Details

Autor(en) / Beteiligte

• Kanda, Neha
• Negi, H. S.
• Rishi, Madhuri S.
• Shekhar, M. S.

Titel

Performance of various techniques in estimating missing climatological data over snowbound mountainous areas of Karakoram Himalaya

Ist Teil von

• Meteorological applications, 2018-07, Vol.25 (3), p.337-349

Ort / Verlag

Chichester, UK: John Wiley & Sons, Ltd

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• ABSTRACT Filling gaps in climate data concerning mountainous areas with high spatial variability is significantly important since gaps tend to decrease the accuracy of trend estimation. In this study, the performance of seven classical methods in estimating missing values of maximum temperature, minimum temperature and precipitation at different time scales, i.e. daily (with different cases of missing data), weekly, biweekly and monthly, over Karakoram Himalaya was evaluated. Four performance indicators, i.e. mean absolute error, root mean squared error, co‐efficient of efficiency and skill score, were used to evaluate the relative performance of the methods; the mean absolute error was preferred over the other three measures for selecting the best method. The results indicate that multiple linear regression using the least absolute deviation criterion is best suited for estimation of all variables at all temporal scales except monthly precipitation data. It was also found that, for any variable, the deviation from the observed values decreased with increasing time step, i.e. there was more deviation on a daily scale than monthly. Filling data gaps in climate studies concerning snowbound mountainous areas is significantly important. This study evaluated the performance of seven classical methods in estimating missing values of maximum temperature, minimum temperature and precipitation on different time scales. The results reveal that multiple linear regression using the least absolute deviation criterion showed best estimations for all variables at all temporal scales except monthly precipitation data. It was also found that, for any variable, the estimates were more biased for smaller time scales.