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This study investigates the impacts of summer monsoon activity on aerosols over the Indian region. We analyze the variability of aerosols during active and break monsoon phases, as well as strong and weak monsoon years, using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Our findings show a clear distinction in aerosol distribution between active and break phases. During active phases, the Aerosol Optical Depth (AOD) and aerosol extinction are lower across the Indian region, while break phases are associated with higher AOD and extinction. Furthermore, we observed a significant increase in AOD over Central India during strong monsoon years, compared to weak monsoon years. Utilizing the vertical feature mask (VFM) data from CALIPSO, we identified polluted dust and dusty marine aerosols as the dominant types during both active/break phases and strong/weak monsoon years. Notably, the contributions of these pollutants are significantly higher during break phases compared to during active phases. Our analysis also reveals a shift in the origin of these aerosol masses. During active phases, the majority originate from the Arabian Sea; in contrast, break phases are associated with a higher contribution from the African region.

Abstract The overarching goal of this paper is to shed light on the human influence on the changing patterns of heat waves in India using the Heat Wave Magnitude Index daily (HWMId). The HWMId obtained from the observational data sets shows a large increase in the heat waves during the past decades. Investigating the effects of natural (e.g., solar variations and volcanic forcings) and anthropogenic (e.g., greenhouse gas emissions, anthropogenic, land use, and land cover) forcings revealed that the anthropogenic factors have cause a two-fold increase in the occurrence probability of severe heat waves in central and mid-southern India during twentieth century. The spatial distribution of maximum HWMId values under natural and all forcings (including anthropogenic) indicates that in most places human activities have increases the frequency, duration and intensity of extreme heat waves. Under the Representative Concentration Pathway (RCP) 4.5, the risk of heat waves is projected to increase tenfold during the twenty-first century. More than ~ 70% of the land areas in India is projected to be influenced by heat waves with magnitudes greater than 9. Furthermore, we find a significant relationship between heat waves and deficits in precipitation. Results show that concurrent heat waves and droughts are projected to increase in most places in India during the twenty-first century.

Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)=0.91), followed by Cochin (NSE=0.89), Chennai (NSE=0.88), Cuddalore (NSE=0.88), and Vishakhapatnam (NSE=0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models was proven effective in predicting the ST of coastal cities. HIGHLIGHTS Surface temperature prediction model based on hybrid machine learning algorithms.; Hybrid data-driven algorithm was more effective compared to the individual ML algorithms.; Surface temperature prediction of major coastal cities of India.;

Despite being rare, large volcanic eruptions can have a long-lasting impact on the chemistry, radiation, and dynamics of the stratosphere. This study attempts to quantify the changes in the stratospheric water vapour and its relationship to temperature and ozone observed from space-based Microwave Limb Sounder (MLS) observations during the submarine volcano eruption Hunga Tonga-Hunga Ha’apai that occurred on 15 January 2022. The most notable aspect of this eruption is the plumes, which are water vapour columns that reached higher altitudes (1 hPa (47.6 km)) than earlier eruptions. We discovered that the eruption injected a record amount of water vapour (6–8 ppmv) directly into the stratosphere from 38–10 hPa vertically, which is present even after one year. The majority of water vapour is confined to the Southern Hemisphere (SH) tropics, i.e., 30°S to 5°N, and gradually descends to the SH polar latitudes over time. The WV from the lower stratosphere reaches mesospheric altitudes during January 2023. We quantify the impact of increased water vapour on temperature and ozone as well. Temperatures begin to fall during the month of March in the regions where there is an increase in water vapour. A ~5 K cooling occurs in July and August as a result of the thermal adjustment to the extra water vapour IR cooling. Our analysis shows a decrease in ozone caused by an increase in water vapour. Significant variability is observed in all three parameters at 26 km compared to other levels. Further, we noticed that after one year of eruption, the water vapour, Temperature and Ozone did not reach the background values. It is possible that this unusual eruption produced a different atmospheric reaction than other significant volcanic eruptions that have been well investigated.

Abstract Changing patterns in aerosol concentrations over the Asian region is well documented with a concurrent increase over India and a marked reduction over China. However, aerosol vertical distribution in the changing climate is not fully understood. By combining long-term satellite observations from MODIS and CALIOP, here we show rapid changes in the aerosol vertical distribution over the South and East Asia covering India and China. A statistically significant decreasing (increasing) trend in the boundary layer (free troposphere) aerosol concentrations is noticed over India. ERA-Interim reanalysis model suggests that this increase in free tropospheric aerosol concentrations are due to the lifting of boundary layer pollutants through an increase in convection (and vertical velocity) in a changing climate. In contrast, a consistent decreasing trend is observed over China irrespective of the altitude. Interestingly, a decreasing trend in Aerosol Optical Depth is observed over the northwest India and we relate this to an observed increase in precipitation leading to increase in the vegetation. It is also found that long-term oscillations like QBO, ENSO and solar cycle significantly affect the aerosol concentrations. Thus, it is prudent to conclude that background meteorology and dynamics play an important role in changing patterns of aerosol vertical distribution.

