Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Kim, Tae‐Woong; Ahn, Hosung

doi:10.1007/s00477-008-0223-9

Cited by 23 publications

(12 citation statements)

References 12 publications

(10 reference statements)

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“…A full description of EM algorithm can be found in McLachlan and Krishnan (). EM has been widely used in recent years for computation of missing rainfall data (Nelwamondo et al ., ; Kalteh and Hjorth, ; Kim and Ahn, ; Firat et al ., ; Tsidu, ).…”

Section: Data and Sourcesmentioning

confidence: 99%

Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh

Alamgir

Shahid

Hazarika

et al. 2015

J American Water Resour Assoc

110

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Drought is one of the most frequent natural disasters in Bangladesh which severely affect agro‐based economy and people's livelihood in almost every year. Characterization of droughts in a systematic way is therefore critical in order to take necessary actions toward drought mitigation and sustainable development. In this study, standardized precipitation index is used to understand the spatial distribution of meteorological droughts during various climatic seasons such as premonsoon, monsoon, and winter seasons as well as cropping seasons such as Pre‐Kharif (March‐May), Kharif (May‐October), and Rabi (December‐February). Rainfall data collected from 29 rainfall gauge stations located in different parts of the country were used for a period of 50 years (1961‐2010). The study reveals that the spatial characteristics of droughts vary widely according to season. Premonsoon droughts are more frequent in the northwest, monsoon droughts mainly occur in the west and northwest, winter droughts in the west, and the Rabi and Kharif droughts are more frequent in the north and northwest of Bangladesh. It is expected that the findings of the study will support drought monitoring and mitigation activities in Bangladesh.

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Section: Data and Sourcesmentioning

confidence: 99%

Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh

Alamgir

Shahid

Hazarika

et al. 2015

J American Water Resour Assoc

110

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“…It was found that the in-filling done by GP rivals that by ANN and many times becomes more satisfactory, especially when the gap lengths are smaller. Kim and Ahn (2009) developed a new spatial daily rainfall model to fill in gaps in a daily rainfall dataset. The model was based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of efficiency of different estimation methods for missing climatological data

Kashani

Dinpashoh

2011

Stoch Environ Res Risk Assess

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Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001)(2002)(2003)(2004)(2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.

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“…The gaps in the meteorological archives are caused mainly by absence of observers, vandalism, loss of records, data contamination, data-processing errors, effects of natural disasters like tornadoes or human-induced factors like wars, lack of funds for replacing broken instruments as well as instrument malfunctioning (Tang et al, 1996;Elshorbagy et al, 2000;Smithers and Schulze, 2000;Kim and Ahn, 2009;Villazón and Willems, 2010). Faulty data is mainly caused by observer's negligence, uncalibrated sensors and faultiness of the electronic sensors.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Moeletsi¹,

Shabalala²,

Nysschen³

et al. 2016

WSA

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Climate data recorded by national meteorological agencies is either incomplete or faulty for some periods due to a number of reasons. Multi-functional utilization of climate data in complete form necessitates the filling of these gaps. In this study an inverse distance weighting (IDW) method was used to estimate rainfall utilizing neighbouring station data in the Free State Province of South Africa. Six weather stations evenly distributed across the province, and with data for 1950 to 2008, were used to evaluate this patching IDW approach at daily and dekadal time steps. Coefficient of determination (r 2 ), mean absolute error (MAE) and mean bias error (MBE) were the statistics used in the assessment. Firstly, the study conducted a sensitivity analysis of the IDW exponent (p) which showed that the best results are obtained when p is either 2 or 2.5. The estimated values at all six stations were highly correlated with the measured rainfall data with an overall r 2 value exceeding 0.70 for both daily and dekadal estimates. MAE showed low miscalculations with values with an average of 1 mm per day and 4.4 mm per dekad. MBE was very low for both daily and dekadal evaluations but the disaggregated data showed underestimation of the IDW mostly for daily rainfall exceeding 10 mm. Thus, IDW methodology proved to be an acceptable approach for estimating both daily and dekadal rainfall in the Free State Province.

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Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Cited by 23 publications

References 12 publications

Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh

Analysis of Meteorological Drought Pattern During Different Climatic and Cropping Seasons in Bangladesh

Evaluation of efficiency of different estimation methods for missing climatological data

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

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