MAUSAM

The Impact of Data Uncertainty on Identifying Precipitation Trends in India

Pramod Soni — 2024-07-01

The study investigates the effect of missing data, variability, and measurement errors on precipitation data in India, as well as the potential errors in trend analysis that result from neglecting these factors. Daily rainfall data and RSTN (ratio of station grids) for the entire country from 1951 to 2015 were obtained from APHRODITE and used both simple and weighted linear regression to estimate trends. The findings of the simple linear regression indicated that on an annual scale, roughly 1489 grids (31% significant at the 95% confidence level) displayed positive trends, whereas 3150 grids (57% significant at the 95% confidence level) exhibited negative trends. In contrast, using the WLR method, a total of 2274 grids displayed positive trends, and 2365 grids showed negative trends, with approximately 25-30% of the grids having significant trends at an annual timescale. Overall, approximately 86% of positive trends and 68% of negative trends remained unchanged when using the WLR method instead of the LR method, respectively. However, approximately 23-26% of significant positive trends and about 46% of significant negative trends in the LR method were converted to non-significant trends in the WLR method. Moreover, only approximately 0.5% of positive significant trends and 2-3% of negative significant trends reversed to significant negative and positive trends, respectively. The study emphasizes the importance of considering missing records and data variability over time to obtain accurate trend analysis.

Weather based wheat yield prediction using machine learning

Ananta Vashisth — 2024-07-01

Wheat crop is highly affected by the influence of weather parameter, adverse weather drastically reduce wheat yield. Thus, there is need to develop and validate weather-based models using machine learning for its reliable prediction at multiple stages. Wheat yield and weather data during crop growing period were collected from IARI, New Delhi, Hisar, Amritsar, Ludhiana and Patiala. The yield prediction model was developed using stepwise multi linear regression (SMLR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) and hybrid model LASSO-SVR, and SMLR-SVR in R software. Analysis was done by fixing 70% of the data for calibration and remaining data for validation. Wheat yield prediction was done at tillering, flowering and grain filling stage for wheat crop by considering 46^th to 4^th, 46^thto 8^th and 46^th to 11^th standard meteorological week for model development. On examining these models for yield prediction at tillering, flowering and grain filling stage, LASSO performed excellent having nRMSE value ranged between 0.02 % at grain filling stage for IARI, New Delhi to 8.36 % for Hisar at flowering stage. The model performance of SVR is increased if hybrid model in combination with LASSO and SMLR is applied. Hybrid model LASSO-SVR has shown more improvement in SVR model compared with SMLR-SVR.

Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand

Chetan Kumar Bhatt — 2024-07-01

In this study, the utility of assimilation of multi-temporal and multi-polarized Sentinel-1A SAR (Synthetic Aperture Radar) data with rice crop model for mapping and predicting rice yield for district Udham Singh Nagar, Uttarakhand has been discussed. In this approach information regarding rice distribution over the district was achieved by mapping of rice fields from Sentinel-1A SAR images using support vector classification, and then the CERES RICE model which is embedded in DSSAT-4.7 was re-initialized by performing assimilation method in which the temporal single-polarized rice backscattering coefficients were grouped for each rice pixel for the district. The optimal input parameters with assimilation method in re-initializing the model allows a good temporal agreement between rice backscattering coefficients derived from Sentinel-1A SAR images and the rice backscattering coefficient derived from coupled model i.e. integration of CERES RICE (DSSAT-4.7) and semi-empirical rice backscatter model through Leaf Area Index (LAI). After re-initialization the yield of rice was calculated from each rice pixel and yield map of the area of study was developed. The results showed that the coupled model gave an estimate of rice yield of 3190 kg/ha which was quite near to the five years average district yield which was 3160 kg/ha with a difference of 30 kg/ha between coupled and five years average rice yield of the district y. On the basis of results obtained it can be concluded that Sentinel-1A SAR data has great potential for monitoring and mapping of rice with the ability to predict the yield of rice crop. The prediction of rice crop is an important step that could be used to assist farmers and policy makers by providing in-season estimates of the rice yield and production. This information could be used for better planning of the resources.

