Моделирование снегонакопления и снеготаяния в бассейне р. Кама с применением данных глобальных моделей прогноза погоды

Currently, the improvement of numerical models of weather forecasting allows using them for hydrological problems, including calculations of snow water equivalent  (SWE) or snow storage. In this paper, we discuss the applicability of daily precipitation forecasts for three global atmospheric models: GFS (USA), GEM (Canada) and PL-AV (Russia) for calculating snow storage (SWE) in the Kama river basin for the cold season of 2017–2018. As the main components of the balance of snow storages the following parameters were taken into account: precipitation (with regard for the phase); snow melting during thaws; evaporation from the surface of the snow cover; interception of solid precipitation by forest vegetation. The calculation of snow accumulation and melting was based on empirical methods and performed with the GIS technologies. The degree-day factor was used to calculate snowmelt intensity, and snow sublimation was estimated by P.P. Kuz’min formula. The accuracy of numerical precipitation forecasts was estimated by comparing the results with the data of 101 weather stations. Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). During the snowmelt period, the part of the snow-covered area of the basin was also calculated using satellite images of Terra/Aqua MODIS on the basis of the NDFSI index. The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. It is difficult to determine which model provides the maximum accuracy of the snow storage calculation since each one has individual limitations. According to the PL-AV model, the mean square error of snow storage calculation was minimal, but there was a significant underestimation of snow accumulation in the mountainous part of the basin. According to the GEM model, snow storages were overestimated by 10–25%. When calculating with use of the GFS model data, a lot of local maximums and minimums are detected in the field of snow storages, which are not confirmed by the data of weather stations. The main sources of uncertainty in the calculation are possible systematic errors in the numerical forecasts of precipitation, as well as the empirical coefficients used in the calculation of the intensity of snowmelt and evaporation from the snow cover surface.На примере холодного периода 2017/18  г. выполнено моделирование формирования и таяния снежного покрова в бассейне р.  Кама с применением выходных данных глобальных моделей прогноза погоды GFS (США), GEM (Канада) и ПЛ-АВ (Россия). Валидация результатов проведена по данным 40 полевых и 27 лесных снегомерных маршрутов, а в весенний период – и по спутниковым снимкам MODIS. Ошибка расчёта снегозапасов по данным всех трёх моделей не превысила 25% фактических значений

Towards a More Complete and Accurate Experimental Nuclear Reaction Data Library (EXFOR): International Collaboration Between Nuclear Reaction Data Centres (NRDC)

The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by the IAEA Nuclear Data Section (NDS) is successfully collaborating in the maintenance and development of the EXFOR library. As the scope of published data expands (e.g., to higher energy, to heavier projectile) to meet the needs from the frontier of sciences and applications, it becomes nowadays a hard and challenging task to maintain both completeness and accuracy of the whole EXFOR library. The paper describes evolution of the library with highlights on recent developments.Comment: 4 pages, 2 figure

Simulation of snow accumulation and melting in the Kama river basin using data from global prognostic models

Currently, the improvement of numerical models of weather forecasting allows using them for hydrological problems, including calculations of snow water equivalent  (SWE) or snow storage. In this paper, we discuss the applicability of daily precipitation forecasts for three global atmospheric models: GFS (USA), GEM (Canada) and PL-AV (Russia) for calculating snow storage (SWE) in the Kama river basin for the cold season of 2017–2018. As the main components of the balance of snow storages the following parameters were taken into account: precipitation (with regard for the phase); snow melting during thaws; evaporation from the surface of the snow cover; interception of solid precipitation by forest vegetation. The calculation of snow accumulation and melting was based on empirical methods and performed with the GIS technologies. The degree-day factor was used to calculate snowmelt intensity, and snow sublimation was estimated by P.P. Kuz’min formula. The accuracy of numerical precipitation forecasts was estimated by comparing the results with the data of 101 weather stations. Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). During the snowmelt period, the part of the snow-covered area of the basin was also calculated using satellite images of Terra/Aqua MODIS on the basis of the NDFSI index. The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. It is difficult to determine which model provides the maximum accuracy of the snow storage calculation since each one has individual limitations. According to the PL-AV model, the mean square error of snow storage calculation was minimal, but there was a significant underestimation of snow accumulation in the mountainous part of the basin. According to the GEM model, snow storages were overestimated by 10–25%. When calculating with use of the GFS model data, a lot of local maximums and minimums are detected in the field of snow storages, which are not confirmed by the data of weather stations. The main sources of uncertainty in the calculation are possible systematic errors in the numerical forecasts of precipitation, as well as the empirical coefficients used in the calculation of the intensity of snowmelt and evaporation from the snow cover surface

The art of collecting experimental data internationally: EXFOR, CINDA and the NRDC network

The world-wide network of nuclear reaction data centres (NRDC) has, for about 40 years, provided data services to the scientific community. This network covers all types of nuclear reaction data, including neutron-induced, charged-particle-induced, and photonuclear data, used in a wide range of applications, such as fission reactors, accelerator driven systems, fusion facilities, nuclear medicine, materials analysis, environmental monitoring, and basic research. The now 13 nuclear data centres included in the NRDC are dividing the efforts of compilation and distribution for particular types of reactions and/or geographic regions all over the world. A central activity of the network is the collection and compilation of experimental nuclear reaction data and the related bibliographic information in the EXFOR and CINDA databases. Many of the individual data centres also distribute other types of nuclear data information, including evaluated data libraries, nuclear structure and decay data, and nuclear data reports. The network today ensures the world-wide transfer of information and coordinated evolution of an important source of nuclear data for current and future nuclear applications