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High temporal and spatial resolution of radar measurements enables to continuously observe dynamically evolving meteorological phenomena. Three-dimensional (3D) weather radar reflectivity data assimilated into the numerical weather prediction model has the potential to improve initial description of the atmospheric model state. The paper is concentrated on the development of radar reflectivity assimilation technique into COAMPS mesoscale model using an Ensemble Kalman Filter (EnKF) type assimilation schemes available in Data Assimilation Research Testbed (DART) programming environment. Before weather radar data enter into the assimilation system, the measurement errors are eliminated through quality control procedures. At first artifacts associated with non-meteorological errors are removed using the algorithms based on analysis of reflectivity field pattern. Then procedures for correction of the reflectivity data are employed, especially due to radar beam blockage and attenuation in rain. Each of the correction algorithms is connected with generation of the data quality characteristic expressed quantitatively by so called quality index (QI). In order to avoid transformation of data uncertainty into assimilation scheme only the radar gates successfully verified by means of the quality algorithms were employed in the assimilation. The proposed methodology has been applied to simulate selected intense precipitation events in Poland in May and August 2010.
The MeteoGIS system developed at the Institute of Meteorology and Water Management – National Research Institute in Poland is a GIS-based system for real-time monitoring of weather and the generation of meteorological warnings. Apart from its monitoring features, it can also provide more advanced analysis, including SQL (Structured Query Language) queries and statistical analyses. Input data are provided mainly by the INCA-PL 2 nowcasting model which employs forecasts from the high-resolution AROME numerical weather prediction model and measurement data from the Polish weather radar network POLRAD and surface meteorological stations. As well as this, data from the PERUN lighting detection system are used. Ingestion of such data allows for the mitigation of risk from potentially hazardous weather phenomena such as extreme temperatures, strong wind, thunderstorms, heavy rain and subsequent impending floods. The following meteorological parameters at ground level are visualised in the MeteoGIS: (i) precipitation (accumulation and type), (ii) temperature, (iii) wind (speed and direction), (iv) lightning (locations and type). End users of the system are workers from civil protection services who are interested in shortterm warnings against severe weather events, especially area-oriented ones (related to districts, catchments, etc.). The reliability of visualised data is a very important issue, and from the MeteoGIS user’s point of view the improvement in data quality is a continuous process.
The purpose of the research was to find a time frame for the beginning, duration of maximum intensity, and end of nocturnal bird migration. The research was carried out using photo registration on a radar screen during the spring and autumn seasons of 1999-2001. Examination of the average length of daylight at the beginning of migration and the onset of civil twilight yielded a high correlation factor for both spring and autumn. The results showed that, on average, nocturnal migration began at the onset of civil twilight, that is, half an hour after sunset. The time elapsing between the onset of migration and the maximum concentration of migratory birds averaged about 70 minutes in both spring and autumn. Nocturnal migration usually came to an end within the one and a half to two hours after sunrise. We ascertained the seasonal time shift for the onset of nocturnal migration corresponding to the seasonal time shift related to the approach of darkness. The average times of the beginning, maximum intensity and end of nocturnal migration were found to be related to photoperiodic factors.
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