Climate change induced the altering in precipitation characteristics across the rice cultivation paddies of Vietnam

Q. C. Nguyen — 2024-07-01

Globally, it has enough convincing evidence to assert that climate variability is the main factor causing the change in precipitation characteristics (CPCs). Appraising the CPCs are, therefore, crucial for local agricultural activities. The aim of this study is to investigate the CPCs across the rice cultivation paddies (RCPs) of the Long Xuyen Quadrangle (LXQ) in Vietnam as a typical case of the impacts of climate change (ICC) on the agricultural sector.

To achieve the intended objective, firstly daily precipitation data series at 9 observation stations across the LXQ throughout 1978-2021 were appraised for quality by applying the homogeneity tests. Secondly, appraised precipitation data were investigated the CPCs applying a set of non-parametric approaches, and finally, the spatial distribution maps of the CPCs across the study area were shown off based on the Thiessen polygon algorithm integrated into the ArcGIS (Version 10.8) software.

With a 95% confidence level, the findings revealed that a statistically insignificant downward trend for annual precipitation and wet seasonal precipitation was recorded along the coastal arable land edges while the statistically insignificant upward trend was detected along the RCPs in the northeast to southeast part. One striking feature the study results revealed is that a statistically insignificant uptrend (Z_MK = 0.043 ~ 0.126) was observed for precipitation in the dry season. The findings could contribute positively to agricultural activities during the summer-autumn crop season.

The Climatic variability and its impact on maize and wheat yield in Himachal Pradesh

Pardeep Singh — 2024-07-01

The present study has analyzed variability in climate variables annually and seasonally (kharif and rabi season), viz. rainfall and temperature during from 1985 to 2021 and found its their impact on productivity of maize and wheat crops at different seven locations of Himachal Pradesh. The study found that, except for the maximum temperature in the kharif season, practically all of the seven sites had significant increases in maximum and minimum temperatures annually and seasonally over the study period. Highest positive deviation was found in minimum temperature of Kangra district rabi season i.e., 2.12ºC while lowest was found in kharif season i.e., 0.50 ºC. Pettit’s homogeneity test showed climate change year 1998 and afterward annually minimum temperature was significantly rise. Climate change year showing positive change in rainfall that is showing increasing trend after 1998 at all the locations. Increases in maximum temperature had adverse impact on maize crop yield in the kharif season and a positive impact on wheat yield in the rabi season, whereas rainfall had a positive impact on maize and wheat crop yield.

The Accuracy of CHIRPS rainfall data and its utilization in determining the onset of the wet and dry seasons in North Sumatra

Giarno . — 2024-07-01

Rainfall is a weather and climate parameter with high variability in space and time. Geographical conditions and atmospheric dynamics cause high rainfall intensity in the Indonesian Maritime Continent (IMC). The availability of rainfall data is crucial to determine the onset of the wet and dry seasons that affect community activities and the formulation of water resources policies. The limited number of rain gauges is a major obstacle in monitoring rainfall. Overcoming this, satellites can be used for rainfall estimation data, one of which is Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). However, the use of CHIRPS data needs to be tested for accuracy. This study aims to calculate the accuracy of CHIRPS data and its utilization in determining the onset of the wet season and the onset of the dry season. Validation is conducted using in-situ observation data at four BMKG stations in the North Sumatra region using the Contingency Table method. The results show that the four stations have an Equatorial rain pattern, with rain peaks in the September-October-November (SON) and March-April-May (MAM) periods. The monthly CHIRPS data has the highest performance based on the Pearson Correlation value. While the Proportion Correct (PC) of daily rainfall averages 62%, the highest value is at Binaka Meteorological Station. Calculating decade (10-day) and monthly rainfall results in the West Coast region is more accurate than the East Coast of North Sumatra. Determining dry season onset with CHIRPS data tends to be more advanced than dry season onset using the observation data. Meanwhile, the determination of wet season onset shows that most wet season onset occurs simultaneously on the west coast of North Sumatra

