India Meteorological Department

This research examines the fog conditions in Delhi, India, for the winter season of December 2023–January 2024 (D–J). Known for its severe air quality challenges, Delhi experiences frequent and intense fog episodes during winter season, significantly affecting transportation, public health, and daily routines. The analysis is mainly based on data from ground-based observations at the Palam and Safdarjung meteorological stations, focusing on the frequency, duration, and intensity of fog events in winter months. The data reveals that Delhi encountered periods of dense to very dense fog (DDF) during the winter season, particularly in the last week of December 2023 and from the second week to the end of January 2024. This study examined various meteorological factors including temperature inversion, relative humidity, dew point depression, wind patterns and synoptic systems. The analysis reveals that relative humidity exceeds 95% on 78% of the days with DDF conditions. In December, minimum temperatures over Delhi were in the range from 5 to 16 °C, while in January, these were in the range from 3 to 10 °C. Wind speeds generally ranged from 0 to 6 knots, with westerly and northwesterly directions prevailing during most periods of DDF conditions. The dew point depression was ≤ 1.0 °C, observed 75–100% of the time over Delhi during DDF conditions. Temperature inversions and DDF conditions coincided in 87% of cases, while 24% of DDF events occurred without an inversion. Synoptically, 17 Western Disturbances (WDs) moved eastward towards Delhi, during December 2023–January 2024, with 16 reaching within a 1000 km radius of the city. Although DDF events in Delhi are typically associated with the presence of a WD, there were also notable instances of DDF occurring without any WD in the region. This study may be useful for understanding and management of fog conditions in urban areas like Delhi, focusing on improving preparedness and response strategies.

This study investigates the synergistic impacts of conventional and non‐conventional atmospheric data assimilation (DA) and fine‐gridded soil state assimilation on wintertime fog formation over the Indo‐Gangetic Plain (IGP), with a specific focus on Delhi. Two DA experiments were conducted using the Weather Research and Forecasting (WRF) model: the first (DA1) assimilated temperature and humidity profiles from a microwave radiometer (MWR) using 3DVar/GSI‐based system, while second (DA2) extended DA1 by incorporating fine‐gridded initial soil fields from the High‐Resolution Land Data Assimilation System (HRLDAS). The effectiveness of these data sets in improving the forecast accuracy of wintertime meteorological parameters within the boundary layer was evaluated. MWR profiles were validated against simultaneous radiosonde (RS) measurements during the winter seasons of 2017–2019, and bias correction using RS data was implemented to enhance MWR profile accuracy. The results indicated that the assimilation of MWR profiles (DA1) improves the accuracy of near surface temperature and humidity forecasts, conducive for fog conditions. The inclusion of soil state assimilation (DA2) further improves the representation of soil states, thereby better capturing the physical processes associated with fog formation. With DA2, biases in near‐surface meteorological and soil variables were significantly reduced (50% in T2, 16% in RH2, 66% in SM, 46% in ST) compared to DA1. DA2 also improved the representation of surface fog heterogeneity and lifecycle across the IGP, with a spatial skill score of 0.36, versus 0.29 for DA1 and 0.24 without assimilation. Additionally, DA2 achieved a higher critical success index (CSI) of 0.75, compared to 0.50 for DA1.

Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities.

An extremely severe cyclonic storm (ESCS) Biparjoy crossed the Gujarat coast near Jakhau around 1730 UTC 15 th June 2023 with maximum sustained wind speed (MSW) of 32-35 ms ⁻¹ gusting to 39 ms ⁻¹ . From 1891 to 2023, 22 tropical cyclones (TCs) have crossed the Gujarat coast (India). Out of these, the ESCS in 1998 (Kandla TC) was similar to the recent TC Biparjoy. During June 1998, Kandla TC crossed the Gujarat coast near Porbandar around 0130 UTC 9 June with MSW of 44-47 gusting to 50 ms ⁻¹ and again near Kandla around 0900 UTC of the same day with MSW of 40-43 ms ⁻¹ gusting to 47 ms ⁻¹ . Official government records indicate that Kandla TC caused deaths of about 1173 people and TC Biparjoy caused no death in Gujarat. Despite frequently changing track & intensity and rapid intensification, the India Meteorological Department predicted all the features of TC Biparjoy including genesis, track, intensity, landfall, associated heavy rain, winds & storm surge, and damage accurately with sufficient lead time. Active response actions from stakeholders resulted in zero loss of life in Gujarat state. A comparative analysis of the early warning system (EWS) of TC Biparjoy in 2023 and Kandla TC in 1998 is made to evaluate the progress made over the years and identify gap areas for further improvement. The study shows that the success in EWS of TCs could be achieved through improvements in all components of EWS including observations, data communication, analysis, modeling, forecasting, and warning services.

