A systematic review of natural language processing applications for hydrometeorological hazards assessment.

Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.

On the crucial role of atmospheric rivers in the two major Weddell Polynya events in 1973 and 2017 in Antarctica.

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.

Analysis of bloom conditions in 2013 in the Strait of Hormuz using satellite observations and model simulations.

In this study an algal bloom event in fall 2013 in the Strait of Hormuz was thoroughly investigated using satellite remote sensing and hydrodynamic modeling. The motivation of this study is to deduce ambient conditions prior to and during the bloom outbreak and understand its trigger. Bloom tracking was achieved by sequential MODIS imagery and numerical simulations. Satellite observations showed that the bloom was initiated in late October 2013 and dissipated in early June 2014. Trajectories of bloom patches were simulated using a Lagrangian transport model. Model-based predictions of bloom patches' trajectories were in good agreement with satellite observations with a probability of detection (POD) reaching 0.85. Analysis of ancillary data, including sea surface temperature, ocean circulation, and wind, indicated that the bloom was likely caused by upwelling conditions in the Strait of Hormuz. Combined with numerical models, satellite observations provide an essential tool for investigating bloom conditions.

Analysis of the spatio-temporal variability of seawater quality in the southeastern Arabian Gulf.

In this study, seawater quality measurements, including salinity, sea surface temperature (SST), chlorophyll-a (Chl-a), Secchi disk depth (SDD), pH, and dissolved oxygen (DO), were made from June 2013 to November 2014 at 52 stations in the southeastern Arabian Gulf. Significant variability was noticed for all collected parameters. Salinity showed a decreasing trend, and Chl-a, DO, pH, and SDD demonstrated increasing trends from shallow onshore stations to deep offshore ones, which could be attributed to variations of ocean circulation and meteorological conditions from onshore to offshore waters, and the likely effects of desalination plants along the coast. Salinity and temperature were high in summer and low in winter while Chl-a, SDD, pH, and DO indicated an opposite trend. The CTD profiles showed vertically well-mixed structures. Qualitative analysis of phytoplankton showed a high diversity of species without anomalous species found except in Ras Al Khaimah stations where diatoms were the dominating ones.

Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters--a case study in the Arabian Gulf.

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.