AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society
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Operational hazard assessment of waves and storm surges from tropical cyclones in
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Mexico
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Christian M. Appendini*a, Michel Rosengausb, Rafael Meza-Padillaa and Victor
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Camacho-Magañaa
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a
Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad
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Nacional Autónoma de México, Sisal, Yucatán 97356, México b
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Advisor to the National Water Commission of Mexico
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*Corresponding author: Christian M. Appendini, Laboratorio de Ingeniería y Procesos
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Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Puerto de
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Abrigo s/n, 97356, Sisal, México
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E-mail:
[email protected]
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Capsule
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We present a quick hazard assessment tool for wave and storm surge warning areas based
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on pre-computed simulations using synthetic tropical cyclone events.
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Abstract
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Tropical cyclones and their associated impacts along the western and eastern Mexican
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coastlines have led to the recent announcement of the creation of a National Hurricane
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and Severe Storms Center in Mexico. While Mexico falls under the responsibility of the
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Regional Specialized Meteorological Center in Miami, the newly announced center aims
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to provide local warning advisories to local governments and emergency managers. This
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study developed their first operational tool, which provides rapid forecasts of hazard
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areas under the presence of waves and storm surges from tropical cyclones threating
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Mexico. The tool is based on pre-computed wave parameters and storm surges from 3100
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synthetic tropical cyclones. Maximum envelop maps for all of the events are stored in a
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system database that is accessed through a graphical interface. Using a search function of
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synthetic events, the user can select those events most analogous to the tropical cyclone
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in question in order to make an assessment of warning areas. The tool allows users to plot
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maximum envelop maps for individual events, or maxima of maximum maps combining
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several events, either using pre-computed values for the different parameters (wind,
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waves, and storm surge) or a normalized map. To demonstrate the capabilities of the
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operational tool, we present an example application based on hurricane Patricia (2015).
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This tool could also be implemented by developing countries affected by tropical
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cyclones, which otherwise are often limited by numerical modeling capabilities, time, and
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budgets.
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Introduction
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In September 2013, two simultaneous tropical cyclones made landfall in Mexico within a
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24-hour window: Ingrid in the Gulf of Mexico and Manuel in the Pacific. These events
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generated exceptional rainfall (Pedrozo-Acuña et al. 2014) that resulted in 192 deaths and
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estimated economic losses of $5.7 billion USD (Impact Forecasting 2014). Only four
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months later, on 16 January 2014, the Mexican president announced the creation of a
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National Hurricane and Severe Storms Center (CNHyTS) tasked with increasing the
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prevention of hydro-meteorological hazards in Mexico. The simultaneous events of 2013
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almost certainly served as a catalysis to the development CNHyTS; however, Mexico has
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always faced disasters associated with tropical cyclones.
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Mexico is exposed to tropical cyclones from two cyclogenesis regions (North
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Atlantic and northeastern Pacific), creating different challenges at the Federal level
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toward emergency response. Tropical cyclones in the northeast Pacific represent
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approximately 18% of global events (Frank and Young 2007), and typically strike
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Mexico or have a direct impact when traveling along the Mexican coast, even without
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landfall. One of the world’s tropical cyclone “hotspots” is located ~500 km south of Los
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Cabos (tip of the Baja California peninsula) and 500 km southwest of Cabo Corrientes
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(southern end of the bay of Bahia Banderas, where Puerto Vallarta is located). Between
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1949 and 2000, 83 named storms occurred within this area, corresponding to
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approximately eight times the density in the Atlantic just east of the Florida Peninsula
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(Rosengaus-Moshinsky et al. 2002).
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Mexico experienced the fourth highest number of landfall events between 1970 and 2009, surpassed only by China, the Philippines, and Japan (Gibney 2010). The
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destructive effects of tropical cyclones threaten the ~11,000 km of Mexican coastline,
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with approximately half of the population (~55 million) exposed to the direct effects of
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tropical cyclones, representing a sizable fraction of the total population under tropical
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cyclone risk in Region IV (North America, Central America and the Caribbean) of the
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World Metrological Organization (WMO).
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The Servicio Meteorológico Nacional (SMN), which is analogous to the National
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Weather Service (NWS) in the USA, is responsible for weather and meteorological
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analysis in Mexico. Working under the World Meteorological Organization (WMO)
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framework, SMN has provided tropical cyclone forecasts and guidance to other
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authorities and to the general public since before the announcement of the CNHyTS.
