Empirically Determined Distributions of Hourly Temperatures

Spreen, William C.

doi:10.1175/1520-0469(1956)013<0351:eddoht>2.0.co;2

Cited by 2 publications

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“…The forecasting of surface temperature has long been an area of interest of the scientific community in general, and meteorologists in particular. Since the late 1930s, different statistical methodologies have been attempted to forecast the surface temperature on hourly (Spreen, 1956), daily (Mantis and Dickey, 1945; Gilbert, 1953), monthly (Kangieser, 1959), and seasonal (Van Loon and Jenne, 1975; Kumar et al , 1997) time scales. Namias (1948) was among the first to state that the mean monthly geopotential height fields for mid tropospheric levels determine monthly air temperature anomalies.…”

Section: Introductionmentioning

confidence: 99%

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach

Chattopadhyay

Jhajharia

Chattopadhyay

2011

Meteorological Applications

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ABSTRACT:The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (T max ) over northeast India. Through autocorrelation analysis the T max time series of northeast India is identified as non-stationary, with a seasonality of 12 months, and it is also found to show an increasing trend by using both parametric and non-parametric methods. The autoregressive models of the reduced T max time series, which has become stationary on removal of the seasonal and the trend components from the original time series, were generated through Yule-Walker equations. The sixth order autoregressive model (AR (6)) is identified as a suitable representative of the T max time series based on the Akaike information criteria, and the prediction potential of AR (6) is also established statistically through Willmott's indices. Subsequently, autoregressive neural network models were generated as a multilayer perceptron, a generalized feed forward neural network and a modular neural network. An autoregressive neural network model of order four (AR-NN (4)), in the form of a modular neural network (MNN), has performed comparably well with that of AR (6) based on the high values of Willmott's indices and the low values of the prediction error. Therefore, AR-NN(4)-MNN will be a better option than AR(6) to forecast a time series, i.e. the monthly T max time series of northeast India, because AR-NN(4)-MNN requires fewer predictors for a superior forecast of a time series.

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