A Comparison of Rainfall Estimation Using Himawari-8 Satellite Data in Different Indonesian Topographies

Ayasha, Nadine

doi:10.30536/j.ijreses.2020.v17.a3441

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Traditionally, weather stations are being used to forecast the weather conditions around the globe and precipitation prediction is made by using online satellite cloud image analysis systems but due to the change in DCP, rainfall may not be recorded at particular coordinate of earth surface therefore machine learning framework is direly needed to construct a cloud classification model and to predict the precipitation patterns from local sky cloud images [9,10]. Cloud images reveals significant key points in shape of water vapors, cloud droplets and evaporation process which defines the capability of cloud for precipitation [11][12][13]; therefore we obtained key points by using scale-invariant feature transform SIFT features from cloud images and converted key points regions into tensor flow data to train and test the deep learning algorithm such as convolutional neural network. The SIFT algorithm is used to detect the sub-objects from images of clouds because it describes local features (LF) [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Framework for Precipitation Prediction Using Cloud Images

Baig¹,

Mallah²,

Shaikh³

2022

Computers, Materials &Amp; Continua

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Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers to know about the weather conditions and to care their cash crops. This research proposes a framework to classify the dark cloud patterns (DCP) for prediction of precipitation. The framework consists upon three steps to classify the cloud images, first step tackles noise reduction operations, feature selection and preparation of datasets. Second step construct the decision model by using convolutional neural network (CNN) and third step presents the performance visualization by using confusion matrix, precision, recall and accuracy measures. This research contributes (1) real-world clouds datasets (2) method to prepare datasets (3) highest classification accuracy to predict estimated as 96.90%.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Framework for Precipitation Prediction Using Cloud Images

Baig¹,

Mallah²,

Shaikh³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Most studies have carried out inter-comparisons of meteorological data but focused more on satellite based weather parameters and gridded data (Ayasha, 2021;Rivoire et al, 2021;Schumacher et al, 2020;Ford & Quiring, 2019;Zeng et al, 2018). These have largely ignored the significant biases that can be addressed by data from Automatic weather stations relative to surface observation stations.…”

Section: Introductionmentioning

confidence: 99%

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

Muita¹,

Kucera²,

Aura³

et al. 2021

AJCC

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Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations

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A Comparison of Rainfall Estimation Using Himawari-8 Satellite Data in Different Indonesian Topographies

Cited by 2 publications

References 2 publications

Deep Learning Framework for Precipitation Prediction Using Cloud Images

Deep Learning Framework for Precipitation Prediction Using Cloud Images

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

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