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Both coastlines of Mexico also fall under the responsibility of the WMO National
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Hurricane Center (NHC; also known as the Regional Specialized Meteorological Center
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(RSMC)-Miami), which provides frequent forecast information regarding track, intensity,
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and wind field extension (six-hourly when a tropical cyclone has been declared and three-
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hourly when such a storm threatens the coastline). Moreover, in agreement with SMN it
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issues coastline alerts (watches and warnings), using graphical products.
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The NHC products include uncertainty estimates on the forecasted track, but
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forecasts of the destructive effects of tropical cyclones (i.e., wind and rainfall fields over
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the continent, and wave and storm surges along the coastline) are not among its
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international responsibilities. Such estimates are critical for hazard management
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preparedness and response, and each country under the threat of tropical cyclones needs
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to develop their own operational tools for hazards warning. In the case of Mexico, a
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preliminary CNHyTS was embedded within the existing SMN institutional structure in
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May 2015, and included a 24-hour team of forecasters tasked with interpreting NHC
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bulletins and providing more detailed tropical cyclone guidance related to hazard
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management. The CNHyTS seeks to provide forecasts of the destructive effects of
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tropical cyclones by developing its own operational tools, starting with rapid wave and
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storm surge forecasting.
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In an operational setup, wave and storm surge forecasts should be disseminated
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within minutes of receiving NHC forecasts. In the USA, where roughly half of all
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fatalities are related to storm surges (Rappaport 2014), considerable efforts have been
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devoted to storm surge forecasting since the establishment of the Storm Surge Unit by the
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NHC in 1980 (Rappaport et al. 2009). The Meteorological Development Lab (MDL) of
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the National Oceanic and Atmospheric Administration (NOAA) developed the Sea Lake
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and Overland Surge from Hurricanes (SLOSH) model (Jelesnianski et al. 1992) as an aid
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to forecast storm surges. The initial conception of SLOSH was to guide forecasters in the
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development of weather bulletins at the NWS, although more recently it has been used to
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delineate storm surge levels in coastal areas (Glahn et al. 2009). The SLOSH model
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provides information for evacuation planning and advisories based on the Maximum
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Envelop of High Water (MEOW) and the Maximum of MEOWs (MOMs). A MEOW is a
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map composed of the maximum storm surge level obtained at each grid cell for a set of
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simulations of a particular storm category, forward speed, trajectory, and initial tide level,
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where the uncertainty of landfall location is given by the run of the same storm with
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different parallel tracks. A MOM is composed of the maximum storm surge values
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obtained from different MEOWs for a particular storm category, so that it represents the
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worst case scenarios for such a storm category, independent of the storm forward speed,
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trajectory, and initial tide level. More recently, in 2007 the MDL implemented the P-
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surge model (Taylor and Glahn 2008) in experimental mode to provide probabilities of a
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given storm surge level. The P-surge model uses SLOSH to run an ensemble of
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hypothetical storms based on an NHC advisory. The ensemble is based on permutations
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of the forecasted storm, including different tracks, speeds, and wind intensity, with
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historical forecast errors and uncertainty for each parameter incorporated in order to
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assign a weight for each track. The SLOSH model calculates the maximum storm surge
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derived from each storm at every grid cell, and the probability error is included as the
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weight for each storm to create a probabilistic storm surge map.
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Acting above the storm surge, waves are another important hazard from tropical
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cyclones. In the USA, wave guidance is provided by the Environmental Modeling Center
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(EMC) of the NWS, which has a group of experts dedicated to wave forecasting. The
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EMC has provided wave guidance based on nine grids covering their area of
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responsibility, ranging from a global resolution of 0.5° to fine resolution grids of up to
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1/15° covering US coastal areas (Chawla et al. 2013). Recently, the EMC developed the
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Nearshore Wave Prediction System (van der Westhuysen et al. 2013) for local Weather
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Forecast Offices, allowing them to perform high-resolution wave forecasting consistent
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with their wind forecasts, where EMC only provides the wave boundary conditions.
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The use of operational wave and storm surge models in the USA is possible in
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part because of long-term federal funding to the storm surge and wave forecasting
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programs at NOAA (e.g., the Hurricane Forecast Improvement Project started with a $13
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M amendment to NOAA’s budget; Gall et al. 2013). In contrast, developing countries
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under the NHC area of responsibility have limited funding for the development of
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operational forecasts. For instance, the SMN has not been involved in operational
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modeling of ocean hazards, and has only had limited funding available for research
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projects in academic institutions. Furthermore, the use of high fidelity operational waves
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and storm surge models requires high-performance computing power and associated
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support systems (e.g., secure energy, telecom bandwidth, and redundancy), which is not
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as readily available in developing countries as in developed countries. However,
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developing countries will still benefit from less sophisticated tools that can be developed
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despite tight time constraints and budgets. These types of tools could be used by
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forecasters with knowledge of waves and storm surges, independently of their modeling
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capabilities.
