**2. Southern Hemisphere tropical cyclone archive and data portal**

#### **2.1. Tropical cyclone historical data archive**

Accurate historical cyclone records (preferably long-term records covering a few decades) are required for reliable prediction of future TC activity. Thus, the first objective of the 'Climate Change and Southern Hemisphere Tropical Cyclones' International Initiative was to prepare a high quality historical database of occurrences of TCs in the Indian and Pacific oceans. Historical TC records have been significantly improved since the 1970s due to availability of satellite imagery [10–12] and they were extensively used for preparing the Southern Hemisphere (SH) TC archive.

TC activity is affected by climate variability and climate change. Large-scale climatic modes such as the El Niño-Southern Oscillation (ENSO) are known to modulate TC occurrences [2]. In addition, modelling of future climate suggests likely changes in TC activity. As stated in Chapter 14, Climate Phenomena and their Relevance for Future Climate Change of the IPCC Fifth assessment report: 'Based on process understanding and agreement in twenty-first century projections, it is *likely* that the global frequency of occurrence of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a *likely* increase in both global

To improve our knowledge about historical TCs in the Indian and Pacific oceans and develop accurate methodologies for TC seasonal forecasting, 'Climate Change and Southern Hemisphere Tropical Cyclones' International Initiative has been established in 1999 [4]. This International Initiative addressed three key areas: (i) preparing high quality TC historical database, (ii) producing TC climatology and (iii) developing skilful methodologies for TC

TC seasonal forecasting is one of the important elements of a Climate Risk Early Warning System (CREWS) aiming to increase preparedness of coastal communities atrisk. Overthe last few decades, statistical model-based methods for TC seasonal forecasting have been developed, starting with the pioneering work of Gray [5]. Statistical models are based on historical relationships of TC activity with large-scale environmental drivers which modulate TC activity such as the ENSO. Relating the observed numbers of TCs with ENSO indices it is possible to derive linear regression equations which can be used for prediction of future cyclone activity. The TC-ENSO relationship was used in developing statistical methodology for forecasting

However, in a globally warming environment, statistical models may not produce reliable outcomes when values of ENSO indices are outside of the range of historical records. While the developed statistical models performed reasonably well in the past, during a very strong La Niña event in 2010–2011 the statistical models significantly over-predicted the number of TCs in the Australian region [9]. It became evident that improving statistical methodologies and developing new dynamical climate model-based methodologies is essential to improve

In this chapter, prospects for improving the skill of operational seasonal prediction of TC activity in the regions of the Southern Hemisphere (SH) using statistical and dynamical model-

Accurate historical cyclone records (preferably long-term records covering a few decades) are required for reliable prediction of future TC activity. Thus, the first objective of the 'Climate Change and Southern Hemisphere Tropical Cyclones' International Initiative was to prepare

**2. Southern Hemisphere tropical cyclone archive and data portal**

mean tropical cyclone maximum wind speed and precipitation rates' [3].

234 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

seasonal TC activity in the Australian and some other regions [6–8].

seasonal prediction.

prediction skill.

based approaches are presented.

**2.1. Tropical cyclone historical data archive**

The SH TC archive has been prepared at the National Climate Centre (NCC) of the Australian Bureau of Meteorology during 1999–2003 in collaboration with the National Meteorological and Hydrological Services (NMHSs) of Fiji, France and New Zealand (NZ). The first version of the SH TC archive has been released in 2003 [13]. Since then, historical data are regularly updated to keep the archive up to date.

Updating the archive is a two-step procedure which includes (i) collection of best track data (or operational data if best track data are not available) from Tropical Cyclone Warning Centres (TCWCs) in Brisbane, Darwin and Perth (Australia), Jakarta (Indonesia), Port Moresby (PNG), Wellington (NZ), Regional Specialised Meteorological Centres (RSMCs) La Reunion (France) and Nadi (Fiji) and (ii) combining the data in one consolidated archive including quality control, correction for errors and making a consensus expert decision when joining tracks of systems which occurred in two or three areas of responsibilities of different TCWCs and RSMCs.

