Performance of Quality Assurance Procedures for an Applied Climate Information System

Hubbard, Kenneth G.; Goddard, Steve; Sorensen, William; Wells, Nathan P.; Osugi, T.T.

doi:10.1175/jtech-1657.1

Cited by 100 publications

(75 citation statements)

References 10 publications

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“…2, 3 and 4. The results obtained in this work are similar to the results of Hubbard et al (2005). The results for the threshold analysis indicate that approximately 2 % of the data would be flagged for maximum, minimum and mean temperature if an f value of 2.3 is used.…”

Section: Resultssupporting

confidence: 84%

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Data validation procedures in agricultural meteorology – a prerequisite for their use

2011

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Abstract. Quality meteorological data sources are critical to scientists, engineers, climate assessments and to make climate related decisions. Accurate quantification of reference evapotranspiration (ET 0 ) in irrigated agriculture is crucial for optimizing crop production, planning and managing irrigation, and for using water resources efficiently. Validation of data insures that the information needed is been properly generated, identifies incorrect values and detects problems that require immediate maintenance attention. The Agroclimatic Information Network of Andalusia at present provides daily estimations of ET 0 using meteorological information collected by nearly of one hundred automatic weather stations. It is currently used for technicians and farmers to generate irrigation schedules. Data validation is essential in this context and then, diverse quality control procedures have been applied for each station. Daily average of several meteorological variables were analysed (air temperature, relative humidity and rainfall). The main objective of this study was to develop a quality control system for daily meteorological data which could be applied on any platform and using open source code. Each procedure will either accept the datum as being true or reject the datum and label it as an outlier. The number of outliers for each variable is related to a dynamic range used on each test. Finally, geographical distribution of the outliers was analysed. The study underscores the fact that it is necessary to use different ranges for each station, variable and test to keep the rate of error uniform across the region.

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Section: Resultssupporting

confidence: 84%

“…1), using data only from a single site. Three procedures were tuned to the prevailing climate: seasonal thresholds, seasonal rate of change and seasonal persistence (Hubbard et al, 2005). These tests are related to station climatology at the monthly level, using dynamic limits for each variable.…”

Section: Source Of Datamentioning

confidence: 99%

Data validation procedures in agricultural meteorology – a prerequisite for their use

2011

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“…On the basis of these principles, the number of spatial neighbors n is set to 10. Hubbard et al [20,21] suggested that the number of temporal neighbors can be set to about half of the research period. In this study, the research period is 26, therefore the number of temporal neighbors m is also set to 10.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…Therefore, GWR and STGWR are unsuitable for estimating missing data. A spatial regression test (SRT) estimates the missing data in the station of interest by using the neighboring stations [20,21]. For each neighboring station, a regression estimate (x i = a i + b i y i ) is calculated.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

Deng

Zide

Liu

et al. 2016

IJGI

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Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly developed for space-time processes that exhibit stationarity in space and time. It is still challenging to model heterogeneity of space-time data in the interpolation model. To overcome this limitation, in this study, a novel space-time interpolation method considering both spatial and temporal heterogeneity is developed for estimating missing data in space-time datasets. The interpolation operation is first implemented in spatial and temporal dimensions. Heterogeneous covariance functions are constructed to obtain the best linear unbiased estimates in spatial and temporal dimensions. Spatial and temporal correlations are then considered to combine the interpolation results in spatial and temporal dimensions to estimate the missing data. The proposed method is tested on annual average temperature and precipitation data in China (1984China ( -2009. Experimental results show that, for these datasets, the proposed method outperforms three state-of-the-art methods-e.g., spatio-temporal kriging, spatio-temporal inverse distance weighting, and point estimation model of biased hospitals-based area disease estimation methods.

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“…This helps explain that normals estimated from inverse distance weighting or arithmetically averaging were not as accurate as IWSTD, which assigns more weights on the neighboring stations with temperatures closer to the temperature of the target station. IWSTD is also better than other schemes we tested, including the Spatial Regression Test (Hubbard et al, 2004), an interpolation scheme newly developed for quality assurance purposes.…”

Section: Spatial Interpolation Schemementioning

confidence: 86%

Estimating temperature normals for USCRN stations

Sun¹,

Peterson²

2005

Int. J. Climatol.

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Temperature normals have been estimated for stations of the newly developed US Climate Reference Network (USCRN) by using USCRN temperatures and temperature anomalies interpolated from neighboring stations of the National Weather Service Cooperative Station Network (COOP). To seek the best normal estimation approach, several variations on estimation techniques were considered: the sensitivity of error of estimated normals to COOP data quality; the number of neighboring COOP station used; a spatial interpolation scheme; and the number of years of data used in normal estimation. The best estimation method we found is the one in which temperature anomalies are spatially interpolated from COOP stations within approximately 117 km of the target station using a weighting scheme involving the inverse of square difference in temperature (between the neighboring and target station). Using this approach, normals of USCRN stations were generated. Spatial and temporal characteristics of errors are presented, and the applicability of estimated normals in climate monitoring is discussed.

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Performance of Quality Assurance Procedures for an Applied Climate Information System

Cited by 100 publications

References 10 publications

Data validation procedures in agricultural meteorology – a prerequisite for their use

Data validation procedures in agricultural meteorology – a prerequisite for their use

A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

Estimating temperature normals for USCRN stations

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