Probabilistic Forecasting of Diurnal Maximum and Minimum Temperature using a 2-Member Ensemble System Over Iran

Authors

1 Associate Professor, Atmospheric Science and Meteorological Research Center (ASMERC)

2 Atmospheric Science and Meteorological Research Center (ASMERC)

Abstract

To quantify the uncertainty exists in the weather forecasting, and producing the probabilistic forecasting of diurnal maximum and minimum temperature, two important methods named Ensemble Model Output Statistics (EMOS) and Bayesian Model Averaging (BMA) are used to estimate the forecast probabilistic density function. In this study, the ensemble system has two members consisting of 1 to 5 ahead forecasting of diurnal maximum and minimum temperature over synoptic stations of Iran provinces from 10 November 2017 to 31 May 2018. These members are the outputs of the WRF model with two different physical configurations. Results show that the deterministic post-processed forecasts have improved the root mean squared error (RMSE) of deterministic raw forecast, 37% and 7% for mximum and minimum temperature, respectively. Generally, raw forecasts of minimum temperature have less error than maximum temperature, but they are not be improved considerably after post-processing.

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References

 
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Nivar
Volume 43, 106-107 - Serial Number 107
September 2019
Pages 54-62

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History

  • Receive Date: 17 December 2018
  • Revise Date: 04 March 2019
  • Accept Date: 08 June 2019
  • First Publish Date: 23 September 2019

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