Statistics of different public forecast products of temperature and precipitation in Estonia

Abstract

Day (0600-1800 UTC) maximum and night (1800-0600 UTC) minimum temperature forecasts as well as prediction of the occurrence of precipitation are evaluated for different sites in Estonia: southern coast of the Gulf of Finland (Tallinn), West-Estonian archipelago (Kuressaare), and inland Estonia (Tartu). The forecasts are collected from Estonian weather service. Several traditional verification methods are used, first of all reliability (root mean square error (RMSE)) and validity (mean error (ME)). Detailed analysis is carried out by means of the contingency tables that enable the user to calculate percent correct, percent underestimated, and percent overestimated. The contingency tables enable the user to calculate conditional probabilities of the realizations of certain forecasts. The paper is user-oriented and does not analyse the forecast technique. On the other hand, attention is drawn to the subjectivity of such evaluation, as the results may depend on the forecast presentation style and/or on the choice of the features of the meteorological parameter under consideration. For the current case study (the coldest hour during night and the warmest hour during day chosen to validate the temperature forecast, the temperature validation bin size 3 degrees, precipitation forecast validated in three categories based on the 12 h precipitation sums) one may say that the RMSE of the shortterm prediction of night minimum and day maximum temperature is 1.5 °C…3.1 °C. It was also noticed that Estonian weather service predicts lower night minimum temperature than it follows in reality. The skill of the temperature forecast is estimated by comparison of its RMSE with that of the persistence forecast (next night/day will be similar to the previous one). The RMSE of the 1st day/night forecast is by 1.3 °C…1.4 °C less for Tallinn and Tartu and 0.3 °C…0.8 °C for Kuressaare than that of the persistence forecast. For the precipitation forecast, percent correct is 60…70, the probability that dry weather forecast is followed by no precipitation is 70%…80%. At the end of the paper the long-term forecasts of two international weather portals www.gismeteo.ru (Russia) and www.weather.com (USA) are briefly analysed.

Temperature extremes and detection of heat and cold waves at three sites in Estonia

“…These probabilities can be used as a benchmark to estimate the skill of forecasts of various kinds, for example to check how much a certain complicated forecast is better than the climatological one (Hamill and Juras, 2006;Keevallik et al, 2014).…”

Evaluation of Quantitative Precipitation Predictions by ECMWF, CMA, and UKMO for Flood Forecasting: Application to Two Basins in China

“…To evaluate the forecast skills of NWPs, a number of techniques have been developed and applied (Wilks 2011), which can be generally divided into two broad categories: (1) probabilistic evaluation methods to assess ensemble forecasts, such as the continuous ranked probability score (CRPS) (Velázquez et al 2009), Brier skill score (BSS) (Brier 1950), the relative operating characteristic (ROC) (He et al 2009;Duan et al 2012), Peirce skill score (Thornes and Stephenson 2001), odds ratio skill score (Stephenson 2000), cost/loss ratio (Richardson 2000), relative economic value (EV) (Richardson 2000;Zhu et al 2002), and reliability diagram (Hamill 1997); and (2) deterministic evaluation methods for deterministic forecasts, typically including the anomaly correlation coefficient (ACC), root-mean square error (RMSE) (Stanski et al 1989), bias (Shrestha et al 2013), and mean error (ME) (Keevallik et al 2014). Instead of using a single method or metric, a number of NWP performance evaluation studies tend to combine various methods in order to make a comprehensive verification of NWPs, which is helpful to avoid methodological biases (Murphy and Winkler 1987;Murphy 1991;Mullen and Buizza 2001).…”