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Pregled bibliografske jedinice broj: 75841

Časopis

Autori: Antonić, Oleg; Križan, Josip; Marki, Antun; Bukovec, Dragan
Naslov: Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks.
( Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. )
Izvornik: Ecological Modelling (0304-3800) 138 (2001), 1-3; 255-263
Vrsta rada: članak
Ključne riječi: air temperature; dendroecology; digital elevation model; kriging; potential evapotranspiration; precipitation; relative humidity; solar irradiation
( air temperature; dendroecology; digital elevation model; kriging; potential evapotranspiration; precipitation; relative humidity; solar irradiation )
Sažetak:
Empirical models for seven climatic variables (monthly mean air temperature, monthly mean daily minimum and maximum air temperature, monthly mean relative humidity, monthly precipitation, monthly mean global solar irradiation and monthly potential evapotranspiration) were built using neural networks. Climatic data from 127 weather stations were used, comprising more than 30000 cases for each variable. Independent estimators were elevation, latitude, longitude, month and time series of respective climatic variable observed at two weather stations (coastal and inland), which have long time-series of climatic variables (from mid last century). Goodness of fit by model was very high for all climatic variables (R>0.98), except for monthly mean relative humidity and monthly precipitation, for which it was somewhat lower (R=0.84 and R=0.80, respectively). Differences in residuals around model were insignificant between months, but significant between weather stations, both for all climatic variables. This was the reason for calculation of mean residuals for all stations, which were spatially interpolated by kriging and used as a model correction. Similarly interpolated standard deviation and standard error of residuals are estimators of the model precision and model error, respectively. Goodness of fit after the averaging of monthly values between years was very high for all climatic variables, which enables construction of spatial distributions of average climate (climatic atlas) for given period. Presented interpolation models provide reliable, both spatial and temporal estimations of climatic variables, especially useful for dendroecological analysis.
Projekt / tema: 00980004, 119299
Izvorni jezik: eng
Rad je indeksiran u
bazama podataka:
Current Contents Connect (CCC)
Scopus
SCI-EXP, SSCI i/ili A&HCI
Science Citation Index Expanded (SCI-EXP) (sastavni dio Web of Science Core Collectiona)
Kategorija: Znanstveni
Znanstvena područja:
Kemija
URL cjelovitog teksta:
Google Scholar: Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks.



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