Meteorological Department Research Articles

Gap between impact-based and impact forecast and warning: Implications for people-centric early warning system (EWS) in India

This paper explores the scope and potential of Impact-based Forecast and Warning (IbFW) services for hydro-meteorological hazards in India. It builds upon several global and national initiatives which emphasize that the Early Warning System (EWS) requires to be people-centric and inclusive in order to reduce disaster risk. The objectives of the study are: to understand the current Impact-based Forecast and Warnings framework of the India Meteorological Department (IMD) and its usefulness from the people's perspective. Drawing from fieldwork conducted in Krushnaparasad Block, Puri District of India, the paper argues that there is a shifting focus towards meeting the requirements of general public, but there is a long way to go as far as meeting expectations are concerned. It calls for changes such as strengthening risk assessment modeling, clarity over institutional mandates and adoption of flexible governance frameworks to move from an Impact-based paradigm to Impact Forecast and Warning services.

Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques.

The daily soil radon activity has been measured continuously over a year with BARASOL BMC2 probe at a measuring site of Jadavpur University Campus in Kolkata, India. The dependency of soil radon activity with different atmospheric parameters such as soil temperature, soil pressure, humidity, air temperature, and rainfall has been also analyzed. The whole study period is divided in four seasons as proposed by the Indian Meteorological Department (IMD). Minimum soil radon level has been observed during the winter season (December-February). On the other hand, higher soil radon level has been observed both for summer and monsoon. Except soil pressure, all other variables have shown positive correlation with soil radon activity. Among five variables, soil temperature has been the most significant variable in terms of correlation with soil radon level whereas maximum humidity has been the least significant correlated variable. It has been observed that considerable reduction of soil radon level occurred after four heavy rainfall events during the study period. The combined effect of these multi-parameters on soil radon gas has been evaluated using machine learning methods like principal component regression (PCR), support vector regression (SVR), random forest regression (RF), and gradient boosting machine (GBM). In terms of performances, RF and GBM have performed much better than SVR and PCR. More robust and consistent results have been obtained for GBM during both training and testing periods.

Assessing seasonal variation and trends in rainfall patterns of Madhya Pradesh, Central India

Abstract Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.

Effect of Surface Characteristics and Rainfall in Infiltration Rate: A Case Study of Kaligandaki River Basin

Study of infiltration characteristics is crucial for effective watershed management, as it helps determine the water availability for crops, the need of irrigation, groundwater recharge, and surface runoff. This study is aimed at identifying the relation between infiltration rate and surface characteristics as well as rainfall rate in the Kaligandaki River basin of Nepal using Hydrologic Modelling System (HEC-HMS). HEC-HMS is a physically based, semi-distributed model that uses the weather and spatial information of soil and landuse of the basin for deriving the inputs for the model. A hydrological rainfall-runoff model is developed for the Kaligandaki River basin, with outlet at hydrological station 420, Kotgau, of Department of Hydrology and Meteorology (DHM). The model was then validated at DHM station 420, Angsing, of Kaligandaki River. Model generated subbasin wise infiltration rates and existing landuse were analysed and it was found that sub-basins with dominant landuse as forest had high infiltration rate among prevailing landuse types. Conversely, low infiltration rate was observed for barren area, snow/glacier and grassland landcover type dominated subbasins. A positive correlation among rainfall rate and infiltration rate was seen with linear increase in infiltration rate for an exponential increase of average rainfall intensity. Infiltration rate was found to have positive correlation with the Hydrologic Soil Group types as suggested by NRCS. Moreover, the study signified a greater impact of landcover over soil types on infiltration rates. Whereas, no significant impact of basin slope on infiltration rate could be identified, which may be due to preclusion of multiple topographic features. Understanding the impacts of rainfall intensity and surface characteristics on infiltration enables proactive measures to mitigate the issues like landslide and reduce flooding and soil erosion by improving infiltration rates through surface condition assessment.

