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All Journal Megasains Geoid - Journal of Geodesy and Geomatics
Robi Muharsyah
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Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Engineering Environmental Science

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Perbandingan Model Kopel ECMWF System 4 Dan CFSV2 Untuk Prediksi Musim Di Indonesia Robi Muharsyah
Megasains Vol 11 No 1 (2020): Megasains Vol 11 No.1 Tahun 2020
Publisher : Stasiun Pemantau Atmosfer Global Bukit Kototabang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3471.137 KB) | DOI: 10.46824/megasains.v11i01.3

Abstract

Kajian ini bertujuan untuk membandingkan curah hujan harian keluaran langsung (raw) dari dua model kopel: European Center Medium Weather Forecast System 4 (S4) dan Climate Forecast System Version 2 (CFSv2) sebagai model prediksi musim operasional pada periode Juni, Juli dan Agustus (JJA) dan Desember, Januari dan Februari (DJF). Kemampuan kedua model diukur berdasarkan ketersediaan prediksi reforecast yang diverifikasi terhadap data observasi curah hujan Global Precipitation Climatology Project (GPCP) dan Southeast Asian Observation - Southeast Asian Climate Assessment and Dataset (SA-OBS SACAD) untuk wilayah Bumi Maritim Indonesia (BMI). Ukuran verifikasi yang dipakai berupa bias aktual, bias relatif, spread anggota ensemble dalam bentuk boxplot dan akumulasi curah hujan per musim, serta korelasi spasial. Hasilnya, untuk DJF, kemampuan kedua model cenderung overestimate untuk wilayah perairan di sekitar tipe-C. Sebaliknya, untuk prediksi curah hujan di daratan keduanya underestimate. Sementara itu, untuk JJA, bias kedua model saling berkebalikan khususnya di pulau Kalimantan. Kajian ini juga menggunakan metode post-processing statistik koreksi bias untuk mengetahui pengaruhnya terhadap semua anggota ensemble pada kedua model
Identification of the Best Semivariogram Model for the Blending of In-Situ and ERA5-Land Air Temperature Data Using the Kriging with External Drift Technique Fatchiyah; Eko Yuli Handoko; Ardhasena Sopaheluwakan; Robi Muharsyah
GEOID Vol. 21 No. 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.8768

Abstract

Accurate air temperature monitoring is essential for understanding climate dynamics and microclimates, particularly in regions with diverse topography. The limited number of observation stations often results in data that do not fully represent actual conditions. To address this gap, combining in-situ measurements with ERA5-Land reanalysis presents a promising alternative, although ERA5-Land may still exhibit biases in mountainous or urban areas. This study applies Kriging with External Drift (KED) to improve temperature estimation, focusing on identifying the most suitable semivariogram model. Daily and monthly analyses were conducted, with performance evaluated using RMSE, MAE, and MSE. The results indicate that the Spherical model consistently performs best for average and maximum temperatures, while the Exponential model provides better estimates for minimum temperature at the daily scale, and the Linear model at the monthly scale. These findings demonstrate that KED can significantly enhance temperature estimation in areas with sparse observations, while also highlighting the most reliable semivariogram models for different temperature parameters.
Co-Authors Ardhasena Sopaheluwakan Eko Yuli Handoko Fatchiyah