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As one of these tools, one could pre-compute the waves and storm surges of a
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large set of realistic track, intensity, size, and translation speed combinations for tropical
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cyclones and then, under the real-time threat of a tropical cyclone, choose the most
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similar as a proxy for wave and storm surge forecasting. However, the brief historical
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records available do not allow for an analog tropical cyclones set, because the probability
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of finding a proper analog would be too low. Instead, this type of tool could be based on
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synthetic tropical cyclones, with sufficient pre-computing of synthetic track/intensity
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cases to enable users to identify analogous synthetic events to be forecast in real time.
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The creation of the CNHyTS provided the opportunity for the development of such a tool,
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although the tight schedule of the project (six months from initial development to
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implementation) was only possible because of the experience of the working group.
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Here we present version 1.0 of an operational tool for forecasting tropical cyclone
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waves and storm surge hazard areas. The forecasting tool satisfies CNHyTS requirements
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by demanding low computing power and the ability to be applied in real time under the
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threat of a tropical cyclone over Mexico. The tool: a) allows forecasts to be achieved
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within minutes of receiving the track/intensity/extension forecast of the NHC; and b)
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does not stress the limited material and human resources of a forecasting office that must
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provide forecasts every three or six hours. The tool was developed under the assumption
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that users do not need a modeling background, but do need a strong understanding of the
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physical processes driving wave and storm surge generation and propagation. This
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requirement was important because forecasters at CNHyTS are not necessarily modelers,
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as the implementation of deterministic/probabilistic numerical models is not a short-term
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goal of the organization. The operational tool was implemented in test mode in early
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2015 and has already been used during a hurricane season.
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System database
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The Quick Assessment Tool for Waves and Storm Surges under Tropical Cyclones
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(QATWaSS-TC) is based on synthetic tropical cyclone wind fields and pre-calculated
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wave and hydrodynamic simulations gathered into a catalog of events (i.e., the system
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database), which was generated following a series of steps outlined in Figure 1.
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Synthetic events and associated wind fields
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Using historical events to develop the system database would have resulted in a limited
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scope, as only just over 100 events have made landfall in Mexico since 1980. As an
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alternative, we used synthetic events to create a robust database of 3100 events making
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landfall over Mexico (1550 each along the Pacific and Gulf of Mexico/Caribbean Sea
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coasts). The events represent a variety of storm conditions related to track, forward speed,
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intensity, and landfall location. The generation of synthetic events was based on Emanuel
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(2008), with warm core vortices randomly seeded across the ocean. These vortices may
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develop or decay according to the ocean temperature climatology, and if developed, they
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are stirred by a beta-advection model driven by large-scale wind fields obtained through
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NCAR-NCEP reanalysis. The seeded vortices are not considered tropical cyclones unless
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they develop wind speeds of at least 21 m s−1. A detailed description of the generation of
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synthetic events can be found in Emanuel et al. (2006, 2008). Only the seeded vortices
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that became tropical cyclones making landfall along the Mexican coastline were included
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in the system database.
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The database of synthetic events comprised two-hourly information for date (year,
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month, day, hour), position (latitude, longitude), maximum wind speed, radius of
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maximum wind speed, atmospheric pressure in the hurricane eye, and neutral
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atmospheric pressure. This information was used to generate temporal wind and
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atmospheric pressure fields for each of the 3100 synthetic events. The wind fields were
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generated using the parametric model of Emanuel and Rotunno (2011), as shown in Eq.
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(1):
197 198
𝑉𝑟 =
1 2 +𝑟 2
2 ) 2𝑟(𝑅𝑚𝑤 𝑉𝑚 + 𝑓𝑅𝑚𝑤 2 𝑅𝑚𝑤
−
𝑓𝑟 2
(1)
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where Rmw is the radius of maximum winds, Vm is the maximum wind speed, r is the
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radial distance from the eye of the hurricane to any given point surrounding it, f is the
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Coriolis parameter, and Vr is the wind speed of the hurricane at radius r. The atmospheric
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pressure fields were generated based on the model proposed by Holland (1980) as shown
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in Eq. (2):
205 𝑃𝑟 = 𝑃𝑐 + (𝑃𝑛 − 𝑃𝑐 ) exp (−
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𝑅𝑚𝑤⁄ 𝐵 𝑟)
(2)
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where Pc is the central pressure, Pn the ambient pressure, r is any given distance between
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the eye of the hurricane and its surrounding domain, Rmw is the maximum wind speed
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radius, and B is the Holland’s shape parameter. For more information on the synthetic
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events used in this study, the reader is referred to Meza-Padilla et al. (2015).
