eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Statistics of different public forecast products of temperature and precipitation in Estonia; pp. 174–182

Full article in PDF format | doi: 10.3176/proc.2014.2.07

Sirje Keevallik, Natalja Spirina, Eva-Maria Sula, Inga Vau


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 short-term 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 (Russia) and (USA) are briefly analysed.


  1. Stephenson, D. B. Use of the “Odds Ratio” for diagnosing forecast skill. Weather and Forecasting, 2000, 15, 221–232.<0221:UOTORF>2.0.CO;2

  2. Thornes, J. E. and Stephenson, D. B. How to judge the quality and value of weather forecast products. Meteorol. Appl., 2001, 8, 307–314.

  3. Coulson, R., Evans, B., and Skea, A. Operational OpenRoad verification. In Proc. 16th International Road Weather Conference. Helsinki, 2012, ID: 0018.

  4. Heideman, K. F., Stewart, T. R., Moninger, W. R., and Reagan-Cirincione, P. The weather information and skill experiment (WISE): The effect of varying levels of information on forecast skill. Weather and Fore­casting, 1993, 8(1), 2536.<0025:TWIASE>2.0.CO;2

  5. Cuo, L., Pagano, T. C., and Wang, Q. J. A review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J. Hydro­meteorology, 2011, 12, 713728.

  6. Clark, A. J., Kain, J. S., Stensrud, D. J., Xue, M., Kong, F., Coniglio, M. C. et al. Probabilistic precipitation fore­cast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Weather Rev., 2011, 139, 14101418.

  7. Mylne, K. R. Decision-making from probability forecasts based on forecast value. Meteorol. Appl., 2002, 9, 307315.

  8. Lee, K.-K. and Lee, J.-W. The economic value of weather forecasts for decision-making problems in the profit/loss situations. Meteorol. Appl., 2007, 14, 455463.

  9. Teisberg, T. J., Weiher, R. F., and Khotanzad, A. The economic value of temperature forecasts in electric generation. Bull. Amer. Meteor. Soc., 2005, 86, 17651771.

10. Stewart, T. R., Pielke, R. Jr., and Nath, R. Understanding user decision making and the value of improved precipitation forecasts. Bull. Amer. Meteor. Soc., 2004, 85, 223235.

11. Monin, A. S. Weather Forecasting as a Problem in Physics. MIT Press, 1972.

12. Lazo, J. K., Morss, R. E., and Demuth, J. L. 300 billion served: Sources, perceptions, uses, and values of weather forecasts. Bull. Amer. Meteor. Soc., 2009, 90, 785798.

13. Morss, R. E., Demuth, J. L., and Lazo, J. K. Communica­tion uncertainty in weather forecasts: A survey of the U.S. public. Weather and Forecasting, 2008, 23, 974991.

14. Gaia, M. and Fontannaz, L. The human side of weather forecasting. The European Forecaster, 2008, 13, 1720.

15. Erkkilä, T. About the nature of the forecaster profession and the human contribution to very short range forecasts. The European Forecaster, 2009, 14, 611.

16. Gigerenzer, G., Hertwig, R., van den Broek, E., Fasolo, B., and Katsikopoulos, K. V. “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecasts? Risk Analysis, 2005, 25, 623629.

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