MAUSAM

Temporal variation in precipitation and temperature in Arunachal Pradesh, India

Tue, 01 Jul 2025 00:00:00 +0000

This study examines the temporal variations in rainfall and temperature over the Lepa Rada district of Arunachal Pradesh from 1983 to 2021. Using both parametric (Linear Regression) and non-parametric (Mann-Kendall and Sen's slope) methods, significant climatic trends were identified. The analysis revealed a statistically significant declining trend in winter rainfall, with linear regression indicating a decrease of 2.809 mm/year, corroborated by significant Mann-Kendall’s Z statistic and Sen’s slope estimates. Temperature trends showed a significant increase in maximum temperatures across all seasons(spring@ 0.109 °C/year; summer @0.103 °C/year; autumn @0.113 °C/year and winter @0.139 °C/year), with annual rates of increase observed at 0.115°C/year (p<0.01) through linear regression. Similarly, the Mann-Kendall test confirmed these findings, with significant Z values across all seasons. In contrast, minimum temperatures showed a significant declining trend during spring and winter, suggesting increased coldness over time. These trends hold significant implications for agriculture and environmental planning in the region. The decline in winter rainfall and the rise in maximum temperatures could adversely affect crop yields and water resources, emphasizing the need for adaptive agricultural practices and efficient water management strategies. Additionally, the increase in coldness during winter and spring suggests the importance of developing climate-resilient crops and improving forecasting systems to help farmers better prepare for changing weather conditions.

Heavy rainfall events over Rajasthan and Madhya Pradesh during 1st week of August 2021: a meteorological case study

Shashi Kant, Rizwan Ahmed, Sunil Kumar — Tue, 01 Jul 2025 00:00:00 +0000

During southwest Monsoon, heavy rainfall and flood events are usually observed in different parts of the country. Therefore, their documentation and systematic study is an important scientific work. This can help in the improvement of weather forecasting and also mitigation of associated natural disasters. The investigation of associated meteorological features is a matter of concern for operational forecasters. In this study, a case of persistent heavy rainfall events is considered that occurred during the first week of August 2021 over some parts of Rajasthan and Madhya Pradesh. Some of the stations from these regions received an all-time high 24-hour (RR24) cumulative precipitation in August 2021. Due to this event, flooding occurred and a huge loss of agricultural and horticultural crops occurred in some parts of Madhya Pradesh and adjoining areas. The present study attempts to understand the various meteorological features associated with this heavy rainfall event. Following meteorological features are found to be favourable in this weather activity; (i) Synoptically, there was a well-marked low-pressure area/ low-pressure areas over the region with the associated cyclonic circulation extending up to middle/upper tropospheric levels. (ii) Availability of continuous moisture supply at lower levels over the region. (iii) Favourable dynamical parameters like relative vorticity up to upper tropospheric levels, high values of low-level convergence, and upper-level divergence over the region. (iv) CAPE of the order of 1000-2000 J/Kg reported over Rajasthan and Madhya Pradesh mainly over northern parts remained which helped formation of intense convection. (v) In addition to CAPE, KI of the order of 35-40 were reported over Rajasthan and Madhya Pradesh with isolated patches of 40-45; LI of the order of -2 to -4 are reported over most parts of the country except for the order of -4 to -6 with isolated patches of the order of -8 to -10 and SWEAT Index in the range of 400-500 °C over northwest Madhya Pradesh & adjoining east Madhya Pradesh.

Dynamics of Orographic Rainfall for mountain corner over north-east India

Prasanta Das, Somenath Dutta — Tue, 01 Jul 2025 00:00:00 +0000

In north-east India, the Corner Mountain is an almost right angled corner, which is made between the Khasi-Jayantia hills (KJH) & the Assam-Burma hills (ABH). In this paper, has been made to analysis the effects of orographic rainfall during the south west monsoon season (SWMS) over the north-east region of India, in a baroclinic mean flow. To construct this model a 3-D laminar, non-viscous, adiabatic, Boussinesq & non rotational moist airflow has been considered. The basic flow has two components, zonal wind U(z) and meridional wind V(z) and the Brunt-Väisälä frequency (N) is taken to be variant with height. The non-linear governing equations of the airflow are linearized by using the perturbation technique. To compute the rainfall intensity (RFI), the vertical velocity ( ) is obtained by quasi-numerical approach. In this study, two selective dates has been considered and all results have compared to the earlier researchers.