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Numerical modeling
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The atmospheric pressure and wind fields for each synthetic event were used to drive a
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third-generation wave model and hydrodynamic model. Both models are based on
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unstructured meshes, and were constructed for each basin with a coarse resolution
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offshore (~10 km) gradually diminishing to a finer resolution along the coast (~1 km). In
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the case of the hydrodynamic model, a few coastal locations were given resolutions of up
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to ~250 m. The Pacific domain was limited to longitude 92°W–120°W and latitude
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12.5°N–33.5°N. The Gulf of Mexico/Caribbean Sea domain included boundaries at the
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Florida Strait (80.5°W, 25°N to 80.5°W, 23°N) and at the Caribbean Sea between Central
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America (83.3°W, 15°N) and Cuba (78.3°W, 20.7°N). Bathymetry data included local
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surveys of select areas and ETOPO 1 data (Amante and Eakins 2009); both domains and
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their bathymetries are described in Meza-Padilla et al. (2015). Owing to the scarcity of
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topographic information and to computational time constrains, meshes were bounded by
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the shoreline, and flooding and drying were not considered in the simulations. It is
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important to mention that some synthetic events were generated outside the model
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domain, so that the swell generated in the eastern Caribbean Sea or the western and
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southern Pacific was not considered in the simulations. While this can be considered a
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critical flaw in an operational wave forecast system, it was considered acceptable for this
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rapid forecast tool, where the main goal is to provide early warnings to coastal areas in
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the vicinity of a possible landfall. However, forecasters should use other sources of
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information (e.g., global wave models) to account for swell, since the severity of the
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waves generated by a local tropical cyclone is also dependent on underlying sea
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conditions (Ochi 2003). In cases where swell is present in the area of interest, this could
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be accounted for by the forecaster.
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The MIKE 21 SW wave model was used to obtain the wave field corresponding
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to each synthetic tropical cyclone, and the MIKE 21 HD FM model was used to obtain
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the storm surge generated by each event (i.e., surface elevation). The MIKE 21 SW
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model is a third-generation spectral model based on the wave action equation used to
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simulate growth, decay, and transformation of wind-generated waves (Sørensen et al.
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2004). The MIKE 21 HD FM model solves the momentum, continuity, temperature,
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salinity, and density equations with turbulent closure scheme equations. It is based on the
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incompressible Reynolds-averaged Navier–Stokes equations (RANS), which are subject
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to Bousinessq and hydrostatic pressure assumptions. The spatial discretization of the
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equations for both models is based on a centered finite volumes method over unstructured
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meshes. Further information about these models can be found in DHI (2014a,b). The
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models can run in coupled mode so that the feedback between waves, currents, and water
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levels are considered; however, in this implementation, the models were run uncoupled to
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reduce computational time. Both models were run with a constant water level equal to
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mean sea level (i.e., no tides were included in the simulations). Since tidal phase during
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landfall is not considered, forecasters will need to manually account for it in advisories,
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taking into consideration that the tidal phase may nonlinearly increase water levels
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created by the storm surge (Rego and Li 2010).
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As in many other developing countries, Mexico has very few measuring stations
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for waves and sea level. For instance, there are 36 tidal gauges along Texas alone (NOAA
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2016a), compared with 38 tidal stations for the whole of Mexico (UNAM 2016).
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Similarly, the only online wave data for Mexico are from the Mexican Institute of
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Transport (IMT 2016), with data only available as graphic displays for recent dates. This
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makes it difficult to calibrate numerical models for Mexican waters, or even to assess the
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accuracy of operational tools with historical cases. Nonetheless, the hydrodynamic model
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was calibrated based on hurricane Ike (2008) using tidal gauges near Galveston Bay,
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while the wave model was calibrated based on different NOAA buoys in the Gulf of
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Mexico, as presented by Ruiz-Salcines (2013) for historical hurricanes. The final model
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setup is described in full by Meza-Padilla et al. (2015).
