An extreme precipitation/flood event that occurred in the Indonesian capital of Jakarta on Java Island in the middle of January 2013 coincided with an active phase of the Madden-Julian Oscillation (MJO) with the enhanced convective phase centered on the western Pacific. Analysis of upper-air sounding data showed that strong to moderate upper westerly to northwesterly winds persisted 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 observations 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 northwesterly 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.

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.

Underestimation of PM_2.5 emissions from the agricultural sector persists as a major difficulty for air quality studies, partly because of underutilization of high-resolution observation platforms for constructing a global emissions inventory. Coarse-resolution products used for such purposes often miss fine-scale burnt areas created by stubble-burning practices, which are primary sources of agricultural PM_2.5 emissions. For this study, we used the high-resolution Sentinel-2 observations to examine the spatiotemporal variability of burnt areas in Punjab, a major hotspot of agricultural burning in India, during the post-monsoon fire season (October–December) in 2022–2024. The results highlight the Sentinel-2 capability of detecting more than 34,000 km² of burnt areas (approx. 68% of Punjab’s total area) as opposed to the less than 7000 km² (approx. 12% of Punjab’s total area) detected by MODIS. The study also reveals, in unprecedented detail, multi-annual spatial and temporal shifting of burning events from northern to central and southern Punjab. This detection discrepancy has led to marked disparities in estimated monthly emissions, with approximately 217.3 million tons of PM_2.5 emitted in October 2022 compared to 8.7 million tons found by EDGAR v.8.1. This underscores higher-resolution observation systems intended to support construction of a global PM_2.5 emissions inventory.

This study examines contribution of brick kiln (BK) industry to PM 2.5 emission over northern part of India. We analyze NASA’s MODIS and JAXA’s GCOM-C/SGLI land-atmospheric products with 3887 BK locations over the region in the period of the highest PM 2.5 concentration during December 2016 to February 2017. Our preliminary results show that more than 60% of BK occupied silt-rich areas, with more than 80% of them were located over croplands on the outskirts of urban areas, indicating proxies for PM 2.5 emission over Northern India. Initial WRF-Chem model simulations showed prominent diurnal variation of the pollutant with averaged peak concentration exceeding 1μg/m ³ over the capital city of New Delhi. Future works will introduce the use of Google Earth Engine (GEE) cloud platform for identifying BK locations and incorporate them into the model to improve the simulation results.