Analysis of heat wave and mean maximum temperature for the months of March to June over Vidarbha during last 50 years

Bhawna . — 2024-07-01

A study has been carried out to analyze the trend of heat wave and maximum temperature during March to June over Vidarbha. Central India, mainly the Vidarbha region of Maharashtra State, experience extreme temperatures during the month of March to June. Special cases have been identified where most of these stations have reported maximum temperature greater than or equal to 45°C and an attempt has also been made to determine its cause. Various studies have been taken up for examining the trend in heat waves over Indian region and its impact on various sectors. This study focuses on the statistical and meteorological aspects of heat wave. It was found that four out of seven stations displayed a positive trend in the number of heat wave days and three out of six stations displayed positive trend in severe heat wave days.It was observed that for the average maximum temperature,only Buldhana station showed positive trend for all four months and Amravati station showed negative trend for April and June months following Mann Kendall test statistics.

Observational Study on Air-Water Interactions over Poyang Lake during a Cold Season

Ximing Liu — 2024-07-01

ABSTRACT

In a lake, the heat and energy budgets and evaporation are the most fundamental components of the regional weather and climate and are controlled by the interactions between the lake and the atmosphere. Poyang Lake is the largest freshwater lake in China. The surface area of this shallow lake, located in the central and lower catchment of the Yangtze River, southeastern China, varies across the year based on the precipitation. A steel platform was built in the northeast open-water area of the lake to measure surface energy fluxes and other related atmospheric/hydrologic variables during a cold season from 1 December 2020 to 28 February 2021. The results show the average water surface temperature was 1.25°C higher than the 2 m height air temperature, though temperature inversions occasionally occurred for short periods. The average wind speed and friction velocity were small, leading to weak turbulent mechanical mixing. Consequently, consistently positive and moderate latent and sensible heat fluxes (17.4 and 5.4 W/m², respectively) values were produced due to the effects of turbulent mixing from thermal and mechanical factors. The monthly average albedo was large at mid-day (0.086). The low Bowen ratio (about 0.37) also indicates that more latent heat is released than sensible heat. When dry and cold air passed over the lake, the pressure of vapor and air temperature decrease significantly, the turbulent mechanical mixing is enhanced and the energy budget changed. As a consequence, the Ts-Ta, e_s-e_a values, and sensible and latent heat fluxes all increase.

Prediction of rainfall and groundwater using machine learning algorithms for Nagpur Division

Tulshidas M. Jibhakate — 2024-07-01

Rainfall and groundwater predictions are important for water resource planning and also to reduce the consequences of catastrophes like drought and floods. In the present study, Rainfall Anomaly Index (RAI) was estimated for 20 years period (2001–2020) to calculate the positive and negative anomalies. The estimated lowest and highest RAI years were used to compare the effect of rainfall on groundwater level fluctuations. Predictions of rainfall and groundwater were performed using machine learning algorithms. Sktime and scikit-learn libraries were used to predict the rainfall and groundwater levels in the study area using machine learning algorithms such as Naive (N), Exponential Smoothing (ES), Decision Tree Regressor (DT), Random Forest Regressor (RF), AutoARIMA (AA), K-Neighbour regressor (KN), and Gradient Booster Regressor (GB). Based on the observed seasonal rainfall and groundwater data from 2001 to 2015 for Nagpur division, present study predicts values for the 2016–2020 period. Then, using observed and predicted values for 2016–2020, accuracy assessment parameters like correlation coefficient (r), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and Taylor diagram were assessed for validation and to investigate the best and worst model forecasters. The present study observes that in the case of rainfall, the AutoARIMA forecaster is the best-fitted model, and in the case of groundwater, the naïve forecaster is the best-fitted model. The decision tree forecaster is the worst-fitted model in both rainfall and groundwater data. Then, the AutoARIMA and Naïve models were used to predict rainfall and groundwater values, respectively, for the years 2021–2025. Impact of ENSO and IOD on ISMR has been assessed. The ENSO phenomenon was more prominent during 2001–2010, and during 2011–2020, both may be the driving factors impacting ISMR.

