MAUSAM

POST MONSOON SEASON (October-December 2023)

Editor Mausam — 2024-10-01

Decoding The Rain Microphysics for Two Tropical Stations Using Disdrometer Data

Jyoti Chahal — 2024-10-01

Forecasting wheat productivity in Punjab, India: A weather-based model approach using detrended data and regression analysis

Lovepreet Kaur — 2024-10-01

Weather-based models were developed to forecast the wheat productivity of three districts in

Punjab (India) could accurately predict wheat yields two and a half months before the crop harvest. The trends in weather parameters were identified using Mann-Kendal, Sen’s Slope and Pettitt’s test.The logistic model was declared best among linear and non-linear models to remove the effect of technological factors over time. The detrended data was used for forecasting wheat productivity based on fourteen weeks of weather data of the vegetative period. The stepwise regression technique for detrended wheat productivity on weather indices revealed model II best for Amritsar, Ludhiana and Patiala districts, explaining 60%, 67% and 52% variation in the detrended wheat productivity and had root mean square percentage error 8.57%, 6.93% and 6.20% respectively. The model concluded that warm maximum and cool night temperatures of the wheat growing season will increase detrended wheat productivity, whereas an increase in rainfall and relative humidity in the morning will decrease detrended wheat productivity and hence the production.

Study of Natural Disaster in Manali Valley (Himachal Pradesh), India on 09 July 2023

M. Sudhanshu Shekhar — 2024-10-01

Continuous heavy rainfall in July 2023, particularly from the 7th to the 10th, led to devastating flash floods, cloudbursts, and landslides in Himachal Pradesh, causing extensive damage to infrastructure, properties, and loss of lives. The most severely affected districts were Kullu, Mandi, Sirmaur, Shimla, Solan, and nearby areas. Manali sub-division, located in Himachal Pradesh, spans an altitude range of 1074 to 4017 meters above sea level. The region's climate is shaped by its mountainous terrain, with the Beas Valley's weather influenced by factors like relief, aspect, and altitude. The Pir Panjal Range's windward slopes create a barrier to monsoon winds, resulting in heavy rainfall and cloudbursts in the area. The report from the India Meteorological Department (IMD) in Shimla, Himachal Pradesh, on July 12, 2023, highlighted the active monsoon conditions in the state from July 7th to 10th, exacerbated by a Western Disturbance (WD). This combination led to intense and unprecedented rainfall, causing extensive damage to public and private properties, particularly in hilly regions. The report mentioned that similar disasters have occurred in the past due to heavy rainfall, cloudbursts, and landslides, possibly exacerbated by factors like unscientific construction, climate change, and increased human activities, including tourism. The report emphasized the need for accurate predictions and proactive planning to mitigate such disasters in the future.

Analyzing the heavy rainfall event of July 2011 in Niigata using ground, satellite and radar rainfalls

Narayan Prasad Gautam — 2024-10-01

In recent years the application of radar and satellite precipitation observations has become increasingly useful in poorly gauged areas. However, understanding the relationships between these data sources and ground observations is vital to correct the datasets and improve their application in hydrological studies. In this study we analyzed the spatial and temporal relationships between Global Satellite Mapping of Precipitation-Near Real Time (GSMaP_NRT) data set with radar and ground observations at downstream of Shinano River, Japan. GSMap_NRT observation showed better relationship with ground observations for longer-duration observations (eg. 3, 6, 12 and 18 hours) than hourly observations. The GSMap_NRT showed excellent relationship with ground rainfall at satellite observation time compared to non-observation time. Comparison of radar observations at various time scales and spatial resolutions showed that radar estimates at smaller-time-interval ratio and lower-spatial-scale ratio are better than longer-time-interval ratio and higher-spatial-scale ratio. We also observed that radar precipitation estimate well-represent the areal averaged precipitation of ground observations. Results from this study showed that radar precipitation estimates could serve as very important input to improve merging of ground and satellite precipitation as well satellite rainfall improvement systems.