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Catalog of events
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The results from each model (winds, waves, and storm surge) were analyzed in order to
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obtain the maximum values during the lifetime of each synthetic storm, giving a total of
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9300 matrices of maximum envelops (3100 for maximum wind intensity, 3100 for
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maximum significant wave height, and 3100 for maximum surface elevation, with each
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basin containing 1550 synthetic tropical cyclones). The maximum value matrices together
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with the synthetic tropical cyclone tracks and intensity information composed the main
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database of the QATWaSS-TC. As an example of the information in the database, Figure
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2 shows the maximum envelop for the different parameters for event 903 in the Gulf of
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Mexico/Caribbean Sea. It is important to note that the wind speed maximum envelops are
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not to be used for forecasting the track or intensity of the storm, but are part of the system
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as an aid to the forecaster to select the synthetic events to include in the wave and storm
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surge forecast. The QATWaSS-TC database was then populated with all of the event
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information (tropical cyclone tracks and parameters), as well as the maximum envelop
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maps, as shown in Figure 3.
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Characteristics of QATWaSS-TC
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Figure 4 shows the conceptual model of the QATWaSS-TC tool, which comprises the
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catalog of events incorporated into a database accessed through Google Maps. The tool
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aims to help forecasters to delineate vulnerable areas along the coast in relation to waves
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and storm surge hazards by providing maximum envelop maps for wind, waves, and
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storm surges, based on official advisories from the NHC, and on the selection of synthetic
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events by the forecaster.
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The storm parameters represented by tropical cyclones in the database (e.g.,
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translation speed, trajectory, landfall location, storm size, and intensity) are limited to
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those of the 3100 events. However, as the synthetic events used as proxies will most
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likely differ in one or more characteristics from the event being forecasted, additional
294
uncertainty may affect the accuracy of significant wave height and water levels. For
14 295
instance, storm size could be more important than storm intensity for generating higher
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storm surge values (Irish et al. 2008), and slower moving storms may create lesser storm
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surges (Irish et al. 2008; Rego and Li 2009) but higher flooded volumes (Rego and Li
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2009; Appendini et al. 2014). Such uncertainty could be reduced by increasing the
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number of synthetic events; however, the time constrains for implementation did not
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allow for more simulations. While the parameters of the forecasted storm are not
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considered in the automatic selection of synthetic events, the user can manually deselect
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all tropical cyclones that do not comply with the characteristics of the event to be
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forecasted. The hazard assessment areas are then only a result of the events selected by
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the forecaster, based on his or her knowledge of tropical cyclones, waves, and storm
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surge. Other uncertainties involved in the use of this tool reflect the actual forecasts and
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wind fields used in the wave and storm surge modeling. For example, Cardone and Cox
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(2009) showed that the real-time estimates of wind speed and storm size produced by
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warning center advisories may create up to 20% uncertainty in storm surge estimates.
309 310 311
Implementation of QATWaSS-TC To illustrate the use of QATWaSS-TC, we present the case of hurricane Patricia,
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which formed in the eastern Pacific on 20 October 2015. Though initially estimated to
313
make landfall as a category 5 hurricane, post-analysis of data estimated landfall at
314
approximately 67 m s−1 (i.e., a category 4 hurricane; Kimberlain et al. 2016). We selected
315
Patricia both because it represents an extraordinary event, and because 2015 was the first
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hurricane season to be monitored by the preliminary CNHyTS group. Based on the best
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track data (NOAA 2016b), Patricia had a maximum intensification rate of 54 m s−1 in 24
15 318
hours, passing from tropical storm to category 5 hurricane during this period. Patricia
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presented the strongest winds ever recorded in the NHC responsibility area and the lowest
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pressure on record in the western Hemisphere (Kimberlain et al. 2016), second only to
321
super-typhoon Tip (1979) on a global level. Event analysis by Kimberlain et al. (2016)
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indicated that the strongest 1-minute averaged sustained winds were ~95 m s−1 and there
323
was a minimum pressure of 872 mb, occurring 11 hours before landfall.
324
Only 24 hours before Patricia made landfall, the uncertainty cone from the NHC
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covered the coastline from north of San Blas and Nayarit, to Melaque and Jalisco
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(approximately 400 km of coastline), which put the cities of Puerto Vallarta and
327
Manzanillo, in addition to many rural areas, at risk of high seas and storm surges. Only
328
12 hours before landfall, the main cities were still under hurricane warning and voluntary
329
evacuations were taking place. Fortunately, Patricia made landfall in an area of low
330
population density and wind speeds above category 3 force were limited to a concentrated
331
area around the eye, resulting in localized damage.
332
We used QATWaSS-TC to determine the wave and storm surge warning areas.
