Dynamics of Atmospheres and Oceans最新文献

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

The present study delves into the underlying processes responsible for the Chennai deluge during 9th, 15th of November, and 1st December, 2015 by employing both observational data and modeling approaches. The Chennai rainfall, as observed from the GPM satellite data, was substantially higher than the accumulated normal of ∼79 cm for the October-December period. These extreme events coincided with the strongest El Niño event of the century, which persisted from 2014 to early 2016. Further, it is found that the anomalies in Sea Surface Temperature (SST) during this period were more than 1° K above the climatological value and prevailing strong low-level easterly waves over the Indian Oceanic region aided the intensification of previously developing synoptic systems. Soil moisture analysis indicated saturation values nearing 70 %, resulting in increased surface runoff during rainfall events in the backdrop of rapid urban expansion from 1995 to 2015 and aggravated water logging issues. Calculation of thermodynamic indices revealed favourable conditions for the development and intensification of severe convective systems, leading to the catastrophic rainfall events over the Chennai region. A high resolution regional model NCUM (resolution ∼1.5 Km) was utilized to simulate various synoptic features and dynamics of the event over Chennai. Moisture transport at 700 hPa and integrated precipitable water up to 300 hPa were examined, revealing a strong convergence of moisture along the Chennai coast for all cases, with high values of precipitable water observed. Simulations of 3-hourly accumulated rainfall from model generally align with corresponding GPM satellite estimates, despite the model tending to underestimate the intensity of rainfall in all cases. The model simulated location specific rainfall is reasonably well matched with the in-situ observations around Chennai region. However, the model is underestimated the peak rainfall while compare with the observations in all the cases. Further, it successfully depicts the dynamics and structure of extreme rainfall events, including key features such as wind patterns and moisture convergence, demonstrating its utility for forecasting extreme weather events in the Chennai region.

In this study, the relationship between SST diurnal cycle,10 m wind speed (W₁₀) and Outgoing Longwave Radiation (OLR) is investigated. A wavelet spectral analysis was applied to SST hourly data to identify the SST diurnal cycle over the Tropical Western Pacific Ocean (TWPO). The SST diurnal cycle was identified as a prominent spectrum peak in waves with an oscillation period of 1 day. An inverse energy cascade hypothesis suggests that the energy from the SST diurnal cycle propagates and gets absorbed by waves within the subseasonal timescale. Three windows were selected to represent the diurnal (0–2 days), subseasonal (15–60 days), and seasonal (80–200 days) timescales. A wavelet-filtered analysis was performed in these windows, revealing inverse SST/ wind speed and direct SST/ OLR correlations over TWPO. These findings are consistent with empirical parametric models. Additionally, this study demonstrates the rectification mechanism of SST through a wavelet-filtered approach, identifying statistically significant correlations at the 5 % level within the diurnal window (0–2 days), particularly in the central tropical Pacific. Wavelet-filtered anomalies of SST, W₁₀, and OLR along 50–160°E reveal the alternating dry and wet phase propagation across the Indo-Pacific in the subseasonal window, which is associated with the Madden-Julian Oscillation (MJO). Furthermore, westward propagating anomalies in the Indian Ocean and eastward propagating anomalies east of the Maritime Continent (MC) and within the Pacific were identified in the seasonal window, resembling patterns of Rossby and equatorial Kelvin waves, respectively.

One of the most severe snowstorms in recent years occurred in the Tehran region on 27–28 January 2018 and led to roadblocks and closure of airports. In this study, the development of the snowstorm is investigated using a combination of synoptic and mesoscale analysis based on diagnostics of frontogenesis and various forms of moist potential vorticity. A surface cyclone, a mid-tropospheric trough, and an occluded front were the main ingredients of the synoptic environment of the snowstorm in the Tehran region. The high-resolution simulation is performed using the WRF model configured for two nested domains with horizontal grid spacings of 9 and 3 km using the ERA5 data for initial and boundary conditions. The simulation with grid spacing of 3 km makes it possible to investigate the effect of meso-γ environmental conditions on the formation of heavy precipitation and snowbands. Results point to the presence of strong frontogenesis and intense negative saturation equivalent potential vorticity (SEPV) in the lower and middle troposphere during the development and mature stages of the snowstorm. As the snowstorm passed the region, the amounts of negative SEPV and frontogenesis became much weaker through most of the troposphere, meanwhile relative humidity and vertical motions reduced. Detailed analysis shows that conditional, inertial, and conditional symmetric instability all contributed to the formation of heavy precipitation in this snowstorm. Moreover, computation of the area-averaged values of the terms contributing to negative SEPV indicates a considerable non-hydrostatic effect mainly by the term involving the meridional gradient of vertical velocity.

