Harri Hohti

Climatology of Severe Hail in Finland: 1930–2006

Abstract A climatology of severe hail (2 cm in diameter or larger) in Finland was constructed by collecting newspaper, storm-spotter, and eyewitness reports. The climatology covered the warm season (1 May–14 September) during the 77-yr period of 1930–2006. Altogether, 240 severe-hail cases were found. The maximum reported severe-hail size was mainly 4 cm in diameter or less (65% of the cases), with the number of cases decreasing as hail size increased. In a few extreme cases, 7–8-cm (baseball sized) hailstones have been reported in Finland. Most of the severe-hail cases (84%) occurred from late June through early August, with July being the peak month (almost 66% of the cases). Most severe hail fell during the afternoon and early evening hours 1400–2000 local time (LT). Larger hailstones (4 cm or larger) tended to occur a little later (1600–2000 LT) than smaller (2–3.9 cm) hailstones (1400–1800 LT). Most severe-hail cases occurred in southern and western Finland, generally decreasing to the north, with th...

Operational Measurement of Precipitation in Cold Climates

Over the last 50 years, severe weather such as thunderstorms, squall lines, tornadoes, hurricanes, and extreme precipitation events with ensuing flash floods have dominated both scientific and operational radar meteorology (Atlas, 1990, Collier, 2001). This is natural, as one of the main benefits of weather radars is very dense sampling of precipitating systems in time and space, facilitating real-time warning and nowcasting of mesoscale severe weather which may have huge socioecomonic impacts. Also, an implication has been that some commercial radar manufacturers have paid little attention to the sensitivity of weather radar. Maximal sensitivity of a radar system is required for making snowfall measurements. Even greater sensitivity of operational radars is needed for the production of Doppler winds from clear air. The important topic of wind field estimation from Doppler measurements relates to Gekat et al. (2003), Meischner et al. (2003) and Macpherson (2003), all this book. The major proportion of the frequent boundary layer echoes found in summer, even in the north of Europe, originates from insects. For example, classification of 240 000 vertical profiles of reflectivity (VPR) from a one-year-long period in Finland revealed that 40% of all VPRs originated from clear air echoes reaching the ground, 20% from overhanging precipitation, i.e. ice crystal clouds or snowfall layers aloft, and only 40% involved precipitation reaching the ground level (Pohjola and Koistinen, 2002). The reflectivity of both nonprecipitating echo classes was typically between −20 and 5 dBZ. Both Canadian and Nordic radar networks (NORDRAD) mostly operate sensitive C-band systems. The operational Nordic radars are “standard” systems with sensitivities around −110 dBm, beamwidths of around 0.9° and pulse lengths from 0.5–2 µs. In Canada, the sensitivity requirement to detect snowfall and clear air echoes has led to the specification of a multi-pulse length capability (0.8–5 µs) and of a narrow antenna beamwidth (0.65°) (Joe and Lapczak, 2002).

A Climatological Comparison of Radar and Ground Observations of Hail in Finland

Abstract Two approaches to producing a hail climatology for Finland are compared. The first approach is based on 70 yr of hail reports from different sources (newspapers, storm spotters, and other volunteers). The second is derived primarily from radar data. It is shown that a selection of newspaper articles of hail damage covering a period of 70 yr provides a good overview of the typical monthly and diurnal distribution of hail occurrence over the country. Radar data covering five summers (2001–05) provide another data source, but with different potential sources of errors. The two distinct methods compared in this paper give roughly the same results in describing the hail climatology of Finland, which gives additional confidence in each of the methods. On the basis of both methods, most hailstones are observed in the afternoon, 1400–1600 local time. The hail “season” extends from May to early September with maximum occurrences in June, July, and August. This means that hail is most frequently observed w...

Experience of real time spatial adjustment of the Z-R relation according to water phase of hydrometeors

Abstract According to hydrometeor water phase analysis (rain, snow, sleet) an optimal relation between the radar reflectivity factor (Z) and precipitation intensity (R in rain, S in snowfall) is applied individually at each radar pixel in real time. Gauges are used to evaluate the improvement in radar accumulation. The results show that the phase dependent Z-R/S selection does not significantly improve the accuracy of radar measurements. Reflectivity profile effects and wind deflection in gauges are dominating error factors.

Precipitation nowcasting using radar-derived atmospheric motion vectors

Abstract The method to predict precipitation for couple of hours in the future, developed at FMI, applies modified correlation-based atmospheric motion vector (AMV) system provided by EUMETSAT. PseudoCAPPI reflectivity fields with interval of 5 minutes are used as the input to the AMV system. That provides a smooth vector field from which a trajectory field is calculated. Speed and direction inaccuracies along the trajectories gives us an “error ellipse” around each starting point of a trajectory. The reflectivities within the ellipse are then applied to calculate a set of forecast products for 0.5 4 hours from the initial time. The verification results show that some conventional quantities like POD and FAR are quite sensitive to the size of the precipitating area.

Validation of Automatic Cb Observations for METAR Messages without Ground Truth

AbstractIn aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT’s Nowcasting Satellite Application Facility and lightning products from the Finnish lightning...