Recently, as a part of the Pacific Australia Climate Change Science and Adaptation Planning (PACCSAP) program's 'Seasonal tropical cyclone prediction' project, the SH TC archive has been revised. Specifically, data for 1969–1970 to 2010–2011 TC seasons covering the South Pacific Ocean and produced by RSMC Nadi (Fiji), TCWCs Brisbane and Darwin (Australia) and TCWC Wellington (New Zealand) have been examined to eliminate errors and inconsistencies. The following rules have been applied when preparing a consolidated archive. As RSMC Nadi (Fiji) is a designated by the World Meteorological Organization (WMO) centre with responsibilities to issue TC warnings and prepare best track data for the area between the equator and 25°S, 160°E and 120°W, its data have been treated as a primary source of information for this area. However, RSMC Nadi was established in 1995 while the SH TC archive extends to cover TC seasons from the 1970s (satellite era). Thus, TC best track data prepared for this area by TCWCs in Brisbane, Darwin and Wellington have been used for 1969–1970 to 1994–1995 TC seasons, and data from RSMC Nadi—from the 1995–1996 TC season onwards. As for the other areas of the South Pacific Ocean, TC best track data from TCWCs in Brisbane and Darwin for the Australian region (between the equator and 37°S, 135°E and 160°E) and from TCWC Wellington for the New Zealand region (between 25°S and 40°S, 160°E and 120°W) have been used for entire length of records from 1969–1970 to 2010–2011 TC seasons.

As a result of growth of the 'Climate Change and Southern Hemisphere Tropical Cyclones' International Initiative and its geographic expansion to cover the Western North Pacific region, TC best track data produced by RSMC Tokyo for 1977–2011 seasons have been added to the consolidated archive. Similarly, TC best track data produced by RSMC la Réunion for 1969– 2011 have been added to cover the South Indian Ocean region.

#### **2.2. Tropical cyclone data portal**

Tropical cyclone data portal has been created with aims (i) to visualise the data from the SH TC archive and (ii) allow users to perform analysis of historical cyclone data. Based on recent changes of and additions to the SH TC archive, the TC historical data portal has been redesigned to incorporate best track data for the Western Pacific both south and north of the equator and the South Indian Ocean (**Figure 1**).


**Figure 1.** Front page of the portal – the Southern Hemisphere (top panel) and the Western North Pacific (bottom panel).

New functionality has been also added to the portal including enhanced spatial and temporal selection of cyclones. Some examples of the portal's new functionality are given below. The portals are extensively used by NMHSs of island countries in the Pacific and Indian oceans for analysis of historical TC data and consequently it is reflected in the examples.

**2.2. Tropical cyclone data portal**

equator and the South Indian Ocean (**Figure 1**).

236 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

Tropical cyclone data portal has been created with aims (i) to visualise the data from the SH TC archive and (ii) allow users to perform analysis of historical cyclone data. Based on recent changes of and additions to the SH TC archive, the TC historical data portal has been redesigned to incorporate best track data for the Western Pacific both south and north of the

**Figure 1.** Front page of the portal – the Southern Hemisphere (top panel) and the Western North Pacific (bottom panel).

Analysis of historical TC tracks is often required to examine an individual cyclone's impact on a specific location. Such analysis could be performed using 'Place name' and 'Coordinates' options of the portal (**Figures 2** and **3**, respectively).

**Figure 2.** Track of TC *Mick* affecting Suva, Fiji displayed after selecting 'Place name' option.

**Figure 3.** Track of TC *Bingiza* affecting area within 100 km radius of Antananarivo, Madagascar displayed after selecting 'Coordinates' option.

Similarly, analysis of historical TC tracks is often required to examine occurrences of TCs over larger areas, e.g. an exclusive economic zone of an island country, during a specific season or a number of seasons (**Figures 4** and **5**, respectively).

**Figure 4.** Tracks of TCs which passed through an exclusive economic zone of Palau during 2012 season.

**Figure 5.** Track of TCs which passed through an exclusive economic zone of Cook Islands from 2008/2009 to 2010/2011 seasons.

Information about cyclone's occurrence (**Figure 6**) and changes of its intensity (**Figure 7**) is also incorporated in functionality of the portal.

