Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy,Quarterly Journal of the Royal Meteorological Society

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Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2020-06-16 , DOI: 10.1002/qj.3851
Liangliang Zhang ₁ , Tianxiao Li ₁ , Dong Liu _{1,

2,

3,

4} , Qiang Fu ₁ , Mo Li ₁ , Muhammad Abrar Faiz ₁ , Shoaib Ali ₁ , Muhammad Imran Khan ₅

Affiliation

Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity.

中文翻译：

基于优化样本熵的区域降水复杂性空间变异及可能原因分析

样本熵可用于研究降水序列的复杂性。但是，相似公差选择的随机性可能导致结果不准确。为了解决这个问题，引入了区分度理论来优化相似的样本熵容限，并为优化过程提供了一个特定的参考框架。利用优化的样本熵从黑龙江省的月降水量（MP）和极端日降水量（EDP）的角度研究降水复杂性的空间差异及其可能原因。结果表明，黑龙江省的MP和EDP均具有不稳定的复杂波动特征，样品熵的最佳相似容忍度分别为标准差的0.24倍和0.13倍。EDP的复杂度高于MP，其波动范围分别为1.641–2.268和0.433–0.870。研究区MP的复杂性可以在空间上表示为基本模式，从东到西逐渐减小。EDP复杂性的空间分布模式表明，北部高于南部。海拔高度是MP和EDP复杂度的常见影响因素，它对EDP复杂度的空间格局有更大的影响。影响MP复杂性空间差异的潜在因素还包括降水强度，水域以及居民和工业用地面积的变化。除海拔高度外，工业经济发展水平还可能影响EDP的复杂性。

更新日期：2020-06-16

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