A re -assessment study on the onset and withdrawal dates of Indian northeast monsoon for the decade 2011-20

Y. E. A. Raj — 2024-07-01

Abstract: The onset and withdrawal dates of Indian North east monsoon (NEM) over the combined region of Coastal Tamil Nadu (CTN) and South coastal Andhra Pradesh (SCAP) for the 10 year period 2011 -20 have been re -determined based on daily rainfall data of 16 stations by following an objective criteria. The dates thus derived when appended with past set of dates fixed by following similar methodology has resulted in a homogenous set of onset and withdrawal dates of NEM for 150 years. The mean onset / withdrawal dates for 2011-20 has been obtained as 23 October and 31 December respectively. During 2011-20, the normal NEM withdrawal from the southern and northern coasts have taken place on 23 December and 5 January respectively. This characteristic of existence of differential withdrawal dates which was shown in an earlier study based on 50 year data of 1961-2010, has persisted in 2011-20 as well.

The superposed epoch analysis conducted on the daily rainfall of the 16 stations has shown very sharp increase of rainfall at the time of onset and decrease after withdrawal. The daily rainfall has increased from 1-5 mm to 10-33 mm at onset and has decreased from 4-16 mm to 0-2 mm at withdrawal. The empirical orthogonal function analysis conducted on the pentad rainfall of October-January rainfall of 4 sub regions SCAP, North, Central and South CTN (NCTN, CCTN and SCTN) has revealed that the first principal component which could be associated with the overall strength of NEM, explains 78.3% of the variation and that the loadings which are positive for all the regions are higher in NCTN and CCTN. The second principal component which explains 12.7 % of variation has positive loadings in SCAP and NCTN and negative loadings in CCTN and SCTN associated with opposite type of rainfall anomalies in the two regions. The empirical orthogonal function analysis manifesting such a pattern could be partly associated with late withdrawal in the south coast compared to north coast.

An analysis of the frequencies of heavy/very heavy /extremely heavy rainfall derived for several periods has revealed that there have been 429 heavy rainfall events over 16 stations and 10 years during October- December. By defining and computing a probability based heavy rainfall index, it has been shown that frequency of heavy rainfall occurrences commencing from onset date is 91 for 16 stations and 10 years, which is 19 time more in the onset phase compared to pre onset phase and that once withdrawal takes place heavy rainfall occurrence becomes very rare.

Evaluation of INSAT-3DR Hydro-Estimator product for monsoon season rainfall at block-level and its utility in forecast verification: A case study in Karnal district, India

Amit Kumar — 2024-07-01

The availability and assimilation of INSAT-3D and INSAT-3DR weather satellite datasets has improved the accuracy of medium range weather forecasts. Conventionally, the forecast is verified against the in-situ observations. But the distribution of in-situ observatories is not uniform for many reasons like, in-habitable conditions, mountain terrains, operational cost etc. The availability of data from Automatic weather stations is also not guaranteed at all times because of maintenance and operational issues. Therefore, in the absence of in-situ data it becomes very difficult to verify the forecast. In the current study, skill of value added rainfall forecast is assessed by carrying out skill score analysis for Assandh (AS), Gharaunda (GD), Indri (ID), Karnal (KA), Nilokheri (NK) blocks using in-situ rain-gauge data for the southwest monsoon season of 2020 and 2021. For Munak (MU), Kunjpura (KJ) and Nissing (NI) blocks, the data from rain-gauges is not available. In order to fill this gap, in the present study, the block level medium-range value added rainfall forecast issued by IMD, is verified by utilizing INSAT-3DR satellite Hydro-estimator (HE) rainfall product. In order to gain confidence in the approach, the INSAT-3DR derived HE rainfall estimate is validated against the available rain-gauges in Karnal district. The study revealed that the rainfall data received from the satellite can be used for better forecasting of daily rainfall at the block level and preparation of agromet advisory bulletin. The accuracy of forecast of weather parameters in advance is found to be useful for farmers for doing appropriate field operations and crop management practices in the form of preparation of Agromet Advisories Bulletin (AAB).