Fisheries-based Determinants of Fisherfolks’ and their Influence on Access to Impact-based Weather Forecasting in Suba-North sub-County, Homabay County, Kenya

Lake Victoria, the world’s largest freshwater fishery, supports the livelihoods of 4 to 5.6 million economically vulnerable people, with many living on less than $1.25 a day. However, the Lake also witnesses a staggering annual drowning toll of 1,500 to 5,000 fatalities, with two-thirds attributed to storms. Recognising the need for an effective advisory and warning system known as impact-based forecasting (IBF), the HIGHWAY-WISER Project commenced in 2017, focusing on 10 East African beaches, including those in Kenya. Despite its significance, this donor-funded initiative remains relatively unknown and under-researched. This study investigates how fisheries-based factors affect access to IBF in the Lake Victoria region. Data collection involves two key stakeholder groups: policymakers and implementers from the Kenya Meteorological Department (KMD) and the fisherfolk community, segmented across six beaches. A combination of proportional stratified and simple random sampling yields a sample size of 422 fisherfolk who complete semi-closed questionnaires. Additionally, 30 policymakers and beach management unit (BMU) members are purposively selected as key informants and interviewed. The study reveals that fisheries-based determinants significantly influence IBF access among fisherfolk. These determinants encompass BMU politics and leadership, social networks, the adoption of flags and noticeboards, exposure to storms, boat size, energy sources for propulsion, fisherfolk experience, and timing of fishing activities. To enhance awareness and accessibility, the study recommends greater involvement of community-based organisations and increased training on IBF for fisheries decision-making contexts. This research underscores the importance of further exploration into fisheries-based factors as pivotal determinants of IBF access. It illuminates how these understudied variables factor into the decision-making processes of fisherfolk regarding whether to embark on fishing expeditions, irrespective of the presence or absence of IBF at their respective beaches. Such insights can inform more effective strategies for improving IBF access within this vital socioeconomic context

Estimating the Intensity Equations for Rain Intensity Frequency Curves (Mosul /Iraq)

The relationship between the rainfall intensity, duration, and frequency (IDF) is widely used in water resources engineering in designing hydraulic structures, such as culverts and sewage systems, and for reducing and controlling floods. In this study, the curves of (IDF) were found for five rain stations registered in different regions in northern Iraq, i.e., Mosul, Tal-Afar, Sinjar, Rabia, and Tal-Abta, and for different periods extending from 1990 to 2019. The Indian Meteorological Department (IMD) empirical equation was utilized to obtain data during short periods, i.e., 10, 20, 30, 60, 120, 180, 360, 720, and 1440 minutes. Also, three probability distributions were used: the Gumble Distribution, the Log Pearson Type III Distribution, and the Log-Normal Distribution, for various return periods (2, 5, 10, 25, 50, and 100) years. Easy fit 5.6 software includes the tests (Kolmogorov-Smirnov, Anderson -Darling (AD), Chi-Squared (χ2)) was used to determine the most suitable distribution for the observed data among the three used distributions. The results demonstrated an insignificant difference between the three applied distributions and the statistics values that fell within the significance level, with priority to the Log Pearson-III distribution.

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

AbstractThe changing frequency of extreme rain events in the past few decades over the Indian river basins (IRBs) contributed to floods and drought and resulted in economic losses and gross domestic product. In this study, we evaluated the performance of 12 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6‐ experiment with India Meteorological Department observed data sets to reproduce the extreme rainfall events as well as project the changes in frequency and intensity of the hydroclimate extremes in future. We found that under low emission scenarios (SSP1‐2.6), the frequency of extreme rainfall is going to increase over the western ghat and northeast IRBs, while an increase in heavy rainfall intensity (14.3%) noticed under SSP2‐4.5 in the upper Ganga and Indus basin. Also, approximately 4%–10% of the heavy rainfall is projected to increase over the western part of IRBs during the Near (2021–2040) and Mid (2041–2060) future. The study explored the new hotspot regions for future urban flooding due to increasing pattern of heavy rainfall in future. Moreover, the lower Ganga basin will experience agricultural drought in near future due to decreasing areal mean rainfall, which needs to be seen by policymakers for managing the excess (less) water. Also, India's northern, central, and western river basins may experience more extremes under high‐emission (SSP5‐8.5) scenarios that indicate challenges to mitigation. The findings of this study highlight the importance of developing long‐term adaptation and mitigation strategies aimed at reducing hydroclimate vulnerability. It emphasizes the need to implement measures that enhance resilience and minimize risks associated with hydroclimate extremes at the basin level.