Assessment of rainfall trend over Periyar Vaigai Command area of Tamil Nadu

Dharani C. — 2024-10-01

Rainfall trend analysis is critical for studying the effects of climate change. The most crucial aspect of understanding climate change at the basin level is, how it will affect water usage, planning, and development. The present study has been attempted to analyze the trend of annual and seasonal rainfall data over Periyar Vaigai Command area from 1982 – 2021 (40 years) employing different trend analysis methods i.e., Mann-Kendall test for trend significance, Sequential Mann-Kendall test for assessment of start and end of trend, Sen’s slope test for trend magnitude, Innovative Trend Analysis for combination of trends and linear regression analysis for trend detection. Statistically positive trend was detected in summer season using all methods whereas, annual and SWM rainfall showed a statistically significant decreasing trend. Winter and NEM rainfall resulted with no significant trend. The results from this study helps to adopt various water management practices and useful for policy makers to prepare appropriate mitigation strategies.

Assessment of Spatiotemporal Variations in Actual Evapotranspiration using the pySEBAL Model and Remote Sensing Data in Navsari, Gujarat, India

Vivek Virani — 2024-10-01

Evapotranspiration (ET), encompassing both transpiration from plants and evaporation from the Earth's surface, is a vital component of the hydrological cycle and influences water and energy exchanges between the surface and the atmosphere. This study investigates the application of the Surface Energy Balance Algorithm for Land (SEBAL) to estimate actual evapotranspiration (AET) over a selected region. The study area, located in Navsari district of Gujarat, India, was characterized by varying climates and land cover conditions. Cloud-free LANDSAT 8 satellite images were utilized at a spatial resolution of 30 x 30 meters to execute the SEBAL model within the Python-built GRASSGIS software. The SEBAL algorithm, based on the principles of energy balance, calculates AET by considering net radiation, sensible heat flux, and ground heat flux. The results showed that AET correlated with factors such as vegetation cover, land surface temperature, and net radiation. The AET rates exhibited significant spatial and temporal variations, with the highest rates observed in May and the lowest in months with reduced net radiation. Across the study area, the dominant AET rate ranges from 3 to 6 mm/day during the summer months and from 2 to 3 mm/day during the winter months. This pattern encompasses more than 65% of the total study area. Moreover, the highest AET values derived from the SEBAL model are contrasted with potential ET (PET) estimates obtained through diverse empirical techniques. This analysis demonstrates a robust alignment between SEBAL-based AET and PET calculations. Notably, in the months of April and May, when the peak AET closely corresponds to PET, there is a potential indication of impending water scarcity concerns.

Spatio -temporal variability in fire events due to crop residue burning and their impact on atmospheric variables using ground and remote sensing data in Punjab

Yashi Singh — 2024-10-01

We attempted monitoring and mapping of the active fire events in Punjab, during the wheat and rice
harvesting period of 2017-2021 from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m aboard. The
analysis showed that the highest fire counts were observed in the central region, followed by the south-west and the
lowest in the north-east region of Punjab during all the years. Moreover, during the wheat season, highest fire counts
were observed in 2019, being 2535, 11062 and 6212 in north-east, central and south-west regions, respectively. However,
during rice, highest fire counts were observed in 2020 in the north-east being 2857 and in 2021 the central and south-west regions being 40960 and 30351 respectively. In line with the number of fire counts, the highest concentration of gases and particulate matter obtained from the Central Pollution Control Board (CPCB) was also observed in the central zone. During the wheat harvesting season, central zone experienced the highest concentration of PM2.5 and PM10 in May 2018 and that of SO2 and O3 in May 2019. Similarly, during the rice harvesting season, central zone also experienced the
highest concentration of PM2.5 and PM10 during November 2017 and that of SO2 in 2018. However, the highest
concentration of NO2 was observed in October 2018 and that of O3 in October 2020 in the central region. Analysis of the
concentration of NO2 and SO2 obtained from the Soumi-NPP satellite also had similar results to CPCB. Such a high
concentration of gases and particulate matter might be attributed to crop residue burning a significant positive correlation was observed between fire counts and concentration of particulate matter (PM2.5 and PM10). In view of the alarming deterioration of air quality, there is a dire need to check this practice in the region by providing incentives and viable alternatives to the farmers.

Prediction of Low-Visibility Events by Integrating the Potential of Persistence and Machine Learning for Aviation Services

Anand Shankar — 2024-10-01

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.

