In this study, tweets related to fires in Riau, Sumatra, were identified using carefully selected keywords for the 2014–2019 timeframe. The TAGGS algorithm was applied, which allows for geoparsing based on the user’s nationality and hometown and on direct referrals to specific locations such as name of province or name of city in the message itself. Online newspapers covering Riau were analyzed for the year 2019 to provide additional information about the reasons why fires occurred and other factors, such as impact on people’s health, animal mortality related to ecosystem disruption, visibility, decrease in air quality and limitations in the government firefighting response. Correlation analysis between meteorological information, Twitter activity and satellite-derived hotspots was conducted. The existing approaches that BMKG and other Indonesian agencies use to detect fire activity are reviewed and a novel approach for early fire detection is proposed based on the crowdsourcing of tweets. The policy implications of these results suggest that crowdsourced data can be included in the fire management system in Indonesia to support early fire detection and fire disaster mitigation efforts.

Suhu udara permukaan (SUP) merupakan salah satu indikator penting dalam konteks terjadinya perubahan iklim. Penelitian ini bertujuan untuk mengkaji variasi temporal dan spasial beserta trend SUP di Pulau Jawa pada tahun 1990-2019. Data yang digunakan diperoleh dari observasi Badan Meteorologi Klimatologi dan Geofisika (BMKG) dan data pemodelan reanalisis (GHCN_CAMS, ERA-5, JRA-55, dan GISTEMP-v4). Data observasi BMKG diperoleh berdasarkan data bulanan dari stasiun BMKG yang tersebar di seluruh Pulau Jawa (16 stasiun). Hasilnya menunjukkan bahwa terjadi peningkatan SUP dalam 30 tahun sebesar 0,11-1,24℃. Variasi spasial menunjukkan bahwa daerah dengan SUP tertinggi terdapat di Pulau Jawa sebelah utara bagian timur dan terendah di dataran tinggi Pulau Jawa bagian barat. SUP di Pulau Jawa tertinggi terjadi pada periode bulan September-Oktober-November (SON) dan terendah pada periode bulan Juni-Juli-Agustus (JJA). Hasil uji statistik menggunakan metode Mann Kendall test dengan significance level 1% menunjukkan bahwa semua data observasi BMKG mengalami trend peningkatan kecuali untuk Banyuwangi. Trend peningkatan SUP per tahun berkisar antara 0,017-0,048℃. Hal ini menunjukkan bahwa perubahan iklim secara regional telah terjadi di Pulau Jawa selama 30 tahun terakhir berdasarkan trend perubahan SUP.

This study aims to analyze the effect of the differences in intensity and track of tropical cyclones upon significant wave heights and direction of ocean waves in the southeast Indian Ocean. We used the tropical cyclone data from Japan Aerospace Exploration Agency (JAXA) starting from December 1997 to November 2017. The significant wave height and wave direction data are reanalysis data from Copernicus Marine Environment Monitoring Service (CMEMS), and the mean sea level pressure, surface wind speed, and wind direction data are reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) from December 1997 to November 2017. The results show that the significant wave height increases with the increasing intensity of tropical cyclones. Meanwhile, the direction of the waves is influenced by the presence of tropical cyclones when tropical cyclones enter the categories of 3, 4, and 5. Tropical cyclones that move far from land tend to have higher significant wave height and wider affected areas compared to tropical cyclones that move near the mainland following the coastline

Early detection that results in early warning of forest fires occurrences in Indonesia, which are strongly related to land management practices (including peatlands), is necessary to mitigate land and forest fires in Indonesia. Riau has been chosen in this study because of its vulnerability to forest fires. The remoteness of this region is one reason for developing alternative warning tools using meteorological and social media information. This study identified tweets related to fires using carefully selected keywords, geoparsed to select messages relevant to fire occurrences, and binned within several Indonesian sub-regions in Riau Province. Content analysis was performed for 31 related online local newspapers. Assessment to study the correlation between meteorological and Twitter information with the forest fires was conducted. Existing approaches that the BMKG and other Indonesian agencies use to detect fire activities are reviewed, and a novel approach based on crowdsourcing of tweets is proposed. The results show a correlation between meteorological information and Twitter activity with satellites derived hotspot information. The policy implications of these results suggest that information should be included in the fire management system in Indonesia to support fire early detection as part of fire disaster mitigation efforts.

UV (ultraviolet) light is an important factor should be considered to predict coronavirus epidemic growth pace. UV is different from weather temperature since UV is electromagnetic wavelength from 10 nm to 400 nm in size, shorter than of visible lights. For some people, UV light can lead to cancer from unprotected sun exposure, however, for tropical people, which have been used to live in such condition, have resisted from negative effect high UV index. Moreover, UV has the capability to inactivate virus. This conclusion has been discussed deeply with biological experts. Although UV light has the ability to inactivate viruses, it may be meaningless in areas with high air pollution where UV light turns into heat.