Articles | Volume 10, issue 10
Atmos. Meas. Tech., 10, 3947–3961, 2017
Atmos. Meas. Tech., 10, 3947–3961, 2017

Research article 25 Oct 2017

Research article | 25 Oct 2017

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

Francesco De Angelis1, Domenico Cimini2,1, Ulrich Löhnert3, Olivier Caumont4, Alexander Haefele5, Bernhard Pospichal3, Pauline Martinet4, Francisco Navas-Guzmán6, Henk Klein-Baltink7, Jean-Charles Dupont8, and James Hocking9 Francesco De Angelis et al.
  • 1CETEMPS, University of L'Aquila, L'Aquila, Italy
  • 2National Research Council of Italy, Institute of Methodologies for Environmental Analysis (IMAA-CNR), Potenza, Italy
  • 3Institute for geophysics and meteorology, University of Cologne, Cologne, Germany
  • 4CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
  • 5Federal Office of Meteorology and Climatology MeteoSwiss, Payerne, Switzerland
  • 6Institute of Applied Physics (IAP), University of Bern, Bern, Switzerland
  • 7Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 8Institut Pierre-Simon Laplace (IPSL), Université Versailles Saint Quentin, Guyancourt, France
  • 9Met Office, Exeter, UK

Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O–B). Monitoring of O–B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O–B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O–B monitoring can effectively detect instrument malfunctions. O–B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ∼  2–2.5 K) towards the high-frequency wing ( ∼  0.8–1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O–B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54–58 GHz). Opaque channels show lower uncertainties (< 0.8–0.9 K) and little variation with elevation angle. Transparent channels show larger biases ( ∼  2–3 K) with relatively low standard deviations ( ∼  1–1.5 K). The observations minus analysis TB statistics are similar to the O–B statistics, suggesting a possible improvement to be expected by assimilating MWR TB into NWP models. Lastly, the O–B TB differences have been evaluated to verify the normal-distribution hypothesis underlying variational and ensemble Kalman filter-based DA systems. Absolute values of excess kurtosis and skewness are generally within 1 and 0.5, respectively, for all instrumental sites, demonstrating O–B normal distribution for most of the channels and elevations angles.

Short summary
Modern data assimilation systems require knowledge of the typical differences between observations and model background (O–B). This work illustrates a 1-year O–B analysis for ground-based microwave radiometer (MWR) observations in clear-sky conditions for a prototype network of six MWRs in Europe. Observations are MWR brightness temperatures (TB). Background profiles extracted from the output of a convective-scale model are used to simulate TB through the radiative transfer model RTTOV-gb.