Meteorological Subdivisions Research Articles

Multifractal characterization of meteorological to agricultural drought propagation over India

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Impact of cloud properties and aerosols on the spatial distribution of Indian summer monsoon rainfall: A case study

Quantitative seasonal Indian summer monsoon (ISM) rainfall forecasting has been a challenge for the climate models as it is sensitive to cloud properties, which are modulated by large-scale dynamics. Proper understanding of the role of dynamics, aerosols, and cloud microphysical processes is important for the precipitation calculation in dynamical models. The all-India averaged seasonal ISM rainfall (ISMR) from June to September had been normal or above normal, however there were large spatial variations. Some meteorological subdivisions, the majority of which lie in the Gangetic Plains, received deficient seasonal rainfall during recent years 2022 and 2010. The quantitative seasonal prediction of the spatial distribution of ISMR from global coupled models (GCMs) has also shown a hard time for accurate spatial rainfall distribution. Four monsoon years (2022, 2021, 2011, and 2010) are considered, in which the country as a whole (all India land points) was ‘above normal’ or ‘normal’. However, in two specific years (2022 and 2010), some subdivisions received deficient rainfall (17% and 15% of the total area). We have used several datasets from the ground, and satellite observations, along with reanalysis products and computed climatology and anomalies for monsoon years of 2022 and 2010. The low-level wind and vertical velocity along with cloud microphysics played a pivotal role in the heterogeneous distribution of ISMR during 2022 and 2010 as compared to years 2021 and 2011. Here, we have demonstrated that the spatial heterogeneity of the positive and negative precipitation anomalies was consistent with cloud properties. Aerosols might have an impact on the cloud microphysical processes and modulate cloud properties and rainfall over different regions due to the availability of water vapor. Therefore, the inclusion of aerosol indirect effects in climate models is crucial for better interactions among dynamics, aerosol, and cloud microphysics. Proper representation of cloud droplet size distribution and microphysical conversion rates might be helpful for quantitative seasonal ISMR forecasting at a smaller spatial scale.

Evaluation of the Rayalseema Meteorological Subdivision’s Drought Using Multiple Drought Indices

Evaluation of the Rayalseema Meteorological Subdivision’s Drought Using Multiple Drought Indices

How virtuous are the bias corrected CMIP6 models in the simulation of heatwave over different meteorological subdivisions of India?

With rising global temperatures, extreme weather events have become more frequent, intense and of longer duration. CMIP6 GCMs provide improved climate simulations that need robust evaluation for historical period for reliable future projections. The present study assesses the ability of bias corrected CMIP6 14 Global Climate Models (GCMs) in simulating heat wave over India for March–June during the historical period (1951–2014). Heat waves were identified using IITM criteria. Model biases were removed using variance scaling bias correction method that showed higher correlation (0.93) and lower root mean square error (2.15) and improvement in approximating the inter-annual variability as well as spatial patterns as observed maximum temperature after bias correction. Evaluation of model performance for 95th and 99th percentile maximum temperature and heatwaves showed that most of the models simulate these extremes similar to observation. Northwestern, Central and South-central regions recorded highest number of heatwaves with a frequency of 50 heatwave days/decade, which were captured by the most of the GCMs varying in decadal frequency over the region. Among the GCM, although all models were found competent, ACCESS-ESM1–5, MPI-ESM1–2HR and MRI-ESM2–0 models were found to be the best performing models for extreme indices and heat wave simulation over India. The study will aid to the current understanding of CMIP6-GCMs performances over the different meteorological subdivisions of India and pave way for future projection of heat waves as well as reduction in uncertainty among the models.

An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset. Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

Analysis of long-term trends of rainfall and extreme rainfall events over Andaman & Nicobar and Lakshadweep Islands of India

The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.

