Statistical Analysis of SO2 and NO2 with Respect to Meteorological Parameters (Gwalior, India)

The present study is carried out in Gwalior to know the level of pollutants viz sulphur dioxide (SO2) and nitrogen dioxide (NO2). In this study, both NO2 and SO2 were collected during different seasons, and estimation was done using chemical methods. The methods used for the determination of SO2 and NO2 in the ambient air of Gwalior was (Modified West and Geake method) and (Modified Jacob and Hochheisier). The SO2 from the air stream was absorbed in a sodium tetramer curate solution. NO2 was collected by bubbling air through a sodium hydroxide solution to form a stable solution of sodium nitrate. Meteorological parameters like temperature, relative humidity were recorded by thermometers and hygrometry during the sampling. Rainfall data was taken from Indian Meteorological Department, New Delhi, for four sampling years. The statistical analysis was carried out between the level of pollutants SO2 and NO2 measured and meteorological parameters recorded during the sampling. This study observed that pollutants were very high in winter and summer compared to monsoon and post-monsoon periods due to the heavy transport movement and favourable meteorological conditions like temperature, humidity, rainfall, and wind speed and directions.

Water Shortage Analysis based on Reservoir Surfaces with varying Population and Rainfall for 2011-20 at Egra in Purba Medinipur

Egra is one of the blocks of Purba Medinipur in West Bengal. The northing and easting of Egra are 21.9°N and 87.53°E respectively. The total number of reservoirs is a hundred (100) as of the year 2020. Reservoir water is utilized for irrigation and industrial purpose. According to Census report 2011, it has been observed that the total population in Egra was 345,926 and the growth of population in Egra is 2.25% p.a. In Egra the population density is more than that of West Bengal. This is enhanced many more times in the last decade i.e from 2011 to 2020. In 2011 the total perimeter of the reservoir was 18630.00m with an area of 145513.00 m 2 . But in 2020 total perimeter of reservoirs was 13752.40 m with an area of 115255.40 m2 . So the water content has been reduced in reservoirs of Egra location in Purba Medinipur. Now increases in population, irrigation and industrialization have been taken place in the Egra manifold. So the withdrawing of water from the reservoirs of Egra has been taken place in indiscriminate ways. But the filling up of water of the reservoirs is very low as the amount of precipitation in the entire Purba Medinipur is quite less. From the Indian meteorological department, it has been found that in maximum years from 1991 to 2020 in Purba Medinipur annual rainfall was around1600 mm. So abstraction is more than the recharge of reservoirs. It’s an alarming situation for Egra. If such an indiscriminate usage of water from reservoir for increasing irrigation and industrialization has been taken place then reservoirs will become dry. If irrigation and industrialization are taking place by the withdrawal of groundwater then the ground water table will be diminished. So if groundwater harvesting is adapted to store water in the reservoir to supply water required for irrigation and industrialization then it may be benefitted. Keywords: Reservoir, perimeter, area, irrigation, industrialization, population

Impact of meteorological factors on number of new COVID-19 cases in Pune

Background: Coronaviruses are a family of viruses that can result in different types of illnesses, most commonly, as Severe acute respiratory syndrome (SARS). Researches have shown that the atmospheric variables and the density of population have affected the transmission of the disease. Meteorological variables like temperature, humidity among others have found to affect the rise of pandemic in positive or negative ways. Respiratory virus illnesses have shown seasonal variability since the time they have been discovered and managed. This study investigated the relationship between the meteorological variables of temperature, humidity and precipitation in the spread of COVID-19 disease in the city of Pune.Methods: This record based descriptive study is conducted after secondary data analysis of number of new cases of COVID-19 per day from the period 01 May to 24 December 2020 in Pune. Meteorological data of maximum (Tmax), minimum (Tmin) and daily average temperature (Tavg), humidity and precipitation were daily noted from Indian meteorological department website. Trend was identified plotting the daily number of clinically diagnosed cases over time period. Pearson’s correlation was used to estimate association between meteorological variables and daily detected fresh cases of COVID-19 disease. Results: Analysis revealed significant negative correlation (r=-0.3563, p<0.005) between daily detected number of cases and maximum daily temperature. A strong positive correlation was seen between humidity and daily number of cases (r=0.5541, p<0.005).Conclusions: The findings of this study will aid in forecasting epidemics and in preparing for the impact of climate change on the COVID epidemiology through the implementation of public health preventive measures.

A Review Study on Stationary and Non-Stationary IDF Models Used in Rainfall Data Analysis around the World from 1951-2020

This article focuses on an overview of the processes of generating rainfall intensity-duration-frequency (IDF) models, the different types and applications. IDF model is an important tool applied in the design of either hydrologic or hydraulic design such as prediction of rainfall intensities to estimate peak runoff volumes for mitigation of flooding. IDF models evolved from stationary – parametric (empirical) and non-parametric (stochastic) models, to non-stationary models in which variables vary with time. Each category controls the ways models predict rainfall intensities, and reveals their strength and weaknesses. IDF models must therefore, be chosen in terms of the project objective, data availability, size of the study, location, output needed, and the desired simplicity. For instance, while the parametric model predicts better for shorter durations and return periods only, the non-parametric models predict better for both shorter and longer durations and return periods. For projects requiring change of input data over time and evaluation of uncertainty bounds, risk assessment, including incorporation of changes in extreme precipitation, the non-stationary model approach must be selected. Also, of importance for catchments without rainfall amount and corresponding duration records but has daily (24-hourly) record of rainfall depth, the Indian Meteorological Department (IMD) method of shorter duration disaggregation can be adopted to generate in-put data for the development of IDF curves for such a location. Therefore, each model type has limitations that may make it unsuitable for some projects. Reviewing input data and output requirements, and simplicity are all necessary to decide on which model type should be selected.

Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India

The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.

Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

The presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° × 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2, and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4% to 53.9% over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6%–48.4%) and IMERG (4.9%–17.8%). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8%–74.4%, followed by PERSIANN-CDR (34.4%–69.11%) and IMERG (15.3%–39.0%). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5%–78.6%.

Rainfall Contribution of Tropical Cyclones in the Bay of Bengal between 1998 and 2016 using TRMM Satellite Data

In the Bay of Bengal (BoB) area, landfalling Tropical Cyclones (TCs) often produce heavy rainfall that results in coastal flooding and causes enormous loss of life and property. However, the rainfall contribution of TCs in this area has not yet been systematically investigated. To fulfil this objective, firstly, this paper used TC best track data from the Indian Meteorological Department (IMD) to analyze TC activity in this area from 1998 to 2016 (January–December). It showed that on average there were 2.47 TCs per year generated in BoB. In 1998, 1999, 2000, 2005, 2008, 2009, 2010, 2013, and 2016 there were 3 or more TCs; while in 2001, 2004, 2011, 2012, and 2015, there was only 1 TC. On a monthly basis, the maximum TC activity was in May, October, and November, and the lowest TC activity was from January to April and in July. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used to estimate TC rainfall contribution (i.e., how much TC contributed to the total rainfall) on an interannual and monthly scale. The result showed that TCs accounted for around 8% of total overland rainfall during 1998–2016, and with a minimum of 1% in 2011 and a maximum of 34% in 1999. On the monthly basis, TCs’ limited rainfall contribution overland was found from January to April and in July (less than 14%), whereas the maximum TC rainfall contribution overland was in November and December (16%), May (15%), and October (14%). The probability density functions showed that, in a stronger TC, heavier rainfall accounted for more percentages. However, there was little correlation between TC rainfall contribution and TC intensity, because the TC rainfall contribution was also influenced by the TC rainfall area and frequency, and as well the occurrence of other rainfall systems.

Impact of microphysics parameterization schemes in simulation of vardah cyclone using the advanced mesoscale weather research and forecasting model

The very severe Tropical Cyclone Vardah caused huge damage to property and life in south India during December 2016. The sensitivity of numerical simulations of the very severe tropical cyclone Vardah to different physics parameterization schemes is carried out to determine the best microphysics and cumulus physics parameterization schemes. The WRF Numerical weather prediction model configured with two nested domains. The horizontal resolution of domain-1is 27 km and domain-2 is 9 km. The tropical cyclone Vardah simulated track results were compared with the best track data given by the Indian Meteorological Department (IMD). WRF model Simulations were carried out using different microphysics (mp) parameterization schemes by fixing convective cumulus physics (cu) option to Grell-3D ensemble scheme and boundary layer option to updated Yonsei University scheme. The Vardah Cyclone track well simulated using WRF Single Moment-3 (WSM3) microphysics scheme in combination with G3D cumulus physics scheme. The cumulus physics and microphysics parameterization schemes influence the cyclone track prediction skill.

Cyclonic intensity study using sea level pressure estimations from Ocensat-II scatterometer winds over Bay of Bengal during 2013

Sea level pressure (SLP) fields prevailing over Bay of Bengal (BoB) during Phailin, Lehar and Madi cyclones (2013) were estimated using University of Washington Planetary Boundary Layer (UWPBL) model with Oceansat-II Scatterometer (OSCAT) sea surface winds as an input parameter. Model performance in estimating SLP variations during cyclonic periods was investigated by comparison against cyclone reports of Indian Meteorological Department (IMD) and in-situ measurements. Pressure drop as per IMD reports were observed to be higher than model estimates, primarily due to limitations in scatterometer wind data. However, model retrieved pressure fields compare well against buoy measurements, with a bias of 0.0042, −0.1279 and −0.4406 observed for Phailin, Lehar and Madi cyclones respectively. The contrast in pressure drops between model estimates and IMD reports was investigated by comparing scatterometer winds against IMD reported maximum sustainable winds.

Climate change and Disasters : Issues and concern of Proposed Sharda –Yamuna Link

Climate is a measure of changes in meteorological variables. Sudden changes arise due to increasing deforestation, pollution, population, depletion of natural resources, global warming and industrialization etc. Uneven monsoon and irregular rainfall distribution causes great variations in climatic conditions which cause disasters; drought and flood. During monsoon season, flood occurs in several river basins of U.P and U.K states. According to Indian Meteorological Department, the average annual rainfall in U.P and U.K is more than other states; 100-120 cm and 120-400 cm respectively. Uttarakhand is among one of the highest rainfall states of India. Sharda river; a tributary of river Ganga overflows many times, causes flood in surrounded regions. Sharda-Yamuna link (S-Y) is one of the proposed links of National Perspective Plan to minimize flood in U.P and U.K states and drought in western parts such as Haryana, Rajasthan, and Gujarat of the country. Excess water will be transferred through S-Y link towards the drought regions of India. Approx 11,680 m3 of surplus water is estimated to transfer from river Sharda to river Yamuna to avoid flood in U.P and U.K states. The lining of the link passes through two states Uttarakhand and Uttar Pradesh. The S-Y may also help in maintaining the water balance in its Enrouted and Command regions. This paper highlights the climate change, flood and drought disaster issues and role of proposed Sharda-Yamuna link.