The northward‐propagating 30–60 days mode of monsoon rainfall anomalies over India, commonly referred to as the monsoon intraseasonal oscillation (MISO), plays a critical role in driving the active and break spells over the monsoon zone of the country. These oscillations are essential to understanding and predicting the variability of the Indian summer monsoon, which has significant implications for agriculture and water management. This study uses daily precipitation data from the TRMM/GPM satellite to derive MISO indices (MISO1 and MISO2). These indices were obtained through an extended empirical orthogonal function analysis conducted on 25 years of daily rainfall anomalies over the Indian region. The long time series of MISO1 and MISO2 indices generated from this analysis were then used to forecast future values using a transformer‐based deep learning model. The deep learning model demonstrated skilful predictions of the MISO indices for 2018–2022, with forecast lead times extending to 18 days. Notably, the model outperformed conventional operational numerical weather prediction models in predicting the MISO indices. These results indicate the potential for more reliable sub‐seasonal to seasonal (S2S) predictions of the Indian monsoon. The findings from this work highlight the effectiveness of using advanced deep learning techniques, such as Transformer architectures, in enhancing the predictability of complex atmospheric phenomena like MISO, thereby improving the outlook for monsoon forecasting.

To assess the respiratory outcomes of newborns receiving Kangaroo mother care (KMC) in remote units of Chhattisgarh, India by providing telementoring to medical staff and parents. In 2022, this comparative study was carried out at one SNCU in Chhattisgarh, India. The study consisted of gathering data before intervention and utilizing video call technology for remote mentoring to encourage KMC among both staff and parents. Quality improvement initiatives were executed under supervision. Subsequently, data on mortality and morbidity was collected and compared with pre-intervention data. The KMC rate increased from 4.5% to 100% by the end of 2022 and remained consistent in 2023. The median latency for starting KMC decreased to 0 d. Long KMC for more than 8 h was seen in 61.4% of babies who received KMC. Oxygen usage dropped from 93.4% to 39.8%. The median days of respiratory support decreased from 3 d to 0 d. The incidence of apnea reduced from 30.3% to 13%. Total adverse outcomes [death, leave-against-medical-advice (LAMA) and referral] reduced from 35.8% (n = 95) to 18.3% (n = 62) (p < 0.05) [Relative Risk (RR): 0.51; 95% Confidence Interval (CI): 0.39–0.67] Using telemedicine technology for promotion and maintenance of KMC is an effective approach to decrease the mortality rate, morbidity rate, and overall cost of care for newborns who are admitted to SNCUs located remotely.

Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting, yet obtaining high-resolution, spatially and temporally extensive data remains a challenge due to the limitations of in-situ measurements. The present study tries to address this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m³) using C-band dual-polarized Doppler Weather Radar (DWR) data from the India Meteorological Department (IMD) Jaipur station over 78 summer monsoon days in 2020–2022. A long-term climatological analysis (2003–2023) of total column cloud liquid water (TCCLW, kg/m²) from ERA5, liquid water cloud water content (LWCP, kg/m²) from MODIS, and rainfall data from IMD, IMERG, and GPCP datasets has been performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of empirically radar derived VIL has been evaluated by comparing with satellite-derived (MODIS) cloud liquid water path (LWP, kg/m²) and TCCLW. The results show that VIL values increase with rainfall intensity, leading to higher estimation errors. Among all relations tested, the hybrid equation (which includes Z and ZDR) consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values. The method also captured more detailed spatial patterns of liquid water distribution with reduced bias, making it the most reliable estimator. Despite limitations such as beam blockage and slight spatial shifts due to interpolation, the current study may provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics by incorporating polarimetric radar products. The future work may aim at refining the methodology through enhanced cloud-type-specific estimators.