Water vapor is the most dominant greenhouse gas in the atmosphere and plays a critical role in Earth’s energy budget and hydrological cycle. This study aims to characterize the long-term seasonal variation of relative humidity (RH), convective available potential energy (CAPE), and convective inhibition (CIN) from surface and radiosonde observations from 1980–2020. The results show that during the monsoon season, very high RH values are depicted while low values are depicted during the pre-monsoon season. West Coast stations represent large RH values compared to other stations throughout the year. Irrespective of the season, the coastal regions show higher RH values during monsoon season. Regardless of season, the coastal regions have higher RH values during the monsoon season. During the pre-monsoon season, the coastal region has high RH values, whereas other regions have high RH values during the monsoon season. The rate of increase in RH in North-West India is 5.4%, followed by the West Coast, Central, and Southern parts of India. An increase in water vapor leads to raised temperature, which alters the instability conditions. In terms of seasonal variation, our findings show that CAPE follows a similar RH pattern. CAPE increases sharply in Central India and the West Coast region, while it declines in South India. Opposite features are observed in CIN with respect to CAPE variability over India. The results of the study provide additional evidence with respect to the role of RH as an influencing factor for an increase in CAPE over India.

The existence of the Asian Summer Monsoon Anticyclone (ASMA) during the summer in the northern hemisphere, upper troposphere and lower stratosphere (UTLS) region plays a significant role in confining the trace gases and aerosols for a long duration, thus affecting regional and global climate. Though several studies have been carried out, our understanding of the trace gases and aerosols variability in the ASMA is limited during different phases of the Indian monsoon. This work quantifies the role of Indian Summer Monsoon (ISM) activity on the tropopause, trace gases (Water Vapor (WV), Ozone (O3), Carbon Monoxide (CO)) and aerosols (Attenuated Scattering Ratio (ASR)) obtained from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC), Microwave Limb Sounder (MLS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite observations, respectively, during the period 2006–2016. Enhancement in the tropopause altitude, WV, CO, ASR and low tropopause temperatures, O3 in the ASMA region is clearly noticed during peak monsoon months (July and August) with large inter-annual variability. Further, a significant increase in the WV and CO, and decrease in O3 during the active phase of the ISM, strong monsoon years and strong La Niña years in the ASMA is noticed. An enhancement in the ASR values during the strong monsoon years and strong La Niña years is also observed. In addition, our results showed that the presence of deep convection spreading from India land regions to the Bay of Bengal with strong updrafts can transport the trace gases and aerosols to the upper troposphere during active spells, strong monsoon years and La Niña years when compared to their counterparts. Observations show that the ASMA is very sensitive to active spells, strong monsoon years and La Niña years compared to break spells, weak monsoon years and El Niño years. It is concluded that the dynamics play a significant role in constraining several trace gases and aerosols in the ASMA and suggested considering the activity of the summer monsoon while dealing with them at sub-seasonal scales.

Abstract Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860–2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20th century and projections during the 21st century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20th century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21st century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21st century shows significant increase during summer. Analysis of rare heat and cold events for 2080–2099 relative to a base period of 1986–2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.

Abstract The state of Kerala, located on the west coast of India, experienced a record 100‐year flood that resulted in major landslides from unprecedented prolonged and extremely heavy rainfall (50–480 mm·day−1) during August 1–19, 2018, causing extensive damage and about 500 causalities. Rainfall observations indicate that the heavy rainfall occurred over two spells (August 7–10 and 14–18) in association with an offshore trough, and a depression over the Bay of Bengal (BOB). High‐resolution 38‐year climatology data (5 km) and the ERA‐Interim reanalysis dataset show a strong low‐level jet over the Arabian Sea and a depression over the BOB with a southwestward tilt during the heavy rainfall. Very high‐resolution (2‐km) mesoscale model simulations suggest that this high convective instability due to the strong westerly jet along with the formation of offshore vortex, the transport of mid‐tropospheric moisture under the presence of conducive vertical shear of horizontal wind, and transport of mid‐tropospheric moisture from the BOB are the major factors (as shown in the schematic diagram) behind the extreme heavy rainfall over Kerala.

It is well reported that the 2015–16 El Niño event is one of the most intense and long lasting events in the 21st century. The quantified changes in the trace gases (Ozone (O3), Carbon Monoxide (CO) and Water Vapour (WV)) in the tropical upper troposphere and lower stratosphere (UTLS) region are delineated using Aura Microwave Limb Sounder (MLS) and Atmosphere Infrared Radio Sounder (AIRS) satellite observations from June to December 2015. Prior to reaching its peak intensity of El Niño 2015–16, large anomalies in the trace gases (O3 and CO) were detected in the tropical UTLS region, which is a record high in the 21st century. A strong decrease in the UTLS (at 100 and 82 hPa) ozone (~200 ppbv) in July-August 2015 was noticed over the entire equatorial region followed by large enhancement in the CO (150 ppbv) from September to November 2015. The enhancement in the CO is more prevalent over the South East Asia (SEA) and Western Pacific (WP) regions where large anomalies of WV in the lower stratosphere are observed in December 2015. Dominant positive cold point tropopause temperature (CPT-T) anomalies (~5 K) are also noticed over the SEA and WP regions from the high-resolution Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Position System (GPS) Radio Occultation (RO) temperature profiles. These observed anomalies are explained in the light of dynamics and circulation changes during El Niño.

