FREEZING CONTAMINATION : AIRCRAFT ICING

Transcription

1 FREEZING CONTAMINATION : AIRCRAFT ICING FORECASTING METHODS Extrapolation of observational icing data Looking for icing scenarios Using numerical model outputs Crossing observations with model outputs (data fusion, icing index...) Towards a conclusion TET1 aircraft icing- 12/2005- V1 1

2 Forecasting techniques (1) Three main approaches for forecasting icing conditions can be identified: 1. Extrapolation in space and time of icing observations. 2. Documenting icing situations with the help of observational data and standard meteorological forecasts. Using this information to identify scenarios known to be favourable for icing. 3. Using an icing index produced by a numerical model. TET1 aircraft icing- 12/2005- V1 2

3 Forecasting techniques: extrapolation of observational icing data (2) If one has a reliable source of icing observations (accretions) coming from airspace users, they can be used to depict the phenomenon and to anticipate it by appropriate extrapolation in time and space. When you have frequent observations (PIREP or SPECIAL AIREP) nicely spaced, one can consider an operational follow-up of the phenomena (very short range forecast) (*) This has the advantage that the users are implicated in the forecasting process which can help in detecting the flaws in the other forecasting techniques. This can also be used to document extreme situations. In any case, this approach is often biased by the difference of the observations. TET1 aircraft icing- 12/2005- V1 3

4 Forecasting techniques: extrapolation of observational icing data (3) This picture displays the inflight icing observations coming from PIREPs over the US. They have been superimposed on a numerical model output of SLD icing potential. (*) TET1 aircraft icing- 12/2005- V1 4

5 Forecasting techniques: looking for icing scenarios (4) With good knowledge of the meteorological situations favourable for icing conditions, each time one finds such a scenario (*) in the observational data (**) or in the numerical model output, one has to inform the users. This can be a good method to identify extreme situations. Drawback: it is a difficult method to put in place over large areas and needs a costly local expertise. TET1 aircraft icing- 12/2005- V1 5

6 Forecasting techniques: using a numerical icing index (5) If you have numerical models with a fine enough mesh (mesoscale), one could consider an output of an icing index (*). This method has to be supported by a regular evaluation through comparison with observational data, covering all the classic scenarios. This method has the advantage that it is easy to put into operation for large areas. The quality of the forecast is correlated to the model s ability to reproduce complex processes on a small scale: realistic microphysics, sophisticated but robust schemes (**). TET1 aircraft icing- 12/2005- V1 6

7 Forecasting techniques: using a numerical model output (6) water vapour condensation-evaporation cloud droplets Bergeron-Findeisen process, homogeneous nucleation melting ice crystals auto-conversion, accretion rain drops riming, dry and wet growth snow graupeln This is an explicit scheme (*) which works within the MESO NH model (**). Cloud LWC is an output of such a process. TET1 aircraft icing- 12/2005- V1 7

8 Forecasting techniques: crossing observations with model outputs (7) For forecasting icing conditions, research is ongoing to automatically combine the model output indices with combined observational data (satellite, radar, PIREP). ADWICE (Germany) combines the output of the LM model with conventional observations (SYNOP, METAR) and radar, but does not use satellite data. CIP (US) is an index of icing severity that combines the output of the RUC model with all possible icing observations, PIREPs and electrical detection included. SIGMA (France) combines the output of the Aladin model (air and surface temperature, humidity) with satellite and radar observations (*). TET1 aircraft icing- 12/2005- V1 8

9 Forecasting techniques: crossing observations with model outputs (8) FZDZ high risk under warm clouds tops light risk of icing under warm clouds tops high risk of icing under warm clouds tops FZDZ light risk under cold clouds tops high risk of icing under cold clouds tops light risk of icing under cold clouds tops FZDZ high risk under cold clouds tops FZRA light risk under cold clouds tops Example of a SIGMA image (Météo France) (*) The use of these combined products needs a good knowledge of the resources and used algorithms and a validation for local use (**), for example in a mountainous region. TET1 aircraft icing- 12/2005- V1 9

10 Forecasting techniques: towards a conclusion (9) In general, it seems that the latest automated products have the ability to distinguish (*) the icing cases from the non icing ones (risk/no risk), but the results are marginal when identifying the situations with severe icing. Moreover, convective situations are usually badly captured by numerical models. It is therefore that when a local study (**) is added, the scores of detecting severe icing will improve to the satisfaction of all aeronautical users (***). But the constantly improving remote sensing techniques and numerical models leave us with a good margin for improvement. TET1 aircraft icing- 12/2005- V1 10

11 Forward to: putting the forecasts in place Notes for teachers TET1 aircraft icing- 12/2005- V1 11