An extreme precipitation/flood event that occurred in the Indo nesian capital of Jakarta on Java Island in the middle of January 2013 coincided with an active phase of the MaddenJulian Oscil lation (MJO) with the enhanced convective phase centered on the western Pacific. Analysis of upperair sounding data showed that strong to moderate upper westerly to northwesterly winds persist ed over the island prior to and during the heavy rain event, which were caused by the active phase of the MJO, while northwesterly winds occurred near the surface. Meteorological radar observa tions indicated regular genesis of convection at night over the sea to the northwest of the island, and southeastward propagation over the island from the nighttime to early morning. The movement of the precipitation systems was dominated by the upper north westerly winds. The results suggest that the eastward propagation of an active phase of the MJO exerted a strong influence on the formation of extreme heavy rain over western Java Island.

This paper presents an overview of the HARIMAU2010 campaign focusing on convective activity with the diurnal rainfall meridional march (DRMM) over Jakarta, which is located on the northern coast of Jawa Island of the Indonesian maritime continent (IMC), based on 1-month intensive observations by a C-band Doppler radar and multi-point atmospheric sounding array conducted during 16 January-14 February 2010. The campaign period corresponded to a phase after large-scale Madden-Julian oscillation (MJO) active convections passed over Jakarta (MJO inactive phase). The cross-equatorial northerly surge (CENS) intruded into the Jawa Sea with a cold tongue (CT) of sea surface temperature (SST) in the beginning of the period (CENS active period: 16-26 January), and then, it started to retreat (transition period: 27 January-05 February); afterward, only a few signs of it were apparent (CENS inactive period: 06-14 February). The observational results showed that (1) rainfall over Jakarta has the nature of DRMM during the MJO inactive phase at least, (2) the DRMM is likely driven primarily by "land-breeze"-like local meridional circulation, and (3) the meridional spatiotemporal variation of rainfall over Jakarta is thus controlled by activities of both the CENS and CT over the Jawa Sea.

An overview of convective activity during the HARIMAU2006 campaign conducted from 26 October to 27 November 2006 was presented, focusing on the di¤erences between coastal land/sea and inactive/active phases of intraseasonal variation (ISV) based on observations using an X-band Doppler radar (XDR) and intensive soundings at Sumatera Island. Diurnal variation (DV) in coastal convections and formation of the coastal heavy rainband (CHeR) along Sumatera Island were also examined in terms of diurnal land-sea migration of coastal convective systems.Convection in the ISV inactive period (PP1) contained convective rain fractions nearly twice as much as stratiform rain fractions, whereas that in the ISV active period (PP2) comprised convective and stratiform elements almost equally. Vertical profiles of radar echo coverage for stratiform rain during PP2 were greater than those during PP1, especially in the lower troposphere over the sea. The radar echo coverage for convective rain over the sea during both periods was nearly double that over land from the near surface up to 6 km high.Convection was generated in the southwestern foothills of the mountain range in the early afternoon (12-15 Local Time, LT). Part of the convective system remained over the coastal land and exhibited weak reflectivity until the next morning. The other part migrated o¤shore at a speed of approximately 4 m s À1 and intensified until around 21 LT while still o¤shore. Additional convective cells also developed o¤shore in the early morning hours, independent of those that formed over land. Results suggested that the CHeR along Sumatera Island is dictated by diurnal variations in coastal convective development and consists of the following phases: 1) migration of convection away from the coastal land and its redevelopment in the late evening, and 2) additional generation of convection just o¤shore during the early morning hours.

Cities lying in the Indo-Gangetic plains of South Asia have the world’s worst anthropogenic air pollution, which is often attributed to urban growth. Brick kilns, facilities for producing fired clay-bricks for construction are often found at peri-urban region of South Asian cities. Although brick kilns are significant air pollutant emitters, their contribution in under-represented in air pollution emission inventories due to unavailability of their distribution. This research overcomes this gap by proposing publicly available remote sensing dataset based approach for mapping brick-kiln locations using object detection and pixel classification. As brick kiln locations are not permanent, an open-dataset based methodology is advantageous for periodically updating their locations. Brick kilns similar to Bull Trench Kilns were identified using the Sentinel-2 imagery around the state of Delhi in India. The unique geometric and spectral features of brick kilns distinguish them from other classes such as built-up, vegetation and fallow-land even in coarse resolution imagery. For object detection, transfer learning was used to overcome the requirement of huge training datasets, while for pixel-classification random forest algorithm was used. The method achieved a recall of 0.72, precision of 0.99 and F1 score of 0.83. Overall 1564 kilns were detected, which are substantially higher than what was reported in an earlier study over the same region. We find that brick kilns are located outside urban areas in proximity to outwardly expanding built-up areas and tall built structures. Duration of brick kiln operation was also estimated by analyzing the time-series of normalized difference vegetation index (NDVI) over the brick kiln locations. The brick kiln locations can be further used for updating land-use emission inventories to assess particulate matter and black carbon emissions.

Intisari  Simulasi WRF pada tanggal 16-17 Januari 2013 dilakukan untuk menguji performa model dalam mendeteksi fenomena seruak dingin dan hujan ekstrim yang merupakan pemicu utama bencana banjir Jakarta pada periode tersebut. Metode verifikasi kualitatif dan kuantitatif pada tiap grid secara dikotomi digunakan untuk membandingkan keluaran model dengan data observasi Global Satellite Mapping of Precipitation (GSMaP) dan NCEP Reanalysis. Performa model WRF dihitung berdasarkan nilai akurasi (ACC), Critical Success Index (CSI), Probability of Detection (POD) dan False Alarm Ratio (FAR) yang diperoleh dari hasil verifikasi numerik. Hasil pengujian menunjukkan bahwa WRF mampu melakukan deteksi waktu awal kejadian hujan ekstrim dengan tepat setelah 6-7 jam sejak inisiasi model dilakukan. Performa terbaik WRF teramati pada pukul 02-09 WIB (LT) dengan nilai CSI mencapai 0,32, POD 0,82 dan FAR 0,66. Hasil verifikasi secara kualitatif dan kuantitatif juga menunjukkan bahwa WRF dapat melakukan deteksi seruak dingin dan hujan ekstrim sebelum banjir terjadi, walaupun dengan ketepatan durasi waktu dan lokasi kejadian yang masih relatif rendah bila dibandingkan dengan data observasi.  Abstract  WRF simulation on January 16-17, 2013 has been conducted to evaluate the model performance in detecting cold surge and extreme precipitation phenomena which were the triggers of Jakarta flood event during the period. Qualitative and quantitative dichotomous grid-to-grid verification methods are utilized to compare the model output with Global Satellite Mapping of Precipitation (GSMaP) observation and NCEP Reanalysis dataset. WRF model performance is calculated based on the scores of accuracy (ACC), Critical Success Index (CSI), Probability of Detection (POD) and False Alarm Ration (FAR) which are generated from numerical verification. The results show that WRF could precisely detect the onset of extreme precipitation event in 6-7 hours after the model initiation.The best performance of the model is observed at 02-09 WIB (LT) with CSI score of 0.32, POD 0.82 and FAR 0.66. Despite the model inability to accurately predict the duration and location of cold surge and extreme precipitation, the qualitative and quantitative verification results also show that WRF could detect the phenomena just before the flood event occured.