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface⁻atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

One of the most important processes in smart antennas, is beam forming. The major function of beam forming is to change the beam pattern of the antenna for a given angle. If the antenna does not change its direction based on the receiving signal, the signal losses will be high. So, in order to increase the efficiency and to reduce the signal losses in the system, here a new method is proposed for beam forming in smart antenna along with spatial diversity using fuzzy interference system and neural network (NN). In order to provide training for NN and fuzzy logic, a huge personalised training dataset is generated using Genetic Algorithm. Given a desired angle as input, the proposed method gives the corresponding antenna parameters and phase angle as output. The length of the beam, interference, phase angle, number of patterns and spatial diversity are the smart antenna parameters that are considered in the proposed method. In addition, the proposed method increases the efficiency in determining the smart antenna parameters. The proposed method is implemented and the results are analysed. The comparative and analytical results prove the performance of the proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

The most imperative process in smart antenna system is beam forming, which changes the beam pattern of an antenna for a particular angle. If the antenna does not change the position for the specified angle, then the signal loss will be very high. By considering the aforesaid drawback, here a new genetic algorithm based technique for beam forming in smart antenna system is proposed. In the proposed technique, if the angle is given as input, the maximum signal gain in the beam pattern of the antenna with corresponding position and phase angle is obtained as output. The length of the beam, interference, phase angle, and the number of patterns are the factors that are considered in our proposed technique. By using this technique, the gain of the system gets increased as well as the interference is reduced considerably. The implementation result exhibits the efficiency of the system in beam forming. [ABSTRACT FROM AUTHOR]

Emotion recognition plays a crucial role in understanding human behaviour and improving human–computer interaction. However, the conventional methods were failed to detect the multiple emotions from image, speech, and video data. So, this study proposes a comprehensive approach for emotion recognition from multiple modalities, including face, speech, and video data, which is named as Emotion-Net. In the preprocessing stage, the input data from face, speech, and video sources is preprocessed to enhance the quality and consistency of the information. Various techniques such as noise removal, normalization, and alignment are applied to ensure reliable feature extraction. Then, the Histogram of Oriented Gradients (HOG) technique is employed to capture important visual patterns and spatial information from facial images, video frames, and speech data. It provides a compact and descriptive representation of facial features, enabling effective emotion recognition. To select the most informative and discriminative features, Effective Seeker Optimization Algorithm (ESOA) feature selection algorithm is employed. The ESOA used to select the best features from HOG using similarity selection process. Finally, the Hidden Markov Convolutional Neural Network (HMCNN) classifier is utilized for emotion recognition. This classifier combines the strengths of Hidden Markov Models (HMMs) and CNNs, allowing the model to capture temporal dynamics and spatial dependencies in the input ESOA features. Experimental evaluations show that, the proposed Emotion-Net achieves high performance in recognizing different emotions from face, speech, and video data.

Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC), consisting of six Low Earth Orbit (LEO) Global Position System (GPS) receivers, on board the Formosat Satellite 3 (FORMOSAT-3) is providing dense observations of density, refractivity, temperature and water vapor profiles of the neutral atmosphere since middle of July 2006. Special radiosonde (Väisälä) campaign was conducted at Gadanki (13.48°N, 79.18°E), a tropical site in India, during July 2006 to March 2007 to validate these meteorological parameters. Co-located Nd: YAG Rayleigh lidar was also operated during the overpass of COSMIC and is utilized to validate the temperatures in the height range of 30 to 40 km. Atotal of 142 overpasses occurred during the above mentioned period within 300 km distance from Gadanki out of which 41 overpasses occurred within a time difference of ±4 hours of radiosonde launch. In addition, 18 overpasses occurred within the time difference of ±4 hours of lidar operation. A detailed comparison has been made with all these overpasses for the refractivity, temperature and water vapor obtained from COSMIC. The water vapor comparison has shown generally a good agreement with a mean difference of 5 - 10% below 6 - 7 km. Although there is a colder bias between COSMIC and radiosonde, a very good comparison in temperature is also found between 10 and 27 km with a mean difference of less than 1 K (RMS difference is only 0.64 K). There exists a large difference in temperature of about 8 K between 30 and 40 km (between COSMIC and lidar). Possible reasons for these large differences are given. There was one event that occurred just over Gadanki for which a detailed comparison has been made with special emphasis on water vapor retrievals. Sensitivity test is also done on the fractional difference in N for the event that occurred on 24 July 2006 between COSMIC (1D-var) and radiosonde and found that pressure plays a key role than temperature in determining the refractivity.