Exploring the impact of climate change on long-term and short-term variability of rainfall in Madhya Pradesh, Central India

The present study assesses trends in rainfall in Madhya Pradesh, Central India, over the long (1871–2016) as well as short (1987–2016) temporal scales. The region is home to disadvantageous groups of the population including vulnerable tribals who depend on rainfall-based agriculture and forest products. Therefore, any disruption in rainfall trends due to climate change needs to be documented for better planning. This study focuses on two meteorological subdivisions, East and West Madhya Pradesh, which receive a major portion of rainfall through the Bay of Bengal branch and the Arabian Sea branch of the Indian Summer Monsoon. The study is based on the rainfall series provided by the Indian Institute of Tropical Meteorology, Pune, India. This study used the Mann–Kendall test to detect a trend and Sen’s Slope to estimate the magnitude of change in rainfall. Statistically significant negative trends were detected in total annual (− 1.125 mm y − 1) and monsoon (− 1.242 mm y − 1) rainfall over long-term observation in East Madhya Pradesh only. However, during the last 30 years, no trends have been recorded in East Madhya Pradesh. In West Madhya Pradesh, rainfall has not recorded any trend for long- as well as short-term observation periods. The non-availability of a trend in rainfall during May and October at both meteorological subdivisions shows that there is no shift in the duration of the monsoon season over long as well as short temporal observations. The decreasing rainfall trends in Eastern Madhya Pradesh over the long-term observation seem to be caused by changing patterns of tropical cyclone frequency in the Bay of Bengal.

Heat Waves and Its Impact on Crop Production and Mitigation Techniques: A Review

Globally, heat waves are getting worse and more common. India experienced its warmest March and April on record, with maximum and minimum temperatures unusually rising in most of the nation. In 10 out of the 36 meteorological subdivisions during this time, the extreme temperatures were found to be higher by +8 to +10.8°C and the rainfall lower by -60 to -99 percent, respectively, compared to normal. Additionally, 2022 will be remembered as a prime example of the combined effects of high temperatures and low rainfall on agricultural production systems, particularly in northern and central India. Climate variability and change have an impact on India's agricultural production. In the states of Punjab, Haryana, and Rajasthan, abnormally high maximum and minimum temperatures in 2022 had an impact on livestock, crops, and fruits and vegetables. Himachal Pradesh, Uttar Pradesh, Madhya Pradesh, Bihar, and Maharashtra, as well as Jammu and Kashmir. The heat wave coincided with the wheat crop's grain filling and development stage, causing the grain to yellow and shrivel, forcing it to mature, and lowering yields by 15 to 25 percent. A variety of technologies are available to reduce the yield loss in wheat caused by a heat wave, and some of them have been implemented on farmer fields. As compared to the local variety HD-3086, several heat-tolerant wheat varieties, including PBW 803, DBW 187, and DBW 222, can withstand high temperatures while still producing normal yields.

Annual and seasonal trends in actual evapotranspiration over different meteorological sub-divisions in India using satellite-based data

Annual and seasonal trends in actual evapotranspiration over different meteorological sub-divisions in India using satellite-based data

Short to medium range impact based forecasting of heavy rainfall in India

There have been major advances in the last few decades in our understanding of heavy rainfall during monsoon season due to substantial progress in both observation and numerical modelling of monsoon. All these resulted in more accurate forecast of heavy rainfall in short to medium range, (upto five days) with 40% improvement in accuracy of heavy rainfall forecast in recent five years (2018-2022) as compared to previous five years. However, improvement of forecast and warning skill is not sufficient to minimize damage to lives and property. It is essential to extend to hazard forecast systems (hazard models) and then to impact and risk assessment with stakeholder interaction for risk based warning (RBW) and response action to protect lives and livelihoods Considering all these, India Meteorological Department (IMD) has introduced impact based forecast (IBF) for heavy rainfall at meteorological sub-division level since July 2013 and at district and city scale in August, 2019 in its short to medium range forecasts and nowcasts indicating the likely impact of the heavy rainfall in different sectors and required response actions. Thereafter the IBF of heavy rainfall has undergone several changes over the years. Currently, the IBF being implemented by IMD includes all the four components, viz., (i) meteorological hazards, (ii) geophysical hazards, (iii) geospatial applications and (iv) socio-economic conditions and it utilises a web-GIS based decision support system (DSS). In this study we have reviewed various approaches and stages of development of IBF of heavy rainfall in India. The success of IBF of heavy rainfall will enhance the management of critical resources like agriculture, water & power and support urban and disaster management sectors among others while reducing loss of life and property.