Assessing seismicity in Bangladesh: an application of Guttenberg-Richter relationship and spectral analysis

Bangladesh has a high risk of earthquakes because the Dauki, Jamuna, and Chittagong-Myanmar faults are still active. However, the assessment of seismicity remains a big challenge due to the complex geologic setting of Bangladesh. This study employed the Guttenberg-Richter relationship and the spectral models to assess and analyze the earthquake conditions in Bangladesh. Besides, an instrumental earthquake catalogue, obtained from the Bangladesh Meteorological Department (BMD), covering 1985–2017, is established. The results revealed that the Guttenberg-Richter constants of a and b were 2.981 and 0.392, which propagated a strain release from 1992 to 2017. The spectral model analyses, e.g. wavelet transform (WT), short-time Fourier transformation (STFT), and multitaper model (MTM), demonstrated the magnitude and strain release anomalies of the same magnitude ranging from 4.8 to 5.7, indicating the probable precursor of an upcoming earthquake. Notably, magnitudes have been running around 4.5–5.8, which may act as a signal to major earthquakes that have not been evident before. The proposed models allowed for the completion of the Bangladesh earthquake catalogue and provided a platform for future seismicity assessment and earthquake probability analysis. These results should be considered in determining how likely earthquakes are to happen in an area or region.

Spatial and Temporal Analysis of Drought Forecasting on Rivers of South India

Extreme weather events such as droughts are catastrophic and can have serious consequences for people and the environment. Drought may be managed if measures are taken in advance. The success of this endeavor depends on a number of factors, not the least of which is accurate descriptions and measurements of drought conditions. Reducing the negative consequences of droughts requires an early forecast of drought conditions. The primary objective of this research is, hence, to establish a process for the assessment and prediction of drought. The drought evaluation was carried out using the standards established by the SPI and the Indian Meteorological Department. Maps of drought severity were generated using severe drought data. Thirty years’ worth of SPI readings was analyzed. Fuzzy-based drought forecasting model parameters were determined during a 25-year period, and the model was validated throughout the remaining years. The findings of this study can be used by the community to help combat the drought. Before the drought worsens, the local government can implement lifesaving mitigating measures.

Future Projections of Rainfall for Tamil Nadu from Coupled Model Intercomparison Project- 6 (CMIP6)

Precipitation is a crucial input for agriculture and living things in the world, which changes drastically under a warmer climate due to climate change. Hence, the study was carried out to project the changes in annual and seasonal precipitation based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model. In the present study, Coupled Model Intercomparison Project phase six (CMIP6) datasets were used for two SSP scenarios: SSP2-4.5 and SSP5-8.5 and three-time slices for the future viz., near (2021–2050), mid (2051–2080) and end-century (2081–2099) and base period (1991–2020) dataset obtained from the India Meteorological Department (IMD) was used to compare with the future climate over Tamil Nadu. The result revealed that the highest positive mean deviations in annual (81%), SWM (21%), NEM (79%) and summer (163%) were observed in the projected precipitation under the SSP5-8.5 scenario during the Near, mid, near and mid-century respectively. For winter, SSP2-4.5 showed the highest mean deviation of 122% in the near century. According to the three future time scale simulations for the twenty-first century, annual rainfall is predicted to increase by 81% in the near future and 19% in the mid-century, while it is expected to decline by 1.5% at the end of the century under SSP5-8.5. In the SSP2-4.5 scenario, rainfall would increase by 1% in the near future, decrease by 30% in the end century and decrease by 30.5% in the mid-century. From the result, it is concluded that there would be an increase in heavy precipitation occurrences at the near, mid and end of the 21st century under both the SSP5-8.5 and SSP2-4.5 scenarios. These findings might be helpful in framing future agricultural water management regulations to deal with threats from heavy precipitation and researchers to study precipitation changes at the global level.

