Muhlis, Sandi, Anita Ramadhani, Halmar Halide
ABSTRACT
The spatial and temporal distribution of Significant Meteorological Information (SIGMET) in Eastern Indonesia reflects the complex dynamics of geographical and meteorological interactions that influence the formation of extreme weather. This study highlights the SIGMET distribution pattern from 2019 to 2022, focusing on the regions of Papua, Southeast Sulawesi, and South Kalimantan. SIGMET is identified as an important indicator in mitigating aviation risks, especially related to thunderstorms, convective clouds, and turbulence. Spatially, Papua is the region with the highest SIGMET activity, supported by high mountainous topography that creates a local convergence zone. Southeast Sulawesi shows a significant SIGMET pattern, especially around Bone Bay, due to the interaction of the southwest monsoon wind with complex topography. South Kalimantan, despite its relatively flat topography, still records SIGMET activity due to the influence of airflow from the Java Sea. Temporally, SIGMET is more active during the rainy season (October-March), when the west monsoon wind brings water vapor from the Indian Ocean, creating ideal conditions for the formation of convective clouds. In contrast, activity decreases during the dry season (April-September), except in certain areas such as Papua which continue to record SIGMET sporadically. The transition season, both from rainy to dry (March-May) and dry to rainy (September-November), shows a varied SIGMET distribution pattern. The analysis shows a concentration of activity in Papua and South Kalimantan, which is influenced by moisture transport, sea surface temperature (SST), and tropopause temperature. These factors support an unstable atmosphere and the formation of extreme weather phenomena. These findings provide important insights for further understanding of SIGMET distribution and its implications for aviation safety in Eastern Indonesia.
By: Syamsinar, Halmar Halide, Aini Suci Febrianti, Andri Moh. Wahyu Laode
https://doi.org/10.15243/jdmlm.2025.122.7255
ABSTRACT
The snow cover in Jayawijaya, Papua, Indonesia, has been rapidly declining due to various climatic factors, posing significant threats to both the ecosystem and local culture. This research focused on the analysis of the impact of weather factors (temperature, relative humidity, wind direction, and wind speed) on the decrease in snow cover in Mt. Jayawijaya. Using the datasets from 2013 to 2022, a stepwise multiple regression analysis was performed to ascertain the predictors for snow cover loss. The findings indicated that out of all the weather factors, relative humidity and wind direction were the most important, with a p-value of 0.005 and 0.032, respectively. The regression model indicates that higher humidity increases snow sublimation, while wind direction brings warm air that accelerates snow melting. Pearson correlation analysis showed a strong correlation (r = 0.81) between the observed snow cover decline and the model, with an RMSE of 20.70 ha. These findings contribute to the understanding of how atmospheric factors interact with snow dynamics in tropical regions and can aid in future conservation efforts for Jayawijaya’s snow cover.
Keywords: climate change; Mt. Jayawijaya; relative humidity; snow coverage; wind direction.
Abstract. Environmental monitoring based on the Internet of Things (IoT) is the
key technology to monitoring the environment in real-time using advanced
sensors to acquire data efficiently. The research aims to develop microclimate
monitoring using Arduino with a BME280 sensor and a TEMT6000 sensor located
in a greenhouse and a poultry farm (closed house and closed open). The BME280
sensor measures temperature, humidity, and air pressure. The TEMT6000
measures light intensity. The tool is designed to acquire data in a large database
directory using Influx DB Cloud. The data is sent to Grafana for visualization in a
dashboard. The research\'s findings show successful real-time environmental
monitoring of acquiring data can be seen both numerically and visually. The tool
has inherent simplicity, affordability, and adaptability. The tool is easily
implemented in a variety of contexts.
Keywords: IoT, Arduino, Microclimate, Data Acquisition