scholarly journals Tropical littoral ambient noise probability density function model based on sea surface temperature

2016 ◽  
Vol 140 (5) ◽  
pp. EL452-EL457 ◽  
Author(s):  
Piyush Asolkar ◽  
Arnab Das ◽  
Suhas Gajre ◽  
Yashwant Joshi
Keyword(s):  
Probability Density Function ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Probability Density ◽  
Density Function ◽  
Ambient Noise ◽  
Sea Surface ◽  
Function Model ◽  
Model Based

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

A Lagrangian probability-density-function model for turbulent particle-laden channel flow in the dense regime

2021 ◽  
Vol 33 (5) ◽  
pp. 053308
Author(s):  
Alessio Innocenti ◽  
Rodney O. Fox ◽  
Sergio Chibbaro
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Channel Flow ◽  
Function Model
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