We study the carbon monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua satellite. The focus of this study is to analyze CO variability and short-term trends separately for background CO and fresh CO emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm utilizes cloud clearing to treat cloud contaminations in the signals, and this increases the data coverage significantly to a yield of more than 50% of the total measurements. We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask to identify AIRS clear sky scenes. We then carry out a science analysis using AIRS CO data individually for the clear and cloud-cleared scenes to identify any potential effects due to cloud clearing. We also introduce a new technique to separate background and recently emitted CO observations, which aims to constrain emissions using only satellite CO data. We validate the CO variability of the recent emissions estimated from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO recently emitted and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH). The high degree of agreement between emissions identified using only AIRS CO and independent inventory sources demonstrates the validity of this approach to separate recent emissions from the background CO using one satellite data set.

The characterisation of errors and uncertainties in numerical weather prediction (NWP) model fields is a major challenge that is addressed as part of the Horizon 2020 Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring (GAIA-CLIM) project. In that regard, observations from the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) radiosondes are being used at the Met Office and European Centre for Medium-Range Weather Forecasts (ECMWF) to assess errors and uncertainties associated with model data. The software introduced in this study and referred to as the GRUAN processor has been developed to collocate GRUAN radiosonde profiles and NWP model fields, simulate top-of-atmosphere brightness temperature at frequencies used by space-borne instruments, and propagate GRUAN uncertainties in that simulation. A mathematical framework used to estimate and assess the uncertainty budget of the comparison of simulated brightness temperature is also proposed. A total of 1 year of GRUAN radiosondes and matching NWP fields from the Met Office and ECMWF have been processed and analysed for the purposes of demonstration of capability. We present preliminary results confirming the presence of known biases in the temperature and humidity profiles of both NWP centres. The night-time difference between GRUAN and Met Office (ECMWF) simulated brightness temperature at microwave frequencies predominantly sensitive to temperature is on average smaller than 0.1 K (0.4 K). Similarly, this difference is on average smaller than 0.5 K (0.4 K) at microwave frequencies predominantly sensitive to humidity. The uncertainty estimated for the Met Office–GRUAN difference ranges from 0.08 to 0.13 K for temperature-sensitive frequencies and from 1.6 to 2.5 K for humidity-sensitive frequencies. From the analysed sampling, 90 % of the comparisons are found to be in statistical agreement. This initial study has the potential to be extended to a larger collection of GRUAN profiles, covering multiple sites and years, with the aim of providing a robust estimation of both errors and uncertainties of NWP model fields in radiance space for a selection of key microwave and infrared frequencies.

The objective of this paper is to deliver the most accurate ozone (O3) and carbon monoxide (CO) climatology for the pure troposphere only, i.e. exclusively from the ground to the dynamical tropopause on an individual profile basis. The results (profiles and columns) are derived solely from the Measurements of OZone and water vapour by in-service Alrbus airCraft programme (MOZAIC) over 15 years (1994–2009). The study, focused on the northern mid-latitudes [24–50° N] and [119° W–140° E], includes more than 40 000 profiles over 11 sites to give a quasi-global zonal picture. Considering all the sites, the pure tropospheric column peak-to-peak seasonal cycle ranges are 23.7–43.2 DU for O3 and 1.7–6.9 × 10 18 molecules cm−2 for CO. The maxima of the seasonal cycles are not in phase, occurring in February–April for CO and May–July for O3. The phase shift is related to the photochemistry and OH removal efficiencies. The purely tropospheric seasonal profiles are characterized by a typical autumn–winter/spring–summer O3 dichotomy (except in Los Angeles, Eastmed – a cluster of Cairo and Tel Aviv – and the regions impacted by the summer monsoon) and a summer–autumn/winter–spring CO dichotomy. We revisit the boundary-layer, mid-tropospheric (MT) and upper-tropospheric (UT) partial columns using a~new monthly varying MT ceiling. Interestingly, the seasonal cycle maximum of the UT partial columns is shifted from summer to spring for O3 and to very early spring for CO. Conversely, the MT maximum is shifted from spring to summer and is associated with a summer (winter) MT thickening (thinning). Lastly, the pure tropospheric seasonal cycles derived from our analysis are consistent with the cycles derived from spaceborne measurements, the correlation coefficients being r=0.6–0.9 for O3 and r>0.9 for CO. The cycles observed from space are nevertheless greater than MOZAIC for O3 (by 9–18 DU) and smaller for CO (up to 1 × 10 18 molecules cm−2). The larger winter O3 difference between the two data sets suggests probable stratospheric contamination in satellite data due to the tropopause position. The study underlines the importance of rigorously discriminating between the stratospheric and tropospheric reservoirs and avoiding use of a~monthly averaged tropopause position without this strict discrimination in order to assess the pure O3 and CO tropospheric trends.

This paper evaluates the microwave instruments onboard the latest Chinese polar-orbiting satellite, Feng-Yun 3D (FY-3D). Comparing three months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave Radiation Imager (MWRI) to Met Office short-range forecasts, we characterize the instrumental biases, show how those biases have changed with respect to their predecessors onboard FY-3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) onboard NOAA-20 and the Global Precipitation Measurement Microwave Imager (GMI). The MWTS-2 global bias is much reduced with respect to its predecessor and compares well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending—descending bias. MWHS-2 benefits from a new calibration method improving the 183-GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements, with reduced global bias and standard deviation with respect to FY-3C; although, spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarization). The strong solar-dependent bias that affects the instrument on FY-3C has been reduced to less than 0.2 K on average for FY-3D MWRI. Experiments where radiances from these instruments were assimilated on top of a full global system demonstrated a neutral to positive impact on the forecasts, as well as on the fit to the background of independent instruments.

Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%.

This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.