In RO–RS comparisons, RO observations are usually co-located with RS profiles within a fixed time window and distance, i.e. within 3–6 h and circles of radii ranging between 100 and 500 km. In this study, we first show that vertical filtering of RO and RS profiles to a common vertical resolution reduces representativeness errors. We then test two methods of reducing horizontal sampling errors during RO–RS comparisons: restricting co-location pairs to within ellipses oriented along the direction of wind flow rather than circles and applying a spatial–temporal sampling correction based on model data. Using data from 2011 to 2014, we compare RO and RS differences at four GCOS Reference Upper-Air Network (GRUAN) RS stations in different climatic locations, in which co-location pairs were constrained to a large circle (∼666 km radius), small circle (∼300 km radius), and ellipse parallel to the wind direction (∼666 km semi-major axis, ∼133 km semi-minor axis). We hypothesize that refractivity gradients lie perpendicular to wind flow, therefore co-locating RO observations within ellipses of semi-major axis a oriented along the direction of wind flow should reduce sampling errors relative to circles of radii a. We also apply a spatial–temporal sampling correction using European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) gridded data. The spatial-temporal sampling correction uses a double-differencing method, correcting the computed RO–RS differences relative to the model background field.
Our results indicate that restricting RO–RS co-locations to within ellipses oriented along the direction of wind flow and applying the spatial-temporal sampling correction can significantly reduce sampling errors during RO–RS comparisons. The full results, impacts, and implications of applying these methods will be presented. Further implications of the spatial and temporal dependencies of the spatial-temporal sampling correction will also be discussed. These methods of reducing sampling errors are not limited to RO–RS comparisons and can be used to reduce spatial and temporal sampling errors during comparisons between other sounding systems.
Figure 1. Root-mean-square (RMS) differences for RO–RS co-locations at the Lindenberg RS station during 2014. RMS differences with the ellipse method only (solid; large circle: orange; small circle: grey; ellipse: magenta) and RMS differences with the ellipse method and sampling correction (dashed, abbreviated SC) are shown for three variables: (b) refractivity, (c) temperature, and (d) water vapor pressure. The number of pairs within each ellipse (solid) and ellipse plus sampling correction (dashed) are given in (a).