A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India

Precise rainfall measurement is essential for achieving reliable results in hydrologic applications. The technological advancement has brought numerous rainfall datasets that can be available to assess rainfall patterns. However, the suitability of a given dataset for a specific location remains an open question. The objective of this study is to find which rainfall datasets perform well in India at various spatial resolutions: pixel level, meteorological sub-divisions (MSDs) level, and India as a whole and temporal resolutions: monthly and yearly. This study performs skill metrics analysis on seven widely used rainfall datasets—GPM, CRU, CHIRPS, GLDAS, PERSIANN-CDR, SM2RAIN, and TerraClimate—using the Indian Meteorological Department’s (IMD) gridded data as a reference. The rule-based decision tree techniques are employed on the obtained skill metrics analysis values to find the good-performing rainfall dataset at each pixel value among all the datasets used. The MSD and pixel-wise analyses reveal that GPM performs well, while TerraClimate performed the most poorly in almost all MSDs. The analysis suggests that of the satellite-derived, gauged, and merged datasets, merged-type are the good-performing datasets at the MSD level, with approximately 17 MSDs demonstrating the same. The temporal analysis (in both month- and year-wise scales) also suggests that GPM is a good-performing dataset. This study obtained the optimal dataset for each pixel among the seven selected datasets. The GPM dataset typically ranks as a good-performing fit, followed by CHIRPS and then PERSIANN-CDR. Despite its finer resolution, the TerraClimate dataset ranks lowest at the pixel level. This research will aid in selecting the optimal dataset for MSDs and pixels to obtain reliable results for hydrologic and agricultural applications, which will contribute to sustainable development.

Unravelling the influence of teleconnection patterns on monsoon extreme precipitation indices over the Sikkim Himalayas and West Bengal

The influence of major climate indices of tropical seas and equatorial oceans on the variability of monsoon (June to September) extreme precipitation indices (EPIs) is explored for observed (1961–2017) period over two meteorological subdivisions of India. The strength and span of covariate relations for each EPI and the Indian Ocean Dipole (IOD), the Multivariate El Niño-Southern Oscillation Index (MEI), the Pacific Decadal Oscillation (PDO), the Southern Oscillation Index (SOI), and sea surface temperature (SST) anomaly of the Bay of Bengal (SST-BoB) as well as the Arabian Sea (SST-AS) are evaluated by linear and nonlinear functions. The periodicities and coherent patterns of each EPI and the climate indices are investigated in time–frequency space using wavelet power spectra. This is to identify the periodic behaviour, strength of nonlinear linkages and major climatic factors involved in the variability of monsoon precipitation extremes. EPIs show significant negative association for MEI and SST-AS. Consecutive dry days (CDD) develop significant positive (negative) correlation with MEI (SST-BoB) over Bihar, GWB, Orissa, Jharkhand and Sub-Himalayan West Bengal (SWB). The duration, intensity and absolute indices over the Sikkim (SIK) Himalayas exhibit significant negative correlation with the SST-BoB. The EPIs develop significant positive relation with PDO phases across the regions. EPIs show asymmetric correlation pattern for SST-BoB and SST-AS with the changes in latitudes and altitudes. Interannual periodic bands from low to medium frequency regions are observed in EPIs. CDD shows in-phase coherency with MEI, and a complex periodic oscillation with SST-BoB. Consecutive wet days (CWD) develop strong consistent decadal coherency with IOD. Monsoon total precipitation (PRCPTOT) is dominantly influenced by MEI and SST-BoB. Heavy precipitation events show strong response to the MEI and SST-AS over GWB. Absolute indices show regional variability in power spectra with intermittent breaks. For absolute indices, major controlling factors considerably vary at both subdivisions and timescales. This study reveals corresponding changes in active- and break-spells, drought and flood events, regional availability of water resources and its possible consequences.