333
The first step was to provide the system with the actual position of the tropical cyclone,
334
as well as a forecast location. We selected the location of the event as provided by the
335
NHC in advisory #14, approximately 14 hours before landfall (with estimated landfall at
336
2315 UTC) and corresponding to the time when Patricia achieved maximum intensity
337
winds. The location was used together with a search radius to identify all synthetic events
338
whose tracks passed through both radii. When QATWaSS-TC is initialized, a display
339
shows an empty map and input dialog boxes (Fig. 5) related to the type of event (e.g.,
340
tropical storm, minor category 1 and 2 hurricanes, and major category 3 to 5 hurricanes),
16 341
the event center position (present location of the tropical cyclone taking place), the
342
forecast position (this could be a landfall location or any other location of interest), and
343
the search radius for both positions. The default search radius for the present position is
344
set to 30 km, which we found to be a reasonable value after several sensitivity tests. For
345
the forecast location, the default is set to a three-day uncertainty cone radius as
346
determined at the beginning of each hurricane season. Both search radii can be modified
347
by the user during searches, without restarting the system.
348
After the user inputs search information, a map is displayed showing all synthetic
349
events that meet the search criteria (Fig. 5a,b), from which the user can manually select
350
the events to use for the warning assessment (Fig. 5c), and a flag is introduced to the map
351
at every landfall location showing coordinates and the wind speed during landfall. The
352
text is given in Spanish since the system is to be used by an official Mexican institution
353
(for an English translation please see Appendix A). The resolution of the output map
354
interface corresponds to the numerical modeling mesh.
355
The user can interact several times with the search of events, as well as inspect the
356
individual maximum envelop maps for the different parameters (significant wave height,
357
storm surge, wind speeds) of the events selected and listed. This allows the user to select
358
the events most suitable for the warning assessment. The system database also contains
359
information on the wave power for each synthetic event, which can be used to assess
360
swell at a particular location far from the storm (Innocentini et al. 2014). Based on
361
Patricia advisory #14 and user selected synthetic events, QATWaSS-TC generates
362
maximum envelop maps that include individual events maximum envelop maps (Fig. 6a),
17 363
maxima of maximum envelop maps from several events (Fig. 6b), and normalized
364
maxima of maximum envelop maps (Fig. 6c).
365
Based on the individual plots (e.g., the maximum envelop map of significant wave
366
height; Fig. 6a), the user can select and deselect events that passed the first selection filter
367
(Fig. 5a,b), and then decide which events to use for the warning area assessment (Fig.
368
5c). The user criteria are critical at this stage, since the accuracy of the warning area
369
forecast is based on the events included in the maxima of maximum envelop maps. After
370
several interactions, and when the user is satisfied with the choice of events, the system
371
can plot of the maximum values at each element mesh considering all selected synthetic
372
events (i.e., the maxima of maximum envelop; Fig. 6b).
373
While QATWaSS-TC is comprised of 3100 events, it is likely that the user will
374
have to use synthetic events with different characteristics (e.g., intensity, storm size, and
375
storm speed) in order to assess hazard areas and uncertainty for a given storm. For
376
instance, only the Mexican coastline of the Caribbean Sea and near the US-Mexican
377
border is covered by synthetic events from all tropical cyclone categories when making
378
landfall (Fig. 7). For other areas, the forecaster will need to select a combination of
379
storms with different categories to cover the uncertainty cone. In such maps (e.g., Fig.
380
5b), the maxima of maximum envelops will be dominated by the most intense storm, and
381
direct interpretation will provide an inaccurate estimate of potentially affected areas. In
382
this case, forecasters will have to rely on their own understanding of storm surge and
383
wave processes and take into consideration the uncertainties imposed by the use of a
384
combination of events. To aid the forecaster, we implemented a normalized plot for the
385
maxima of maximum envelop maps, in which the values for each storm (i.e., waves,
18 386
surface elevation, and wind speed) are normalized by the maximum value. In this
387
manner, all events in the normalized maxima of maximum have the same scale, with the
388
highest intensity set to unity (1) for each individual event. For example, if the user selects
389
a tropical storm, a category 2 event, and a category 5 event, the normalization will set the
390
maximum values of each to 1, so that the user can infer the warning areas. If the user is
391
aware of the normalization process and a category 3 hurricane is approaching land, he or
392
she will know, based on the events used for the mapping, that the potential areas under
393
threat may differ because none of the events in the system was a category 3 hurricane.
