| 1. |
- Lembrechts, Jonas J., et al.
(författare)
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SoilTemp : A global database of near-surface temperature
- 2020
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 26:11, s. 6616-6629
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Tidskriftsartikel (refereegranskat)abstract
- Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
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| 2. |
- Abbasi, Rasha, et al.
(författare)
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IceCube search for neutrinos from GRB 221009A
- 2023
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Ingår i: Proceedings of 38th International Cosmic Ray Conference (ICRC 2023). - : Sissa Medialab Srl.
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Konferensbidrag (refereegranskat)abstract
- GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremelyhigh flux of high and very-high-energy photons provide a unique opportunity to probe the predictedneutrino counterpart to the electromagnetic emission. We have used a variety of methods to searchfor neutrinos in coincidence with the GRB over several time windows during the precursor, promptand afterglow phases of the GRB. MeV scale neutrinos are studied using photo-multiplier ratescalers which are normally used to search for galactic core-collapse supernovae neutrinos. GeVneutrinos are searched starting with DeepCore triggers. These events don’t have directionallocalization, but instead can indicate an excess in the rate of events. 10 GeV - 1 TeV and >TeVneutrinos are searched using traditional neutrino point source methods which take into accountthe direction and time of events with DeepCore and the entire IceCube detector respectively. The>TeV results include both a fast-response analysis conducted by IceCube in real-time with timewindows of T0 − 1 to T0 + 2 hours and T0 ± 1 day around the time of GRB 221009A, as well asan offline analysis with 3 new time windows up to a time window of T0 − 1 to T0 + 14 days, thelongest time period we consider. The combination of observations by IceCube covers 9 ordersof magnitude in neutrino energy, from MeV to PeV, placing upper limits across the range forpredicted neutrino emission.
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| 3. |
- Liemohn, Michael W., et al.
(författare)
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Model Evaluation Guidelines for Geomagnetic Index Predictions
- 2018
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Ingår i: Space Weather. - 1542-7390. ; 16:12, s. 2079-2102
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Tidskriftsartikel (refereegranskat)abstract
- Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.
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