Rainfall characteristics under changing climate in Sundergarh district of Odisha

Manjari Singh — 2024-10-01

Most pronounced signal of warming world is the increase in intensity and frequency of extreme
events worldwide. Many researchers have reported the nature of these events in coastal district of Odisha, but for
Sundergarh district which comes under North-western Plateau Agro-Climatic Zone, it still needed to be studied. The
present study was conducted with an objective to study rainfall characteristics - rainfall climatology, rainy days, trend -
their deviation and resulting meteorological drought in Sundergarh district of Odisha at block level using 34 years of
daily rainfall data (1988-2022). IMD classification for rainy days was adopted to determine the frequency of rainy days in
different categories. SPI was computed using Climpact to determine the occurrence and severity of meteorological
drought and identify episodes of dry and wet events during the study period for each block. MK test and Sen’s slope
estimator was used for detecting trend in rainfall (annual and seasonal) and rainy days at 95% significance level. Long
term rainfall analysis reveals that the mean annual rainfall of the district is 1290 ± 314 mm, most of which is received
during SW monsoon (86%) followed by post-monsoon (6.2%), pre-monsoon (5.7%) and least during winter (2.1%).
Rainfall received in the months of July and August together accounts for more than 50% of the total. Spatial distribution
of rainfall indicated that mean annual rainfall varies from 1071 mm (Subdega) to 1578 mm (Bonai). Annually, rainfall is
dependable in most of the blocks. However, during pre-monsoon, post-monsoon and winter, CV is very high, so the
rainfall is not dependable. Bulk of annual rainfall received in the district is contributed by rain events of light to moderate
category. Direction and magnitude of the trend of rainfall amount and rainy days varies a lot in different blocks. SPI values calculated for the district as a whole shows that frequency of occurrence of these drought events were: mild (34% of months); moderate (7.6% of months); severe (3.3% of months) and extreme (3.2% of months). The present study provides pertinent information on the rainfall climatology and its trend under climate change over the Sundergarh district of Odisha. Finding of the study suggests that there is an urgent need to raise awareness about the climate resilient society, train personnels about sustainable development approaches and highlight the necessity of water harvesting techniques at community level.

Desertification of Bengal Dryland Areas Possible under Projected Climate Conditions

Kartic Bera — 2024-10-01

Drylands are some of the most sensitive areas to climate change and human activities around the globe. Assessment of future climate trend scenarios provides valuable practical information for dryland management decision-making. According to Huang et al. (2017), more than 50% of global drylands will expand by this century, with a maximum (78%) of newly expanded dryland occurring in developing countries. To understand the potential for expansion of drylands and desertification, we examine critical predictor variables (temperature and precipitation) of Bengal dryland expansion to guide early actions to mitigate and prevent desertification. Using trend analysis of bias-corrected CMIP6 projected climate change data for temperature and precipitation (2022-2041), results indicate future dryland expansion is possible from increases in temperature and declines in monsoonal precipitation. Over the next two decades (2022-2041), Bengal dryland areas will be 0.1-0.5°C warmer, and rainfall will decrease by 2.57-13.43cm total during the monsoon period. Given these variables are critical predictors of dryland expansion because of their role in driving evapotranspiration and soil moisture deficits; we anticipate an increase in the population affected by water scarcity, land degradation, and desertification. Our work provides information critical for effective dryland management, biodiversity conservation, and land-use planning under future climate conditions.

Investigating the Long-Term Effects of Anthropogenic Practices on Soil Features

T. V. H. Hoang — 2024-10-01

Anthropogenic practices have been increasingly conducted on the rise in addressing the escalating
demands for socioeconomic development, resulting in significant impacts on land ecosystems. This research seeks to
evaluate the influence of anthropogenic activities on soil features in mountainous regions of Vietnam by focusing 84
samples collected from 12 locations across Lam River Basin at seven soil profiles (0-10, 10-20, 20-30, 30-40, 40-60, 60-
80 and 80-100 cm).
The findings reveal notable differences in soil texture among land use types (LUTs). Forest cover lands (FCLs),
showed the least amount of sand content, varying from 29.7% to 37.6%, while unplanted and bare lands (UBLs) had the
highest sand ratios, up to 53.9%. FCLs exhibited lowest bulk density (BD), soil porosity (SP) and soil electrical
conductivity (EC) and C:N ratio with respective ranges of 0.93-1.29 g.cm-3, 32.7-36.5%, 0.526-0.743 mS.m-1 and
6.74 -8.52, respectively.
In contrast, crop cultivation lands (CCLs) demonstrated higher values for BD (1.17-1.25 g/cm3), SP (39.25-
43.19%), EC (0.583-0.792 mS.m-1) and C:N ratio (11.27-15.77). UBLs, on the other hand, exhibited even highest values
up to 1.23-1.36 g.cm-1, 43.19-49.62%, 0.437-0.619 mS.m-1 and 11.68-16.58% and displayed high levels of exchange irons
and soil organic content compared to FCLs. Other factors such as pH varied little in space between the sampled soil
locations and along the soil profiles. Overall, the study indicates that anthropogenic practices have impacts on the soil
features across the study area.