394
Furthermore, in reality, a storm’s behavior also depends on a variety of other parameters
395
(e.g., bathymetry, coastal morphology, storm size, and translation speed). The user should
396
be aware of the real conditions of the forecast position, and the event that is being
397
assessed, to provide a sound estimate of the warning areas. It is important to note that all
398
maps derived from QATWaSS-TC are subject to misinterpretation and are intended for
399
use by trained forecasters only. The maps are not suitable or intended for release to the
400
public.
401
The QATWaSS-TC is conceived as a qualitative tool to aid forecasters and not to
402
provide estimates of wind speed, significant wave height, or surface elevation. However,
403
under an operational environment it is desirable to have a minimum threshold for the
404
hazard parameters to determine the warning areas. Therefore, the background of the
405
forecaster and his or her knowledge of the area are critical. For instance, in the Mexican
406
Pacific, the mean annual significant wave height in deep water is around 1.5 m, while
407
extreme waves (based on 12 hours of exceeding the 99th percentile) are above 3.5 m
408
(Reguero et al. 2013; Cox and Swail 2001), which could provide the threshold for the
19 409
warning areas. In the particular case of Patricia, the maxima of maximum map of
410
significant wave height (as obtained using QATWaSS-TC) showed values above 4 m
411
between the locations of San Blas and Manzanillo (Fig. 6b), which would trigger a
412
warning of hazardous waves for this coastline. For this assessment, there were synthetic
413
events making landfall as major hurricanes at the uncertainty cone limits, so that the
414
normalized map (Fig. 6c) does not provide an asset to the forecaster. In the case that there
415
was only one major hurricane in the synthetic database, the individual maximum envelop
416
map for that event would provide the baseline for the significant wave height values that
417
can be obtained and then the normalized maxima of maximum map will provide the
418
extend of the area under risk. We do acknowledge this is a rough approximation, but one
419
that can provide accurate estimates to delineate warning areas when forecasters have a
420
background in the physical processes underlying waves and storm surge generation and
421
propagation, as well as in the local characteristic of the area.
422
To test the accuracy of estimates from QATWaSS-TC for Patricia-generated
423
waves, we compared the results to those of the WaveWatch III model of NOAA’s
424
EMC/National Centers for Environmental Prediction (not shown). The wave model
425
provided similar results to QATWaSS-TC, with estimates of significant wave height
426
between 4 and 8 m along the coast from San Blas to Manzanillo. Nevertheless, the system
427
should still be considered a qualitative aid for the estimation of warning areas and should
428
not be used for quantitative estimates.
429
One of the main advantages of QATWaSS-TC is that it does not rely on high-
430
performance computing, which would allow computation of waves and storm surges
431
using data from NHC advisories. To compare results that could be obtained using real-
20 432
time forecast models to the results from QATWaSS-TC, we computed wave maximum
433
wave fields from Advisory #14 and best track data for Patricia (Fig. 8). The significant
434
wave height values near the coastline from the pre-computed synthetic events
435
(QATWaSS-TC) showed similar intensities to the analogous events (uncertainty cone
436
tracks), although the values near San Blas were overestimated by the synthetic events.
437
For the operational forecast, this suggests that warning areas would be similar whether
438
QATWaSS-TC or real-time models based on the advisory were used. Here, we only
439
included two synthetic events, which covered the extremes of the uncertainty cone, so
440
high waves could be expected anywhere in between. Comparing the results from both
441
QATWaSS-TC and the simulations using the advisory information to the results using the
442
best track data, we found that the wave warning area for waves above 4 m was equal to
443
those from both the simulations based on the advisory and QATWaSS-TC, with the
444
exception of the area south of San Blas; however, the values at the coastline were smaller
445
for the best track simulation.
446
Finally, we performed a qualitative assessment of QATWaSS-TC using the post-
447
storm damage survey conducted by CNHyTS and NWS/NOAA. The results of the survey
448
show property damage and flooding up to 3.5 m above mean sea level resulting from the
449
combined effects of waves and a storm surge ~120 km southeast of the landfall point
450
(Playa Paraíso). This area was part of the extension of the coastline identified as under
451
risk by QATWaSS-TC, lending additional credibility to the system.
452
21 453
Conclusions
454
In this study, we developed a quick wave and storm surge warning tool for tropical
455
cyclones (QATWaSS-TC), which is the first operational tool for the recently announced
456
National Hurricane and Severe Storms Center in Mexico. The tool was developed on a
457
tight budget and within limited time constraints: six months from conception to
458
implementation. Based on pre-run high fidelity models, the tool allows forecasters to
459
provide rapid estimates of wave and storm surge warning areas related to tropical
460
cyclones along the Mexican coastline. When tested using hurricane Patricia (2015) as an
461
example, the tool provided accurate estimates for warning areas.