Assessment Of Rainfall Pattern And Future Change For Kelantan River Basin, Malaysia Using Statistically Downscaled Local Climate Models

<p>Climate change has been discussed frequently in recent decades, and it has increased the probability of extreme flood occurrence. This study aims to provide an analysis of future rainfall patterns and flood occurrences specifically for the Kelantan River Basin which is identified as one of flood prone areas in Malaysia. The study area was divided into five regions of the Kelantan River Basin, - Kota Bharu (Northern), Kuala Krai (Center), Pos Lebir (Southeastern), Pos Hua (Southwestern) and Pos Gob (Northwestern). The historical rainfall data (1986-2019) was then retrieved from the Malaysian Meteorological Department (MMD) based on the five regions. The statistical approach was applied to downscaled climate model data from the CanESM2 GCM forced by the Representative Concentration Pathway (RCP) 4.5 and 8.5. The reliability assessment using a Cronbach’s Alpha, Linear Regression and Pearson Correlation results show that local climates (2006-2019) forced by RCP4.5 have a similar trend to historical rainfall within the same period. The spatial analysis outcomes showed that the northeastern region of the Kelantan River Basin received its highest average annual rainfall (5,000 mm) in 1990 and caused severe flooding in the area. However, there is a significant change of rainfall pattern in all regions, with a steady increase in annual rainfall in the southwestern region (2021-2100).</p>

Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.

Doppler Sodar Measured Winds and Sea Breeze Intrusions over Gadanki (13.5° N, 79.2° E), India

Doppler sodar measurements were made at the tropical Indian station, i.e., Gadanki (13.5° N, 79. 2° E). According to wind climatologies, the wind pattern changes from month to month. In July and August, the predominant wind direction during the monsoon season was the southwest. In September, it was the northwest and south. While the winds in November came from the northeast, they came from the northwest and southwest in October. The winds in December were out of the southeast. The diurnal cycle of winds at 60-m above the ground was visible, with disturbed wind directions in September and October. This may be connected to the Indian subcontinent’s southeastern monsoon recession. To better understand the monsoon circulation on a monthly basis, the present work is innovative in that it uses high-resolution winds measured using the Doppler sodar at the atmospheric boundary layer. The convergence of a sea breeze and the background wind might result in a sudden change in wind direction, and forecasting such a chaotic atmospheric event is crucial in the aviation sector. As a result, the wind shear that is produced may pose a serious threat to airplanes that are landing. In the current study, we present a few cases of sea breeze intrusions. The physics underlying these intrusions may help modelers better understand these chaotic wind structures and use them as inputs in their models. Based on surface-based atmospheric characteristics, there have been two reports of deep sea breeze intrusions that we report in this research. The sea breeze days were marked by substantial (moderate) drops in temperature (dewpoint temperatures) and increased wind speed and relative humidity. The India Meteorological Department (IMD) rainfall data showed a rise in precipitation over this location on 23 July (4.8 mm) and 24 July (9.5 mm) when sea breeze intrusions over Gadanki were noticed. Sea breeze intrusions could have brought precipitation (intrusion-laden precipitation) to this area due to conducive meteorological conditions. A simple schematic model is proposed through a diagrammatic illustration that explains how a sea breeze triggers precipitation over adjacent locations to the seacoast. The skew-T log-P diagrams have been drawn using the balloon-borne radiosonde measured atmospheric data over Chennai (a nearby location to Gadanki) to examine the thermodynamic parameters to gain insights into the underlying mechanisms and meteorological conditions during sea breeze intrusion events. It is found that the convective available potential energy (CAPE), which is presented as a thermos diagram, was associated with large values on 23 July and 24 July (898 J/kg and 1250 J/kg), which could have triggered thunderstorms over Chennai.

ANALYSIS OF FREQUENCY CONTENT AND STATISTICAL RELATIONSHIP AMONG EARTHQUAKE PARAMETERS OF SEISMIC DATA

The frequency content of the strong-motion seismic data collected from seismic stations by the Bangladesh Meteorological Department (BMD) using GL-S60/120 broadband seismometers of earthquakes in and around Bangladesh is examined. In addition, the statistical relationships among various earthquake parameters, such as the peak ground velocity (PGV), magnitude, epicentral distance, and time of arrival of p-and s-waves of the collected data, are analyzed. Approximate seismic wave velocities are also obtained. The Fourier and wavelet analysis results reveal delicate differences in the frequency content of the p-and s-waves occurring during earthquakes. These results are compared to those generated from the STEAD dataset, which contains seismic waveforms collected in the U.S., France, and Greece regions. Regression analysis on the BMD data derives relationships among the aforementioned parameters. Collected data from some stations show perfect linear relationships among PGV and the travel time of seismic waves with magnitude and epicentral distance, whereas, in other stations, such relationships are not significant. Plausible explanations for these disparities are provided along with similar regression analysis using the STEAD data. The findings are useful for earthquake early-warning systems, geology research, seismic risk zone delineation, and seismic hazard forecasts.