Triple-dip La Niña (2020-2022) and its impact on Indian Summer Monsoon Rainfall: Insight from the Monsoon Mission Coupled Forecasting System

Satyaban B Ratna — 2024-07-01

The El Niño-Southern Oscillation (ENSO) is the most important driver of the Indian summer monsoon rainfall (ISMR) interannual variability. In recent years, there have been three consecutive La Niña years (2020–2022), which are widely known as the ‘triple-dip’ La Niña. This study discusses the observed variations in SST and ISMR during these triple-dip La Niña episodes. It was seen from the observed record that there have been four instances of triple-dip La Niña occurrences (1954–1956, 1973–1975, 1998–2000, and 2020–2022) during which ISMR was on the positive side of normal for most of the years. This study also evaluates the performance of the operational Monsoon Mission Climate Forecasting System (MMCFS) model in forecasting La Niña and its associated variability in ISMR during the recent triple-dip La Niña period (2020–2022). The results indicate that the model successfully forecasts the strength and patterns of La Niña during the monsoon season when initialized with April and May initial conditions. The model also forecasts the above-normal rainfall over many parts of India accurately. However, it fails to forecast the observed below-normal rainfall over the central-east Indo-Gangetic plains and northeast India for all three years. While climate models generally exhibit skill in forecasting ENSO-associated Indian summer monsoon seasonal rainfall, accurately predicting the spatial variability of rainfall over India remains a challenge.

Hybrid deep learning algorithms on the dimensionally reduced dataset with optimized parameters for high-precision predictions of rainfall in Chhattisgarh State

Nisha Thakur — 2024-06-28

Time series forecasting of multi-variant rainfall data was done using a sequential hybrid model. In this model, principal component analysis (PCA) was used to reduce the dimension of the dataset with minimal loss of the original information. The optimized value of window size and the number of Long Short Term Memory (LSTM) units to be used in the deep learning algorithm (LSTM) were estimated using the Genetic algorithm (GA). Thereafter, the dataset retrieved using PCA was inputted with the parameters optimized using GA. Because of these reasons; the model was named PCA-O-LSTM. A comprehensive, comparative study of various models, such as LSTM, PCA–LSTM, GA-LSTM, and PCA-O-LSTM was carried out. For a better interpretation of the results, each of the models was run for various epochs, like 10, 20, 50, 100 and 200. The quality of prediction done using the PCA-O-LSTM model was evaluated by different parameters like using determination coefficient (R²), mean square error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), Normalized error (NORM), RMSE-observations standard deviation ratio (RSR) and cosine similarity (CS). The value of R², were in the range of (0.962874, 0.972276), (0.970131-0.955826) and (0.950982- 0.972991) with the best value of the said parameter for 200, 200 and 100 epochs in case of GA-LSTM, PCA-LSTM and PCA-O-LSTM, respectively. The best possible value of R² was seen in the case of the PCA-O-LSTM model in which a dimensional-reduced dataset along with GA optimized the window size and numbers of units were incorporated.

