Variations in meteorological floods during summer monsoon over India

In this study, rainfall data of 31 mteorological sub-divisioins in India for 113 years (1875-1987) have been used to develop a flood index and statistical properties of the Index are discussed. Relationship of the index with the seasonal rainfall, number of depressions and El-N/no phenomenon are examined. The study revealed that 1971-80 decade, had more number of flood years than the drought years. The flood index was found to be significantly related to flood situation over India. It IS difficult to associate any particular phase of the quasi-biennial oscillations (QBO) with occurrence of floods.

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Maximum Product of Spacing Parameter Estimation of Gompertz Rayleigh Distribution and Application to Rainfall Datasets

Asian Journal of Probability and Statistics ◽

10.9734/ajpas/2021/v14i230325 ◽

2021 ◽

pp. 41-59

Author(s):

Hussein Ahmad Abdulsalam ◽

Sule Omeiza Bashiru ◽

Alhaji Modu Isa ◽

Yunusa Adavi Ojirobe

Keyword(s):

Goodness Of Fit ◽

Negative Binomial ◽

Likelihood Estimation ◽

Statistical Properties ◽

Rainfall Data ◽

Parameter Estimates ◽

Data Sets ◽

Unknown Parameters ◽

Exponentiated Weibull ◽

Integrated Hazard

Gompertz Rayleigh (GomR) distribution was introduced in an earlier study with few statistical properties derived and parameters estimated using only the most common traditional method, Maximum Likelihood Estimation (MLE). This paper aimed at deriving more statistical properties of the GomR distribution, estimating the three unknown parameters via a competitive method, Maximum Product of Spacing (MPS) and evaluating goodness of fit using rainfall data sets from Nigeria, Malaysia and Argentina. Properties of statistical distributions including distribution of smallest and largest order statistics, cumulative or integrated hazard function, odds function, rth non-central moments, moment generating function, mean, variance and entropy measures for GomR distribution were explicitly derived. The fitted data sets reveal the flexibility of GomR distribution over other distributions been compared with. Simulation study was used to evaluate the consistency, accuracy and unbiasedness of the GomR distribution parameter estimates obtained from the method of MPS. The study found that GomR distribution could not provide a better fit for Argentine rainfall data but it was the best distribution for the rainfall data sets from Nigeria and Malaysia in comparison with the distributions; Generalized Weibull Rayleigh (GWR), Exponentiated Weibull Rayleigh (EWR), Type (II) Topp Leone Generalized Inverse Rayleigh (TIITLGIR), Kumarawamy Exponential Inverse Raylrigh (KEIR), Negative Binomial Marshall-Olkin Rayleigh (NBMOR) and Exponentiated Weibull (EW). Furthermore, the estimates from MPSE were consistent as the sample size increases but not as efficient as those from MLE.

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Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products

Remote Sensing ◽

10.3390/rs11091080 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1080 ◽

Cited By ~ 2

Author(s):

Amit Bhardwaj ◽

Vasubandhu Misra

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Spatial Scales ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Seasonal Rainfall ◽

Onset Date ◽

Rainfall Anomalies ◽

Global Precipitation ◽

Seasonal Length

We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions.

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ANALISIS CURAH HUJAN BULANAN DI KOTA AMBON MENGGUNAKAN MODEL HETEROSKEDASTISITAS: SARIMA-GARCH

MEDIA STATISTIKA ◽

10.14710/medstat.13.1.68-79 ◽

2020 ◽

Vol 13 (1) ◽

pp. 68-79

Author(s):

Lexy Janzen Sinay ◽

Ferry Kondo Lembang ◽

Salmon Notje Aulele ◽

Dominique Mustamu

Keyword(s):

White Noise ◽

Secondary Data ◽

Monthly Rainfall ◽

Rainfall Data ◽

The Other ◽

Seasonal Rainfall ◽

Volatility Clustering ◽

Arch Effect ◽

Data Form ◽

Sample Data

Non-linear characteritics in rainfall allow volatility clustering. This condition occurs in Ambon City with seasonal rainfall patterns. The aims of this research are to find the best model and to forecast monthly rainfall in Ambon City using heteroscedasticity model. This research examines secondary data from BMKG for monthly rainfall data in Ambon City from January 2005 – December 2018. The data is divided into two parts. First part, is called in-sample data, consist of data form January 2005 – December 2017. Second part, is called out-sample data, consist data from Januari 2018 – December 2018. The research used SARIMA–GARCH to model the data. The results are the is the best model and the residual model satisfied assumptions of normality, white noise, and there is no ARCH effect. The MAPE value in simulation using in-sample data is 0.73%. On the other side, the MAPE value of forecast results is 30%.

