Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September-October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered "extremely hazardous to health" by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO 2 , CH 4 and CO) and PM 2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of "pure" sub-surface peat burning and spreading vegetation fires atop burning peat. PM 2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM 2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg −1 , and for spreading vegetation fires atop burning peat 44 to 61 g·kg −1 , both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM 2.5 emissions factors measured worldwide. Using our peatland CO 2 , CH 4 and CO emissions factors (1779 ± 55 g·kg −1 , 238 ± 36 g·kg −1 , and 7.8 ± 2.3 g·kg −1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg −1 ) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EF CH4 produces far less (~3×) methane. However, our mean in situ derived EF PM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg −1 ) is far higher than current emissions inventories assume, resulting in our total Remote Sens. 2018, 10, 495; doi:10.3390/rs10040495 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 495 2 of 31 PM 2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM 2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions' spatio-temporal variations in far greater detail than ever before (hourly, 0.05 • ), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM 2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in...

MODIS (Moderate Resolution Imaging Spectroradiometer) hotspot and precipitation data for the most recent 11-year period (2002 to 2012) were analyzed to elucidate recent trends in the seasonal and spatial fire occurrence in Sumatra and the relationship with precipitation. Using a latitude line of S 0.5°, Sumatra was divided into two regions, N. (north) and S. (south) Sumatra. Different trends in seasonal fire occur-rence were discussed and further defined by considering two different precipitation patterns. Analysis of hotspot (fire) data was carried out using 0.5° × 0.5° grid cells to evaluate recent trends of spatial fire oc-currence. Analysis results of hotspot and precipitation data were also tallied every 10-day to find the rela-tionship between seasonal fire occurrence and the dry season. Standard deviation (SD) and variance (V) were then used to evaluate fire occurrences in Sumatra and Kalimantan objectively. The relatively mild fire occurrence tendency in Sumatra compared to Kalimantan could be the result of different stages of forest development or the high deforestation rate in Sumatra compared with Kalimantan. This paper also shows that the two different seasonal fire activities in N. and S. Sumatra were closely related to the two different dry season types: a winter and summer dry season type (WD & SD) in N. Sumatra, and a summer dry season type (SD) in S. Sumatra. Extreme fire occurrences in the Dumai region in 2005 and Palembang region in 2006 could be partially explained by a severe drought occurrence enhanced by two different kinds of El Niño events

Teknik RGB (Red-Green-Blue) merupakan salah satu teknik intepretasi citra satelit dengan mengombinasikan beberapa kanal secara tumpang tindih warna merah, hijau dan biru untuk menyajikan informasi yang lebih mudah dipahami. Teknik RGB dapat digunakan dalam kajian analisis cuaca, terutama untuk mengidentifikasi kondisi khusus seperti bencana hidrometeorologi. Kejadian banjir pada tanggal 20 - 21 Februari 2017 yang merendam sekurangnya 7 kabupaten dan 1 kota di Provinsi Lampung yang dindikasikan terjadi karena hujan ekstrim yang merata di wilayah Lampung. Hasil pelaporan curah hujan di stasiun Klimatologi Masgar terukur 107.0 mm/hari, Pos Pengamatan Politeknik Negeri Lampung terukur 159.6 mm/hari dan Pos Pengamatan Kemiling Bandar Lampung terukur 154.0 mm/hari dimana curah hujan termasuk dalam kategori hujan sangat lebat BMKG (> 100 mm/hari). Hasil analisis kondisi regional menunjukan adanya tekanan udara rendah di barat lampung dan daerah konvergensi serta shearline di Lampung bagian barat dan tengah. Analisis citra satelit menunjukan adanya kumpulan awan dengan suhu puncak yang sangat dingin, teknik RGB menggunakan identifikasi mikrofisis atmosfer pada malam hari (Night Microphysics) dan sebaran massa udara (Air Mass) menunjukan adanya proses mikrofisis yang intensif serta aliran massa udara penyebab awan hujan yang tumbuh dan meluas di wilayah Lampung sebelum dan saat terjadinya banjir. Hasil produk olahan HCAI (Highresolution Cloud Analysis Information) menunjukan awan didominasi oleh awan Comulonimbus (Cb) dan awan konvektif padat (Dense Cloud).

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