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A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz

  • Hélène Brogniez
  • , Stephen English
  • , Jean François Mahfouf
  • , Andreas Behrendt
  • , Wesley Berg
  • , Sid Boukabara
  • , Stefan Alexander Buehler
  • , Philippe Chambon
  • , Antonia Gambacorta
  • , Alan Geer
  • , William Ingram
  • , E. Robert Kursinski
  • , Marco Matricardi
  • , Tatyana A. Odintsova
  • , Vivienne H. Payne
  • , Peter W. Thorne
  • , Mikhail Yu Tretyakov
  • , Junhong Wang
  • CNRS
  • European Centre for Medium-Range Weather Forecasts
  • Centre National de Recherches Météorologiques
  • University of Hohenheim
  • Colorado State University
  • National Oceanic and Atmospheric Administration
  • University of Hamburg
  • Science and Technology Corporation, Hampton
  • Met Office
  • University of Oxford
  • Space Sciences and Engineering
  • Institute of Applied Physics of the Russian Academy of Sciences
  • Jet Propulsion Laboratory, California Institute of Technology
  • Maynooth University

Research output: Contribution to journalReview articlepeer-review

46 Scopus citations

Abstract

Several recent studies have observed systematic differences between measurements in the 183.31 GHz water vapor line by space-borne sounders and calculations using radiative transfer models, with inputs from either radiosondes (radiosonde observations, RAOBs) or short-range forecasts by numerical weather prediction (NWP) models. This paper discusses all the relevant categories of observation-based or model-based data, quantifies their uncertainties and separates biases that could be common to all causes from those attributable to a particular cause. Reference observations from radiosondes, Global Navigation Satellite System (GNSS) receivers, differential absorption lidar (DIAL) and Raman lidar are thus overviewed. Biases arising from their calibration procedures, NWP models and data assimilation, instrument biases and radiative transfer models (both the models themselves and the underlying spectroscopy) are presented and discussed. Although presently no single process in the comparisons seems capable of explaining the observed structure of bias, recommendations are made in order to better understand the causes.

Original languageEnglish
Pages (from-to)2207-2221
Number of pages15
JournalAtmospheric Measurement Techniques
Volume9
Issue number5
DOIs
StatePublished - May 18 2016

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