scholarly journals TREND OF DAILY RAINFALL AND TEMPERATURE IN PENINSULAR MALAYSIA BASED ON GRIDDED DATA SET

2018 ◽  
Vol 14 (44) ◽  
Author(s):  
Chee-Loong Wong
Keyword(s):  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Gridded Data ◽  
Data Set

scholarly journals Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 500 ◽  
Author(s):  
Chee Wong ◽  
Juneng Liew ◽  
Zulkifli Yusop ◽  
Tarmizi Ismail ◽  
Raymond Venneker ◽  
...  
Keyword(s):  
High Resolution ◽  
Peninsular Malaysia ◽  
Gridded Data ◽  
Data Set ◽  
Rainfall Characteristics

scholarly journals Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 1-18 ◽  
Author(s):  
D.S Pai ◽  
M Rajeevan ◽  
O.P Sreejith ◽  
B. Mukhopadhyay ◽  
N.S Satbha
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Data Sets ◽  
Gridded Data ◽  
Rainfall Distribution ◽  
Data Set ◽  
The Western Ghats

ABSTRACT. The study discusses development of a new daily gridded rainfall data set (IMD4) at a high spatial resolution (0.25° × 0.25°, latitude × longitude) covering a longer period of 110 years (1901-2010) over the Indian main land.  A comparison of IMD4 with 4 other existing daily gridded rainfall data sets of different spatial resolutions and time periods has also been discussed. For preparing the new gridded data, daily rainfall records from 6955 rain gauge stations in India were used, highest  number of stations used by any studies so far for such a purpose. The gridded data set was developed after making quality control of basic rain-gauge stations. The comparison of IMD4 with other data sets suggested that the climatological and variability features of rainfall over India derived from IMD4 were comparable with the existing gridded daily rainfall data sets. In addition, the spatial rainfall distribution like heavy rainfall areas in the orographic regions of the west coast and over northeast, low rainfall in the lee ward side of the Western Ghats etc. were more realistic and better presented in IMD4 due to its higher spatial resolution and to the higher density of rainfall stations used for its development.

scholarly journals TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Siti Mariam Saad ◽  
Abdul Aziz Jemain ◽  
Noriszura Ismail
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Poisson Model ◽  
Rain Gauge ◽  
Peninsular Malaysia ◽  
Cascade Model ◽  
Statistical Measures ◽  
Hourly Rainfall ◽  
Cascade Models ◽  
Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal  models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal  model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal  models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

scholarly journals A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Siti Mariana Che Mat Nor ◽  
Shazlyn Milleana Shaharudin ◽  
Shuhaida Ismail ◽  
Nurul Hila Zainuddin ◽  
Mou Leong Tan
Keyword(s):  
Missing Values ◽  
Nearest Neighbor ◽  
East Coast ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Peninsular Malaysia ◽  
Partial Least Square ◽  
Least Square ◽  
Rainfall Data ◽  
Efficiency Coefficient

Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.

Developing a demand model integrating end uses of water (DMEUW): structure and process of integration

2015 ◽  
Vol 71 (4) ◽  
pp. 529-537 ◽  
Author(s):  
R. C. Sarker ◽  
S. Gato-Trinidad
Keyword(s):  
Water Consumption ◽  
Water Conservation ◽  
Water Demand ◽  
Daily Rainfall ◽  
Demand Model ◽  
Data Set ◽  
Residential Water Consumption ◽  
End Use ◽  
Hypertext Markup Language ◽  
End Uses

The process of developing an integrated water demand model integrating end uses of water has been presented. The model estimates and forecasts average daily water demand based on the end-use pattern and trend of residential water consumption, daily rainfall and temperature, water restrictions and water conservation programmes. The end-use model uses the latest end-use data set collected from Yarra Valley Water, Australia. A computer interface has also been developed using hypertext markup language and hypertext pre-processor. The developed model can be used by water authorities and water resource planners in forecasting water demand and by household owners in determining household water consumption.

scholarly journals Surface Ocean CO<sub>2</sub> Atlas (SOCAT) gridded data products

2012 ◽  
Vol 5 (2) ◽  
pp. 781-804 ◽  
Author(s):  
C. L. Sabine ◽  
S. Hankin ◽  
H. Koyuk ◽  
D. C. E. Bakker ◽  
B. Pfeil ◽  
...  
Keyword(s):  
Gridded Data ◽  
Data Set ◽  
Surface Ocean ◽  
Marine Surface ◽  
Science Community ◽  
Ocean Carbon ◽  
To Come ◽  
Carbon Dioxide Co2 ◽  
Global Oceans ◽  
Global Data

Abstract. A well documented, publicly available, global data set for surface ocean carbon dioxide (CO2) parameters has been called for by international groups for nearly two decades. The Surface Ocean CO2 Atlas (SOCAT) project was initiated by the international marine carbon science community in 2007 with the aim of providing a comprehensive, publicly available, regularly updated, global data set of marine surface CO2, which had been subject to quality control (QC). SOCAT version 1.5 was made public in September 2011 and holds 6.3 million quality controlled surface CO2 data from the global oceans and coastal seas, spanning four decades (1968–2007). The SOCAT gridded data is the second data product to come from the SOCAT project. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust regularly spaced fCO2 product with minimal spatial and temporal interpolation which should be easier to work with for many applications. Gridded SOCAT is rich with information that has not been fully explored yet, but also contains biases and limitations that the user needs to recognize and address.

scholarly journals Probability Distribution of Philippine Daily Rainfall Data

2017 ◽  
Author(s):  
Vanessa Althea B. Bermudez ◽  
Ariel Bettina B. Abilgos ◽  
Diane Carmeliza N. Cuaresma ◽  
Jomar F. Rabajante
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Economic Systems ◽  
Tropical Country ◽  
Data Set ◽  
Cumulative Rainfall ◽  
The Pacific ◽  
The Pacific Ocean

Philippines as an archipelago and tropical country, which is situated near the Pacific ocean, faces uncertain rainfall intensities. This makes environmental, agricultural and economic systems affected by precipitation difficult to manage. Time series analysis of Philippine rainfall pattern has been previously done, but there is no study investigating its probability distribution. Modeling the Philippine rainfall using probability distributions is essential, especially in managing risks and designing insurance products. Here, daily and cumulative rainfall data (January 1961 - August 2016) from 28 PAGASA weather stations are fitted to probability distributions. Moreover, the fitted distributions are examined for invariance under subsets of the rainfall data set. We observe that the Gamma distribution is a suitable fit for the daily up to the ten-day cumulative rainfall data. Our results can be used in agriculture, especially in forecasting claims in weather index-based insurance.

scholarly journals Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lu Ye ◽  
Saadya Fahad Jabbar ◽  
Musaddak M. Abdul Zahra ◽  
Mou Leong Tan
Keyword(s):  
Sea Level ◽  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Gradient Boosting ◽  
Sea Level Pressure ◽  
Extreme Rainfall Events ◽  
Inference System ◽  
Rainfall Events ◽  
Level Pressure ◽  
Extreme Gradient Boosting

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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