An IoT-based handheld environmental and air quality monitoring station

Weather and air quality play an important role in determining environmental pollution. Fluctuation of these parameters not only causes environmental pollution but also causes severe injuries to human health. With the emergence of the Internet of Things (IoT), sensor-based weather devices with easy observation facilities started to develop. In this regard, this study focused on developing an IoT-based portable weather monitoring gadget that can measure weather and air parameters which are more often required in our day-to-day life. The proposed system is capable to measure temperature, pressure, humidity, altitude, PM2.5, PM10 level, VOC, and CO level. It consists of a portable display and a mobile app with a thing speak cloud platform. Further, the system has a Wi-Fi and GSM connection to communicate data. A mobile application was developed to monitor the readings in real-time which are stored in the cloud platform. The developed hardware was carefully calibrated in the national meteorological department to make sure our system is practically usable. Compared to existing models, our prototype is very handholdable, easily installable which does not require trained technicians, and is easily maintainable. Also, it is possible to access the data from anywhere in the world through Wi-Fi connectivity, and possible to make data visualization and analysis. On the other hand, it is very difficult to find a single, portable size, and low-cost device to collect all these parameters together. Nonetheless, our prototype has the potential of connecting with multiple similar devices to create a larger IoT network grid. Overall, our proposed product has a combination of weather and air quality parameters in a portable and handholdable size with low-cost and low power consumption which other devices do not have according to the latest literature.

Assessing the potential impacts of LULC change on urban air quality in Dhaka city

Dhaka is among the top five metropolises with the most particulate pollution and polluted air alone, responsible for 17.6% of the risk of mortality and disability. As a result of recent global episodes of urban air pollution, policymakers and administration agencies are endeavoring to regulate urban land use and land cover change (LULC) and sustainably enhance air quality. This study aims to establish a relationship between alterations in PM2.5 concentration and LULC features. The Bangladesh Meteorological Department provided PM2.5 and meteorological data for 2012 and 2018, and RapidEye image data was used to classify land-use for 2012 and 2018. Between 2012 and 2018, waterbodies and vegetation fell by 2% and 5.3%, and construction and bare land increased by 4% and 3.4%, respectively. Reduced vegetation and waterbodies might result in an increase in barren land and built-up areas. Numerous industrial sites and the unlawful acquisition of canals may also contribute to the growth of barren land. In 2018, homogeneity of the landscape pattern is higher than 2011. The aggregation and contagion indices, landscape metrices, have inverse correlation of −0.01 and −0.158 respectively with PM2.5 concentrations. However, the landscape index has positive correlation of 0.06 with PM2.5 concentrations. On the other hand, the variability in PM2.5 concentrations is correlated with the altered characteristics of LULCs and related landscape indicators. According to the spatial correlation, PM2.5 levels have decreased as vegetation and waterbodies have increased, as the PM2.5 concentration is negatively correlated with vegetation and waterbodies by −0.212 and −0.028, respectively. Furthermore, meteorological indices are significantly correlated with PM2.5 concentration, which was used as an independent variable to estimate the annual average PM2.5 concentration levels for 2024 using an Artificial Neural Network, which range from 312 to 188 μg/m3. The government should take appropriate measures to curb polluting air emissions, ensuring a livable city in the future.

Study on Effect of Rainfall Variability on Crop Productivity of Tur (Arhar) and Cotton Crops for Various Districts of Maharashtra, India