Satellite based analysis of rapid intensification of Super Cyclone Amphan

Chinmay Rajendra Khadke — 2024-10-01

Tropical Cyclones (TCs) are formidable natural hazards with destructive impacts on life and property along the tropical belt. TCs are responsible for multiple hazards such as riverine and urban floods, extreme winds, and lightning, storm surge significantly amplifying the threats they pose. Rapid Intensification (RI), defined as a sudden increase in Maximum Sustained Winds (MSWs) by 30 kt or more in 24 h, has been a growing concern. These events, characterized by a swift escalation in MSW are challenging to the forecasters and remain a considerable operational challenge, amplifying risks for coastal communities. Numerical modeling also struggles to predict RI, with limitations in describing inner core convective scale processes cited as a major limitation. Monitoring and assessing TCs heavily rely on satellite-based observations due to the absence of adequate in-situ measurements over the ocean. In the present study, we examine Super Cyclonic Storm (SuCS) Amphan, the first Super Cyclone of this century, that formed over the Bay of Bengal in 2020 using satellite imagery and derived products available in near-real time to monitor and identify the early signatures for the RI. It is observed that the intensification is intricately tied to substantial expansions in both the outer and inner core sizes along with its symmetricity before the RI. Notably, no significant alterations in size are observed between the very severe cyclonic storm (VSCS) and SuCS stages. The convection characteristics of TCs are found to provide crucial markers of their intensification, with a particular emphasis on polar satellite-based passive microwave imagery for analyzing embedded convection and early eye detection. Key observations indicate that inner convective ring patterns, identified through 37Ghz microwave imagery in lower-level convection, act as precursors to rapid intensification. Concurrently, the outer ring pattern observed during the mid-life course is found to permit further intensification of the system.

Machine Learning approach in the prediction of Fog: An Early Warning System.

Anand Shankar — 2024-10-01

The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential for
aviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather prediction
models and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to grasp
the micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks at
how well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India's
Jay Prakash Narayan International Airport (ICAO Index-VEPT), a representative station of the Indo-Gangetic Plains
(IGP). The proposed ensemble ML-based model was trained using hourly synoptic data from 2014 to 2020 and tested
using data from 2021 to 2022 (December to February). Once the features are chosen and the forecasters' local knowledge
is taken into account, the dry bulb temperature (°C), dew point temperature (°C), relative humidity (%), cloud amount
(octa), wind direction (degrees from true north) and wind speed (knots) are used to build the proposed ML models. ML
algorithms were trained on meteorological data from 1500 to 2200 UTC to predict fog (Visibility <1000 m) and dense
fog (Visibility <200 m) for the next day at 0000 UTC, with a two-hour lead time. For fog forecasting at 0000 UTC with a 4-hour lead time, ML models were trained with data from 1300 to 2000 UTC and so on. This study evaluates parameter tuning in six level 0 ML models: distributed random forest (DFR), deep learning (DL), gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized tree (XRT), XG Boost and stacked ensemble at level 1. The performance metrics and statistical skill scores indicate that DRF and DL models perform well for lead times of 2 and 4 hours at level 0 for fog (visibility <1000 m) and dense fog (visibility <200 m). But the proposed ensemble models outperform all the base models at level 0 and are recognized as the best instrument for predicting fog at Patna Airport.

Impact of meteorological parameters on black carbon mass concentrations over Silicon City “ Bengaluru, Southern part of India” – A Case Study

