使用先进的时间序列相似算法为尼日利亚选择最佳的网格式气候数据集
Bashir Tanimu1,2, Mohammed Magdy Hamed3, Al-Amin Danladi Bello1
1Department of Water Resources and Environmental Engineering, Ahmadu Bello University Zaria, Kaduna, Nigeria.
Environmental science and pollution research international
|February 3, 2024
概括
这项研究使用先进的相似算法评估了尼日利亚的五个格式气候数据集. 气候研究单位 (CRU) 的数据在降雨和温度方面是最准确的,表现优于TERRA,CPC,ERA5和CHELSA.
科学领域:
- 水文和气候科学 水文和气候科学
- 地理空间数据分析.
- 环境监测 环境监测
背景情况:
- 格式化气候数据集对于水气候研究至关重要,特别是在数据稀缺的地区.
- 选择最可靠的数据集是具有挑战性的,因为数据质量和空间分辨率各不相同.
- 尼日利亚的水气候研究受到缺乏长期,密集的观测数据的阻碍.
研究的目的:
- 评估,比较和排名尼日利亚五个主要的网格式气候数据集 (CRU,TERRA,CPC,ERA5,CHELSA).
- 评估数据集在现场复制降雨和温度数据方面的性能.
- 引入和验证先进的时间序列相似性算法 (STS,CCD) 用于网格数据评估.
主要方法:
- 应用珀金斯技能得分 (PSS),短时间序列距离 (STS) 和交叉相关距离 (CCD) 来比较数据集.
- 使用妥协编程 (CP) 用四个统计标准进行绩效评估.
- 在26个尼日利亚地点对现场降雨量,最高和最低温度数据的验证性能.
主要成果:
- 气候研究单位 (CRU) 的数据集被评为尼日利亚最好的数据集,其次是CHELSA,TERRA,ERA5和CPC.
- STS和CCD方法证实了CRU的优越性能,显示了17 (降雨量),10.1 (最大温度) 和20.8 (最小温度) 的集成值.
- 对于Pbias (0.5-1),KGE (0.5-0.9),NSE (0.3-0.8) 和NRMSE (-30到68.2) 的数据,CRU的统计表现非常出色.
结论:
- STS和CCD是评估格式气候数据性能的有效和高效替代方案.
- CRU是尼日利亚水气候研究推的网格气候数据集.
- 该研究强调了先进算法的实用性,以克服传统的多标准决策方法的局限性.
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