Development of high-resolution daily gridded temperature datasets for the central north region of Egypt

Mohamed Salem Nashwan,Shamsuddin Shahid,Eun-Sung Chung

doi:10.1038/s41597-019-0144-0

Abstract

This study developed 0.05° × 0.05° land-only datasets of daily maximum and minimum temperatures in the densely populated Central North region of Egypt (CNE) for the period 1981–2017. Existing coarse-resolution datasets were evaluated to find the best dataset for the study area to use as a base of the new datasets. The Climate Prediction Centre (CPC) global temperature dataset was found to be the best. The CPC data were interpolated to a spatial resolution of 0.05° latitude/longitude using linear interpolation technique considering the flat topography of the study area. The robust kernel density distribution mapping method was used to correct the bias using observations, and WorldClim v.2 temperature climatology was used to adjust the spatial variability in temperature. The validation of CNE datasets using probability density function skill score and hot and cold extremes tail skill scores showed remarkable improvement in replicating the spatial and temporal variability in observed temperature. Because CNE datasets are the best available high-resolution estimate of daily temperatures, they will be beneficial for climatic and hydrological studies.

Highlights

Background & SummaryRegularly gridded meteorological observation data are important for climate analyses[1]
The methodology adopted in this study was structured using the following steps: (1) the 0.5° × 0.5° Climate Prediction Center (CPC) Tmx and Tmn datasets were regridded to a 0.05° × 0.05° spatial resolution using the inverse distance weighting (IDW) method; (2) the KDDM bias correction was applied to correct the bias in daily temperature data against the observed data; and (3) the spatial variability in temperature from the regridded data were corrected using the WorldClim v.2 temperature climatology[12], which is available at a 2.5 arc minute spatial resolution
The performance of Central North region of Egypt (CNE) with respect to the CPC and CRU datasets was assessed according to their abilities to replicate the daily observed temperature at 13 stations that were used during data development

Summary

Introduction

Background & SummaryRegularly gridded meteorological observation data are important for climate analyses[1]. The methodology adopted in this study was structured using the following steps (demonstrated in Fig. 1): (1) the 0.5° × 0.5° CPC Tmx and Tmn datasets were regridded to a 0.05° × 0.05° spatial resolution using the IDW method; (2) the KDDM bias correction was applied to correct the bias in daily temperature data against the observed data; and (3) the spatial variability in temperature from the regridded data were corrected using the WorldClim v.2 temperature climatology[12], which is available at a 2.5 arc minute spatial resolution. The performance of CNE with respect to the CPC and CRU datasets was assessed according to their abilities to replicate the daily observed temperature at 13 stations that were used during data development.

Results

Conclusion