Assessing medium range weather forecast and economic impact of agro advisories on wheat in Jalandhar, Punjab

Baljeet Kaur — 2024-07-01

The study was conducted to analyze the usability of medium range weather forecast in Jalandhar district of Punjab and economic impact on wheat. In this context, qualitative and quantitative analysis of medium range weather forecast (MRWF) was done for the rainfall over the actual data. Thereupon, to study the economic impact of Agromet Advisory Services (AAS), prepared on the basis of MRWF, on wheat; an experiment was conducted during rabi 2020-21 at PAU, Krishi Vigyan Kendra, Jalandhar. The results of the study stated that the usability of rainfall which was more than 80 per cent during post-monsoon, winter and Rabi. Further, the economic impact varies from Rs. 2324 to Rs. 3142 economic gain to the adopted AAS over non- adopted AAS for wheat. This profit was due to the crop management done according to agromet advisory bulletins. Thus, the application of agromet advisory bulletin, based on current and forecast weather is a useful tool for enhancing the production and income.

Assessment of Uncertainty in Estimation of Rainfall using EV1 Distribution with Reference to Data Length

N. Vivekanandan — 2024-07-01

Estimation of rainfall for a given return period is considered as an essential input to a hydrologic model that is used to estimate design discharge, which is needed for the planning and design of civil engineering infrastructure projects viz., roads, bridges, green highways, etc. This can be estimated by using recorded rainfall data over many stations in a given region. Uncertainties in design rainfall estimates arise from various sources such as data error, sampling error, regionalization error, model error, etc. Further, in model error, the data length is having direct impact in assessing the uncertainty of error in estimation of rainfall. This paper aims to assess the uncertainty in rainfall estimates of Pune and Vadgaon Maval, which can be carried out through extreme value analysis (EVA) by fitting Method of Moments (MoM), Maximum Likelihood Method (MLM), Method of Least Squares (MLS), Probability Weighted Moments (PWM) and Method of L-Moments (LMO) of Extreme Value Type-1 (EV1) distribution to the annual 1-day maximum series of rainfall. The characteristics of data series with different data length used in EVA of rainfall is examined through statistical tests viz., Wald-Wolfowitz runs test for randomness, Mann-Whitney U-test for homogeneity and Grubbs' test for identifying the outliers in data series. The adequacy of EV1 distribution in rainfall estimation is evaluated by Goodness-of-Fit (viz., Anderson-Darling and Kolmogorov-Smirnov) tests. The EVA results of Pune and Vadgaon Maval presents that (i) the estimated rainfall increases when data length increases; (ii) the standard error in the estimated rainfall decreases when the data length increases; and (iii) the standard error in rainfall estimates given by MLM is less than those values of MoM, MLS, PWM and LMO

Probability Analysis of Annual and Monthly Rainfall in Mizoram, India: Evaluating Goodness of Fit and Identifying Best Probability Distributions

Sundararajan Muniyan — 2024-07-01

This study presents a comprehensive analysis of the probability distribution of rainfall data in Mizoram, a state in Northeast India, from 1986 to 2021 by 18 probability distributions. The objective of this study is to identified the best-fit probability distribution of annual and monthly rainfall of Mizoram. The Kolmogorov-Smirnov, Anderson-Darling, and Chi-Square tests were conducted to determine the goodness of fit for each distribution. Additionally, based on the total score obtained from all three tests, the probability distribution with the highest score was included as a fourth distribution. After identifying the three best-fitting distributions from the respective tests, the parameters were used to generate random numbers for each period of analysis. The best-fit probability distribution was determined based on the minimum absolute deviation between actual and estimated values. The results show that the General Extreme Value distribution was found to be the best fit for 5 out of 12 months, followed by Log Pearson 3 for two months out of 12months. Gamma (3P) distribution was found to be the best fit for the annual rainfall of Mizoram. Additionally, the month of August contributes the highest annual rainfall with 16.8% while January contributes the lowest with 0.4%. These findings can be useful for hydrological and agricultural planning in Mizoram in light of climate change and variability.