This research article investigates the impact of rainfall variability on the productivity of Tur (Arhar) and Cotton crops in the districts of Maharashtra, India. The study analyzes the correlation between rainfall departure (deviation from the normal) and crop productivity, focusing on specific months when rainfall has the most significant influence on crop yield. Secondary data from the India Meteorological Department and the Ministry of Agriculture and Farmers Welfare were utilized for the analysis. Statistical methods such as detrending, correlation analysis, and significance testing were employed to measure the relationship between rainfall variability and crop productivity. For the Tur (Arhar) crop, the study covers data from 1997-1998 to 2018-2019 for 34 districts in Maharashtra. The analysis reveals the effects of rainfall on different stages of crop growth, such as sowing, germination, vegetative growth, flowering, grain formation, and harvesting. Similarly, for the Cotton crop, data from 28 districts were examined to determine the impact of rainfall on various stages of crop development. The findings indicate that rainfall plays a crucial role in crop productivity, with both positive and negative correlations observed in different districts and months. The study highlights the significance of rainfall during specific stages of crop growth and emphasizes the importance of considering rainfall patterns when making agricultural decisions. This research contributes valuable insights into the relationship between rainfall variability and crop productivity, providing farmers and policymakers with knowledge to develop sustainable agricultural strategies in Maharashtra. By understanding the influence of rainfall on crop yield, farmers can make informed decisions regarding crop selection and adopt appropriate agricultural practices to mitigate the effects of rainfall variability.

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

A novel model for rainfall prediction using hybrid stochastic-based Bayesian optimization algorithm.

Rainfall forecasting is considered one of the key concerns in the meteorological department because it is related strongly to social as well as economic factors. But, because of modern context of climatic conditions and the intense activities of humans, the forecasting procedure of rainfall patterns becomes more problematic. Therefore, this paper proposes a novel timely and reliable rainfall prediction model using a hybrid stochastic Bayesian optimization approach (HS-BOA). The weather dataset containing different meteorological geographical features is provided as input to the introduced prediction method. Hybrid stochastic (HS) specifications are tuned by the Bayesian optimization algorithm (BOA) to upgrade the prediction accuracy. The weather data are initially preprocessed through the pipelines, namely, data separation, missing value prediction, weather condition cod separation, and normalization. After preprocessing, the highly correlated features are removed by correlation matrix using the Pearson correlation coefficient. Then, the most significant features which contribute more to predicting rainfall are selected through the feature selection process. At last, the suggested rainfall forecasting model accurately predicts rainfall using optimized parameters. The experimental analysis is performed, and for the proposed HS-BOA, MAE, RMSE, and COD, values attained for rainfall prediction are 0.513mm, 59.90mm, and 40.56mm respectively. As a result, the proposed HS-BOA approach achieves minimum error rates with increased prediction accuracy than other existing approaches.

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

Parameter estimation strategies have long been a focal point in research due to their significant implications for understanding data behavior, including the dynamics of big data. This study offers an advancement in these strategies by proposing an adaptive parameter estimation approach for the Generalized Extreme Value distribution (GEVD) using an artificial neural network (ANN). Through the proposed adaptive parameter estimation approach, based on ANNs, this study addresses the parameter estimation challenges associated with the GEVD. By harnessing the power of ANNs, the proposed methodology provides an innovative and effective solution for estimating the parameters of the GEVD, enhancing our understanding of extreme value analysis. To predict the flood risk areas in the Chi river watershed in Thailand, we first determine the variables that are significant in estimation of the three GEVD parameters μ,σ, and ξ by considering the respective correlation coefficient and then estimating these parameters. The data were compiled from satellite and meteorological data in the Chi watershed gathered from the Meteorological Department and 92 meteorological stations from 2010 to 2021, and consist of such variables as the Normalized Difference Vegetation Index (NDVI), climate, rainfall, runoff, and so on. The parameter estimation focuses on the GEVD. Taking into consideration that the processes could be stationary (parameters are constant over time, S) or non-stationary (parameters change over time, NS), maximum likelihood estimation and ANN approaches are applied, respectively. Both cases are modeled with the GEVD for the monthly maximum rainfall. The Nash-Sutcliffe coefficient (NSE), is used to compare the performance and accuracy of the models. The results illustrate that the non-stationary model was suitable for 82 stations, while the stationary model was suitable for only 10 stations. The NSE values in each model range from 0.6 to 0.9. This indicated that all 92 models were highly accurate. Furthermore, it is found that meteorological variables, geographical coordinates, and NDVI, that are correlated with the shape parameter in the ANN model, are more significant than others. Finally, two-dimensional maps of the return levels in the 2, 5, 10, 20, 50, and 100-year return periods are presented for further application. Overall, this study contributes to the advancement of parameter estimation strategies in the context of extreme value analysis and offers practical implications for water resource management and flood risk mitigation.