A further study of a special event of low-level windshear at the Hong Kong International Airport

P. W. Chan, K. K. Lai — Tue, 01 Jul 2025 00:00:00 +0000

This is a follow-up study of a special windshear case at the Hong Kong International Airport. In the previous study, the airflow disturbances observed by the Doppler LIDAR for cross-mountain airflow could reasonably be reproduced in the simulation, but there are two aspects that are not simulated so satisfactorily, namely, the jumps in temperatures and dew points at the weather buoys over the sea to the west of the airport and the reverse airflow at that area. The present study considers a number of model setups in order to reproduce these two features, namely, the use of different numerical weather prediction models, the choice of vertical co-ordinate system and the choice of turbulence parameterization scheme. It is found that the use of Weather Forecasting and Research (WRF) model with MYNN turbulence parameterization scheme seems to give the best results. The choice of vertical co-ordinate system appears to be secondary. The mechanism for the rapid fall and rise in temperatures and dew points is investigated further based on the simulation result. They are found to be related to a change of the flow regime in the airport area, namely, from prevailing easterly flow from the airport, to a more southerly flow crossing the mountains. The present study may serve as a reference for simulating wind flow in areas of complex terrain with a high spatial resolution using mesoscale numerical weather prediction models.

Impact assessment of climate change and adaptation measures through different RCPs scenarios using CERES-Wheat model for wheat yield under different agroclimatic zones of Punjab, India

Sarabjit Singh, Prabhjyot Kaur, Amarinder Singh Riar — Tue, 01 Jul 2025 00:00:00 +0000

The Climate change impact under Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) was studied in Punjab state conditions. The study area includes three locations for which the bias corrected temperature and rainfall meteorological data for Ensemble model was collected and used as input in calibrated and validated DSSAT CERES-Wheat model. The model run for the assessment of yield for two wheat cultivars (HD 2967 and PBW 725) using deviation from the baseline period (2010-2021) for a 70 years (2025-2095) time period in near (2025-2055) and far (2066-2095) future scenarios. The current dates of sowing observed a yield decline at different agroclimatic zones for near and far future under four scenarios as agroclimatic zone II Ballowal Saunkhri (4-37%and 0.6- 35%) agroclimatic zone III Amritsar (0.4-32% and 0.3 to 38%) and Ludhiana 0.65-32% and 0.32-38%). The major decline in yield was indicated under high emission scenario i.e. RCP 8.5 during the far future whereas RCP 2.6 indicated low decline in wheat yield during both near and far future. The declination in yield at different locations indicated a requirement of optimized sowing window under different future scenarios. The results for optimized sowing window showed that adjusting in the sowing dates of all the three locations at last week of November for both the wheat cultivars to get maximum yield.

Weather in India MONSOON SEASON (June to September 2024)

Editor Mausam — Tue, 01 Jul 2025 00:00:00 +0000

Cyclonic storms & depressions over the North Indian Ocean during 2024

Editor Mausam — Tue, 01 Jul 2025 00:00:00 +0000

Comparative analysis of recurrent neural networks for weather prediction in the Antarctic region

V Sakthivel Samy, Veena Thenkanidiyoor — Tue, 01 Jul 2025 00:00:00 +0000

Numerical weather prediction is a well-established method that uses current atmospheric conditions as inputs to solve wind, temperature, pressure and humidity equations. This study examines the use of deep learning for meteorological forecasting using historical data from the Bharati Station, Antarctica. Different unique recurrent neural network models have been developed using a deep learning framework and explicitly trained to predict the weather conditions of the next 24 to 48 hours.The effectiveness of our proposed approach is compared against state-of-the-art neural network algorithms, and the results demonstrate better forecasting performance.In this study, the Transformer model has the lowestRoot Mean Square Error (RMSE) of 0.000478, making it one of the most efficient models in the neural networks investigated. This progress facilitates a more efficient development process, which, in turn, enhances the accuracy of weather forecasts.