Assimilation of KOMPSAT-5 Bending Angle in GSI 4D-EnVar Assimilation System

Suryakanti Dutta — 2024-07-01

Bending Angle from KOMPSAT-5 (Korea Multi-Purpose Satellite-5) GNSS-RO (Global Navigation Satellite System - Radio Occultation) data is assimilated in Global Data Assimilation and Forecast System. The observations are incorporated using the GSI (Grid-point Statistical Interpolation) 4D-EnVAR analysis scheme. Before assimilation, the data is processed and undergoes quality control by CDAAC (COSMIC Data Analysis and Archive Center), UCAR (University Corporation for Atmospheric Research). The evaluation & assessment procedure goes through four phases: 1) diagnostics through assimilation in cold start mode; cyclic assimilation for a 2) summer and a 3) winter month; 4) a case study for investigating the impact on the severe weather event. Two separate NWP forecast experiments, a control (named CTRL) and the experiment (called KOMPSAT5), are run simultaneously. The only difference between the two is the presence (in KOMPSAT5) and absence (in CTRL) of the KOMPSAT-5 GNSS-RO. Summer cycle statistics show better, significant and consistent improvement compared to the winter cycle.

An Innovative approach of meteorological observation integration to develop state-of-the-art web based visualization tool

Arpita Rastogi — 2024-07-01

Nowcasting plays a crucial role in assessment of current conditions of the atmosphere. Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings. Therefore, development of some tools through objective methods has immense importance in upgrading the capabilities of India Meteorological Department. At present, conventional subjective methods are running which are not much accurate. The biggest limitation is non-availability of various datasets at a same platform which makes nowcasting redundant and inefficient. Thus a robust web based tool is required which has ability to Integrate all available dataset at a same place in real time. This web based tool will be of great help to forecaster for delivering best and accurate state of the atmosphere in current condition. For development of this tool various datasets like radar, satellite, upper air sounding data and lightning data etc has been utilized. By using this tool as an integrated display, forecaster shall be able to analyze small-scale features present in a small area such as a city and make an accurate forecast for the next few hours. This tool is capable of providing warning to the public for hazardous, high-impact weather phenomenon including tropical cyclones, thunderstorms, lightning strikes and destructive winds as well. This paper is an attempt for development of a web based visualization tool by integration of various near-real time weather observation datasets for nowcasting purpose.

Maximum Cloud Zone Monitoring through INSAT Outgoing Long Wave Radiation (OLR) data

Rahul Sharma — 2024-07-01

Scanning radiometers on-board the meteorological satellites measure the radiance in narrow windows within the visible and infra-red spectra. For example, in the case of the INSAT VHRR these windows are 0.55-0.75µ and 10.5-12.5µ respectively. The broad-band outgoing longwave radiation and the planetary albedo are derived indirectly from such window measurements by applying physical and/or statistical algorithms. Geostationary satellite (INSAT-3D/3R) narrowband based OLR observations offer the significant advantage of an instantaneous response to surface temperature changes and played an important role in Indian Monsoon activity.

It has been shown that Indian Summer Monsoon (ISM) phases starting from onset, active and withdrawal are associated with the development of a Maximum Cloud Zone (MCZ) near the equatorial belt and its northward propagation. The alterations or oscillations between onset, active, break periods of the ISM can easily be monitored through meridionally propagating cloud bands as 30 to 50 days periodicity. Localized weather events also affect the periodicity of these MCZ every year differently. These varying periodicities and their amplitude peaks (30 days, 40 days, 20, days 8 days or lower) of OLR propagation/amplification modes during monsoon season were attributed to evolution and distribution of monsoon elements, like low level jet, Tibetan High, Mascarine High, Cross equatorial flow and dry and moist static stability and their cooperative interaction with lower troposphere over India.

