Studies on the trend analysis of surface air temperature have never been conducted in Central Sulawesi Province. This study aims to determine changes in the average surface air temperature trend in Central Sulawesi Province period 1981 to 2019. Linear regression analysis is used to identify the trend of temperature changes over a certain period. The results show that the average surface air temperature is significantly increased by 0.015–0.045°C per year (p < 0.05). The results provide the information on climate change analysis that the average surface air temperature has increased in Central Sulawesi Province over 39 years.

Satellite-based rainfall estimation is evolving rapidly. Most studies use data, which is spatially fine, but poorly regarding time. On the other hand, availability of verification data is also quite rare. This study used Hillman Form B report that was corrected by ME-48 from Malang Climatological Station. 2009-2016 IR1 satellite data were used in hourly temporal resolution (only less than 3% data missing). Four estimation methods were compared: Auto Estimator, CST, mCST, and Quantile Analysis Equation. Data processing was carried out using Python and R statistic as a quality control. The analysis was done by creating a graph that combines False Alarm and Miss Information for each rainfall intensity. Binary transformation was done for enabling information to be plotted. All rainfall estimation methods have a high false alarm (more than 74% at 1 mm) but quite low miss (less than 0.03%). By taking into account its error pattern, satellite data can be used in rainfall observation. The Quantile equation is slightly superior to other methods. This study is relatively inexpensive to be duplicated so it can be used as an evaluation tool for rainfall estimation best practice for Meteorological and Climatological Agency’s network.

This study aims to evaluate the performance of the long-term Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection 6.1 (C6.1) in determining the spatiotemporal variation of aerosol optical depth (AOD) and aerosol types over Indonesia. For this purpose, monthly MODIS DB AOD datasets are directly compared with Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 8 sites throughout Indonesia. The results indicate that MODIS DB AOD retrievals and AERONET AOD measurements have a high correlation in Sumatra Island (i.e., Kototabang (r = 0.88) and Jambi (r = 0.9)) and Kalimantan Island (i.e., Palangkaraya (r = 0.89) and Pontianak (r = 0.92)). However, the correlations are low in Bandung, Palu, and Sorong. In general, MODIS DB AOD tends to overestimate AERONET AOD at all sites by 16 to 61% and can detect extreme fire events in Sumatra and Kalimantan Islands quite well. Aerosol types in Indonesia mostly consist of clean continental, followed by biomass burning/urban industrial and mixed aerosols. Palu and Sorong had the highest clean continental aerosol contribution (90%), while Bandung had the highest biomass burning/urban-industrial aerosol contribution to atmospheric composition (93.7%). For mixed aerosols, the highest contribution was found in Pontianak, with a proportion of 48.4%. Spatially, the annual mean AOD in the western part of Indonesia is higher than in the eastern part. Seasonally, the highest AOD is observed during the period of September–November, which is associated with the emergence of fire events.

Waktu komputasi dalam interpolasi spasial merupakan hal yang penting karena berkaitan dengan pengelolaan dan penyediaan informasi meteorologi yang efektif dan efisien. Oleh karena itu, penelitian ini bertujuan untuk menganalisis beban kerja waktu komputasi serta mendapatkan fungsi waktu dari parameter - parameter interpolasi spasial dengan memperhatikan nilai angka penting, resolusi, dan jumlah titik data awal. Data yang digunakan berupa data acak yang dihasilkan oleh fungsi runif di R. Data acak tersebut memiliki 0, 3, dan 6 angka desimal. Jumlah titik yang digunakan yaitu berjumlah 10, 50, 100, 200, 500, 1000, 1500, 2000, 3000, 5000 dan 8000 dengan variasi resolusi luaran 0.5, 0.25, 0.05, 0.01 dan 0,005. Data-data ini selanjutnya diolah menggunakan piranti keras dan piranti lunak yang berbeda. Data kemudian dijalankan untuk interpolasi Inverse Distance Weighted. Lama waktu suatu pekerjaan interpolasi dapat dihitung melalui nilai jumlah titik (n) dan resolusi (r). Hasil menunjukkan bahwa nilai waktu (T) dapat didekati dengan persamaan: . Dimana konstanta C1, C2, C3 dan C4 berbeda sesuai dengan spesifikasi perangkat lunak dan perangkat keras. Dengan menggunakan pendekatan ini, standar penyelesaian waktu suatu pekerjaan interpolasi diharapkan dapat dikaji lebih baik.