Jaswanth Gowda — 2024-10-01

In the context of Bengaluru, the impact of black carbon (BC) aerosols on regional climate dynamics emerges as a crucial area of investigation. Due to the lack of high-resolution BC data, it was collected at 5 different locations in Bengaluru, Karnataka, from January 2019 to December 2019. The mean mass concentration of BC was 5.91 microg m-3, whereas the higher mass concentration of BC was 7.71 microg m-3 over the city railway station and the lower (3.69 microg m-3) was in Jayanagara. The contribution of BC in higher-traffic and industrial locations was approximately 41% higher than the residential locations, which indicates a large fraction of soot particles are from anthropogenic activities mainly from fossil fuel combustion. The seasonal concentrations of BC have also exhibited a large variability, with the highest sequence in magnitude during the winter season (9 microg m-3) followed by the summer (6.3 microg m-3), post-monsoon (5.8 microg m-3) and monsoon (3.4 microg m-3) seasons during the study period. The concentrations of BC during the monsoon season were very low as compared to the winter season due to the impact of the washout effect. The BC was negatively significantly correlated (-0.70) with rainfall indicating the washout effect, however, during the winter season, it was due to impact of boundary layer condition. Overall (annually) the mean concentrations of BC have increased by around 55% within five years over the urban region, which indicates the impact of man-made activities. The higher mass concentration of BC over Bangalore in the southern part of India, indicates serious implications for the regional climate that need to be investigated and mitigated. Finally, the study suggests that immediate action is required to mitigate the emission of BC into the urban environment in the southern part of India.

District wise spatiotemporal analysis of precipitation trend during 1900-2022 in Bihar state, India

Priyanka Singh — 2024-10-01

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900–2022. Major findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter, and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post-monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas, and extreme eastern districts like Purnia, Katihar, Araria, and Supaul had negative trend.

Lightning activity in India an important cause of increase in fatalities

Manish R. Ranalakar — 2024-10-01

With Recent increase in deaths due to lightning activity in India we are posed with a question - are these fatalities related to increase in the lightning activity owing to climate change? The IPCC report (2013) projects global warming of 1-5 °C by the end of 21^st Century. The global warming is closely related to increased concentration of greenhouse gases. Previous studies have shown that on different temporal and spatial scales small increases in surface temperature leads to increase in thunderstorm and lightning activity. The lightning induced deaths are on the rise especially in the tropical south Asia and Africa but IPCC report does not explicitly deals with lightning activity and its future projection. Studies on this aspect become all the more important as the subgrid scale phenomena such as convective clouds and hence lightning are poorly resolved and taken in to account by climate models.

In order to address this issue we have analyzed trends of lightning activity, surface temperature, upper tropospheric water vapour, cloud ice, Convective Available Potential Energy (CAPE) and aerosols. We also present correlation of these parameters with lightning activity using lightning flash rate data of Lightning Imaging Sensor aboard TRMM, TRMM Level -2 Precipitation Radar data, gridded temperature data of IMD, aerosol data acquired by MODIS.

The result indicates that upper tropospheric temperature rise is more than surface temperature rise. This imply stable atmosphere with fewer thunderstorms. The increased convection transports additional water vapour into upper troposphere. The water vapour acts as green house has by absorbing infrared radiation emitted by the surface of the Earth. This results in more warming in the upper troposphere than at the surface and stabilizing of the atmosphere. However, results also show that within the thunderstorm the instability measured by CAPE is positively correlated with lightning activity.

This paradox of stabilization of global mean atmosphere with increase in lighting activity leads us to conclude that tough thunderstorm activity has subdued but those develop are much more explosive producing more lightning activity and perhaps lead to more fatalities.

Inter-comparison of GNSS- IPWV with ERA-5 IPWV and monitoring of convective events over the Indian region

C. S. Tomar — 2024-10-01

The present study deals with (i) An Inter-comparison study of ground based Global Navigation Satellite System (GNSS) derived Integrated Precipitable Water Vapour (IPWV) with 5^th generation global climate reanalysis data of European Centre for Medium Range Weather Forecast (ERA-5) (ii) IPWV thresholds of GNSS data (iii) case studies of IPWV analysis.

It is found that both the datasets (GNSS and ERA-5) are strongly correlated & the correlation coefficient ranging between 0.97 and 0.99. Monthly Thresholds of IPWV (MTI) are generated with 2017 -2020 data sets from Indian GNSS station having continuous data and found very useful input as value addition to know the possibility of building up /decaying of convection in day to day daily forecast issued to the public. Case studies shows an increase of IPWV, 3 to 4 hour prior to the occurrence of convective event around the GNSS station and found very useful for nowcasting.

Therefore, utilization of IPWV (model as wells GNSS based) have incremented value in enriching the understanding about the weather event and its forecasting. This supplement information along with other analysis products (model, surface, upper air, satellite or radar etc) can support further in appropriate decision making to the forecasters, decision makers and the end users of the society.