOPITT measurements and their impacts on the retrieval of tropospheric carbon monoxide profiles

The Measurements of Pollution In The Troposphere (MOPITT) instrument is designed to measure the spatial and temporal variation of the carbon monoxide (CO) profile and total column amount in the troposphere from the space. MOPITT channels are sensitive to both thermal emission from the surface and target gas absorption and emission. Surface temperature and emissivity are retrieved simultaneously with the CO profile. To obtain the desired 10% precision for the retrieved CO from MOPITT measurements, it is important to understand MOPITT CO channel sensitivity to surface temperature and emissivity and the impacts of the effects of any errors in retrieved skin temperature and emissivity on retrieved CO for various underlying surfaces. To demonstrate the impacts of the surface temperature and emissivity on the retrieval of the tropospheric CO profile, simulation studies are performed. The collocated Moderate Resolution Imaging Spectroradiometer (MODIS) surface products are used to assess the accuracy of the retrieved MOPITT surface temperature and emissivity.

MATR thermal channel analysis and results for autumn 2001 campaign

The MOPITT (Measurements of Pollution in the Troposphere) Airborne Test Radiometer (MATR) uses gas filter correlation radiometry from high altitude aircraft to measure tropospheric carbon monoxide. This is in support of the ongoing validation campaign for the MOPITT instrument on board the Tera Satellite. This paper reports on a recent study of MATR CO retrievals using observations of thermal radiation during the autumn of 2001 in western United States. Retrievals of CO were performed and compared to in-situ sampling with less than 7% retrieval error relative to the in-situ total column amount. The effects that influence the retrieval such as the instrument sensitivity, the retrieval sensitivity, and bias between observations and the radiation model are discussed.

MOPITT cloud detection and its validation

The measurements of Pollution in the Troposphere (MOPITT) instrument aboard the Earth Observing System (EOS) Terra spacecraft measures tropospheric CO and CH4 by use of a nadir-viewing geometry. MOPITT cloud algorithm detects and removes measurements contaminated by clouds before retrieving CO profiles and CO and CH4 total columns. The collocation between MOPITT and MODIS is also established and MODIS cloud mask will be used in the MOPITT cloud algorithm. The cloud detection results in the use of MOPITT data alone agree with MODIS cloud mask for more than 80% of the tested cases.