Predicting thunderstorms, with their small spatial and temporal scales and complex nonlinear dynamics, poses a significant challenge in meteorology. Predicting severe thunderstorms accurately is essential for various community members. The paper employs an artificial neural network model for predicting severe thunderstorms in northeastern India during April 1st and 17th, 2018, based on meteorological data affected by thunderstorms. To evaluate the abilities with many statistical measures were used including sophisticated learning algorithms (Levenberg-Marquardt, Conjugate Gradient, Quick Propagation, Momentum, Step, and Delta-Bar-Delta) in the prediction of thunderstorms. The Levenberg-Marquardt method accurately forecasts thunderstorm-impacted surface strictures and effectively modeled hourly changes in temperature and comparative humidity, including unexpected drops and rises, one, three, and twenty-four hours in advance. It uniquely identifies nonlinear meteorological time series. Meteorologists and other real-time forecast professionals can benefit from the application of the advanced model.

This case highlights an underreported zoonosis in our dermatology clinic. The diagnosis of furuncular myiasis was made based on typical clinical findings and with a characteristic history in skin of colour.

The Indo-Gangetic Plain (IGP) frequently experiences low-visibility events (fog or dense fog) during November–February, significantly impacting aviation, transportation, and public safety. Predicting the critical transitions—onset and dissipation—of these events remains challenging due to their localized nature and the limitations of numerical weather prediction (NWP) models. Current operational forecasting and nowcasting methods rely heavily on human expertise, making prediction accuracy dependent on individual experience.This study presents a machine learning (ML)-based approach to predict the critical transitions (onset and dissipation) of low-visibility events for the lead time of 1 to 3 h using an extensive dataset spanning 2016 to 2023. The dataset comprises historical surface meteorological parameters and co-located air pollution data from Patna Airport, a key station in the IGP. Given its high heterogeneity, the dataset is well-suited for ML applications.To address the prediction of the transition of low-visibility events as a classification task, an ensemble learning framework employing a voting-based approach integrates diverse base learners. The methodology incorporates robust preprocessing techniques, including feature selection through decision tree-based models, to enhance model interpretability and performance. Additionally, imbalanced data distributions and the unequal costs of false classification are considered, improving prediction accuracy.A comparative analysis of machine learning classifiers reveals that the Support Vector Machine (SVM) achieves the highest individual accuracy, while the voting ensemble (VE) model consistently outperforms all classifiers, particularly for short-term predictions of 1 to 3 h. The VE model achieves an accuracy of 99%, an F1 score of 0.99, and a ROC-AUC of 0.98, demonstrating high reliability in predicting low-visibility events.Simulation results confirm the robustness and resilience of the proposed weighted voting ensemble learning model in operational nowcasting. The findings highlight the potential of ML-based techniques to complement and enhance traditional low visbiliyty forecasting methods, providing aviation services with a reliable decision-support tool for real-time operations.

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current LST estimates for field-level hydrological, agricultural, and ecological operations is challenging due to their coarse spatiotemporal resolution. In the current article, we compared three different models, namely 1) Thermal Sharpening (TsHARP), 2) Thin Plate Spline (TPS), and 3) Random Forest (RF) for downscaling LST from 100 to 10 m by using high-resolution Sentinel-1,2 optical-microwave data. TsHARP, TPS, and RF are commonly used methods for improving the spatial resolution of large-scale environmental or climate data to finer scales for field-level applications. The analysis was performed at agricultural farms in the semi-arid, arid, and per-humid regions of India during the winter and summer seasons of 2020–21 and 2021–22. The calibration accuracy of the RF model was in better agreement with the coefficient of determination (R²), root mean square error (RMSE), and normalized RMSE (nRMSE) values ranging between 0.961–0.997, 0.103–0.439 K, and 0.034–0.143%, respectively, and lower values of standard errors for all three locations. Though the validation accuracy of models varied between the regions, RF and TPS consistently outperformed the TsHARP model. Further the impact of individual features on LST downscaling was analyzed using Accumulated Local Effects (ALE) plot. The study concluded that RF is an effective and adaptable strategy that can be used in various agroclimatic zones and land cover types, suggesting its broader applicability in agricultural and ecological operations. Finer resolution LST data with enhanced precision can support tailored field-level decision-making and interventions in agriculture and environmental monitoring.