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Low-Entropy Stochastic Processes for Generating k-Distributed and Normal Sequences, and the Relationship of These Processes with Random Number Generators †

Mathematics ◽

10.3390/math7090838 ◽

2019 ◽

Vol 7 (9) ◽

pp. 838

Author(s):

Boris Ryabko

Keyword(s):

Stochastic Processes ◽

Random Number ◽

Infinite Sequence ◽

Statistical Properties ◽

Random Number Generators ◽

Long Run ◽

Relationship Of ◽

Low Entropy ◽

The Relationship

An infinite sequence x 1 x 2 . . . of letters from some alphabet { 0 , 1 , . . . , b - 1 } , b ≥ 2 , is called k-distributed ( k ≥ 1 ) if any k-letter block of successive digits appears with the frequency b - k in the long run. The sequence is called normal (or ∞-distributed) if it is k-distributed for any k ≥ 1 . We describe two classes of low-entropy processes that with probability 1 generate either k-distributed sequences or ∞-distributed sequences. Then, we show how those processes can be used for building random number generators whose outputs are either k-distributed or ∞-distributed. Thus, these generators have statistical properties that are mathematically proven.

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Effect of rainfall on the yield of major cereals in Darchula District of Nepal

International Journal of Environment ◽

10.3126/ije.v3i1.9954 ◽

2014 ◽

Vol 3 (1) ◽

pp. 205-213 ◽

Cited By ~ 4

Author(s):

Govinda Bhandari

Keyword(s):

Rainfall Data ◽

Seasonal Rainfall ◽

Poor Growth ◽

The Relationship

Rainfall is one of the most important factors for the growth of cereals. Inadequate water results poor growth and reduced yield. This study is aimed to explore the relationship between rainfall and yield of major cereals in Darchula district of Nepal. The yield of individual cereals is correlated with the seasonal rainfall data using MS Excel to identify the effect of rainfall on yield of cereals. The amount of rainfall in the years 1974, 1977, 1980, 1985, 1986, 1987, 1991, 1992, 1994, 1996, 1997, 1999 and 2000 was reduced which has greatly affected the yield of rice, wheat and maize in 1986 and 1987. In the years 1976, 1977, 1999 and 2000, the decrease in the amount of rainfall has reduced the yield of all major cereals in Darchula district of Nepal. DOI: http://dx.doi.org/10.3126/ije.v3i1.9954 International Journal of Environment Vol.3(1) 2014: 205-213

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Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh

Water ◽

10.3390/w11020349 ◽

2019 ◽

Vol 11 (2) ◽

pp. 349 ◽

Cited By ~ 22

Author(s):

Mohamed Salem Nashwan ◽

Shamsuddin Shahid ◽

Xiaojun Wang

Keyword(s):

Spatial Patterns ◽

Similarity Index ◽

Skill Score ◽

Rainfall Data ◽

Seasonal Rainfall ◽

Annual Rainfall ◽

Global Precipitation Climatology Centre ◽

Rainfall Trends ◽

Grid Points

This study assessed the uncertainty in the spatial pattern of rainfall trends in six widely used monthly gridded rainfall datasets for 1979–2010. Bangladesh is considered as the case study area where changes in rainfall are the highest concern due to global warming-induced climate change. The evaluation was based on the ability of the gridded data to estimate the spatial patterns of the magnitude and significance of annual and seasonal rainfall trends estimated using Mann–Kendall (MK) and modified MK (mMK) tests at 34 gauges. A set of statistical indices including Kling–Gupta efficiency, modified index of agreement (md), skill score (SS), and Jaccard similarity index (JSI) were used. The results showed a large variation in the spatial patterns of rainfall trends obtained using different gridded datasets. Global Precipitation Climatology Centre (GPCC) data was found to be the most suitable rainfall data for the assessment of annual and seasonal rainfall trends in Bangladesh which showed a JSI, md, and SS of 22%, 0.61, and 0.73, respectively, when compared with the observed annual trend. Assessment of long-term trend in rainfall (1901–2017) using mMK test revealed no change in annual rainfall and changes in seasonal rainfall only at a few grid points in Bangladesh over the last century.

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On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Water ◽

10.3390/w11081578 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1578 ◽

Cited By ~ 3

Author(s):

Kyunghun Kim ◽

Hongjun Joo ◽

Daegun Han ◽

Soojun Kim ◽

Taewoo Lee ◽

...

Keyword(s):

Network Analysis ◽

Mutual Information ◽

Complex Network ◽

Pearson Correlation ◽

Daily Rainfall ◽

Analysis Data ◽

Rain Gauge ◽

Rainfall Data ◽

Relationship Of ◽

Rain Gauge Network

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics.

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