Comparison of in‐flight aircraft icing algorithms based on ECMWF forecasts

Abstract

This study presents an objective verification of different icing algorithms for two periods from 5 December 2012 to 17 April 2013 and from 9 October to 11 December 2013. The predicted icing potential and severity are evaluated against pilot reports of aircraft icing below 23 000 feet over Eastern United States. The icing algorithms are applied to the deterministic forecasts of European Centre for Medium-Range Weather Forecasts (ECMWF). Two of these algorithms are based only on forecasts of temperature (T) and relative humidity (RH), but the first uses fixed thresholds and the second uses a fuzzy logic approach. Although the second algorithm detects less icing events, it is more efficient with respect to the warning area, i.e. it forecasts icing over a smaller percentage of the airspace than the first algorithm. The impact of adding information from cloud liquid water (CLW) content and/or vertical velocity in the fuzzy logic scheme is assessed. Algorithms using T, CLW and vertical velocity as predictors are also tested. Most of the algorithms reveal a good performance in discriminating between icing and no-icing conditions, but are less skilful in forecasting the icing severity. The results also show that the use of CLW is essential to avoid issuing too many warnings. Moreover, the algorithm combining T, RH, CLW and vertical velocity attains the best performance. This algorithm, SFIP O , has been operational at the Portuguese Meteorological Watch Office since June 2013, and an example of a severe icing episode in February 2014 over mainland Portugal is presented.

Cloud icing by mineral dust and impacts to aviation safety

“…Following the needs of aviation for timely and reliable information on expected icing on aircraft cruising routes, several icing indices/parameter shave been developed and routinely implemented in order to identify conditions for icing occurrence 1,[12][13][14] . These methods use either predicted or observed temperature, cloud liquid water, relative humidity and vertical velocity to estimate the icing intensity.…”

“…There have been more than 150 accidents reported by commercial airplanes 1 , 2 related to cloud ice impacts, such as engine power loss, blade damage, and the icing of instrument sensors. Most of these incidences have been linked to icing in the upper troposphere in the vicinity of deep summer convection systems.…”

Development and Evaluation of In-Flight Icing Index Forecast for Aviation

“…The new icing indices being tested in the Met Office system are based on the simplified forecast icing potential (SFIP) described by Belo-Pereira (2015), itself based on the work of McDonough et al (2004), Bernstein et al (2005), and Wolff et al (2009). The SFIP is defined as…”

“…The temperature membership function M T used here differs slightly from the one used by Bernstein et al (2005) and Wolff et al (2009); for simplicity it does not differentiate between convective and stratiform cloud conditions. Additionally, in contrast to Belo-Pereira (2015), a different temperature membership function is not used for the lowest flight levels. Here, M T is defined as…”

Objective verification of global in‐flight icing forecasts using satellite observations: Verification of WAFS icing forecasts using satellite observations

“…Over many years, observations from instruments on‐board research aircraft have played an important role in the verification of icing algorithms to improve predictions (Sand et al , ; Politovich, ; Miller et al , ; Bernstein et al , ). Studies have been carried out to assess icing forecasts using pilot reports (PIREPs) of icing alone (Carriere et al , ; Belo‐Pereira, ) and in combination with data from research aircraft (Thompson et al , ). Although a pilot report seems the natural choice for a truth against which to verify icing forecasts, there are well‐documented reasons why it is less suitable (Schwartz, ).…”