Assessment of Heavy Rainfall cases during the monsoon 2022 over Arunachal Pradesh

Sandeep Araveti , Arun V. H., S. I. Laskar , Sunit Das — Tue, 01 Jul 2025 00:00:00 +0000

The performance of IMD-Global Forecast System (GFS) model forecasts is evaluated against observed rainfall data during the southwest monsoon of 2022 for Arunachal Pradesh (ARP). A total of 14 heavy rainfall (HRF) cases were examined when the rainfall exceeds 64.5 mm in 24-hours. With a correlation coefficient (R) of 0.71 between the observed and model day-1 predicted rainfall exhibits a very good agreement, however, R -values were decreased by 2-4% as the forecast lead time increased. In addition, for day-1 model overestimated the observed rainfall by 10-20% and has increased further by 4-6% as the lead time progresses. Interestingly, the model forecasts noticed the 7-homogeneous HRF zones in ARP during the monsoon 2022, whereas the observations noticed only 5 zones. To assess forecast performance further, skill metrics such as Probability of Detection (POD), False Alarm Rate (FAR), Equitable Threat Score (ETS) and Heidke-Kuiper skill score (HK) were computed. Results show that the POD for day-1 forecasts is 0.82, and the FAR is 0.32 indicating a high rate of correctly predicting HRF events. In addition, ETS of 0.43 and HK score of 0.38 suggest moderate model performance. These skill scores decrease by 2-4 percent as lead time increases. Furthermore, the model forecasts are evaluated based on the warning color category (yellow, orange, and red). The results suggest during the days when the yellow alert is given the POD rate is about 0.80, whilst the orange (red) alert days the POD rate is 0.52 (0.25), respectively at one-day lead time. It indicates that the model performs is best in predicting HRF events during the yellow alert days but less accurate for orange, and red alert days for all lead times.

On the increasing length of the southwest monsoon season over India

Satya Prakash , R. K. Giri, S. C. Bhan — Tue, 01 Jul 2025 00:00:00 +0000

The southwest monsoon rainfall has a paramount impact on agricultural practices and water resources management across India. This study investigates long-term changes in the southwest monsoon length over India for the period from 1971 to 2020. The length of the southwest monsoon season across the country shows an increase at the rate of 1.6 days per decade during 1971-2020. The rate of increase in the southwest monsoon length coincides with the rate of delay in the monsoon withdrawal dates during this period. Although the monsoon onset date over Kerala (MOK) shows notable interannual variability, it does not show significant change during the last 50 years. Rainfall between 01 June and 30 September (JJAS) is typically considered for the southwest monsoon rainfall analyses. This study investigates the difference in rainfall between MOK and withdrawal dates (termed as MOK2WDRL), and JJAS rainfall for 1971-2020 using a rain gauge-based gridded rainfall dataset. JJAS rainfall contributes 75% to all-India annual rainfall, whereas it is 79% by MOK2WDRL rainfall. The interannual variability of all-India monsoon rainfall is also shown to be different for several years from MOK2WDRL and JJAS rainfall estimates. The correlation coefficients with Nino 3.4 index, all-India rabi crop yield, and all-India rapeseed & mustard yield are shown to be higher with MOK2WDRL rainfall than JJAS rainfall over the country.

The vertical and horizontal structure on the first intensification of tropical cyclone Seroja

Ida Pramuwardani , Fakhrul Alam, Andri Ramdani , Guswanto . — Tue, 01 Jul 2025 00:00:00 +0000

Since Indonesia lies in the equatorial region, tropical cyclones (TC) are quite rare. The cyclogenesis phase which is prior to the TC stage usually takes place in the low latitude region like Indonesia, where the sea surface temperature is quite high and hence favourable to induce low-pressure areas, apparently giving a meaningful impact as well as during the TC stage. However, some tropical cyclones ever occurred very close to the Indonesia region such as TC Vamei in 2001-2002 and TC Cempaka in 2017. The most recent tropical cyclone in Indonesia, namely TC Seroja, occurred very close to Indonesia and hit Nusa Tenggara Timur in 2021. For Seroja itself, the system tends to be unique because it started from a cyclogenesis stage near the equator line and dissipated far in western Australia which survived more than one week before landfall. The first intensification that was categorized into a TC was just a short moment, which then dissipated and intensified again reaching its strongest intensity over the Indian Ocean. TC Seroja was evaluated in this paper to understand the system characteristics, environmental condition, and structure, especially for TC that took place in the near tropical area. The vertical structure is evaluated as well as the horizontal structure to understand the characteristics of the system. This paper revealed that the TC Seroja, with a typical cyclogenesis phase when it struck Indonesia, had the most tremendous impact caused especially by precipitation. The strong environment wind shear is one of the factors that makes TC Seroja unique vertically and horizontally. By this paper, the information during the TC stage in the tropics is expected to be achieved, thus, not only the TC characteristics and structures during the mature or strongest intensity are acknowledged, but also the first TC intensification stage, especially in the tropical region.

