Anomalous propagation (anaprop), analogous to the upper mirage in the visual wavelengths, is still a major problem in radar meteorology. This phenomenon assumes particular importance in automatic recognition and estimation of rainfall. Anaprop echoes from terrain features such as hills and coasts Often give echoes up to 50-60 dBZ equivalent to heavy rain or hail in severe thunderstorms. Anaprop echoes from sea waves may be comparable in strength to those from moderate precipitation and also form similar patterns. Based on the evidence that the vertical reflectivity profile of precipitation is quite different from the anaprop profile, two methods for anaprop identification are presented. The method proposed by the Servizio Meteorologico Regionale (SMR, Italy) simply uses the operational scan procedure to discriminate between precipitation and anaprop. At the Swedish Meteorological and Hydrological Institute an 'ad hoc' scan strategy has been developed in order to obtain much more detail of the lowest reflectivity profile. A number of statistical parameters have been used to achieve a better discrimination between precipitation, land and sea clutter. A number of case studies, representing different echo intensities and patterns, and including a case of anaprop with embedded precipitation, are presented to assess the impact of these methods.

We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (> 5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces.

This paper presents a prediction system for regional crop growth in Sweden, recently set up at SMHI (Swedish Meteorological and Hydrological Institute). The system includes a state-of-the-art crop growth model, WOFOST (WOrld FOod STudies) and inputs from meteorological mesoscale analysis. The simulated crops dye spring barley, spring rape, oats and winter wheat, and the period of investigation is 1985-98. The simulated water-limited grain yield is used as a predictor in the yield prediction procedure. The technological time trend describing the yearly increase of the production level is accounted for as well. Yield prediction based on crop growth modelling is justified since the ability to forecast the yield is higher compared to that using the technological time trend alone. The prediction errors are of the order of 8 to 16%, with the lowest errors for winter wheat and spring barley.

Precipitation forecasting experiments have been carried out with HIRLAM, a numerical weather prediction model. The model has been run with three different horizontal gridlengths: 22, 11 and 5.5 Km. An attempt has been made to estimate the appropriate magnitude of the horizontal diffusion, which is used to control small-scale noise in the model, by looking at Kinetic energy spectra. It is shown that, for the higher resolutions, smoothing the orography gives smoother precipitation patterns. The small-scale precipitation resulting from runs with the original orography has negligible extra skill compared to the smoothed orography runs. The results show that the model is able to forecast good precipitation amounts, even with 22 Km gridlengths. No significant improvements occur when the higher horizontal resolutions are used. Experiments have been performed using the tendencies of the physical parameterisations computed on a coarser grid than that of the dynamics. The resulting precipitation patterns are very similar and this indicates a more economical way of integrating the model.

A simple and pragmatic method utilising the difference between analysed near-surface and Meteosat IR temperatures (DeltaT) is presented and applied with the aim of identifying and removing non-precipitation echoes in weather radar composite imagery. Despite inherent deficiencies in these multisource data, such as lower spatial and temporal resolutions relative to the radar data, DeltaT is demonstrated to efficiently identify efficiently those areas void of potentially precipitating clouds, and to remove radar echoes in them. A set of 243 manually analysed composites from the summer of 2000 was used to evaluate the method. False alarm rates (FAR), percent correct (PC) and Hanssen-Kuipers skill (HKS) scores were calculated from standard contingency tables for five echo classes: weak, strong, land, sea, and all. FAR was lowered in all classes, PC was generally raised by a few percent to be over 95%, while HKS either remained unchanged or was slightly lowered through the application of DeltaT. These results indicate that DeltaT successfully removes a significant amount of non-precipitation, sometimes at the expense of a small amount of true precipitation. This penalty is larger over sea, which indicates that the method may need to be tuned differently for land and sea environments. This method may act as a foundation on which improvements to radar data quality control can be made with the introduction Of new and improved satellite instrumentation such as that found on board the Meteosat Second Generation platform. However, this type of method should remain complementary to improved signal processing and radar data analysis techniques.

A new index to estimate aircraft icing in clouds from operational meteorological models has been developed by Swedish meteorologists. Although rather simple it takes into account, directly or indirectly, all the principal meteorological variables for icing. The index has been evaluated during three winter seasons and is now operational in the Swedish HIRLAM model. A graphical representation of the index is presented.