The transient eddies in the atmosphere are short‐lived, moving disturbances prominent over mid‐latitude. Transient eddy transport enables the exchange of mass, energy, and moisture between extratropical and tropical regions. Based on observations, about 40% of the rain that falls in northern India during the summer monsoons is influenced by transient eddy heat and momentum fluxes. During these rainfall cases, the four‐stage cycle of transient eddy heat and a momentum feedback process exists. Global‐scale circulation anomalies are generated due to their forcing on the mean flow. This impact of transient eddies on mean flow is referred to as eddy–eddy feedback, commencing in around 22 days. On a daily scale, the enhancement of rainfall over the Western Ghats and north‐western India is linked with upper tropospheric poleward transient eddy heat flux transport and equatorward transient eddy momentum flux transport. On a quasi‐biweekly scale, however, the transport direction reverses. Additionally, this rainfall pattern is governed by the meridional passage of monsoon intraseasonal oscillation phases (MISO, tropical mode) and the zonal passage of wave number 7–8 patterns (Rossby wave, extratropical mode). Interactions of tropical–extratropical modes are associated with this eddy –eddy feedback that drives the hemispheric upper‐tropospheric circulation patterns. This includes wave generation, propagation, and the dissipation of waves away from the source region. The hindcast skill analysis of subseasonal to seasonal scale models, namely Extended Range Prediction Application to Society (ERPAS) and Global Seasonal Forecast version 5 (GloSea5), shows that the models can predict northern Indian rainfall associated with eddy–mean flow interactions at 1‐week lead times. After a week, the skill of both models diminishes under the influence of transient eddy transport. The monsoon circulation is more consistent and predictable in both models when transient eddies are absent. A theoretical understanding of the dynamical feedback of upper‐tropospheric transient eddies is crucial for improving rainfall prediction.

The rising temperatures impact the environment, economy, public health, and global climate. This rise can be attributed to greenhouse gas emissions, urbanization, deforestation, and changes in oceanic currents. Higher temperatures pose a health risk and can result in dehydration and heat stroke while also affecting agricultural yields, aggravating water scarcity, increase in the frequency and intensity of hydroclimatic extreme weather events such as heatwaves, flooding, or droughts in different regions. Further, it can affect the construction, energy generation, and tourism industries. This paper highlights the fundamental factors behind the summer temperature rise in India and its impacts. The recommendations aim to improve the adaptation to the changes on individual and governmental levels. There is a need to encourage a carbon-neutral economy and tap into the resources for research and development of technologies. The paper also underscores the relationship between increased temperatures and the possibility of a pandemic in the future, as increased temperatures have the ability to change the pathogen behavior, and understanding the relationship between both is essential to formulate policies and future interventions.

This paper presents a novel approach where tunable graphene is loaded on an ultra wide-band (UWB) antenna which is used as a switch in THz spectrum and shifts the frequency of notch band. The proposed tunable technology may be a replacement of MEMS or any other switching diodes which are being used for reconfigurability in antenna design. The impedance matching of the THz antenna is enhanced with a partial ground and a tapered-shaped radiator, offering an operating bandwidth from 3.2 to 13.2 THz. Accomplishment of a complementary split-ring resonator (CSRR) in the radiator creates a notch band from 3.55 to 4.25 THz which can be tuned with graphene strips. The changes in the chemical potential of graphene modulate the conductivity and shift the notch band from 3.55–4.25 to 4.05–5.05 THz. The antenna achieves high radiation efficiency with stable radiation pattern. The presented antenna can be used in different THz applications such as 6G, bio-sensing, and bio-imaging applications.