Source characterisation of June 2023 Doda (Kashmir) earthquake

Tue, 01 Jul 2025 00:00:00 +0000

On the 13^th of June 2023, at 13:33:42 IST, an earthquake of magnitude Ml5.1 occurred in the Doda region of Jammu & Kashmir (J&K). The earthquake's epicenter was at coordinates 33.15 N and 75.89 E, with a shallow depth of 10 km. This seismic event unfolded within the geological context of the Himalayan orogeny, which has taken shape as a consequence of the collision between the Indian plate and the Eurasian plate. The Himalayan region, characterized by the intricate dynamics of these tectonic collisions, harbours numerous significant regional and local fault systems. A substantial portion of these fault lines remains active, continually generating seismic activity throughout the Himalayan region and its adjacent areas. Despite causing minor structural damage to a few buildings in the source region, this particular earthquake was well-documented and was accurately located by the Indian National Seismic Network, which is operated by the National Centre for Seismology (NCS), Ministry of Earth Sciences (MoES). Notably, the NCS-MoES maintains three Seismological observatories in Jammu & Kashmir (specifically in Jammu, Srinagar, and Udhampur), along with two additional Seismological observatories in Ladakh (in Hanley and Alchi). In the present study, we have harnessed waveform data collected from a network of 15 Seismological observatories managed by NCS-MoES, strategically positioned across J&K, Ladakh, and Himachal Pradesh. Our research endeavours to estimate the source characteristics including the precise determination of the earthquake's geographical location. Our analysis employed specialized software tools, SEISAN for epicenter localization and ISOLA for fault plane solution. Moreover, these derived seismic parameters served as foundational data for further analyses. Specifically, we leveraged these parameters to quantify the energy released and determine the source radius. These additional insights provided a comprehensive characterization of this earthquake and its implications.

Mapping the lightning hotspot: reducing deaths in Jharkhand, India, using spatiotemporal and statistical insights

Anand Shankar, Abhishek Anand, Ashish Kumar, S. P. Singh — Tue, 01 Jul 2025 00:00:00 +0000

This research investigates the spatial and temporal variations of lightning flashes, specifically in-tra-clouds (IC), negative cloud-to-ground (NCG) and positive cloud-to-ground (PCG) strikes, across the state of Jharkhand, India. Actual georeferenced lightning data (IC, NCG, and PCG) were collected from the Indian Institute of Tropical Meteorology's (IITM) Lightning Location Network (LLN) sensor deployed over the Indian subcontinent during 2019–2023. Corresponding the Department of Home, Jail, and Disaster Management, Govt. of Jharkhand, provided lightning-related casualty data for the studied periods. Jharkhand's diverse geography, encompassing hills, plateaus, and forests, creates favorable conditions for the development of thunderstorms and lightning strikes, particularly during the pre-monsoon (March to May) and monsoon seasons (June to September). These periods of moist air masses are marked by atmospheric instability, increasing the likelihood of lightning strikes. Rural areas, lacking adequate lightning protection systems and awareness of safety protocols, are especially vulnerable, posing risks to lives and property during thunderstorms. The study employs spatiotemporal statistical techniques, including interpolation methods, to analyze lightning data. Geospatial heat maps illustrate the spatial and temporal variability of lightning occurrences and associated casualties in Jharkhand's lightning hotspots. The research comprehensively examines seasonal lightning patterns, location-specific susceptibility, temporal hazards, and related meteorological factors.

Furthermore, the research proposes strategies to enhance disaster preparedness in Jharkhand, emphasizing the need for strengthened meteorological monitoring and early warning systems, particularly in remote areas. Public awareness campaigns targeted at vulnerable groups such as farmers and tribal communities are crucial to improving lightning safety knowledge and preparedness. Community resilience-building initiatives, including disaster management training and local involvement in emergency response planning, are recommended to mitigate the impacts of lightning strikes effectively.

