The catastrophic implication of harmful algal bloom (HAB) events in the Arabian Gulf is a strong indication that the study of the spatiotemporal distribution of chlorophyll-a and its relationship with other variables is critical. This study analyzes the relationship between chlorophyll-a (Chl-a) and sea surface temperature (SST) and their trends in the Arabian Gulf and the Gulf of Oman along the United Arab Emirates coast. Additionally, the relationship between bathymetry and Chl-a and SST was examined. The MODIS Aqua product with a resolution of 1 × 1 km² was employed for both chlorophyll-a and SST covering a timeframe from 2003 to 2019. The highest concentration of chlorophyll-a was seen in the Strait of Hormuz with an average of 2.8 mg m⁻³, which is 1.1 mg m⁻³ higher than the average for the entire study area. Three-quarters of the study area showed a significant correlation between the Chl-a and SST. The shallow (deep) areas showed a strong positive (negative) correlation between the Chl-a and SST. The results indicate the presence of trends for both variables across most of the study area. SST significantly increased in more than two-thirds of the study area in the summer with no significant trends detected in the winter.

The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.

Aerosols in Southeast Asia (SEA) are entangled with complex land–sea–atmosphere–human interactions, and it is difficult for scientists to understand their dynamic behaviors. This study aims to provide an insightful understanding of aerosols across SEA with respect to their radiative properties using several lines of evidence obtained from remote sensing instruments, including those from onboard Earth observation satellites (MODIS/Terra and MODIS/Aqua, CALIOP/CALIPSO) and from ground-based observation (AERONET). The findings, obtained from cluster analysis of aerosol optical properties, showed seven aerosol types which were dominant across the country, exhibiting diverse radiative forcing potentials. The light-absorbing (prone to warm the atmosphere) aerosols were likely found in mainland SEA, both for background and high-aerosol events. The light-scattering aerosols were associated with aging processes and hygroscopic growth. The neutral potential, which comprised a mixture of oceanic and local anthropogenic aerosols, was predominant in background aerosols in insular SEA. Further studies should focus on carbonaceous aerosols (organic carbons, black carbon, and brown carbon), the aging processes, and the hygroscopic growth of these aerosols, since they play significant roles in the regional aerosol optical properties.

Fine particulate matter (PM_2.5) remains a major public-health concern in Thailand, with seasonal peaks amplified by terrain-induced stagnation. We delineate nationwide airshed regimes by integrating topography with multi-variable meteorology in an object-based workflow coupled to K-means clustering. Monthly composites for 2003–2024 were assembled for digital elevation (DEM), planetary boundary-layer pressure (PBL; proxy for PBL height), 10-m wind speed (WS10), surface pressure (SUR), surface shortwave radiation (SRAD), TMAX/TMIN, vapor pressure (VAP), and precipitation (PPT), harmonized to a common grid and aggregated to objects. Candidate K=2–10 was evaluated; the Elbow criterion supported K=4. Four physically interpretable clusters emerge—lowland, two transition belts, and highland—monotonically ordered by elevation and accompanied by coherent atmospheric gradients. From lowland to highland, SUR, TMAX, TMIN, VAP, and PPT decline, winds weaken in transition/highland zones, and PBL pressure decreases (consistent with higher PBL tops); SRAD is modestly lower aloft. Seasonality is systematic: November–February features expansion of transition/highland classes into basins under cooler, more stable conditions, whereas during the Southwest monsoon (May–October) transition belts retract as ventilation and wet scavenging strengthen. Despite boundary shifts, cluster identity at fixed locations is highly repeatable. The zoning provides an operational scaffold for airshed-aware management: targeting monitoring and advisories in transition belts and basin interiors, supporting cross-provincial coordination, and informing regime-conditioned forecasting. Limitations include a dispersion-focused design and reliance on gridded products. Planned extensions will incorporate ventilation capacity (VC=WS10×PBLH), stagnation frequency, and expanded validation (Silhouette/Gap, bootstrap stability, external/temporal holdouts with independent PM_2.5).