EGU21-4918, updated on 27 Jul 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Novel assessment of model relative humidity with satellite probabilistic estimates

Chloé Radice1, Hélène Brogniez1, Pierre-Emmanuel Kirstetter2, and Philippe Chambon3
Chloé Radice et al.
  • 1Université Paris-Saclay, LATMOS / UVSQ / IPSL, Guyancourt, France ( /
  • 2Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, Oklahoma, USA
  • 3Météo France, CNRM/GMAP/OBS, Toulouse, France

Remote sensing data are often used to assess model forecasts on multiple scales, generally by confronting past simulations to observations. This paper introduces a novel probabilistic  method that evaluates  tropical atmospheric relative humidity (RH) profiles simulated by the global numerical model for weather forecasting ARPEGE  with respect to probability distributions of finer scale satellite observations.   

The reference RH is taken from the SAPHIR microwave sounder onboard the Megha-Tropiques satellite in operations since 2011. ARPEGE simulates the RH field every 6h hours on a 0.25° grid over 18 vertical levels ranging between 950hPa and 100hPa. The reference probabilistic RH field is retrieved from brightness temperatures measured by SAPHIR with a footprint resolution ranging from 10 km (nadir) to 23 km (edge of swath) on 6 vertical layers, ranging from 950hPa to 100hPa. Footprint scale RH are aggregated (convoluted) over the spatial and temporal scale of comparison to match the model resolution and summarize the patterns over a significant period. Comparison  results will be shown over the April-May-June 2018 period for two configurations of the ARPEGE model (two parametrization schemes for convection). The probabilistic comparison is discussed with respect to a classical deterministic comparison of RH values.

This probabilistic approach allows to keep all the sub-grid information and, by looking at the distribution as a whole, avoids the classical determinist simplification that consists of working with a simple “best” estimate. This method allows a finer assessment by working on a case-by-case basis and allowing a characterisation of specific situations. It provides an added-value by accounting for  additional information in the evaluation of the simulated field, especially for model simulations that are close to the traditional mean.

How to cite: Radice, C., Brogniez, H., Kirstetter, P.-E., and Chambon, P.: Novel assessment of model relative humidity with satellite probabilistic estimates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4918,, 2021.