Statistical Analysis from Time Series Related to Climate Data

Encinas, Ascensión Hernández; Dios, Araceli Queiruga; Encinas, Luis Hernández; Martínez, Víctor Gayoso

doi:10.7763/ijapm.2013.v3.206

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…This set of algorithms, RHtestsV3 (Wang and Feng, ), can be used to detect multiple change points, both documented and undocumented, in a climate data series that may contain type‐1 autoregressive errors. This algorithm set contains implementations of the penalized maximal t ‐test and the penalized maximal F test (Wang, ) and has been used in numerous studies ranging from wind speed and temperature time series ( Wan et al ., ; Encinas et al ) to ocean wave records (Gemmrich et al , ).…”

Section: Methodsmentioning

confidence: 99%

Climatology and trends of wind speed in the Beaufort/Chukchi Sea coastal region from 1979 to 2009

Baule

Shulski

2013

Int. J. Climatol.

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Climatic records of wind speed are analyzed for eight long-term monitoring locations in the Beaufort/Chukchi coastal region of the Arctic, over the period from 1979 to 2009. Data for this study originated from three national observing networks throughout the Beaufort/Chukchi region and were uniformly quality controlled using a combination of automated and manual checks. The climatology of wind speed was developed over the study period and includes analyses of mean wind speed, extremes, and frequency distributions. Trends in monthly and annual wind speed were examined. The climatology illustrates strong distinctions between coastal and interior locations, strong seasonal characteristics, and diurnal cycles in wind speed. Negative trends in wind speed were apparent at several locations, particularly at locations in Alaska. Many of these trends were statistically significant (95% CI). To assess the impact of artificial changes on trends, station data were homogenized with respect to metadata records. Trends were recalculated and the effects were discussed. Possible drivers of wind speed trends were also discussed, including multi-decadal climate signals. Results from the climatology and trend analyses were compared with output from the North American Regional Reanalysis (NARR). Negative biases in wind speed were apparent at most locations throughout the year in NARR. Trends in NARR wind speed opposed in situ trends, at most locations. Improved knowledge of wind speed records and reanalysis output is important in this rapidly changing region.

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Section: Methodsmentioning

confidence: 99%

Climatology and trends of wind speed in the Beaufort/Chukchi Sea coastal region from 1979 to 2009

Baule

Shulski

2013

Int. J. Climatol.

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“…Sementara Jeelani et al (2014) memanfaatkan perintah bawaan program "R" untuk rancangan percobaan, terutama racangan acak lengkap. Encinas et al (2013) menggunakan paket climdex "R" untuk analisis time series data iklim. Paket ini digunakan untuk empat tahap analisis data iklim, yaitu quality control data, homogenitas data, menghitung berbagai macam nilai indeks iklim dan data wilayah.…”

Section: Paket "R" Yang Dapat Digunakan Untuk Penelitian Agroklimatunclassified

Pemanfaatan Software Open Source “R” Untuk Penelitian Agroklimat

Sarvina¹

2017

Informatika Pertanian

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ABSTRAKAnalisis dalam penelitian agroklimat seringkali menggunakan data time series panjang dan beragam serta melibatkan model-model simulasi yang kompleks. Untuk dapat menghasilkan informasi dengan cepat, tepat, dan akurat dibutuhkan perangkat lunak (software) komputer. Penggunaan software berlisensi membutuhkan biaya yang relatif mahal. Salah satu solusi untuk mengatasi keterbatasan biaya pembelian software ini adalah penggunaan open source software (OSS). Tulisan ini membahas pemanfaatan OSS pengolah data dan statistik yaitu perangkat "R" dalam penelitian agroklimat, mulai dari paket analisis yang tersedia, contoh penelitian yang menggunakan OSS "R" hingga keunggulannya dibandingkan dengan software pengolah data lainnya. Saat ini penggunaan OSS untuk penelitian agroklimat terus meningkat, baik untuk analisis data spasial maupun tabular. Perangkat "R" dapat digunakan untuk pengolahan statistik, mulai dari analisis sederhana seperti analisis ragam dalam penelitian lapangan sampai analisis pemodelan iklim yang kompleks. Berbagai paket "R" untuk penelitian agroklimat telah banyak dikembangkan. Kemampuan perangkat "R" dalam manajerialisasi data, simulasi, modeling, dan machine learning adalah keunggulan dari software ini yang diperlukan dalam penelitian agroklimat. OSS "R" memberikan peluang yang lebih besar kepada para peneliti dan pihak lainnya untuk menggali informasi historis agroklimat. Perangkat "R" perlu dikembangkan dalam penelitian agroklimat dengan paket yang sudah ada. Para peneliti dapat mengembangkan paket baru dari paket yang sudah ada untuk memecahkan berbagai masalah agroklimat dan pertanian umumnya. ABSTRACTAnalysis in agro-climate research often uses long and varied time series data and even involves complex simulation models. Software is required to produce information quickly, precisely, and accurately. Agro-climate research is sometime constrained by the availability proprietary software since cost of proprietary/ licensed software is relatively high. Open source software (OSS) is one solution to overcome this constrain whereas OSS can be used freely. This paper discusses the utilization of "R" for agro-climatic research that comprise of available "R" packages for agro-climate research, several studies have applied " R" and advantage of "R" over other statistics software. Nowadays, there are many agroclimate researches and studies have utilized R both for spatial and tabular analysis. R can be used for simple statistical analysis such as variance analysis for experimental research and even for complex climate model. Many "R" packages for agro-climate research have been developed. The "R" capabilities on data management, model simulation, modelling and machine learning are "R" advantages that very useful for current agro-climate research. By using "R" researchers have greater opportunity to explore the historical agro-climate data. "R" should be developed in agro-climate research with existing packages. Researchers can develop new packages from existing packages to solve agro-climate problems ...

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“…Additionally, several indices have been defined by the World Meteorological Organization (WMO) expert panel on climate change to create a consistent framework for climate change analysis at various spatial and temporal scales [ 29 ]. Studies [ [30] , [31] , [32] , [33] , [34] ] have been undertaken at different areas to understand the changes in climatic indices for the adaptation plan. These indicators are used as analytical instruments for describing the climatic system of a region [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

Haider,

Karim,

Islam

et al. 2024

Heliyon

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Statistical Analysis from Time Series Related to Climate Data

Cited by 4 publications

References 3 publications

Climatology and trends of wind speed in the Beaufort/Chukchi Sea coastal region from 1979 to 2009

Climatology and trends of wind speed in the Beaufort/Chukchi Sea coastal region from 1979 to 2009

Pemanfaatan Software Open Source “R” Untuk Penelitian Agroklimat

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

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