This paper describes the application of machine learning techniques to develop a state-of-the-art detection and prediction system for spatiotemporal events found within remote sensing data; specifically, Harmful Algal Bloom events (HABs). We propose an HAB detection system based on: a ground truth historical record of HAB events, a novel spatiotemporal datacube representation of each event (from MODIS and GEBCO bathymetry data) and a variety of machine learning architectures utilising state-of-the-art spatial and temporal analysis methods based on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) components together with Random Forest and Support Vector Machine (SVM) classification methods. This work has focused specifically on the case study of the detection of Karenia Brevis Algae (K. brevis) HAB events within the coastal waters of Florida (over 2850 events from 2003 to 2018; an order of magnitude larger than any previous machine learning detection study into HAB events). The development of multimodal spatiotemporal datacube data structures and associated novel machine learning methods give a unique architecture for the automatic detection of environmental events. Specifically, when applied to the detection of HAB events it gives a maximum detection accuracy of 91% and a Kappa coefficient of 0.81 for the Florida data considered. A HAB forecast system was also developed where a temporal subset of each datacube was used to predict the presence of a HAB in the future. This system was not significantly less accurate than the detection system being able to predict with 86% accuracy up to 8 days in the future.

This study investigates the atmospheric dynamics of the major dust storm that occurred in June 2020 over the Sahara and during which dust clouds associated with the highest‐on‐record aerosol optical depths were transported toward the America. An anomalous atmospheric circulation pattern in the mid‐latitudes, linked to a circumglobal wavetrain, resulted in the development of a subtropical high‐pressure system to the west of the Saharan heat low. This created a pressure dipole and generated anomalously strong northeasterlies over the Sahara, which caused continuous dust emissions over 4 days. Occurring along the northern fringes of the intertropical discontinuity, the dust was transported to higher altitudes (6 km) by the strong updraft in this region. This injected the dust at the African Easterly Jet (AEJ) altitudes and favored a rapid westward long‐range transport. The AEJ was also anomalously strong, being strengthened by the anticyclonic circulation associated with the anomalous high.

This study reports the occurrence of intense atmospheric rivers (ARs) during the two large Weddell Polynya events in November 1973 and September 2017 and investigates their role in the opening events via their enhancement of sea ice melt. Few days before the polynya openings, persistent ARs maintained a sustained positive total energy flux at the surface, resulting in sea ice thinning and a decline in sea ice concentration in the Maud Rise region. The ARs were associated with anomalously high amounts of total precipitable water and cloud liquid water content exceeding 3 SDs above the climatological mean. The above-normal integrated water vapor transport (IVT above the 99th climatological percentile), as well as opaque cloud bands, warmed the surface (+10°C in skin and air temperature) via substantial increases (+250 W m⁻²) in downward longwave radiation and advection of warm air masses, resulting in sea ice melt and inhibited nighttime refreezing.

Study region: United Arab Emirates. Study focus: Numerous global precipitation products have been developed and calibrated. However, their performance over arid regions such as the United Arab Emirates (UAE) has been revealing notable differences triggered by a combination of climatic and terrestrial attributes. The objective of this study is to cross-validate and analyze the consistency of four global precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the UAE using a network of 53 rain gauges from 2000 to 2010. The spatial analysis of their consistency versus topography and land cover is expected to reveal the factors affecting the country’s rainfall regime. The study also identifies and calibrates the best statistically performing precipitation product as an essential climatic input for monitoring, forecasting, and modeling hydrologic applications over the UAE. New hydrological insights for the region: The UAE, similar to other dryland environments lacking adequate hydrologic monitoring networks, presents a unique area to evaluate satellite remote sensing products and the erratic spatiotemporal nature of precipitation in diverse environments. Statistical analyses indicate that the TMPA V7 precipitation products record the highest overall agreement with the observational network. Within the UAE, areas that receive high rainfall and fall within the vegetated highlands (e.g., >250 m), provide the most promise for incorporating satellite precipitation into hydrologic monitoring, modeling, or water resource management

Remote sensing provides an effective tool for timely oil pollution response. In this paper, the spectral signature in the optical and infrared domains of oil slicks observed in shallow coastal waters of the Arabian Gulf was investigated with MODIS, MERIS, and Landsat data. Images of the Floating Algae Index (FAI) and estimates of sea currents from hydrodynamic models supported the multi-sensor oil tracking technique. Scenes with and without sunglint were studied as the spectral signature of oil slicks in the optical domain depends upon the viewing geometry and the solar angle in addition to the type of oil and its thickness. Depending on the combination of those factors, oil slicks may exhibit dark or bright contrasts with respect to oil-free waters. Three oil spills events were thoroughly analyzed, namely, those detected on May 26 2000 by Landsat 7 ETM + and MODIS/Terra, on October 21 2007 by MERIS and MODIS, and on August 17 2013 by Landsat 8 and MODIS/Aqua. The oil slick with bright contrast observed by Landsat 7 ETM + on May 26 2000 showed lower temperature than oil-free areas. The spectral Rayleigh-corrected reflectance (R_rc) signature of oil-covered areas indicated higher variability due to differences in oil fractions while the R_rc spectra of the oil-free area were persistent. Combined with RGB composites, FAI images showed potentials in differentiating oil slicks from algal blooms. Ocean circulation and wind data were used to track oil slicks and forecast their potential landfall. The developed oil spill maps were in agreement with official records. The synergistic use of satellite observations and hydrodynamic modeling is recommended for establishing an early warning and decision support system for oil pollution response.