DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations.
For further information please refer to:
[in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ]
[in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]
This dataset comprises numerical outputs from the whole atmospheric model GAIA (=Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) and associated simulations (EXP1, EXP2, and EXP3) presented in the article "Excitation mechanism of ionospheric 6-day oscillation during the 2019 September sudden stratospheric warming event" (Miyoshi and Yamazaki, 2020).
Briefly, GAIA is a numerical model of the Earth’s whole atmosphere (e.g., Jin et al., 2011; Miyoshi et al., 2011, 2012). The model consists of mathematical equations that represent various physical and chemical processes in the troposphere, stratosphere, mesosphere, and thermosphere. The neutral atmosphere model (Miyoshi & Fujiwara, 2003) is coupled with an ionospheric model (Shinagawa, 2011) and electrodynamics model (Jin et al., 2008). The lower layers of the model below 40 km are constrained by meteorological reanalysis products by the Japan Meteorological Agency (Kobayashi, et al., 2015).
The model was run for the period 1 September-10 October 2019, when there was a sudden stratospheric warming in the Antarctic region (Yamazaki et al., 2020). The GAIA simulation outputs can be found in the directory 'gaia', while the numerical outputs from the controlled simulations EXP1, EXP2, and EXP3 can be found in the directories 'exp1', 'exp2', and 'exp3', respectively. The model data for the temperature, zonal wind, meridional wind, and geopotential heigh are stored separately for each day in the NetCDF format. 'gt', 'gu', 'gv', and 'gz' in file name indicate the temperature, zonal wind, meridional wind, and geopotential heigh, respectively. For instance, the file 'gv20190915gcm.nc' contains the meridional wind data for 15 September 2019. The model data for the eastward current intensity, eastward electric field, and total electron content can be found as text files, namely, 'East_current_gaia.data', 'East_efield_gaia.data', and 'tec_gaia.data'.
This dataset comprises numerical outputs from the whole atmospheric model GAIA (=Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) and associated simulations presented in the article "Whole atmosphere model simulations of ultra-fast Kelvin wave effects in the ionosphere and thermosphere" (Yamazaki et al., 2020).GAIA is a numerical model of the Earth’s whole atmosphere (e.g., Jin et al., 2011; Miyoshi et al., 2011). The model consists of mathematical equations appropriate for various physical and chemical processes in the troposphere, stratosphere, mesosphere, and thermosphere. The neutral atmosphere model (Miyoshi & Fujiwara, 2003) is coupled with an ionospheric model (Shinagawa, 2011) and electrodynamics model (Jin et al., 2008). The lower layers of the model below 30 km are constrained by meteorological reanalysis products by the Japan Meteorological Agency (Onogi et al., 2007; Kobayashi, et al., 2015).The model was run for the following three time intervals:1. 15 August 2010 - 15 October 20102. 01 August 2011 - 30 September 20113. 01 December 2012 - 31 January 2013The simulation outputs can be found in GAIA/2010, GAIA/2011, and GAIA/2013, respectively. In each directory, the model data are stored in a MATLAB format (.mat).List of model outputs:GZ: "Geopotential height" in [m] as a function of LONGITUDE [˚], LATITUDE [˚], PRESSURE, and TIMEGU: "Zonal wind" in [m/s] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIME (positive eastward)GV: "Meridional wind" in [m/s] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIME (positive northward)GT: "Temperature" in [K] as a function of LONGITUDE, LATITUDE, PRESSURE, and TIMEEEF: "Equatorial zonal electric field" in [V/m] as a function of LONGITUDE and TIME.TEC: "Total electron content" in [TECU] as a function of LONGITUDE, LATITUDE, and TIMEPRESSURE is given at: 10^2, 10^1, 10^0, 10^(-1), 10^(-2), 10^(-3), 10^(-4), 10^(-5), 10^(-6), 10^(-7), 10^(-8) [hPa]For the time period #1, additional controlled simulations "LARGE_WAVES" and "NO_UFKW" were run. See Yamazaki et al. (2020) for details of these runs. The simulation outputs can be found in LARGE_WAVES/2011 and NO_UFKW/2011, respectively.
Original data comes from a project which takes or took place as part of the DFG priority program "Exploratories for large-scale and long-term functional biodiversity research". The data is stored together with descriptive metadata, in combination called a dataset, in the project repository (https://www.bexis.uni-jena.de). Species information was extracted from that original dataset. The second paragraph is part of the metadata of the original dataset. Hypersensitive reactions (HR) are regarded as one of the most important induced defense mechanisms and most effective bottom-up effects in multitrophic food-webs (Cornelissen and Fernandes, 2001; Fernandes, 1998; Fernandes and Negreiros, 2001). Plant genetics and physiological properties are generally considered to constitute the main factors influencing degree and frequency of HR against galling insects (Fernandes et al., 2003; Fernandes and Negreiros, 2001). Physiological plant properties are probably influenced by abiotic site conditions (soil, climate) (Fernandes et al., 2003), but also by other abiotic and biotic habitat characteristics as canopy openings or occurrence of other tree species, which depend on forest management intensity (Collet et al., 2001; Dittmar et al., 2003; Lof et al., 2005; Marusak and Barna, 2002).
The frequency, diversity and community structure of herbivores and parasitoids is expected to differ in dependence of several habitat characteristics induced by forest management as well (Gossner et al., 2006) and may influence the frequency and efficiency of HR of the host plant, bur are otherwise dependent from the host plant quality (Fonseca et al., 2006).
To reveal possible effects of forest use intensity on three-trophic-level interactions, the frequency and density of the most abundant galling insects on Fagus sylvatica (Mikiola fagi and Hartigiola annulipes, Cecidomyiidae) will be recorded on approximately 60 selected trees on plots characterized by clearly different forest management intensities in the exploratory Hainich/Dün. Recording of hypersensitive reactions to gall induction (Fernandes et al., 2003) and predators and parasitoids of M. fagi and H. annulipes (Dziurznski, 1961) will enable the analysis of the interplay of bottom-up and top-down effects in this system in dependence from the forest use intensity. The relation of hypersensitive reactions (bottom-up effect) to living gall-inducers leads to an estimation of the relevance of forest use management for the susceptibility of European beech to galling insects. These investigations lead to an estimation of the possible indicative value of easily recordable plant structures as galls and HP sites for beech susceptibility or restistance.