The Crop Growth Monitoring System (CGMS) is one of the most advanced systems of monitoring the conditions of crops growth and development and forecasting their yields in agrometeorological practice. The CGMS allows to assess the conditions of growth, development and accumulation of productive biomass of a number of agricultural crops - winter wheat, barley, maize, rice, sunflower, potatoes, soybean etc. For each of the crops the system must be adapted to specific territories taking into account meteorological, phenological, biological information and soil characteristics. The paper discusses the peculiarities of technological adaptation of the CGMS system (Crop Growth Monitoring System) including creation of a meteorological database for the period of 2000-2017 using standard meteorological observations of the Ukrainian Hydrometeorological Center (UkrHMC) network; creation of a soil characteristics database by finding a correspondences of taxonomy of the soil map of Ukraine (scale:1:2500000) to classification of soils of the WRB; creation of a database of phenological characteristics such as TSUMEM (sum of temperatures within the period from sowing to coming-up), TSUM1 (sum of temperatures within the period from coming-up to blossoming) and TSUM2 (sum of temperatures within the period from blossoming to maturity) calculated according to the data obtained from agrometeorological posts and stations of the UkrHMC network for the period of 2000 - 2015 with regard to five main crops (winter wheat, maize, spring barley, soybean and sunflower); creation of a statistical crop capacity database at the regional and district levels. In addition, the paper considers spatial schematization of calculations and aggregation of agricultural crops productivity indicators obtained as a result of the WOFOST biophysical model application. It also outlines the scheme of crop capacity forecasting based on administrative units and the estimation of forecast accuracy for winter wheat crop capacity in administrative districts of Kiev region. The link to the website containing results of operation of the CGMS-Ukraine system is as follows: http:/entln.uhmi.org.ua/case/CGMS.
The Ukrainian segment of the Earth Networks lightning finding system created in 2016 is dis-cussed in the paper. It consists of 12 sensors located in different parts of Ukraine which allow identifying both types of lightning: "cloud-to-ground discharge (CG)" and "cloud-to-cloud dis-charge (CC)". The stated number of sensors covers the entire territory of Ukraine and allows the determination of CG with a probability of 95 % with the spatial accuracy of lightning detection constituting about 200 meters. Taking into account the necessity to preserve the equipment, an agreement was reached with the Ukrainian Hydrometeorological Centre on installation of light-ning finding sensors within the territory of meteorological stations. Significant advantage of this lightning finding system is that it allows recording of electromagnetic lightning signals within the range from 1 Hz to 12 MHz. Due to this, the spatial position of CG and CC can be determined more accurately by analyzing the spectrum of electromagnetic signal within the specified range. To localize a lightning discharge using the ENTLN network the method of lightning finding based on the principle of "time of signal arrival (ToA)" is applied. The primary data obtained from the lightning finding sensors are analyzed in the internal system of centralized processing and can be used further by a consumer in two ways: either directly, or serve as output data for series of prod-ucts resulted from processing using mathematical, statistical and geographic information systems. In order to process obtained data the UHMI developed a modular system that allows unification of the means of primary and secondary processing of output data and enabling all necessary channels for transmission of generated data using a wide range of protocols. To visualize the lightning data a subsystem based on the open GeoServer for preprocessing of the geodata and client tools using the mapping data of OpenStreetMap are used. As an example of one of possibilities these lightning data provide, the analysis of the spatial and temporal distribution of lightning activity over the ter-ritory of Ukraine from June 10 to September 30, 2016 has been done and the results showed that these data could be a new, qualitative source of data for climatological studies. In addition, real-time data acquisition allows creation of a series of products for a wide range of consumers inter-ested in a short-term forecasting.
Information of water content of frontal clouds produced strong precipitation has important practical applications. Such type of data is necessary for the estimation of electromagnetic waves attenuation, the calculation of aircrafts' icing possibility, the estimation of necessity of an increase of precipitation or clouds dissipation (for the cases when thick cloudiness is observed over airports and astronomical observatories) by using of weather modification technologies and so on. This information can be obtained by aircrafts' sounding, but not for the whole area of cloud frontal system and not for the all-time of their existence. Nowadays the satellites can provide measurements of cloud systems parameters continuously and on a large scale. The main objective of this research is to define the water content, water balance and liquid water losses for different precipitation intensity levels (especially heavy) of frontal cloud systems in cold period. The analysis of water contents and water balances for the three synoptic situations: 08-13 January, 30 January – 06 February, 27-31 March 2015 have been done. Initial data included: the hourly water content estimated by satellite measurements (P), the precipitation amount (Q) and duration (T) observed on 40 meteorological stations and the wind speed on the cloud level (V) derived from air soundings. Other characteristics as precipitation generation ability (K), the water balance (Q*=0.36×P×V×T) and water balance recovery (Q/Q*) were defined. Some specific values of the typical water content for different precipitation intensity levels were defined. The dependence K on Q is manifested in the form of two straight-line dependencies for each synoptic situation, which is probably due to the peculiarities of the formation of the water content of clouds. For clouds with crystal precipitation the maximal value of water balance was 25 tons and for clouds with liquid water precipitation – 80 tons. The data of water balance recovery during process of precipitation are interesting. For this purpose, the amount of water transported by the clouds over the meteorological stations was calculated during the time of precipitation. The ratio of Q to the value of Q* characterizes the process of water balance recovery. It was shown, that the distribution of Q/Q*was the same for all synoptic situations. For most cases (75-80%) water loses due to precipitation were no more than 0.4 Q*. The all water balance recovery (Q/Q* equals 1.0…1.5) was in 1 % cases.
The article studies one of the most important issues of agricultural production maintenance – development of a system of crops area estimation in Ukraine. The objective of this paper is to describe the similar system that uses high resolution satellite data and operational agrometeorological data from the network of the Hydrometeorological Centre of Ukraine as input information. The system is based on step-by-step solving of the following tasks: obtaining geoinformation data for individual agricultural crops; development of methods for multispectral satellite images classification; development of software applications to automate the process of these images classification with subsequent classification of crop areas. The research uses the following algorithms (or classifiers) to classify the agricultural land: SVM (support vector machine), RF ("random forest") and NN (neural networks). The choice of the most accurate of them formed the basis of the general method of classification. The values of spectral characteristics of red and infrared channels of a complete set of cloudless satellite images during the growing period were used as input data (features). As a result, in 2018 some test calculations were conducted to estimate the area of agricultural crops in Kyiv Region. The results of evaluation of accuracy of the satellite-based agricultural crops area estimation using the statistical data showed that the lowest accuracy is typical for winter wheat and corn. The accuracy of soybeans and spring barley classification is quite low for most of the tested fields. Sunflower and rapeseed crops showed the highest accuracy. In order to improve the accuracy of classification, it is necessary to introduce more classification features (in a temporary aspect) by processing more satellite images during the growing period, and to increase the number of test samples through systematic sampling of ground data across the regions in Ukraine. We suggest using the scheme of main agricultural crops area estimation satellite-based system by the Hydrometeorological Centre of Ukraine.
Severe weather events associated with deep convection such as flash floods, large hail, damaging winds and even occasional tornadoes are reported every year in Ukraine. There is an increased demand for the assessment of operational strategies for the forecasting and/or nowcasting of severe convective clouds in this country. The forecast of the location of severe convective clouds in advance of their formation is possible with the use of remote sensing techniques such as radar and/or satellite. Ukraine currently has a few EUMETCast stations in the different parts of the country which provide real-time geostationary Meteosat Second Generation satellite images. An experience in the use of Meteosat for detection and tracking of severe convective clouds is discussed.
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