The seas and oceans are essential in preserving energy in the Earth, Ocean, and atmosphere system. Assessing past trends and expected sea surface temperature (SST) shifts is critical for future climate scenarios within this framework. Previous studies have emphasized the importance of SST in the emergence and intensification of heavy rainfall events in the South Asian basin and the development of heat waves in the coastal regions of South Asia. This study has focused to investigate the relationship between SST of the Arabian Sea and mean rainfall in South Asia using Singular Value Decomposition Analysis (SVDA) for the July, August, and September (JAS) season, as most of the region's rainfall is in this season. The first mode accounts for 69 % of the covariance between Arabian Sea SST and regional JAS rainfall, with three dominant SVDA modes explaining 96 % of the total squared covariance. The degree to which the connected fields are correlated has been investigated using Root Mean Squared Covariance. By heterogeneous correlation maps, it is concluded how significant is the impact of one field (SST) on the other (rainfall), which validates our hypothesis that the SST of the Arabian Sea affects variation in precipitation. The empirical results from the SVDA are consistent with those of the composite diagrams. Dry and wet years have been defined and analyzed to examine the impact of SST on regional JAS rainfall further. Vertically integrated moisture transport reveals a significant difference in moisture transport over the Arabian Sea between wet and dry years during the JAS season. Water vapor transport is much stronger during wet years compared to dry years.

The interannual rainfall variability over the southeast Australian state of Victoria is known to be influenced by a number of large scale and regional phenomena, including the Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), and Southern Annular Mode (SAM). However, the role of ‘upstream’ regional circulation or pressure anomalies has received only modest attention. The amount of winter (May-August) rainfall over the state has declined over the past few decades, especially from 1960 to 2017. Using the Center of Action (COA) technique this study examines the relationship between winter precipitation over Victoria and the characteristics of the Indian Ocean High (IOH) over the period 1951–2021. We show that variations of the IOH are strongly linked with those of precipitation over Victoria. The strongest link is with the Indian Ocean High pressure (IOH_P) and its longitudinal position (IOH_LN), whereas the Indian Ocean High latitude (IOH_LT) has little impact. Less precipitation is observed across the state when IOH_P anomalies are positive, whereas the eastward shift of the IOH_LN is a major factor in the reduction of precipitation. Using correlation and multiple regression analyses, we find that the IOH indices explain 54 % of the winter precipitation variation. The strength of this relationship is somewhat weaker in the northern part of the state, partly because of the additional influence of ‘north-west cloud bands’ north of the Great Diving Range. Finally, we perform composite analyses of anomalous high (low) years of IOH to establish evidence of IOH influencing Victorian rainfall. This allows us to reveal the dynamical mechanisms behind the revealed associations.

In this study, recent decadal changes in Sea Ice Concentration (SIC) and Sea Surface Temperature (SST) between the decade of 2010–2019 and 2000–2009 have been studied in the Antarctic regions of the Southern Ocean. Satellite-derived the Advanced Very High-Resolution Radiometer (AVHRR) showed the significant dominance of the decadal increase of SIC in both eastern and western Antarctic sea-ice regions. The maximum decadal increase of SIC has been observed in the Bellingshausen and Amundsen Sea (BAS) sectors of the Antarctic sea-ice regions. The AVHRR data also showed a decadal decrease for SST, but changes are weak compared to SIC. The above observed decadal change of SIC and SST are reasonably well simulated by a global ocean sea-ice coupled model, known as the Modular Ocean Model of version 5 with Sea Ice Simulator (MOMSIS). A simple mixed-layer heat budget analysis has been performed using the model MOMSIS to quantify the contribution of various ocean and atmospheric thermodynamics processes. The significant role of ocean horizontal advection and vertical entrainment has been observed along with atmospheric heat fluxes for a strong decadal increase of SIC in the BAS sectors of the Antarctic sea-ice. The strength of recent decadal variability in the Antarctic sea-ice regions critically depends on both oceanic processes and atmospheric fluxes. Decadal weakening of wind stress and increase of negative wind curl also have a dominant role in association with the decadal increase of SIC in the Antarctic regions of the Southern Ocean.

Generated under hurricane conditions, a slip layer composed of foam, spray, bubble emulsion, etc. determines the behavior of surface drag with wind speed. This study estimates foam's contribution to this behavior. A logarithmic parametrization of surface drag is introduced, where the effective roughness length of the underlying surface is decomposed into three fractional roughness lengths. These correspond to the foam-free area (as determined by open-ocean data at low wind speeds and laboratory data at high wind speeds), which includes the effects of spray, bubble emulsion, etc., and ocean areas covered by whitecaps and streaks, each weighted by their respective coverage coefficients. A key concept of this approach is the use of well-established experimental bubble-size spectra produced by breaking surface waves to obtain the foam-produced effective roughness length. This method provides a fair correlation of the logarithmic parametrization of surface drag against wind speed with a wide class of experimental data. Additionally, this approach estimates the hurricane's potential intensity, demonstrating reasonable agreement with experimental findings.

Information on wave direction and height is an important input to the coastal engineers. The availability of measured data at every location in the ocean makes maritime operations smoother. However, the practical impossibility makes it to look for alternative datasets like ERA5 reanalysis data. In this study, we compare the significant wave height and mean wave direction in the ERA5 with the buoy-measured data available at the nearest locations in the coastal waters of India. Even though the ERA5 overestimates the measured significant wave heights at certain instances, they both are in good agreement at most of the locations. The correlation coefficient varies from 0.82 to 0.99, with the RMSE falling between 0.15 and 0.31 m. However, the ERA5 wave direction deviates significantly from the measured buoy data at certain locations due to the substantial difference between the measured and ERA5 mean direction of wind-seas. The ERA5 dataset matches the measured mean wave direction when swell dominates i.e., during the southwest monsoon for the locations in the Arabian Sea and during post-monsoon season for the locations in the western Bay of Bengal.