scholarly journals Deep learning‐based precipitation bias correction approach for Yin–He global spectral model

2021 ◽  
Vol 28 (5) ◽  
Author(s):  
Yi‐Fan Hu ◽  
Fu‐Kang Yin ◽  
Wei‐Min Zhang
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Spectral Model ◽  
Global Spectral Model ◽  
Precipitation Bias

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

A Global Spectral Model for Simulation of the General Circulation

1978 ◽  
Vol 35 (9) ◽  
pp. 1557-1583 ◽  
Author(s):  
Bryant J. McAvaney ◽  
William Bourke ◽  
Kamal Puri
Keyword(s):  
General Circulation ◽  
Spectral Model ◽  
Global Spectral Model

Experimental Weekly to Seasonal U.S. Forecasts with the Regional Spectral Model

2004 ◽  
Vol 85 (12) ◽  
pp. 1887-1902 ◽  
Author(s):  
J. Roads
Keyword(s):  
Spectral Model ◽  
Planetary Boundary Layer Height ◽  
Forecast System ◽  
Global Spectral Model ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Regional Spectral Model ◽  
Environmental Prediction ◽  
Climate Prediction Center ◽  
Regional Forecast

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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