Extreme Temperature Forecast in Mbonge, Cameroon through Return Level Analysis of the Generalized Extreme Value (GEV) Distribution
In this paper, temperature extremes are forecast by
employing the block maxima method of the Generalized extreme
value(GEV) distribution to analyse temperature data from the
Cameroon Development Corporation (C.D.C). By considering two sets
of data (Raw data and simulated data) and two (stationary and
non-stationary) models of the GEV distribution, return levels analysis
is carried out and it was found that in the stationary model, the
return values are constant over time with the raw data while in the
simulated data, the return values show an increasing trend but with
an upper bound. In the non-stationary model, the return levels of
both the raw data and simulated data show an increasing trend but
with an upper bound. This clearly shows that temperatures in the
tropics even-though show a sign of increasing in the future, there
is a maximum temperature at which there is no exceedence. The
results of this paper are very vital in Agricultural and Environmental
research.
[1] Myriam Charras-Garrido, Pascal Lezaud. Extreme Value Analysis : an
Introduction. Journal de la Societe Francaise de Statistique et Societe
Mathematique de France, 2013, 154 (2), pp 66-97. (hal-00917995).
[2] S. Eduardo. Martins et al, Generalized maximum-likelihood generalised
extreme-value quantile estimators for hydrologic data, 2000.
[3] Hosking et al Estimation of the Generalized Extreme value distribution
by the method of the probability-weighted moments, 1985 .
[4] Husna Hasan, N. Salam Modelling extreme temperature in Malaysia using
generalised extreme value distribution, 2012.
[5] Meehl, George A, Tebaldi, Claudia, More Intense, More Frequent, and
Longer Lasting Heat Waves in the 21st Century. Science 305 (5686):
9947. Bibcode:2004Sci...305..994M. doi:10.1126/science.1098704.
PMID 15310900, (13 August 2004).
[6] Sheng Ngong Estimation of hot and cold spells with extreme value theory,
2012.
[7] Notes on probability distributions in Easy-fit pro.
[8] H. W. Van Den Brink, Estimating return periods of extreme evens from
ECMWF seasonal forecast ensembles, 2004.
[1] Myriam Charras-Garrido, Pascal Lezaud. Extreme Value Analysis : an
Introduction. Journal de la Societe Francaise de Statistique et Societe
Mathematique de France, 2013, 154 (2), pp 66-97. (hal-00917995).
[2] S. Eduardo. Martins et al, Generalized maximum-likelihood generalised
extreme-value quantile estimators for hydrologic data, 2000.
[3] Hosking et al Estimation of the Generalized Extreme value distribution
by the method of the probability-weighted moments, 1985 .
[4] Husna Hasan, N. Salam Modelling extreme temperature in Malaysia using
generalised extreme value distribution, 2012.
[5] Meehl, George A, Tebaldi, Claudia, More Intense, More Frequent, and
Longer Lasting Heat Waves in the 21st Century. Science 305 (5686):
9947. Bibcode:2004Sci...305..994M. doi:10.1126/science.1098704.
PMID 15310900, (13 August 2004).
[6] Sheng Ngong Estimation of hot and cold spells with extreme value theory,
2012.
[7] Notes on probability distributions in Easy-fit pro.
[8] H. W. Van Den Brink, Estimating return periods of extreme evens from
ECMWF seasonal forecast ensembles, 2004.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:70987", author = "Nkongho Ayuketang Arreyndip and Ebobenow Joseph", title = "Extreme Temperature Forecast in Mbonge, Cameroon through Return Level Analysis of the Generalized Extreme Value (GEV) Distribution", abstract = "In this paper, temperature extremes are forecast by
employing the block maxima method of the Generalized extreme
value(GEV) distribution to analyse temperature data from the
Cameroon Development Corporation (C.D.C). By considering two sets
of data (Raw data and simulated data) and two (stationary and
non-stationary) models of the GEV distribution, return levels analysis
is carried out and it was found that in the stationary model, the
return values are constant over time with the raw data while in the
simulated data, the return values show an increasing trend but with
an upper bound. In the non-stationary model, the return levels of
both the raw data and simulated data show an increasing trend but
with an upper bound. This clearly shows that temperatures in the
tropics even-though show a sign of increasing in the future, there
is a maximum temperature at which there is no exceedence. The
results of this paper are very vital in Agricultural and Environmental
research.", keywords = "Return level, Generalized extreme value (GEV),
Meteorology, Forecasting.", volume = "9", number = "6", pages = "336-6", }