Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization
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 forecast...
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ftopenaccessrep:oai:zenodo.org:106881 2023-10-25T01:41:41+02:00 Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization Enrico Scoccimarro Andrea Alessandri Andrea Borrelli Simona Masina Silvio Gualdi 2011-06-15 https://www.openaccessrepository.it/record/106881 https://doi.org/10.1175/2010jcli3585.1 eng eng url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/106881 doi:10.1175/2010jcli3585.1 info:eu-repo/semantics/openAccess Atmospheric Science info:eu-repo/semantics/article publication-article 2011 ftopenaccessrep https://doi.org/10.1175/2010jcli3585.1 2023-09-26T22:22:04Z 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. Article in Journal/Newspaper North Atlantic Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository Austral Pacific Indian Journal of Climate 24 12 2963 2982 |
institution |
Open Polar |
collection |
Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository |
op_collection_id |
ftopenaccessrep |
language |
English |
topic |
Atmospheric Science |
spellingShingle |
Atmospheric Science Enrico Scoccimarro Andrea Alessandri Andrea Borrelli Simona Masina Silvio Gualdi Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
topic_facet |
Atmospheric Science |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Enrico Scoccimarro Andrea Alessandri Andrea Borrelli Simona Masina Silvio Gualdi |
author_facet |
Enrico Scoccimarro Andrea Alessandri Andrea Borrelli Simona Masina Silvio Gualdi |
author_sort |
Enrico Scoccimarro |
title |
Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
title_short |
Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
title_full |
Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
title_fullStr |
Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
title_full_unstemmed |
Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization |
title_sort |
tropical cyclone count forecasting using a dynamical seasonal prediction system: sensitivity to improved ocean initialization |
publishDate |
2011 |
url |
https://www.openaccessrepository.it/record/106881 https://doi.org/10.1175/2010jcli3585.1 |
geographic |
Austral Pacific Indian |
geographic_facet |
Austral Pacific Indian |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/106881 doi:10.1175/2010jcli3585.1 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1175/2010jcli3585.1 |
container_title |
Journal of Climate |
container_volume |
24 |
container_issue |
12 |
container_start_page |
2963 |
op_container_end_page |
2982 |
_version_ |
1780737905609670656 |