scholarly journals Waves and the equilibrium range at Ocean Weather Station P

2013 ◽  
Vol 118 (11) ◽  
pp. 5951-5962 ◽  
Author(s):  
J. Thomson ◽  
E. A. D'Asaro ◽  
M. F. Cronin ◽  
W. E. Rogers ◽  
R. R. Harcourt ◽  
...  
Keyword(s):  
Weather Station ◽  
Equilibrium Range

scholarly journals Uncertainty Evaluation of Wind Speed Sensor in Automatic Weather Station

2021 ◽  
Vol 768 (1) ◽  
pp. 012008
Author(s):  
Zhen Yang ◽  
Husheng Zhang ◽  
Qiang Wang ◽  
Cuicui Li ◽  
Wenlong Xu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Uncertainty Evaluation ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Speed Sensor

scholarly journals Twenty-first century regional temperature response in Chile based on empirical-statistical downscaling

2021 ◽  
Author(s):  
Sebastian G. Mutz ◽  
Samuel Scherrer ◽  
Ilze Muceniece ◽  
Todd A. Ehlers
Keyword(s):  
Statistical Models ◽  
Temperature Response ◽  
Local Scale ◽  
First Century ◽  
Local Temperature ◽  
Future Climate Change ◽  
Weather Station ◽  
Temperature Changes ◽  
Scale Temperature ◽  
Twenty First Century

AbstractLocal scale estimates of temperature change in the twenty-first century are necessary for informed decision making in both the public and private sector. In order to generate such estimates for Chile, weather station data of the Dirección Meteorológica de Chile are used to identify large-scale predictors for local-scale temperature changes and construct individual empirical-statistical models for each station. The geographical coverage of weather stations ranges from Arica in the North to Punta Arenas in the South. Each model is trained in a cross-validated stepwise linear multiple regression procedure based on (24) weather station records and predictor time series derived from ERA-Interim reanalysis data. The time period 1979–2000 is used for training, while independent data from 2001 to 2015 serves as a basis for assessing model performance. The resulting transfer functions for each station are then directly coupled to MPI-ESM simulations for future climate change under emission scenarios RCP2.6, RCP4.5 and RCP 8.5 to estimate the local temperature response until 2100 A.D. Our investigation into predictors for local scale temperature changes support established knowledge of the main drivers of Chilean climate, i.e. a strong influence of the El Niño Southern Oscillation in northern Chile and frontal system-governed climate in central and southern Chile. Temperature downscaling yields high prediction skill scores (ca. 0.8), with highest scores for the mid-latitudes. When forced with MPI-ESM simulations, the statistical models predict local temperature deviations from the 1979–2015 mean that range between − 0.5–2 K, 0.5–3 K and 2–7 K for RCP2.6, RCP4.5 and RCP8.5 respectively.

scholarly journals Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

2016 ◽  
Vol 8 (4) ◽  
pp. 409-419 ◽  
Author(s):  
Tobias Dalhaus ◽  
Robert Finger
Keyword(s):  
Crop Production ◽  
Market Failure ◽  
Weather Station ◽  
Yield Losses ◽  
Grid Data ◽  
Weather Index ◽  
Basis Risk ◽  
Promising Alternative ◽  
Station Data ◽  
Adverse Weather

Abstract Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

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