The distribution of hydrometeors in thunderstorms is still under investigation as well as the process of electrification in thunderclouds leading to lightning discharges. One indicator of cloud electrification might be high values of the Linear Depolarization Ratio (LDR) at higher vertical levels. This study focuses on LDR values derived from vertically pointing cloud radars and the distribution of five hydrometeor species during 38 days with thunderstorms which occurred in 2018 and 2019 in Central Europe, close to our radar site. The study shows improved algorithms for de-aliasing, the derivation of vertical air velocity and the classification of hydrometeors in clouds using radar data. The comparison of vertical profiles with observed lightning discharges in the vicinity of the radar site (≤1 km) suggested that cloud radar data can indirectly identify “lightning” areas by high LDR values observed at higher gates due to the alignment of ice crystals, likely because of an intensified electric field in thunderclouds. Simultaneously, the results indicated that at higher gates, there is a mixture of several hydrometeor species, which suggests a well-known electrification process by collisions of hydrometeors.

We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements can be used to analyze the internal cloud structure of severe convective storms. The XCLASS classification was developed by major innovation of a previously published algorithm. The XCLASS algorithm identifies seven types of hydrometeors: light rain, rain, wet snow, dry snow, ice, graupel, and hail. It uses measured horizontal and vertical radar reflectivity, specific differential phase, co-polar correlation coefficient, and temperature, and applies fuzzy logic to determine the type of hydrometeor. The new algorithm practically eliminates unrealistic results around and below the melting layer provided by the original algorithm. It identifies wet snow in more cases, and areas with individual hydrometeors have more realistic shapes compared to the original algorithm. The XCLASS algorithm shows reasonable results for the classification of hydrometeors and can be used to study the structure of convective storms.

We processed five years of measurements (2018–2022) of a vertically pointing radar MIRA 35c at the Milešovka meteorological observatory with the aim of analyzing the cloud structure of thunderstorms and comparing differences in measured data for cases when lightning discharges were observed very close to the radar position, and for cases when lightning discharges were observed at a greater distance from the radar position. The MIRA 35c radar is a Doppler polarimetric radar working at 35 GHz (Ka-band) with a vertical resolution of 28.9 m and a time resolution of approximately 2 s. For the analysis, we considered radar data whose radar reflectivity was at least 10 dBZ at 5 km or higher above the radar to ensure that there was a cloud above the radar. We divided the radar data into “near” data (a lightning discharge was registered up to 1 km from the radar position) and “far” data (a lightning discharge was registered from 7.5 to 10 km from the radar position). We compared the following quantities: (i) Power in co-channel (pow), (ii) power in cross-channel (pow-cx), (iii) phase in co-channel (pha), (iv) phase in cross-channel (pha-cx), (v) equivalent radar reflectivity (Ze), (vi) Linear Depolarization Ratio (LDR), (vii) co-polar correlation coefficient (RHO), (viii) Doppler radial velocity (V), (ix) Doppler spectrum width (RMS), and (x) Differential phase (Phi). Pow, pow-cx, pha, pha-cx, and V are basic data measured by the radar, while Ze, LDR, RHO, RMS, and Phi are derived quantities. Our results showed that the characteristics of the compared radar quantities are clearly distinct for “near” dataset from “far” dataset. Furthermore, we found out that there is a clear evolution close to the time of discharges of the observed radar quantities in the “near” dataset, which is not that obvious in the “far” dataset.

Institute of Atmospheric Physics of Czech Academy measures atmospheric attenuation on 60 m experimental FSO link on 830 and 1550 nm for more than three years. Visibility sensors and two 3D sonic anemometers on both transmitting and receiving site, rain gauge and many sensors enabling the refractivity index computation are spaced along the optical link. Meteorological visibility, wind turbulent energy, sonic temperature, structure index and rain rate are correlated with measured attenuation. FSO link attenuation dependence on the above mentioned parameters is analyzed. The paper shows also basic statistical behavior of the long-term FSO signal level and also the simulation of hybrid link techniques.

This contribution compares retrieval algorithms after Mie (quick, but neglecting the real shape of rain drops) and after Oguchi (slower, but more exact) for radar forward scattering and backscatter at frequencies of 10 and 35 GHz. The retrieved quantities were drop size distribution (DSD), rain rate and attenuation (e.g. of microwave link). Measurements of drop size distributions over one year in the Czech Republic served as reference. The simulation shows that the simple Mie algorithm can be used on a good accuracy level.

The paper deals with wave propagation in the troposphere and discusses practical estimates of the refractive index, which determines how much the path of radio-wave is bent, or refracted, in the troposphere. Refractive index (RI) is a function of relative air humidity, air temperature and air pressure while the tropospheric refractivity depends on their height profile. Based on balloon measurements in the Prague meteorological station and mast measurements at Kopisty meteorological station, statistical characteristics of RI are calculated and their sensitivity to changes in pressure, temperature and relative humidity is quantitatively estimated. The paper includes figures and tables that can be used to provide a basic estimate of RI for specified atmospheric properties given by temperature, pressure and relative humidity. The results show that the RI is not very sensitive to pressure values and instead depends strongly on temperature and this dependence increases with increasing relative humidity. It should be emphasized that the RI undoubtedly depends on the local climatology of the quantities from which it is calculated.