Mapping Low Earthquake Risk Areas In North Maluku Indonesia

Muh. Farid Wajedy — 2024-07-01

Complex tectonics in the Maluku Sea cause frequent earthquakes in the mainland of Maluku, especially North Maluku. During 2017-2021, 1099 earthquakes occurred in the North, with an average magnitude of 4.4. The current study aims to map the maximum ground acceleration, maximum ground velocity and earthquake intensity in the North Maluku region between 2017 and 2021 to find areas with high and low earthquake impact. The method used in this study was to calculate the PGA (Peak Ground Acceleration) value with PGV (Peak Ground Velocity). Earthquake intensity is calculated using the equation that relates PGA to MMI. The PGA value in theNorth Maluku region ranges from 18-20.3 gal. and PGV values range from 8.6 to 9.6 cm/s. The earthquake's intensity was on the MMI III scale because several active faults in North Maluku cause earthquakes with a magnitude of 4-5 with a depth varying from 10-50 km. Finally, the result of this study is expected to provide important information about areas with a low or high risk of earthquake impacts in North Maluku.

Weather in India MONSOON SEASON

Editor Mausam — 2024-07-01

Calibration and validation of CERES-rice model using varied transplanting dates and seedling ages of RNR 15048 and assessing high temperature sensitivity in the North Eastern Ghat region of Odisha

Lalichetti Sagar — 2024-07-01

A field experiment was carried out with four dates of transplanting (29th July, 7th August, 17th August and 27th August) and three age of seedlings (15, 25 and 35 days old) at PG Experimental farm, M. S. Swaminathan School of Agriculture, Parlakahemundi, Gajapati district to calibrate and validate the CERES-Rice model for RNR 15048 in the north eastern ghat region of Odisha. The model evaluation with respect to simulation of phenology at anthesis and physiological maturity is considered to be excellent with RMSEn of 3 and 1 as influenced by both dates of transplanting and age of seedlings, respectively. Similarly, the simulated grain yields were also closely related to observed yields with lower RMSE and RMSEn values. The CRM values obtained in simulating both phenology and grain yields were negative, reporting an over-estimation of predictions. Further, the model simulated yields were used to study the influence of elevated temperatures 0.3 and 0.9 C on grain yield which showed reduction in grain yields with an increase in temperatures. Therefore, the model was found to be enough sensitive to be used as a research tool in the variable agro-environments of eastenghat region of Odisha to suggest suitable management practices for RNR 15048.

Impact of Elevated CO2 and Temperature on Greengram (Vigna radiata L.) and Cowpea (Vigna unguiculata L.) under Soil Plant Atmospheric Research (SPAR)

Govindraj T. — 2024-07-01

The experiment was carried out during 2022 to assess the effect of elevated temperature and elevated CO₂ on growth and yield parameters of Greengram and cowpea under Soil Plant Atmospheric Research (SPAR) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore with four treatments viz.,T₁-(aG): Ambient Greengram , T₂ - (eG): +3°C elevated temperature and CO₂ (600 ppm) Greengram, T₃ - (aC): Ambient Cowpea, T₄ - (eC): +3°C elevated temperature and CO₂ 600 ppm in Cowpea which was replicated four times. Results revealed that cowpea recorded maximum plant height, leaf area under elevated temperature cum elevated CO₂ which were significantly higher than other treatments and Greengram CO 8 variety recorded maximum yield attributes under elevated temperature cum elevated CO₂ which were found significantly higher than other treatments. Elevated CO₂ and elevated temperature shown a positive effect on greengram and cowpea plant growth and yield attributes.

Usability assessment of district level rainfall forecast in Mizoram

Saurav Saha — 2024-07-01

India Meteorological Department (IMD) issued periodic district level rainfall forecast in Mizoram over past twelve years. We evaluated the accuracy and usability of forecast using several index based approaches. The accuracy was more but with limited forecast skills during non-rainy over major rain-receiving months. Principal component analysis identified four indices viz.Odds ratio skill score(ORSS),Probability of Detection (PoD), Odds ratio (OR) and Frequency bias (BIAS); essential for forecast accuray evaluation using minimum rainfall datasets. Relative operating characteristic(ROC) curvesignified that there was considerable scope for increaseing forecast accuracy through multi model-ensemble (MME)calibration under Gramin Krishi Mausam Seva (GKMS) network.

Cyclonic storms & depressions over the north Indian Ocean during 2023

Editor Mausam — 2024-07-01