Improving Predictions of Precipitation Type at the Surface: Description and Verification of Two New Products from the ECMWF Ensemble
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FEBRUARY 2018 G A S C Ó NETAL. 89 Improving Predictions of Precipitation Type at the Surface: Description and Verification of Two New Products from the ECMWF Ensemble ESTÍBALIZ GASCÓN,TIM HEWSON, AND THOMAS HAIDEN European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom (Manuscript received 9 August 2017, in final form 7 November 2017) ABSTRACT The medium-range ensemble (ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) is used to create two new products intended to face the challenges of winter precipitation-type forecasting. The products themselves are a map product that repre- sents which precipitation type is most likely whenever the probability of precipitation is .50% (also including information on lower probability outcomes) and a meteogram product, showing the temporal evolution of the instantaneous precipitation-type probabilities for a specific location, classified into three categories of pre- cipitation rate. A minimum precipitation rate is also used to distinguish dry from precipitating conditions, setting this value according to type, in order to try to enforce a zero frequency bias for all precipitation types. The new products differ from other ECMWF products in three important respects: first, the input variable is discretized, rather than continuous; second, the postprocessing increases the output information content; and, third, the map-based product condenses information into a more accessible format. The verification of both products was developed using four months’ worth of 3-hourly observations of present weather from manual surface synoptic observation (SYNOPs) in Europe during the 2016/17 winter period. This verification shows that the IFS is highly skillful when forecasting rain and snow, but only moderately skillful for freezing rain and rain and snow mixed, while the ability to predict the occurrence of ice pellets is negligible. Typical outputs are also illustrated via a freezing-rain case study, showing interesting changes with lead time. 1. Introduction types completely paralyzed, and with major long-term damage to infrastructure and vegetation (DeGaetano One of the greatest difficulties facing forecasters 2000; Chang et al. 2007; Call 2010). Accurate predictions during the winter season is the accurate identification of from weather forecast models of timing (onset and du- precipitation type at ground level (Ralph et al. 2005). ration), intensity, spatial extent, and phase (i.e., pre- Certain types of precipitation can be a threat to human cipitation type) are crucial for decision-making and can health and public safety and can disrupt travel and help minimize the potential impacts (Branick 1997; commerce, seriously affecting the economy (Ralph et al. Ikeda et al. 2013; Grout et al. 2012; Ikeda et al. 2017). 2005; Reeves et al. 2016). Freezing rain (FZRA) is Nevertheless, although it is self-evident that correct particularly hazardous due to its ice-loading effects on predictions of precipitation type are vitally important, power wires, and because it can make travel extremely only limited attention has been paid to wintertime dangerous. In the most severe cases with heavy and precipitation-type forecasting in Europe. prolonged freezing precipitation, the consequences can There are numerous sources of uncertainty in be catastrophic, with collapsed power lines causing precipitation-type forecasts, in particular mixed phases prolonged power outages, with travel networks of many [FZRA, ice pellets (IP), and rain and snow mixed (RASN)] are not well predicted (Wandishin et al. 2005; Reeves et al. 2014; Elmore et al. 2015; Ikeda et al. 2017) Denotes content that is immediately available upon publica- and continue to pose a substantial forecast challenge for tion as open access. numerical weather prediction (NWP) models. The thermodynamic structures of the atmosphere in IP and Corresponding author:Estíbaliz Gascón, estibaliz.gascon@ FZRA situations are so similar that small errors in ecmwf.int the predicted thicknesses of an elevated melting layer DOI: 10.1175/WAF-D-17-0114.