Evaluation of Climate Model Performance for Water Supply Studies: Case Study for New York City

Anandhi, Aavudai; Pierson, Donald C.; Frei, Allan

doi:10.1061/(asce)wr.1943-5452.0001054

Cited by 1 publication

(2 citation statements)

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“…The capabilities of the spatially interpolated temperature datasets to measure closely the distributions of the observed minimum and maximum temperature data over the basin was evaluated using PDF metric along with Perkin's skill score. Fu et al (2013), andAnandhi et al (2019). Although, the deviations of the gridded datasets from observed data are substantial in Gembu and Yola for minimum temperature, but have improved skill in replicating the observed maximum temperature.…”

Section: Analysis Of Pdfs For Minimum and Maximum Temperaturesmentioning

confidence: 90%

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Validating Gauge-based Spatial Surface Atmospheric Temperature Datasets for Upper Benue River Basin, Nigeria

Salaudeen

Ismail

Adeogun

et al. 2021

Nig. J. Env. Sci. & Tech.

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Like most other countries of Africa, one of the main problems threatening effective impact modelling in Nigeria including Upper Benue river basin, dwells in lack of high-quality in-situ observation datasets at appropriate spatiotemporal scales. Gridded meteorological variables can serve as promising alternatives to in-situ measurements in data sparse regions, but then, require validations to assess quantitatively their level of accuracies and reliabilities. As a consequence, this study makes comparative analysis of two gauge-based, spatially interpolated surface atmospheric temperature datasets with in-situ measurements in seven distinct meteorological stations covering the period of 1982-2006. Correspondingly, spatial analysis and statistical measures were used to assess the performances of the gridded datasets from the Climate Research Unit (CRU) and the Climate Prediction Centre (CPC). Results from spatial distributions depict 8, 11 and 10 °C as observed minimum temperatures and 33, 36, 42 °C as observed maximum temperatures over the Cameroon highland (Gembu), the Jos plateau and at the northern fringes of the basin respectively. Consequently, both the CRU and CPC datasets captured remarkably well the observed temperature gradients along the varying topography, though with differing margins. The interannual variabilities indicate CRU dataset to better capture the signs and magnitudes of the observed anomalies as compared to the CPC data. Moreover, the CRU data was noted to be more outstanding in representing the observed features in seasonal temperature variations over most stations. Also, the shapes of the probability density function (PDF) for both datasets in minimum and maximum temperatures measured closely the shapes of the observed PDF. Trend analysis suggests CRU datasets to better represent the warming and the cooling trends than the CPC. Overall, the CRU datasets are the most outstanding in this study and is therefore preferred for water resource application over the study area.

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Section: Analysis Of Pdfs For Minimum and Maximum Temperaturesmentioning

confidence: 90%

“…The PDF has been widely used by earlier researchers to compare between two climate datasets e.g. Maxino et al (2008), Anandhi et al (2019) and Kabela and Carbone (2015). To quantify the area of overlap, which may reveal the degree of correspondence of the two datasets, Perkins et al (2007) proposed a metric known as Skill Score.…”

Section: Methodsmentioning

confidence: 99%