Earthquake and tsunami forecasting are critical components of disaster preparedness and mitigation efforts. This paper presents a novel HawkTide ProForecast model that integrates the Refined Red-Tailed Hawk (RR-TH) algorithm for feature optimization and the Enhanced Time-series Dense Encoder (ETiDE) model for forecasting seismic events and tsunamis. The RR-TH algorithm mimics the hunting behavior of hawks to efficiently select the most relevant features from seismic data, enhancing the model's capacity to seize essential patterns. The ETiDE model, known for its accuracy in time-series forecasting, utilizes dense encoding techniques to capture intricate temporal patterns and dependencies in sequential data streams. To evaluate the proposed model, we use standard metrics including precision, accuracy, recall, and F1 score. These metrics provide the model's performance in predicting earthquake alert levels and tsunami possibilities. The model is trained and tested on historical seismic data to demonstrate its effectiveness in real-world scenarios. Our experimental results show that the integrated model outperforms traditional methods in terms of prediction accuracy and reliability. The precision, accuracy, recall, and F1 score metrics demonstrate the model's ability to accurately forecast seismic events and tsunamis, highlighting its potential for improving early warning systems and disaster response strategies.

Studies related to impact of black carbon (BC) aerosols on weather phenomena like monsoon breaks, winter fog events, pre-monsoon heatwaves etc. are sparse in India. This study fills the gap of observational information of BC aerosols and their relationship with meteorological phenomenon. We examined the interaction between BC aerosols and precipitation during the monsoon's active-break cycle, a critical period for agriculture, water resources, and weather patterns. Data from stations in rural and urban areas provided contrasting seasonal and diurnal variation. The diurnal pattern is closely linked to anthropogenic activities and meteorological factors. The study examines significant diurnal and seasonal variation in relation to local and regional meteorological variation. BC concentrations show distinct bimodal diurnal patterns, with major peak in the evening, between 2000 h to 2300 h IST and secondary peak in the morning between 0700 h to 0900 h IST. Seasonal variations show the lowest BC levels during the monsoon due to efficient wet scavenging, while the highest levels occur during the post-monsoon, primarily from agricultural burning. Meteorological factors like temperature, humidity, rainfall, and wind speed significantly influence BC dynamics. Higher temperatures and lower humidity increase BC levels, while rainfall reduces them, and wind disperses BC aerosols, affecting their concentration and distribution. Analysis of pre-monsoon heatwaves, winter fog events, and monsoon conditions reveals the complex interplay between BC aerosols and weather patterns. Local meteorological factors such as temperature inversions and wind patterns significantly influence the BC impact on weather phenomena. This research enhances the understanding of BC pollution and its diverse effects on weather and climate, emphasizing the importance of integrating meteorological factors into air quality management and policymaking. It lays the groundwork for developing targeted strategies to mitigate BC's adverse effects on health and environment in India.

The aim of the proposed work was to design SNPs by green synthesis employing plant-based reducing agents, optimized through a 3² factorial design for antioxidant, antimicrobial, and anticancer activities. The novelty of this study lies in the synergistic use of piperine and chamomile extracts to enhance the bioactivity of silver nanoparticles compared to methods that used a single agent synthesis, which has been reported in previous studies. The OSNPs were characterized for SEM, FTIR, XRD, in vitro drug release studies, and biological activities. Characterization resulted in the description of the piperine as well as the chamomile synthesized SNPs being triangular, which had the appropriate size for efficient biomedical application. In vitro drug release studies demonstrated a rapid initial release (33.7% within 3 h), followed by sustained release (78.5% by 7 h) and near-complete release (97.8% by 10 h), which made them suitable for cancer therapy by providing high initial doses and prolonged therapeutic levels. The OSNPs had antioxidant activity much stronger with the smallest IC50 value (10⁶ µg/ml) than the plant-based SNPs when used as individuals, with significantly better antibacterial and antifungal efficiency with large inhibition zones against E. coli, S. aureus, B. subtilis, and Aspergillus fumigatus. OSNPs also exhibited higher dose-dependent cytotoxicity against HeLa cells, as shown by their lower IC50 value of 60.25 µg/ml, whereas in the case of the crude mixture, the value was 66.6 µg/ml. The results of the present work also reveal the more significant implications of using multi-compound green synthesis methods in the synthesis of nanoparticles that have greater therapeutic potential. On account of their strong antioxidant, antimicrobial, and anticancer activities, OSNPs are promising applications for the treatment of oxidative stress-related diseases, antimicrobial resistance, and cancer, therefore allowing further studies that may be justified for clinical and pharmaceutical progress.