Climate Risk Early Warning System for Island Nations: Tropical Cyclones http://dx.doi.org/10.5772/64029 239

**Figure 6.** Tracks of TCs over selected area in the Western North Pacific in 2013 season together with information about start and end of TC *Jebi*.

**Figure 7.** Track of TC *Zoe* displayed over 'Elevation and Bathymetry' background. Changes in the cyclone's intensity are colour-coded; legend is displayed on the right side.

#### **3. Statistical prediction models**

Similarly, analysis of historical TC tracks is often required to examine occurrences of TCs over larger areas, e.g. an exclusive economic zone of an island country, during a specific season or

**Figure 4.** Tracks of TCs which passed through an exclusive economic zone of Palau during 2012 season.

**Figure 5.** Track of TCs which passed through an exclusive economic zone of Cook Islands from 2008/2009 to 2010/2011

Information about cyclone's occurrence (**Figure 6**) and changes of its intensity (**Figure 7**) is also

seasons.

incorporated in functionality of the portal.

a number of seasons (**Figures 4** and **5**, respectively).

238 Recent Developments in Tropical Cyclone Dynamics, Prediction, and Detection

Currently, a statistical technique is used by the Australian Bureau of Meteorology to prepare operational TC seasonal prediction for the Australian and the South Pacific Ocean regions. The operational statistical model consists of linear discriminant analysis (LDA) models, based on ENSO indices as predictors.

#### **3.1. The ENSO indices and linear discriminant analysis model**

The Southern Oscillation Index (SOI) and sea surface temperature anomalies (SSTAs) in Niño3.4 and Niño4 regions (NIÑO3.4 and NIÑO4) are commonly used indices in defining ENSO phases which describe oceanic and atmospheric responses, respectively.

It has been demonstrated by Kuleshov et al. [2, 7] and Ramsay et al. [14] that in the Australian region a strong correlation (about −0.7) exists between the annual number of TCs and the NIÑO4 and NIÑO3.4 indices averaged over 3 months preceding the onset of a Southern Hemisphere TC season (August-September-October). In the eastern South Pacific Ocean, better correlation of the annual TC number with ENSO indices was found for the NIÑO3.4 and the SOI [7]. Based on these findings, the NIÑO3.4 and the SOI indices have been selected by the Australian Bureau of Meteorology for use in operational LDA statistical TC-ENSO model for seasonal prediction of TCs in both the Australian and the South Pacific regions.

A multivariate ENSO index has been developed at the NCC with the aim to integrate both atmospheric and oceanic responses in one index [2]. It is based on the first principal component of monthly Darwin mean sea level pressure (MSLP), Tahiti MSLP and the NIÑO3, NIÑO3.4 and NIÑO4 SST indices [2, 7]. This standardised monthly anomaly index is usually denoted as the 5VAR index. Further examining correlation of ENSO indices with TC occurrences in the Australian region, Kuleshov et al. [15] found that the 5VAR performs better than the SOI and NIÑO3.4 demonstrating the strongest monthly (−0.67, pre-season September), bi-monthly (−0.67, August and September) and tri-monthly correlation (−0.66, July, August and September).

Incorporating into statistical model a decreasing trend in TC activity over the Australian region in recent years [2, 16] and using 5VAR, SOI and NIÑO3.4 indices as predictors, Kuleshov et al. [15] demonstrated potential for improving skill of the LDA operational model. Brief description of the developed statistical model is presented in the Appendix. Cross-validation employed to assess the models' performance demonstrated that the models which used the preseason July-August-September SOI and September 5VAR indices and the time trend as the predictors [15] demonstrated increased skill in TC seasonal forecasting compared with currently used LDA model [7].

#### **3.2. Support vector regression (SVR) models**

Recently, application of advanced statistical methodologies for seasonal prediction of TCs has been explored. It has been demonstrated that improvement in prediction skill compared to the LDA model can be achieved using support vector regression (SVR) models, exploring new environmental indicators and non-linear relationships between them [9]. Detailed description of the developed SVR models for the Australian and South Pacific Ocean regions could be found in [17] and its brief description is presented in the Appendix. Analysis of the results of the SVR models shows that the Dipole Mode Index, the 5VAR index and the SOI are the most frequently used indices selected for TC seasonal forecasting in the Australian and South Pacific regions.
