Remedy 1: Improved high-resolution modelling to quantify mismatch effects
Remedy 2: Use of statistical analysis techniques based upon available and targeted additional observations
Spatiotemporal variability of the atmosphere at the scale of the airmass being measured or - in the case of a measurement intercomparison - at the scale of the co-location, leads to additional uncertainties, not accounted for by the uncertainty budget reported with an individual measurement (Lambert et al., 2012). To quantify these additional uncertainties (cf. gaps G3.04 and G3.06), or to ensure that they remain negligible through the use of appropriate co-location criteria (cf. G3.02), a prerequisite is a proper understanding of atmospheric variability of the targeted ECV on those scales.
While scales above approx. 100km and 1h are relatively well captured for several GAIA-CLIM target ECVs in model or satellite gridded data (e.g., Verhoelst et al., 2015, for total ozone), information on smaller scales is most often restricted to results from dedicated campaigns or specific case studies, e.g., Sparling et al. (2006) for ozone profiles, Hewison (2013) for meteorological variables, and Pappalardo et al. (2010) for aerosols. Due to the exploratory nature of these studies, neither global nor complete vertical coverage is achieved. For instance, information on small-scale variability in the ozone field is limited to altitudes and regions probed with dedicated aircraft campaigns. The validation of satellite data records with pseudo global networks of ground-based reference instruments on the other hand requires an appropriate quantification of atmospheric variability in very diverse conditions, covering all latitudes, altitudes, dynamical conditions, degrees of pollution etc.
This gap therefore concerns the need for a better, more comprehensive, quantification of the spatiotemporal variability of the ECVs targeted by GAIA-CLIM.
Within GAIA-CLIM, a work package (WP3) was dedicated to research on co-location mismatch in an inhomogeneous and variable atmosphere. In the context of this work package, some studies were performed that quantified spatiotemporal variability for a few ECVs at a limited scale domain (e.g. temperature and water vapour temporal variability at 6-hour scale from radiosonde inter-comparisons, and aerosol optical depth variability at the scale of a satellite-ground co-location in the North-East US). Although this work was limited to a few ECVs, scales, geographical coverage etc. owing to the limited resources and data availability, GAIA-CLIM has demonstrated use cases / case studies which may permit a more exhaustive approach in future. Fully addressing this gap requires significant observational and modelling work, far beyond the scope of GAIA-CLIM, as described in detail in the remedies.
G3.04. To be addressed after G3.01
Argument: To estimate the additional uncertainties on a measurement that result from spatiotemporal atmospheric variability at the measurement sampling and smoothing scales, a quantification of that spatiotemporal variability is a prerequisite.
G3.06. To be addressed after G3.01
Argument: Understanding the uncertainty budget of a comparison (in a validation context) requires a quantification of the impact of co-location mismatch. This cannot be done without an estimate of the spatiotemporal variability of the ECV under study.
- Butterfield et al.:” Determining the temporal variability in atmospheric temperature profiles measured using radiosondes and assessment of correction factors for different launch schedules”, AMT, v8, 2015
- Lambert, J.-C., et al., “Comparing and merging water vapour observations: A multi-dimensional perspective on smoothing and sampling issues”, in “Monitoring Atmospheric Water Vapour: Ground-Based Remote Sensing and In-situ Methods”, N. Kämpfer (Ed.), ISSI Scientific Report Series, Vol. 10, Edition 1, 326 pp., ISBN: 978-1-4614-3908-0, DOI 10.1007/978-1-4614-3909-7_2, © Springer New York 2012
- Pappalardo et al., “EARLINET correlative measurements for CALIPSO: First intercomparison results”, J.G.R.: Atmospheres v115, 2010
- Sparling et al., “Estimating the impact of small-scale variability in satellite measurement validation”, J.G.R.: Atmospheres v111, 2006
The atmospheric concentration of nearly all ECVs varies in space and time at the scale of the individual measurements, and at the scale of their co-location in the context of data comparisons (e.g., for the purpose of satellite validation, data merging, and data assimilation). However, the amplitude and patterns of these variations are often unknown on such small scales. Consequently, it is impossible to quantify the uncertainties that result from sampling and smoothing properties of the measurements of the variable, structured atmospheric field. This gap thus concerns the need for a better quantification of atmospheric spatiotemporal variability at the small scales of individual measurements and co-locations.