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Description
  • Knowledge of the time series structure is essential for generating synthetic daily series of meteorological data in creating climate change scenarios. Weather generators, which contain parameters describing the series structure, have commonly been used in order to meet demands for temporal resolution of one day in the series. In climate change assessments, namely in agriculture and hydrology sectors, daily variables are necessary inputs to impact models. This paper attempts to identify the AR order in daily maximum temperature series by using a new nonparametric method. The method is applied to both measured and simulated temperature series. The simulated series are produced by a general circulation model (GCM) developed in Germany (ECHAM3/T42 - DKRZ1993). The ECHAM output used here consists of daily maximum temperatures simulated by the control and perturbed runs in a gridpoint located in the Czech Republic (south Moravia). Daily series measured in Moravia serve as the observation counterpart.
  • Knowledge of the time series structure is essential for generating synthetic daily series of meteorological data in creating climate change scenarios. Weather generators, which contain parameters describing the series structure, have commonly been used in order to meet demands for temporal resolution of one day in the series. In climate change assessments, namely in agriculture and hydrology sectors, daily variables are necessary inputs to impact models. This paper attempts to identify the AR order in daily maximum temperature series by using a new nonparametric method. The method is applied to both measured and simulated temperature series. The simulated series are produced by a general circulation model (GCM) developed in Germany (ECHAM3/T42 - DKRZ1993). The ECHAM output used here consists of daily maximum temperatures simulated by the control and perturbed runs in a gridpoint located in the Czech Republic (south Moravia). Daily series measured in Moravia serve as the observation counterpart. (en)
Title
  • On the order of autoregressive model in temperature series.
  • On the order of autoregressive model in temperature series. (en)
skos:prefLabel
  • On the order of autoregressive model in temperature series.
  • On the order of autoregressive model in temperature series. (en)
skos:notation
  • RIV/46747885:24510/00:00000024!RIV/2001/MSM/245101/N
http://linked.open.../vavai/riv/strany
  • 19
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 245100303)
http://linked.open...iv/cisloPeriodika
  • 1
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Picek, Jan
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 720474
http://linked.open...ai/riv/idVysledku
  • RIV/46747885:24510/00:00000024
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Autoregressive model, order of autoregressive model, autoregressive rank, weather generator. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • SK - Slovenská republika
http://linked.open...ontrolniKodProRIV
  • [9C9EAB2066BC]
http://linked.open...i/riv/nazevZdroje
  • Meteorological journal
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 3
http://linked.open...iv/tvurceVysledku
  • Picek, Jan
http://linked.open...n/vavai/riv/zamer
issn
  • 1335-339X
number of pages
http://localhost/t...ganizacniJednotka
  • 24510
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