Climate Change and Consequential Rainfall Trends: An Indian Perspective

This article presents an overview of different studies pertaining to the trends observed in rainfall over India and its constituent regions. The spatial scale of trend estimation considered in the reviewed studies is an important aspect that dictates the overall trends over a given scale and helps in ascertaining the influence of anthropogenic activities upon the resultant trends. In this article, Indian trend analysis studies conducted on a national, meteorologic sub-divisional, river basin and agro-climatic zonal level as well as for different time periods and time scales have been reviewed. One of the more recent national-scale studies were able to detect overall negative trends in the annual rainfall over India with a more prominent decline observed during the latter half of the 20th century and first decade of 21st century. Also, 17 out of 30 meteorological sub-divisions for which trend analysis was carried out exhibited increasing tendencies though, all were insignificant. Most of the river basins of India have shown an increasing trend in annual rainfall with Indus (lower) and Tapi basins observing maximum rise. Decreasing trends were however, recorded over most of the basins especially after 1970. The conclusions drawn from the review of suitable studies highlight the variation in trends across different studies due to a different set of variables and factors involved in individual studies. The practical significance of trend analysis has also been discussed along with a need of a more subjective assessment of the mathematical outputs obtained from commonly used trend analysis methods.

Comparison of Rainfall and GDP: A Feasibility Study on Introducing Rainfall Derivatives in the Indian Weather Risk Market

Precipitation is an economic indicator for the Indian economy. Because more than half of the Indian population is engaged in agriculture, and they depend on rainfall for their farming activities, along with farming, other sectors like manufacturing, transportation, banking, construction, and others are directly or indirectly affected by rainfall. The proposed study attempts to establish a relationship between the changes in rainfall and the GDP growth rate. The study demonstrates the opportunity and feasibility of introducing rainfall index-based derivatives in the Indian weather risk market. The study considered the average annual rainfall data of all 36 meteorological subdivisions and the GDP annual growth rate for the period from 1961 to 2020. The study performed simple correlation and regression with the SPSS software. The results of the correlation matrix show that there is a positive relationship between the selected two variables. According to the regression analysis, rainfall has a significant positive effect on India's GDP growth rate. This result shows that there is a need for rainfall index based derivatives in the Indian weather risk market to absorb the rainfall risk. The purpose of this paper is to suggest a new kind of versatile tool to overcome the rainfall risk. Rainfall risk is covered by traditional insurance, index insurance, and reinsurance. However, all of them failed to effectively manage the rainfall risk. So, right now, rainfall derivatives are needed to make a full-fledged rainfall risk market.

An information-theoretic study of rainfall time series through the Dempster–Shafer approach over a meteorological subdivision of India

Abstract The present paper reports a study, where we have developed a methodology to understand the relative uncertainty associated with the rainfall amount corresponding to summer monsoon (JJAS) and post monsoon (OND) for the period 1871–2016 over northeast India. After calculating the partial correlation between two random variables after the removal of the effect of the third one, we have standardized all the realizations of the random variables. Subsequently, after applying the Dempster–Shafer theory, we have obtained joint basic assignments through two judging criteria for the fuzzy sets representing the closeness of the observed values to two measures of central tendency for different window sizes obtained from the original time series. The study revealed a higher rate of increase in the uncertainty with a change in the window size for OND than in the case of JJAS. This study finally concluded that this approach could generate some idea about the most advantageous ratio of training and test cases for predictive models with supervised learning procedures.

