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All Journal Jurnal Meteorologi dan Geofisika International Journal of Science and Mathematics Education
Sonni Setiawan
Departemen Geofisika Dan Meteorologi, Institut Pertanian Bogor
Earth & Planetary Sciences Education Energy Mathematics Physics Social Sciences

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Spatial Analysis of Aerosol Optical Depth in Western Java Indonesia Rajwa Hanan; Fithriya Yulisiasih Rohmawati; Idung Risdiyanto; Sonni Setiawan
International Journal of Science and Mathematics Education Vol. 2 No. 2 (2025): June: International Journal of Science and Mathematics Education
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijsme.v2i2.196

Abstract

Air quality in Western Java is highly dynamic and shaped by environmental changes influenced by intense human activities. Aerosols—tiny particulate matter that affects air quality, weather, and climate—can be quantified using Aerosol Optical Depth (AOD), which measures aerosol concentrations in the atmospheric column. This research uses spatial regression analysis to examine the spatial distribution of AOD from GEE’s platform (Google Earth Engine) and its relationship with rainfall and wind patterns during both the wet and dry seasons. The findings indicate that wind speed does not significantly impact AOD values, but wind direction does affect the distribution of rainfall and AOD, likely due to the monsoon system. During the wet season (December to March), high-intensity and widespread rainfall effectively cleanses the atmosphere of aerosols, leading to no significant effect on AOD (p-value > 0.05). In contrast, during the dry season, rainfall significantly influences AOD spatial patterns (p-value < 0.05). These results highlight the intricate interplay between meteorological factors and aerosol’s behavior, emphasizing the seasonal variability in their interactions.
Leveraging Sequential and Attention-Based Deep Learning Architectures for Accurate Daily Rainfall Prediction in Jakarta, Indonesia, Using Atmospheric Predictors Hardiano, Akhdan Fadhilah Yaskur; Setiawan, Sonni
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1194

Abstract

In this study, we developed and evaluated daily rainfall prediction models utilizing deep learning architectures, specifically comparing Long Short-Term Memory (LSTM) and Transformer models integrated with various atmospheric predictors. Our results indicate that the LSTM achieved superior accuracy at short-term lags—reaching an R² of 0.94 and an RMSE as low as 4.81 at lag-3—whereas the Transformer demonstrated higher consistency across all lags, maintaining stable R² values between 0.87 and 0.88. The application of a 5-day smoothing pre-processing step significantly enhanced prediction quality for both architectures by mitigating high-frequency noise, a benefit particularly pronounced in the LSTM due to its sensitivity to data fluctuations. Notably, the inclusion of tropical wave variables did not substantially improve model performance and, in some instances, reduced LSTM accuracy at longer lags by increasing input complexity; conversely, the Transformer remained robust to these additional variables. Among the predictors evaluated, Vertically Integrated Moisture Flux Divergence (VIMD) emerged as the most critical feature, underscoring its physical relevance to precipitation processes in convective and monsoonal regions. These findings suggest that while LSTMs excel at capturing immediate temporal dynamics, Transformers provide a more stable framework for longer-range rainfall forecasting in Jakarta.
Co-Authors Fithriya Yulisiasih Rohmawati Hardiano, Akhdan Fadhilah Yaskur Idung Risdiyanto Rajwa Hanan