Rainfall-runoff modeling and impact assessment of land parameters on water availability in Bisalpur reservoir, semi-arid Banas basin, Rajasthan, India

Sanjay Kumar Agarwal, Priyamitra Munoth, Archana Sarkar, Rohit Goyal — Tue, 01 Jul 2025 00:00:00 +0000

The accessibility of the water in the reservoir, particularly in semi-arid regions, holds paramount significance for the residents of the command and catchment areas. Situated in the semi-arid landscape of Rajasthan, India, the Bisalpur Dam plays a crucial role in providing drinking water to Jaipur, Ajmer, and Tonk districts, apart from catering to the command area's agricultural needs. Knowing the water availability in the reservoir is important for the Water Resources Department (WRD) to optimise the utilisation of water resources and planning. Therefore, in this study, efforts have been made to model and predict inflow at Bisalpur Dam using the SWAT model. The calibration of the model was done for 2001-2008 with a warm-up duration of 2 years (2001-2002) and validated for 2009-2013 on a monthly scale. The performance indices like R², NS, and PBIAS have been estimated, and the values were 0.90, 0.91, and 23.6% in calibration and 0.84, 0.70 and -20.6% in validation, respectively, which conclude that the model for the watershed fits successfully. The results show satisfactory model performance, and the Soil and Water Assessment Tool (SWAT) model effectively captured the dynamics of the Bisalpur watershed. As a result of this study, the most important land parameters that affect the water availability in the reservoir are related to soil characteristics such as permeability, soil evaporation, and deep aquifer percolation fraction. The developed model can be utilised for hydrological, land cover, land use, and climate change analysis of the Bisalpur watershed and may be replicated in other semi-arid areas after validation with the observed data.

17-year trend of tropospheric columnar NO2 over India's northeast region observed by OMI : investigating probable anthropogenic and natural sources

Tue, 01 Jul 2025 00:00:00 +0000

This study presents a comprehensive analysis of Tropospheric Columnar NO₂ (TCN) concentrations spanning 17 years (2005-2022) in the Northeastern Region of India (NERI). Remote sensing data from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite was utilized in analyzing the spatiotemporal patterns of nitrogen dioxide (NO₂) concentrations within the region. NO₂ is a prominent atmospheric pollutant that emerges from diverse sources like industrial emissions, vehicle combustion, biomass burning, and natural processes such as lightning and soil emissions. The varying levels of NO₂ pollution in the NERI, with its distinctive topography and meteorological behaviors, may be attributed to urbanization, population growth, and energy utilization. TCN concentrations peak during pre-monsoon and winter months, driven primarily by factors like biomass burning and anthropogenic activities. Long-term data reveals an overall TCN increase, reflecting growing influences from rising vehicles, industrial expansion, and population density. Monthly variations indicate the significance of the pre-monsoon season, characterized by elevated NO₂ levels influenced by lightning and transported NO₂. Forest fires, biomass burning, and combustion engines contribute as major sources of both natural and anthropogenic NO₂. Frequency distribution analysis results exhibit varying air quality statuses across NERI states, emphasizing the need for targeted interventions in regions consistently experiencing high TCN levels. Furthermore, the study assesses the impact of the COVID-19 pandemic, identifying fluctuations in NO₂ concentrations during lockdowns in pre-monsoon seasons. This research emphasizes the requirement for strong monitoring and mitigation strategies to combat increasing NO₂ pollution in the NERI, addressing air quality and broader environmental health issues, necessitating well-informed measures for healthier living conditions.

Analysis of CO2 over Hubei of China in the first round of COVID-19 scenarios

Xuefu Dan, Xulong Wu, Jinye Zhang, Ruibei Liu, Ziyue Hu, Chang Xu — Tue, 01 Jul 2025 00:00:00 +0000

As global warming intensifies and extreme weather events become more frequent, the concentration of carbon dioxide (CO₂) in the atmosphere has become a major concern worldwide. The COVID-19 pandemic, which began at the end of 2019, led to significant restrictions on human activities, resulting in changes of greenhouse gases’ concentrations. CO₂ Data from GOSAT satellite and COVID-19 data during the first wave of the pandemic in Hubei, China, were analyzed.CO₂ concentration during the outbreak of COVID-19 decreased by 1.54 ppm, an unprecedented decline in previous years. The reduced value of the CO₂ concentration in Hubei province ranked second among the 34 provinces in China, only second to Taiwan province. After the outbreak was under control, CO₂ concentration gradually returned to normal levels. The restrictions on resident mobility and industrial production led to a significant drop in electricity consumption across the primary, secondary and tertiary industries, while residential electricity consumption increased substantially, resulting in a rise in fossil CO₂ emissions from the residential sector. However, emissions from power generation, industry, transport, public services, and aviation all significantly decreased. As the pandemic subsided, these trends began to recover.

