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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 9, 2207-2221, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Review article
18 May 2016
A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz
Hélène Brogniez1, Stephen English2, Jean-François Mahfouf3, Andreas Behrendt4, Wesley Berg5, Sid Boukabara6, Stefan Alexander Buehler7, Philippe Chambon3, Antonia Gambacorta8, Alan Geer2, William Ingram9, E. Robert Kursinski10, Marco Matricardi2, Tatyana A. Odintsova11, Vivienne H. Payne12, Peter W. Thorne13, Mikhail Yu. Tretyakov11, and Junhong Wang14 1LATMOS (IPSL/UVSQ/UPMC/CNRS), 78280, Guyancourt, France
2ECMWF, Reading, RG2 9AX, UK
3CNRM/GAME, Météo-France/CNRS, Toulouse, 31057, France
4University of Hohenheim, Institute of Physics and Meteorology, 70599 Stuttgart, Germany
5Colorado State University, Fort Collins, CO, USA
6NOAA, USA, Camp Spring, MD, USA
7Meteorological Institute, Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany
8Science and Technology Corporation, Columbia, MD, USA
9MetOffice, Hadley Centre, Exeter, UK and AOPP, Department of Physics, Oxford, UK
10Space Sciences and Engineering, Boulder, CO, USA
11Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, Russia
12JPL, California Institute of Technology, Pasadena, CA, USA
13Department of Geography, Maynooth University, Maynooth, Ireland
14State University of New York, Albany, NY, USA
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.

Citation: Brogniez, H., English, S., Mahfouf, J.-F., Behrendt, A., Berg, W., Boukabara, S., Buehler, S. A., Chambon, P., Gambacorta, A., Geer, A., Ingram, W., Kursinski, E. R., Matricardi, M., Odintsova, T. A., Payne, V. H., Thorne, P. W., Tretyakov, M. Yu., and Wang, J.: A review of sources of systematic errors and uncertainties in observations and simulations at 183 GHz, Atmos. Meas. Tech., 9, 2207-2221,, 2016.
Publications Copernicus
Short summary
Because a systematic difference between measurements of water vapor performed by space-borne observing instruments in the microwave spectral domain and their numerical modeling was recently highlighted, this work discusses and gives an overview of the various errors and uncertainties associated with each element in the comparison process. Indeed, the knowledge of absolute errors in any observation of the climate system is key, more specifically because we need to detect small changes.
Because a systematic difference between measurements of water vapor performed by space-borne...