scholarly journals Development of Dynamical Seasonal Prediction System for Northern Winter using the Cryospheric Condition of Late Autumn

Atmosphere ◽  
2013 ◽  
Vol 23 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Taehyoun Shim ◽  
Jee-Hoon Jeong ◽  
Baek-Min Kim ◽  
Seong-Joong Kim ◽  
Hyun-Kyung Kim
Keyword(s):  
Seasonal Prediction ◽  
Prediction System ◽  
Late Autumn ◽  
Northern Winter ◽  
Dynamical Seasonal Prediction

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

scholarly journals Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

2019 ◽  
Vol 32 (3) ◽  
pp. 957-972 ◽  
Author(s):  
Takeshi Doi ◽  
Swadhin K. Behera ◽  
Toshio Yamagata
Keyword(s):  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Economic Losses ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Operational Systems ◽  
The Indian Ocean ◽  
Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

2014 ◽  
Vol 44 (9-10) ◽  
pp. 2723-2735 ◽  
Author(s):  
J. Baehr ◽  
K. Fröhlich ◽  
M. Botzet ◽  
D. I. V. Domeisen ◽  
L. Kornblueh ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Model ◽  
Seasonal Prediction ◽  
Prediction System ◽  
Coupled Climate Model

scholarly journals Configuration and hindcast quality assessment of a Brazilian global sub‐seasonal prediction system

2020 ◽  
Vol 146 (728) ◽  
pp. 1067-1084 ◽  
Author(s):  
Bruno S. Guimarães ◽  
Caio A. S. Coelho ◽  
Steven J. Woolnough ◽  
Paulo Y. Kubota ◽  
Carlos F. Bastarz ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Seasonal Prediction ◽  
Prediction System

scholarly journals Predictability of Persistent Thailand Rainfall during the Mature Monsoon Season in 2011 Using Statistical Downscaling of CGCM Seasonal Prediction

2015 ◽  
Vol 143 (4) ◽  
pp. 1166-1178 ◽  
Author(s):  
Yukiko Imada ◽  
Shinjiro Kanae ◽  
Masahide Kimoto ◽  
Masahiro Watanabe ◽  
Masayoshi Ishii
Keyword(s):  
Statistical Downscaling ◽  
El Niño ◽  
Rainy Season ◽  
El Nino ◽  
Seasonal Prediction ◽  
Precipitation Pattern ◽  
Prediction System ◽  
Central Pacific ◽  
Central Pacific El Niño ◽  
Local Rainfall

Abstract Predictability of above-normal rainfall over Thailand during the rainy season of 2011 was investigated with a one-tier seasonal prediction system based on an atmosphere–ocean coupled general circulation model (CGCM) combined with a statistical downscaling method. The statistical relationship was derived using singular value decomposition analysis (SVDA) between observed regional rainfall and the hindcast of tropical sea surface temperature (SST) from the seasonal prediction system, which has an ability to forecast oceanic variability for lead times up to several months. The downscaled product of 2011 local rainfall was obtained by combining rainfall patterns derived from significant modes of SVDA. This method has the advantage in terms of flexibility that phenomenon-based statistical relationships, such as teleconnections associated with El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), or the newly recognized central Pacific El Niño, are considered separately in each SVDA mode. The downscaled prediction initialized from 1 August 2011 reproduced the anomalously intense precipitation pattern over Indochina including northern Thailand during the latter half of the rainy season, even though the direct hindcast from the CGCM failed to predict the local rainfall distribution and intensity. Further analysis revealed that this method is applicable to the other recent events such as heavy rainfall during the rainy seasons of 2002 and 2008 in Indochina.

Machine learning-based dynamical seasonal prediction of summer rainfall in China

2020 ◽  
Author(s):  
Jialin Wang ◽  
Jing Yang ◽  
Hongli Ren ◽  
Jinxiao Li ◽  
Qing Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Gradient Boosting ◽  
Disaster Reduction ◽  
Best Fitting ◽  
Application Prospects ◽  
Dynamical Seasonal Prediction

<p>The seasonal prediction of summer rainfall is crucial for regional disaster reduction but currently has a low prediction skill. This study developed a machine learning (ML)-based dynamical (MLD) seasonal prediction method for summer rainfall in China based on suitable circulation fields from an operational dynamical prediction model CAS FGOALS-f2. Through choosing optimum hyperparameters for three ML methods to reach the best fitting and the least overfitting, gradient boosting regression trees eventually exhibit the highest prediction skill, obtaining averaged values of 0.33 in the reference training period (1981-2010) and 0.19 in eight individual years (2011-2018) of independent prediction, which significantly improves the previous dynamical prediction skill by more than 300%. Further study suggests that both reducing overfitting and using the best dynamical prediction are imperative in MLD application prospects, which warrants further investigation.</p>

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