Methane (CH₄) concentrations were also analyzed in this study. In February 2020, CH₄ concentration decreased by 4.76 ppb, marking the largest decline during the pandemic and reflecting the most severe stage of the outbreak in Hubei. Furthermore, compared to the methane concentration increments in March 2019 and March 2021 (1.21 and 1.06 ppb, respectively), the increment in methane concentration in March 2020 was -0.23 ppb, which was lower than the increments observed in the previous and following years. Similarly, CH₄ showed substantial fluctuations, with the largest drop observed during the peak of the COVID-19 crisis, followed by a recovery as the pandemic situation improved.

Soil temperature prediction in the upper Gangetic Plain of India using a data-driven approach

Tapendra Kumar Srivastava, Ram Ratan Verma, Pushpa Singh, Raj Kumar Saroj — Tue, 01 Jul 2025 00:00:00 +0000

Soil temperature plays a crucial role in determining the kinetics and fate of physicochemical and biological processesinfluencingcrop growth and development. Soil temperature regimes based sustainable intensification of agricultural systems is needed for climate resilience. The present study involved prediction of soil temperature from the air temperature at the ICAR-Indian Institute of Sugarcane Research located in the upper Gangetic Plains of India. The data-driven empirical regression models were identified for prediction of soil temperature at 5, 10 and 20 cm depths. The power model was found the best fit for predicting daily, weekly and monthly morning soil temperature at all three depths, with the exception of exponential model, found best for weekly temperature at 20 cm. Further, power model was the best fit for daily, weekly and monthly afternoon soil temperature predictions at 10 and 20 cm. However, exponential model was the best fit for daily, weekly and monthly afternoon temperature at 5 cm depth. The minimum air temperature was most suitable for predicting morning soil temperature whereas the maximum air temperature for afternoon soil temperature. The power model also served as the best-fit for soil temperature prediction at 10 and 20 cm through the use of soil temperature at 5 cm depth. The accuracy of the best-fit regression models ranged from 97.1 to 99.1%. The present work offers appropriate models for predicting soil temperature based on ambient air temperature. The findings will be useful for researchers, policy makers and farmers to help mitigate climate change impacts on agriculture.

Trend analysis of monthly and seasonal rainfall of IARI research farm (New Delhi)

Tue, 01 Jul 2025 00:00:00 +0000

Climate change is likely to impact rainfall patterns leading to higher uncertainty in management of scare water resources, flood and drought events. Keep this in view the rainfall data of IARI research farm were analyzed using non-parametric Mann-Kendall (MK) test and Sen’s slope estimator approaches. Analysis of long-term rainfall data (1991-2020) indicated that IARI receives a normal annual rainfall of 802 mm and normal monthly rainfall for June (82 mm), July (219.7 mm), August (239.8 mm), September (117.8 mm), while for remaining months varies from 4.2 to 33.9 mm. Moreover, normal seasonal rainfall for pre-monsoon (63.3 mm), monsoon (639 mm), post-monsoon (32.5 mm) and winter (40.8 mm), besides this for cropping seasons, 680.2 mm (kharif), 142.8 mm (rabi) and 149.6 mm (zaid). The MK and Sen’s slope approach applied to monthly, climatic and cropping seasons indicated intra variability in monthly, and seasonal rainfall trends (increasing/ decreasing) among the period of 1991-00, 2001-10 and 2011-20. Also, significant increasing trend were observed for March, April, July, pre-monsoon and rabi crop season with ZMK of 0.22, 0.23, 0.34 and 0.30, respectively. Overall, it can be concluded that the about 80% of normal annual rainfall during monsoon season of four months (June-September) can be used by crops, conserved in lined water harvesting structures or used for groundwater recharge.

Assessing the air temperature and rainfall trend over the undulating red and lateritic zone of West Bengal using Mann-Kendall test and Innovative trend analysis

Argha Ghosh, Dillip Kumar Swain, Chanraprakash ., Suchismita Tripathy — Tue, 01 Jul 2025 00:00:00 +0000

The present study aimed to assess the trend of maximum and minimum temperature at monthly scale and rainfall at monthly, seasonal and annual scale over the undulating red and lateritic zone of West Bengal, India. “TerraClimate” monthly maximum and minimum air temperature and rainfall data was collected from Google climate engine platform for the period 1958- 2021. The traditional Mann-Kendall (MK) test, Sen’s slope estimator and Innovative trend analysis (ITA) were implemented to detect the trend. The results clearly indicated that the red and lateritic zones experienced significant temperature increase during the last six decades. Both the Sen’s slope estimator and ITA showed that the minimum air temperature of February increased at the highest annual rate followed by March and November. Annual rainfall over the study area followed non- significant increasing trend as confirmed by both the MK test and ITA. The monthly and seasonal rainfall distribution pattern changed. A shift of the South-west monsoon was realized over the study zone characterized by decreasing rainfall in June and increasing rainfall in October. The ITA successfully detected the monotonic and non-monotonic trend of air temperature and rainfall. Historical trend of the extreme weather values were detected by ITA. Higher values of November rainfall showed positive trend. On the contrary, a positive trend was detected for the lower values of pre-monsoon rainfall. The present study indicated that the red and lateritic zone experienced significant changes in the temperature and rainfall pattern. The findings of the present study can be used for assessing the future trend of the weather parameters and planning and sustainable management in this climatologically and socio-economically vulnerable zone.

Spatial Variations of Northeast Monsoon Rainfall over South Peninsular India

Tue, 01 Jul 2025 00:00:00 +0000

This study investigates the spatial variability of Northeast Monsoon (NEM) rainfall over South Peninsular (SP) India, focusing on five meteorological subdivisions: Coastal Andhra Pradesh, Rayalaseema, Tamil Nadu, South Interior Karnataka, and Kerala. Using rainfall data from the Indian Institute of Tropical Meteorology (IITM), seasonal rainfall patterns are assessed for the NEM period (October-December), identifying active and poor monsoon years based on standardized departures. Composite rainfall values and percentage deviations provide insights into NEM performance across subdivisions. The analysis also explores key meteorological parameters, including lower tropospheric winds, relative vorticity and divergence at 1000 hPa, 850 hPa, and 200 hPa, using NCEP/NCAR Reanalysis datasets from 1948 to 2016. Outgoing Longwave Radiation (OLR) data from NOAA (1974–2016) further elucidate atmospheric circulation patterns associated with active and poor monsoon years. Findings reveal that active NEM seasons display negative OLR anomalies over SP India and surrounding seas, indicating enhanced convection, particularly in Coastal Andhra Pradesh, Rayalaseema and Tamil Nadu. Conversely, poor monsoons show positive OLR anomalies, signalling reduced convective activity. Wind and divergence anomalies highlight that active monsoons strengthen easterly flows and promote moisture transport to the Intertropical Convergence Zone (ITCZ), amplifying rainfall. During poor monsoons, weakened easterlies and reduced atmospheric convergence correspond with diminished rainfall. Notably, El Niño events generally enhance NEM rainfall, while La Niña years typically result in deficits, with some exceptions, such as in 2016.

Weather context in the Vietnamese Mekong Delta under the impacts of the typical ENSO phases

T. V. H. Hoang, T. A. Dang — Tue, 01 Jul 2025 00:00:00 +0000

The shifting season features of precipitation due to the El-Niño and La-Nina events, under the impacts of global climate change (GCC), have increased the potential risks of crop yield decline in rice cultivation areas (CCAs) worldwide. This study examines the relationship between the typical El-Niño and La-Nina phases and the season features of precipitation, and their subsequent impact on CCAs within the Vietnamese Mekong Delta (VMD).

Daily rainfall data from 12 observation stations from 1986 to 2022 were analyzed to investigate the connection between ENSO events and season features across the VMD. The results indicate that the El-Niño and La-Nina events significantly influence the timing and duration of season features in the VMD.

During the La-Niña event in the period 1999-2001, the rainy season onset date (RSOD) and rainy season cessation date (RSCD) occurred three weeks earlier and two weeks later, respectively, than the long-term average (1986-2022). Conversely, during the extreme El-Niño event of the stage 2014-2026, the RSOD and RSCD were delayed, occurring 10.9 and 5.0 days later than the long-term average, with a shorter length of rainy season (LRS) by 5.9 days over the entire VMD. These shifts in the season features of precipitation under the GCC have impacted cultivation activities in the VMD. The findings underscore the vulnerability of rice cultivation regions in the VMD to ENSO-driven climate variability, emphasizing the importance of proactively implementing adaptation solutions to mitigate the negative impacts of GCC.

TREND ANALYSIS OF RELATIVE HUMIDITY IN THE AGRO CLIMATIC ZONES (ZONES II &IIIA) OF BIHAR

Tue, 01 Jul 2025 00:00:00 +0000