The problem of the optimal combination of rain-gauge measurements and radar precipitation estimates has been investigated. A method that attempts to generalize wellestablished geostatistical techniques, such as kriging with external drift, is presented. The new method, besides allowing spatial information to be incorporated into the modelling and estimation process, also allows temporal information to be incorporated. This technique employs temporal data as secondary co-kriged variables. The approach can be considered both straightforward and practical as far as design and programming aspects are concerned. Co-kriging with external drift leads to significant improvements in the results compared with typical radar estimates. It also seems to be more advantageous than kriging with external drift in modelling stability terms. Evidence is provided showing the advantages of co-kriging with external drift modelling over kriging with external drift. The difference becomes particularly pronounced for non-robust input data. The theoretical background and mathematical structure of the method is demonstrated. The method has been applied to four events, three during summer and one during winter, that took place over the complex Swiss orography. It is shown that cross-validation skill scores improve when the aggregation period of the input data increases from ten minutes to one hour. This improvement can be attributed to the increasing robustness of the input data with the period of aggregation. Moreover, a straightforward disaggregation scheme, which starts from hourly precipitation maps, produced by means of the aforementioned geostatistical technique and generating precipitation estimates at a temporal resolution of five minutes, is proposed.

A novel analogue-based heuristic tool for nowcasting orographic precipitation is presented. The system takes advantage of the orographic forcing, which determines a strong relation between mesoscale flows, air mass stability and rainfall patterns. These quantities are used as predictors of precipitation. In particular, past situations with the predictors most similar to those observed at the current time are identified by searching a large historical dataset. Deterministic and probabilistic forecasts are then generated every five minutes as new observations are available, based on the rainfall observed by radar after the analogous situations. The analogue method provides a natural way to incorporate evolution of precipitation into the nowcasting system and to express forecast uncertainty by means of ensembles.A total of 127 days of long-lasting orographic precipitation constitutes the historical dataset in which the analogous situations are searched. The system is developed for the Lago Maggiore region in the southern part of the European Alps. Given the availability of radar data and the presence of a strong orographic forcing, it can be extended to other mountainous regions. An evaluation of the skill of the system shows that the heuristic tool performs better than Eulerian persistence for predictions with lead time larger than one hour, and better than the numerical model COSMO2 for forecasts with lead time up to four hours.

Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

The characterization of the alpine extreme precipitation is the basis to study the projected changes in frequency and intensity of heavy rainfall and is needed to improve the resilience of communities to high‐impact weather. Climatological features of extreme daily and sub‐daily precipitation are documented here for the Swiss Alps and surrounding regions at a high spatial resolution (1 km2). The basis is 12 years of data from rain gauges and CombiPrecip, a rainfall field produced by locally adjusting the radar precipitation map to the values measured by rain gauges. The agreement between rain gauges and CombiPrecip concerning both the timing and the magnitude of the extreme events is quantified by cross‐validation; overall, it increases with diminishing the severity of the extremes and increasing accumulation time. If the extremes represent on average the 10 most intense rainfall accumulations per year, in general 50–65% of rain gauges extremes are extremes also for CombiPrecip, 40–50% of CombiPrecip extremes are not extremes according to rain gauges, and CombiPrecip extremes are till 7% lower than rain gauges extremes. The maps presented in this paper show that both daily and sub‐daily extremes are more intense along the alpine slopes compared to the crest of the Alps in all seasons, with the Lago Maggiore region showing the largest values. The fraction of yearly rainfall due to extremes is generally smaller in the Alps than in flat terrain. Extreme 1‐hr precipitation is more clustered in time in the inner Alps, but is less frequent, and exhibits a strong diurnal cycle in summer. The paper also shows that sub‐daily and daily extremes occur essentially over the same 24‐hr period.

[1] Quantitative precipitation estimation based on meteorological radar data potentially provides continuous, high-resolution, large-coverage data that are essential for meteorological and hydrologic analyses. While intense scientific efforts have focused on precipitation estimation in temperate climatic regimes, relatively few studies examined radar-based estimates in dry climatic regions. The paper examines radar-based rain depth estimation for rainfall periods (a series of successive rainy days) in Israel, where the climate ranges between Mediterranean to dry. Three radar-gauge adjustment methods are compared: a one-coefficient bulk adjustment, which simply removes the mean bias; a two-coefficient range adjustment based on a weighted regression (WR); and a four-coefficient adjustment based on a weighted multiple regression (WMR), which assumes a locally varied, nonisotropic correction factor. The WMR technique has been previously applied in the Alps of Europe. Adjustment coefficients have been derived for 28 rainfall periods using 59 independent gauges of a quality-checked training data set. The validation was based on an independent data set composed of gauges located in eleven 20 Â 20 km 2 validation areas, which are representative of different climate, topography and radar distance conditions. The WR and WMR methods were found preferable with a slight better performance of the latter. Furthermore, a novel approach has been adopted in this study, whereby radar estimates are considered useable if they provide information that is better than gauge-only estimates. The latter was derived by spatial interpolation of the gauges belonging to the training data set. Note that these gauges are outside the validation areas. As for the radar-adjusted estimates, gauge-derived estimates were assessed against gauge data in the validation areas. It was found that radar-based estimates are better for the validation areas at the dry climate regime. At distances larger than 100 km, the radar underestimation becomes too large in the two northern validation areas, while in the southern one radar data are still better than gauge interpolation. It is concluded that in ungauged areas of Israel it is preferable to use WMR-adjusted (or alternatively, simply WR-adjusted) radar echoes rather than the standard bulk adjustment method and for dry ungauged areas it is preferable over the conventional gauge-interpolated values derived from point measurements, which are outside the areas themselves. The WR and WMR adjustment methods provide useful rain depth estimates for rainfall periods for the examined areas but within the limitation stated above.Citation: Morin, E., and M. Gabella (2007), Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes,