An Implementation of Single Precision Fast Spherical Harmonic Transform in Yin‐He Global Spectral Model

Author(s):  
Fukang Yin ◽  
Junqiang Song ◽  
Jianping Wu ◽  
Weimin Zhang
2018 ◽  
Vol 146 (10) ◽  
pp. 3163-3182 ◽  
Author(s):  
Fukang Yin ◽  
Guoli Wu ◽  
Jianping Wu ◽  
Jun Zhao ◽  
Junqiang Song

Abstract In this paper, we describe an implementation of the fast spherical harmonic transform (SHT) algorithm in the Yin–He global spectral model (YHGSM). A new analysis method is proposed to study the potential instability of interpolative decomposition and to evaluate the performance of fast SHT on the MilkyWay-2 supercomputer. The novel aspect of the proposed method is the use of the computational complexity analysis method by studying the properties of interpolative decompositions. Furthermore, three test cases are conducted to verify fast SHT in YHGSM. The results demonstrate that fast SHT is feasible and efficient for reducing the computational complexity and memory cost of SHT while still keeping enough accuracy.


1978 ◽  
Vol 35 (9) ◽  
pp. 1557-1583 ◽  
Author(s):  
Bryant J. McAvaney ◽  
William Bourke ◽  
Kamal Puri

2004 ◽  
Vol 85 (12) ◽  
pp. 1887-1902 ◽  
Author(s):  
J. Roads

Since 27 September 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making near real-time experimental global and regional dynamical forecasts with the National Centers for Environmental Prediction (NCEP) global spectral model (GSM) and the corresponding regional spectral model (RSM), which is based on the GSM, but which provides higher-resolution simulations and forecasts for limited regions. The global and regional forecast skill of the GSM was previously described in several papers. The purpose of this paper is to describe the RSM-based U.S. regional forecast system, various biases and errors in these regional U.S. forecasts, as well as the significant skill of the of temperature, precipitation, soil moisture, relative humidity, wind speed, and planetary boundary layer height forecasts at weekly to seasonal time scales. The skill of these RSM forecasts is comparable to the skill of the GSM forecasts.


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