Brief report on the first presentation block
"Uncertainty in numerical weather prediction"

Reporter: Michael Kunz

As a result of many complex and highly nonlinear processes, precipitation is the atmospheric variable which is most important for hydrological purposes but also difficult to predict. In order to quantify the uncertainties of precipitation forecasts, various methods of ensemble prediction systems (EPS) have been developed over the past decades. However, there are many open questions and scientific challenges that have to be overcome.

By considering uncertainties arising from an inaccuracy in the initial conditions, several large forecast centres like the ECMWF or NCEP run global EPS operationally. Each member of the EPS represents one possible physically-based scenario so that the total ensemble allows estimating the probability function for several atmospheric parameters. However, the coarse resolution of the global models gives an underestimation of the precipitation amounts and blurs small-scale variations of the spatial distribution especially over mountainous terrain (Undén) [1]. Regional EPS from a Limited Area Model combines the advantage of a probabilistic forecast from the global model with the better representation of small-scale terrain variations of a high-resolution numerical weather forecast. Walser and Rotach [2] showed in a case study of the flooding event of August 2005 that COSMO-LEPS (clustering approach) yields substantially more information on regional and rather extreme weather events than the global EPS of ECMWF. Hence, the coupling of COSMO-LEPS with a hydrologic model seemed to be an attractive strategy to get probabilistic runoff forecasts which could enable risk-based flood warnings.

Another well established approach in ensemble weather prediction is to create an artificial forecast by combining different numerical weather forecasts (poor man ensemble) as reported by Trepte [3]. Pistotnik [4] introduced the model system INCA (Integrated Nowcasting through Comprehensive Analysis) that additionally includes a nowcasting technique which is based on the extrapolation of current observations. In this approach, where the different model forecasts are weighted according to the forecast lead-time, significantly improves the forecast skills especially during the first forecast hours.

By using an EPS for hydrological purposes, it is important to keep in mind several constraints of the forecasts (Undén) [1]:

• The spread of the members is around a model forecast and not around the nature-truth. All members may be wrong in the same way and/or contain a complex set of biases due to model deficiencies (approximations, numerical algorithms, parameterizations).
• By adding sizeable perturbations to the most likely initial state, each member is likely to be significantly worse then the control run.
• Many EPS are of limited use for short range forecasts when the optimisation time for the growth of the forecast errors lies in the same range.
• The forecast uncertainty increases mostly with forecast lead-time and with precipitation amount. The uncertainty also is dependent on the time and space scales over which the forecast is averaged.

According to the discussion between meteorologists and hydrologists, the central problem with precipitation forecasts is that they tend to be strongly biased compared to the observations. Hydrologists, however, need well-calibrated forecasts on a local scale. Additionally, the ensemble forecasts tend to underestimate the actual uncertainty since they do not account for important model inaccuracies. A crucial issue therefore is the model climatology or Model Output Statistics (MOS) that should be considered in an appropriate form of data pre-processing. Long-term information about model output and its verification is required to limit the effects of random sampling variability of the results. Besides the problem to hold a large sample size, the model climatology may change with changes of the model physics. The users identified a lack of information and communication between model developers and hydrologists that has to be improved in the future.

In general, the meteorological forecaster has a good knowledge about the characteristics of the weather forecast model, its reliabilities and well known limitations. Besides, he has great expertise in the interpretation of the forecasts. He should be able to modify ensemble forecasts, e.g. by systematically shifting precipitation fields. There is a great wish to improve the communication between the operational forecasters and the hydrologists in order to transfer this knowledge. To improve the forecast chain from precipitation to discharge in the future, it is important that both operators need to know the end-user and their demands as well as to understand each other problems.

Finally, it was discussed that the end-users should be actively involved in the development of forecast products to meet their requirements. In the case of extreme flood events, the end-user needs more information including probability estimates that may help in the decision to adopt protective measures like evacuations, and to justify these measures against the population.

[1] - Per Undén - Swedish Meteorological and Hydrological Institute: Global ensemble forecast systems - principles, some applications and limitations
[2] - André Walser / Mathias Rotach - MeteoSwiss: The benefit of a limited-area ensemble prediction system with respect to flood forecasting
[3] - Sebastian Trepte - German Weather Service: Plans for high-resolution forecasts and ensembles at the German Weather Service
[4] - Georg Pistotnik - Central Institute for Meteorology and Geodynamics Vienna: Combination of meteorological nowcasting and ensemble methods in operational flood forecasting