Valuing information from mesoscale forecasts

The development of meso-gamma scale numerical weather prediction (NWP) models requires a substantial investment in research, development and computational resources. Traditional objective verification of deterministic model output fails to demonstrate the added value of high-resolution forecasts made by such models. It is generally accepted from subjective verification that these models nevertheless have a predictive potential for small-scale weather phenomena and extreme weather events. This has prompted an extensive body of research into new verification techniques and scores aimed at developing mesoscale performance measures that objectively demonstrate the return on investment in meso-gamma NWP. In this article it is argued that the evaluation of the information in mesoscale forecasts should be essentially connected to the method that is used to extract this information from the direct model output (DMO). This could be an evaluation by a forecaster, but, given the probabilistic nature of small-scale weather, is more likely a form of statistical post-processing. Using model output statistics (MOS) and traditional verification scores, the potential of this approach is demonstrated both on an educational abstraction and a real world example. The MOS approach for this article incorporates concepts from fuzzy verification. This MOS approach objectively weighs different forecast quality measures and as such it is an essential extension of fuzzy methods

The impact of climate change on the critical weather conditions at Schiphol airport (Impact)

Schiphol is van groot belang voor de economische positie van Nederland. De luchthaven is erg gevoelig voor kritieke weersomstandigheden zoals mist, intensieve neerslag en hevige wind. Als gevolg van klimaatverandering verwachten we dat ook de variabiliteit van het weer op de luchthaven en de frequentie en intensiteit van kritieke weersomstandigheden zullen veranderen, maar een precieze kwantificering daarvan ontbreekt. De belangrijkste doelstelling van dit project is daarom het verstrekken en demonstreren van het volgende generatie weer‐ en klimaatmodel HARMONIE. Dit is een nieuw model dat beter geschikt lijkt om het effect van klimaatverandering op lokale kritieke weersomstandigheden op de luchthaven te kwantificeren en te begrijpen. Bovendien zal kennis uit dit project worden gebruikt om de kwaliteit van onze huidige en toekomstige weersvoorspellingen te verbeteren. In dit project wordt het potentieel van het HARMONIE model, om meer gedetailleerdere en nauwkeurigere weersvoorspellingen voor luchthaven Schiphol te leveren dan ons huidige operationele weermodel HIRLAM, nagegaan in het huidige klimaat

The effect of improved nowcasting of precipitation on air quality modeling

The predictive potential of air quality models and thus their value in emergency management and public health support are critically dependent on the quality of their meteorological inputs. The atmospheric flow is the primary cause of the dispersion of airborne substances. The scavenging of pollutants by cloud particles and precipitation is an important sink of atmospheric pollution and subsequently determines the spatial distribution of the deposition of pollutants. The long-standing problem of the spin-up of clouds and precipitation in numerical weather prediction models limits the accuracy of the prediction of short-range dispersion and deposition from local sources. The resulting errors in the atmospheric concentration of pollutants also affect the initial conditions for the calculation of the long-range transport of these pollutants. Customary the spin-up problem is avoided by only using NWP (Numerical Weather Prediction) forecasts with a lead time greater than the spin-up time of the model. Due to the increase of uncertainty with forecast range this reduces the quality of the associated forecasts of the atmospheric flow. In this article recent improvements through diabatic initialization in the spin-up of large-scale precipitation in the Hirlam NWP model are discussed. In a synthetic example using a puff dispersion model the effect is demonstrated of these improvements on the deposition and dispersion of pollutants with a high scavenging coefficient, such as sulphur, and a low scavenging coefficient, such as cesium-137. The analysis presented in this article leads to the conclusion that, at least for situations where large-scale precipitation dominates, the improved model has a limited spin-up so that its full forecast range can be used. The implication for dispersion modeling is that the improved model is particularly useful for short-range forecasts and the calculation of local deposition. The sensitivity of the hydrological processes to proper initialization implies that the spin-up problem may reoccur with changes in the model and increased model resolution. Spin-up should be an ongoing concern for atmospheric modelers

Modeling and Forecasting the Onset and Duration of Severe Radiation Fog under Frost Conditions

A case of a severe radiation fog during frost conditions is analyzed as a benchmark for the development of a very high resolution NWP model. Results by the Weather Research and Forecasting model (WRF) and the High resolution limited area model (HIRLAM) are evaluated against detailed observations to determine the state of the art in fog forecasting and to derive requirements for further research and development. For this particular difficult case, WRF is unable to correctly simulate the fog for none of the parameterizations and model configurations utilized. Contrary, HIRLAM does model the onset of fog, but is unable to represent it beyond the lowest model layer, which leads to an early dispersal of fog in the morning transition. The sensitivity of fog forecasts to model formulation is further analyzed with a high resolution single column version of HIRLAM, and with the Duynkerke (1991) single column model as a reference. The single column results are found to be sensitive to the proper specification of the external forcings. It is reconfirmed that high vertical resolution is essential for modeling the fog formation, the growth of the fog layer and when the fog lifts for the maintenance of a stratus deck. The properly configured column models are able to accurately model the onset of fog and its maturation, but fail in the simulation of fog persistence and subsequent dispersal. Details of the turbulence parameterization appear to be important in this process. It is concluded that, despite of all advances in numerical weather prediction, fog forecasting is still a major challeng