Abstract. From 1 May 2017 until 15 June 2017, the E-AMDAR operational service from EUMETNET disseminated more commercial aircraft data than usual on the Global Telecommunication System (GTS). Meteo-France specifically requested the implementation of such a trial. It lead to an increase in the number of aircraft data over France, especially vertical profiles (ascents and descents). Though Meteo-France routinely buys additional data with respect to the basic E-AMDAR service, this trial aimed at assessing the potential of French airlines to produce further data in collaboration with E-AMDAR and yield an observation network as dense as possible.
This was the opportunity to check the impact of these additional data on forecast skill scores of the limited area and convective scale model AROME-France. A data denial experiment (OSE) was carried out on May 2017, by removing E-AMDAR profiles (about 14 % of data) to mimic the routine observing system. The reference was the operational AROME-France 3D-Var that assimilated all extra data in real-time. However, no dedicated flag allowed to distinguish supplementary data from routine ones. Therefore, a necessary step of the experimental methodology was to identify which data profile could be considered as supplementary. The examination of forecast skill scores from the denial experiment showed that the impact of the removal of the additional observations is rather small and mixed, depending upon the parameter of interest, the atmospheric level, and the forecast range. The case studies done did not exhibit any particular additional skill for the suite with augmented observations. The experimental set-up is described and the results are discussed on the basis of forecast scores, including precipitation scores. Finally, a number of recommendations are given for a more optimal assimilation of AMDAR data in the AROME-France model.
[1]
Eric P. James,et al.
Observation System Experiments with the Hourly-Updating Rapid Refresh (RAP) Model Using GSI Hybrid Ensemble/Variational Data Assimilation
,
2017
.
[2]
L. Berre,et al.
A Posteriori Diagnostics in an Ensemble of Perturbed Analyses
,
2009
.
[3]
N. Bormann,et al.
Global observing system experiments in the ECMWF assimilation system
,
2019
.
[4]
D. Ricard,et al.
Improvement of the forecast of convective activity from the AROME‐France system
,
2016
.
[5]
Joël Stein,et al.
Deterministic and fuzzy verification methods for a hierarchy of numerical models
,
2009
.
[6]
Stanley G. Benjamin,et al.
Evaluation of Regional Aircraft Observations Using TAMDAR
,
2010
.
[7]
Gérald Desroziers,et al.
A posteriori diagnostics of the impact of observations on the AROME‐France convective‐scale data assimilation system
,
2014
.
[8]
Siebren de Haan,et al.
Estimates of Mode-S EHS aircraft-derived wind observation errors using triple collocation
,
2015
.
[9]
Ralph A. Petersen,et al.
On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports
,
2016
.
[10]
Barry E. Schwartz,et al.
Relative Short-Range Forecast Impact from Aircraft, Profiler, Radiosonde, VAD, GPS-PW, METAR, and Mesonet Observations via the RUC Hourly Assimilation Cycle
,
2010
.
[11]
V. Masson,et al.
The AROME-France Convective-Scale Operational Model
,
2011
.
[12]
Clive D Rodgers,et al.
Inverse Methods for Atmospheric Sounding: Theory and Practice
,
2000
.