Ionospheric index MF2n for monthly median foF2 modeling and long-term prediction over European area

Author(s):  
A.V. Mikhailov
2013 ◽  
Vol 31 (10) ◽  
pp. 1653-1671 ◽  
Author(s):  
M. Pietrella

Abstract. Twelve empirical local models have been developed for the long-term prediction of the ionospheric characteristic M3000F2, and then used as starting point for the development of a short-term forecasting empirical regional model of M3000F2 under not quiet geomagnetic conditions. Under the assumption that the monthly median measurements of M3000F2 are linearly correlated to the solar activity, a set of regression coefficients were calculated over 12 months and 24 h for each of 12 ionospheric observatories located in the European area, and then used for the long-term prediction of M3000F2 at each station under consideration. Based on the 12 long-term prediction empirical local models of M3000F2, an empirical regional model for the prediction of the monthly median field of M3000F2 over Europe (indicated as RM_M3000F2) was developed. Thanks to the IFELM_foF2 models, which are able to provide short-term forecasts of the critical frequency of the F2 layer (foF2STF) up to three hours in advance, it was possible to considerer the Brudley–Dudeney algorithm as a function of foF2STF to correct RM_M3000F2 and thus obtain an empirical regional model for the short-term forecasting of M3000F2 (indicated as RM_M3000F2_BD) up to three hours in advance under not quiet geomagnetic conditions. From the long-term predictions of M3000F2 provided by the IRI model, an empirical regional model for the forecast of the monthly median field of M3000F2 over Europe (indicated as IRI_RM_M3000F2) was derived. IRI_RM_M3000F2 predictions were modified with the Bradley–Dudeney correction factor, and another empirical regional model for the short-term forecasting of M3000F2 (indicated as IRI_RM_M3000F2_BD) up to three hours ahead under not quiet geomagnetic conditions was obtained. The main results achieved comparing the performance of RM_M3000F2, RM_M3000F2_BD, IRI_RM_M3000F2, and IRI_RM_M3000F2_BD are (1) in the case of moderate geomagnetic activity, the Bradley–Dudeney correction factor does not improve significantly the predictions; (2) under disturbed geomagnetic conditions, the Bradley–Dudeney formula improves the predictions of RM_M3000F2 in the entire European area; (3) in the case of very disturbed geomagnetic conditions, the Bradley–Dudeney algorithm is very effective in improving the performance of IRI_RM_M3000F2; (4) under moderate geomagnetic conditions, the long-term prediction maps of M3000F2 generated by RM_M3000F2 can be considered as short-term forecasting maps providing very satisfactory results because quiet geomagnetic conditions are not so diverse from moderate geomagnetic conditions; (5) the forecasting maps originated by RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD show some regions where the forecasts are not satisfactory, but also wide sectors where the M3000F2 forecasts quite faithfully match the M3000F2 observations, and therefore RM_M3000F2, RM_M3000F2_BD, and IRI_RM_M3000F2_BD could be exploited to produce short-term forecasting maps of M3000F2 over Europe up to 3 h in advance.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.


1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas

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