Retrieval of cloud parameters in weather radar benefits from polarimetric measurements. Most polarimetric radars measure the full backscatter matrix (BSM) using a few alternating polarized sounding signals. Using specially encoded orthogonal frequency division multiplexing (OFDM) signals however, the BSM can be measured in a single simultaneous transmission of two orthogonally polarized signals. Based on a set of parameters for weather radar, the properties of such a signal are explored and its merit as a useful capability is shown.

Most operational hail detection algorithms for single-polarisation radars are based on the analysis of the vertical profiles of radar reflectivity. At KNMI (Royal Netherlands Meteorological Institute) and RMI (Royal Meteorological Institute of Belgium) the probability of hail is derived from the height of the freezing level and the 45-dBZ radar echo top height (maximum height of the 45-dBZ echo). Echo tops are affected by errors in the measured reflectivity itself and by errors in the height assigned to these reflectivities. This study investigates the quality of radar echo top heights as a function of range and explores the implications for hail detection.

The method is based on the comparison between reflectivity measurements from two radars on the vertical cross-section extending between these radars. In a first step, sampling errors related to the radar Volume Coverage Patterns are analysed using idealised storm profiles. Subsequently, real reflectivity data for 25 thunderstorm episodes are compared. It is found that the quality of the maximum reflectivity measurements strongly deteriorates with range and that about half of this degradation can be attributed to overshooting effects. Height assignment differences between the two radars are limited to about 0.5 km. Errors on the reflectivity measurements strongly affect the frequency of 45-dBZ threshold exceedances. However, once the threshold is exceeded, errors in measuring the 45-dBZ echo top heights generally affect the derived probability of hail by less than 20%.

Radar rainfall nowcasting at short lead times has important hydrometeorological applications in the fields of weather prediction and flood forecasting. The predictability of rainfall events can vary significantly with scale as smaller storm features are less predictable than the storm envelope motion. As a result, various techniques have been developed for filtering a radar image and deriving short-term forecasts from the more predictable, larger storm scales. In this study, the effects of image filtering on radar nowcasting performance using the Storm Tracker Nowcasting Model (STNM) are evaluated. Radar rainfall nowcasts are evaluated for three storms exhibiting varying degrees of organisation over the Arkansas-Red River basin. In each case, it is found that the nowcast skill decreases with the forecast lead time, increases with the verification area used around a forecast location, and decreases with higher rainfall thresholds. Furthermore, it is demonstrated that a set of properly tuned filtering nowcasts are superior to simple ‘persistence’ and slightly better than ‘uniform advection’. At the scale of a large hydrologic basin (∼ 6000 km2), filter-based nowcasting is shown to capture the temporal variation in rainfall amount and its spatial distribution based on a set of catchment-based metrics. Finally, a method for relating changes in nowcasting skill to errors associated with storm dynamics not captured by image filtering techniques is evaluated.

This paper reports on work that demonstrates that weather radar can yield remarkably accurate rainfall measurements in Scotland. Analysis of data during 1999 and 2000 shows Nimrod radar data to have no consistent error bias and a 24% mean error in storm rainfall totals. For rivers such as the Ruchill Water in the Perthshire Highlands this highlights the fact that the observations and quality control corrections utilised by the Met Office Nimrod system may offer particular benefits in certain flood warning applications. Assessment of wind data indicates that smaller errors occur in Nimrod radar observations during lower wind speeds (less than 15 knots) and with an easterly airflow. However, errors may also be attributed to occasions when the Numerical Weather Prediction model fails to represent the wind conditions correctly at ground level.