Rainfall dynamics observed over India during last century (1901–2020) using innovative trend methodology

Abstract This study investigates the spatio-temporal distribution and trends of seasonal rainfall for different meteorological sub-divisions (MSDs) of India using statistical analysis and the innovative trend analysis (ITA) method. The gridded dataset of daily rainfall for 120 years from 1901 to 2020 was obtained from the India Meteorological Department (IMD) and analysed using statistical results of mean rainfall, standard deviation, coefficient of variation, skewness, kurtosis, maximum seasonal rainfall, percent deviation of rainfall, number of rainy days, rainfall intensity, rainfall categorization, trend detection, and cross-correlation coefficients. The period was divided into three quad-decadal times (QDT) of 40 years each (i.e., 1901–1940: QDT1, 1941–1980: QDT2, and 1981–2020: QDT3). A general decrease in the number of rainfall events was observed in all the seasons except for a few MSDs of northwest India showing a rise throughout the pre-monsoon season in recent times (QDT3). Significant trends were detected using the ITA method in seasonal rainfall in nearly all the MSDs of India. Our findings are highlighting the qualitative and quantitative characteristics of seasonal rainfall dynamics at the MSDs level which will be useful for comprehending the rainfall dynamics as impacted by climate change and climate variability in India, and may further lead the policymakers and stakeholders for making the best use of available water resources.

Modeling and Designing of Rainfall Derivative Contract in India

There has been a prolonged discussion on the use of rainfall derivatives in the Indian conditions. The use of rainfall derivative instruments that could empower the ecosystem of trading and absorbing rainfall risk. The study adopted a unique approach of designing the rainfall indexation based on proposed metrics which is DRD/ERD index, this could be used as building blocks for designing rainfall derivatives similar to HDDs/CDDs underlying temperature derivatives. DRD/ERD values are computed for 36 meteorological subdivisions of India. The basic rainfall future contract structure is defined and analyse how it can reduce the rainfall risk. Like CAT bonds and weather derivatives, rainfall derivatives constitute potentially a distinct asset class and hence could be an added arsenal in the hands of investors for enriching their portfolios.

Assessing the consistency of trends in Indian summer monsoon rainfall

Adequate and consistent monsoonal rainfall is essential for the economic and societal well-being of India. Even though most past studies analysed trends in Indian summer monsoon rainfall (ISMR) to capture any changes, the nature and consistency (stability) of those trends remained unexplored. Therefore, this study investigates the monotonic, non-monotonic, and trendless nature as well as the stability of ISMR trends from 1897 to 2016 using both graphical and statistical approaches at different meteorological sub-divisions of India. Also, the trend stability is examined considering high, medium, and low clusters of ISMR. Results show a predominance of monotonic trends in the long-term series, and maximum eastern sub-divisions exhibit a decreasing trend and vice versa. However, further analysis revealed instabilities in the ISMR trend and its respective clusters. The possible mechanisms behind ISMR instabilities and the spatio-temporal changes are discussed, along with certain methodological challenges, which might aid in formulating climate action plans.

Hydraulic jump: The cause of heavy rainfall on the immediate lee side of the Western Ghats in the Maharashtra state of India

AbstractNorth–South‐oriented mountains (known as “The Western Ghats [WG]”) running parallel to the west coast of peninsular India separate the Maharashtra state (lying on the northwest of peninsular India) into two meteorological subdivisions (a) Konkan lying on the windward side of WG receiving high monsoon rainfall (2,390 mm) and (b) Madhya Maharashtra (MM) lying on the leeward side receiving low monsoon rainfall (579 mm). The state of Maharashtra has two more subdivisions on the leeward side of WG, viz. Marathwada and Vidarbha, which however do not lie on the immediate leeward side of WG. In this study, a subdivision of MM is further subdivided into (a) north–south strip of 80 km width on the immediate lee side (ILS) from the crest of WG and (b) area extending eastwards from ILS to 280 km, referred to as “distant lee side (DLS).” DLS contains some parts of the Marathwada subdivision also. The study indicates that the mean monsoon rainfall over ILS region is higher than DLS region (proportion is 1.4:1). Higher rainfall over ILS region has been shown due to the occurrence of the “hydraulic jump (HJ).” The dynamics of HJ are explained using Froude number and vertical velocities. The climatological high rainfall over ILS remained an unnoticed feature in the spatial monsoon variability over peninsular India. The study focuses on the unnoticed high rainfall in the ILS region and attributes it to HJ. The incorporation of HJ has the potential to impact on forecasting of monsoon rainfall over the region by numerical models.