Naslov
Improving spatio-temporal interpolation of daily precipitation using parallelized machine learning and precipitation derived from MSG

Autori
Perčec Tadić, Melita ; Hengl, Tom

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Skup
MedCLIVAR 2018: Bridging the Mediterranean Climates

Mjesto i datum
Beograd, Srbija, 18.09.2018. - 21.09.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
daily precipitation, machine learning, random forest, MSG satellite precipitation

Sažetak
Preliminary analysis of 1 008 812 daily precipitation records measured on the 572 ground stations during 2005-2010 period in Croatia revealed that the 73% of data are zeros or the precipitation is less than 1 mm, hence data are zero inflated. Most non-zero data belong to [0, 1] mm and [1, 10] mm class, and just 5% of precipitation amounts are larger. That strongly affects the predictions that underestimate the observations, especially for large precipitation amounts. Spatio-temporal prediction using machine learning algorithm on the daily precipitation data accounts for 55% of the spatio-temporal variability. The machine learning algorithm of random forest RF is implemented through R ranger package. Even though the RF in general does not account for spatial location and auto-correlation which are important in spatio-temporal analysis of precipitation, the attempt is made to include it through covariates and distance fields. The preliminary set of covariates detected that the most influential predictors are time variables cdate representing cumulative time and doy representing seasonality effect. Than follow annual precipitation CHELSA_precip, altitude, distance to the sea and different buffer distances. Further attempt in improving the results is by taking into account larger data set for the 1981-2016 period, than monthly precipitation grids for Croatia instead of CHELSA precipitation and satellite precipitation data from MSG missions as additional time dependent predictors.

Izvorni jezik
Engleski

Znanstvena područja
Geofizika

POVEZANOST RADA