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/23/21 01:57 PM UTC 90 WEATHER AND FORECASTING VOLUME 33 and/or a near-surface subzero layer can result in an The main surface observations used by national mete- incorrect prediction of precipitation type at ground orological services in Europe are surface synoptic ob- level (Reeves et al. 2014, 2016). Precipitation rate also servations (SYNOPs), of both automatic and manual plays an important role in the correct determination of types. Manual SYNOP observations are generated by precipitation type, because melting, which is an in- trained observers and are generally accurate with regard tegral feature of IP/FZRA situations, will absorb la- to the determination of precipitation type. However, tent heat from the atmosphere and thereby cool and automatic SYNOP station precipitation-type reports are modify the thermodynamic structure in proportion to often erroneous, with mixed precipitation types often precipitation rate. Furthermore, precipitation rate is misdiagnosed (Elmore et al. 2015). influenced by snowflake type, density, and the degree Regarding NWP, sophisticated microphysical pa- of riming, as well as by interaction with other particles rameterizations schemes are widely used in high- during passage through different atmospheric layers resolution regional forecast models, which should help with different temperature and moisture profiles with precipitation-type prediction, but even with such (Sankaré and Thériault 2016; Reeves et al. 2016; complex algorithms, correctly predicting what phase of Ikeda et al. 2017). Temporal variability is an added precipitation ends up at the ground remains a chal- complication since rain (RA) and snow (SN) are in lenging task (Ikeda et al. 2013). The study by Thériault general longer-lived phenomena than FZRA, IP, or et al. (2010) demonstrated that precipitation type at the RASN. Various authors (such as Reeves 2016), have ground is highly sensitive to temperature profile varia- also highlighted the existence of strong biases in tions as small as 60.58C, meaning that predictive diffi- precipitation-type forecasts, especially for FZRA and culties are particularly acute within snow–rain transition IP (Manikin et al. 2004; Schuur et al. 2012; Reeves regions. Other factors such as proximity to water bodies, et al. 2014), but also with RA and SN. The causes of terrain height variations, or the precipitation rate are these systematic errors can in principle be diagnosed very important as well (Stewart 1985; Bernstein 2000; using observational data (Reeves et al. 2014). Forbes Robbins and Cortinas 2002; Minder and Durran 2011). et al. (2014) compared two freezing-rain case studies Some authors consider these uncertainties to be difficult between the last version of the cloud and precipitation to reduce, but they can potentially be quantified by the parameterizations in Integrated Forecasting System use of ensemble forecasts (Cortinas et al. 2002; Manikin (IFS) cycle 41r1 (2015) and the version in IFS cycle et al. 2004; Wandishin et al. 2005; Shafer and Rudack 36r4 (2010). In the newer version, the freezing-rain 2014; Scheuerer et al. 2017). Brooks et al. (1996), processes for elevated warm layers are modified. They Wandishin et al. (2005), and Reeves (2016) point out include a more representative time scale for the re- that a more desirable approach to increasing the accu- freezing of raindrops that depends on the temperature racy of precipitation-type forecasts for mixed pre- and crucially on whether the snow particles have cipitation events is to use ensemble prediction to completely melted or not (Zerr 1997). Forbes et al. provide probabilistic forecasts of precipitation type. (2014) found large errors in the previous depiction of Naturally, this provides the forecaster with a broader precipitation type, specifically an inability to predict perspective on the likelihood of occurrence of different the extent of the freezing-rain event, whereas the mixed phases during (potential) FZRA episodes. model with the new physics is able to predict freezing Wandishin et al. (2005) published the first study to in- rain that is in general agreement with the observations. vestigate extensively the potential use of ensembles for The present study is an extension of Forbes et al. forecasting precipitation types during the winter period. (2014), who showed the advantages of using ensemble They used 10 ensemble members and examined 0–48-h forecasts of precipitation type and a capacity to detect lead times, showing that ensemble forecasts have the potential freezing-rain areas even with low precipita- capacity to be of substantial value to potential users and tion rate thresholds. how skill increased with the number of members, espe- The correct choice of observations is another impor- cially for mixed-phase precipitation forecasts. tant aspect of precipitation-type verification but few ECMWF IFS ensemble forecasts (ENS) have been authors have paid attention to this topic (Huntemann operational for over 25 years and currently comprise 1 et al. 2014; Reeves 2016). When the true surface tem- control and 50 perturbed forecasts running out to perature is near to 08C, small errors in forecasts can 15 days, twice per day. Instantaneous