462
Despite the advantages presented by QATWaSS-TC, the approach has several
463
limitations. First, the system database contains only 3100 synthetic events, so events for
464
use as proxies will likely differ in at least one characteristic (e.g., track, translation speed,
465
maximum wind speed, or storm size) from the event being forecasted. Second, fine-scale
466
bathymetry is only available in some localized areas and topography has not been
467
included, thus no overland flooding is calculated. Finally, quantitative estimates can only
468
provide aids for the qualitative assessment of warning areas as the limitations discussed
469
above result in high uncertainties related to quantitative estimates in nearshore areas.
470
To reduce the uncertainty imposed by the limitation of events, the database could
471
be updated with additional synthetic events. For examples, high fidelity models could be
472
run outside of hurricane season to produce more pre-computed scenarios, which in the
473
case of Mexico could add at least 3100 events per year, considering the same meshes are
474
used. To increase the quantitative precision of the system, high fidelity models should
475
also include more accurate bathymetric data and topography. However, with
22 476
approximately 11,000 km of coastline, Mexico is unlikely to perform surveys to gather
477
precise bathymetric and topographical data; although this could be feasible for other
478
Latin-American countries and the Caribbean islands with considerably shorter coastlines
479
(i.e., Cuba and the Bahamas have about 40% of the coastline of Mexico, and 2/3 of the
480
countries in the area of NHC responsibility have less than 5% of Mexico’s coastline). In
481
the case of smaller countries, the use of 3100 events could provide a sufficiently large
482
dataset to reduce the uncertainty imposed. Furthermore, these updates would allow
483
greater automatization of the tool, enabling a more quantitative usage, and in particular
484
would reduce discrepancies that may arise owing to different interpretations by different
485
forecasters.
486
QATWaSS-TC could be easily adopted in countries with limited numerical
487
modeling capabilities and without a complex forecasting system (e.g., many countries in
488
the Caribbean and Central America), although forecasters would be required to have
489
knowledge of waves and storm surge generation and propagation. While high computing
490
resources are needed to pre-compute scenarios for the system, none are needed during the
491
tropical cyclones season, when forecasts of warning areas can be done in minutes. The
492
system can be developed and implemented under low budgets and tight schedules, both
493
of which are common in many developing countries.
494 495
Acknowledgements
496
The authors would like to thank the Comisión Nacional del Agua (CONAGUA) for
497
providing support under project No. CNA-SGT-GASIR-14/2014. The authors are very
498
grateful to Professor Kerry Emanuel for supplying the synthetic events and for allowing
23 499
their use in this study, Gonzalo Martin for IT support, and two anonymous reviewers who
500
greatly helped to improve the manuscript. The CNHyTS and NWS/NOAA storm damage
501
survey was conducted by Orlando Bermudez (NWS), Pedro Restrepo (NWS), Humberto
502
Hernandez Peralta (SMN), and Michel Rosengaus (advisor to CONAGUA). The views
503
and opinions expressed in this manuscript do not reflect the opinion of the donor institute.
504
24 505
Appendix A
506
Glossary
507
Altura de ola significante: Significant wave height
508
Buscar: Search
509
Categoría: Category
510
Envolvente: Envelop
511
Evento(s): Event(s)
512
Eventos seleccionados: Selected events
513
Generar envolvente normalizado: Generate normalized envelop (normalized maxima of
514
maximum envelop)
515
Generar envolvente: Generate envelop (maxima of maximum envelop map)
516
Gráficar máximos: Plot maxima (maximum envelop map)
517
Huracán mayor: Major hurricane
518
Huracán menor: Minor hurricane
519
Incluir: Include
520
Máximo: Maximum
521
Medida: Measured (in this case, parameters to display are wind, waves, and storm surge)
522
Nomalizada: Normalized
523
Oleaje: Waves
524
Posición actual: Present position
525
Posición esperada: Expected position
526
Quitar: Remove
527
Tormenta tropical: Tropical storm
25 528
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30 627
Figure captions
628
Fig. 1. Flow diagram used for the generation of the system database.
629 630
Fig. 2. Examples of maximum envelops for synthetic event 903 in the Gulf of
631
Mexico/Caribbean Sea, showing: a) maximum wind speed (m s−1); b) maximum water
632
level (m); c) maximum significant wave height (m); and d) maximum wave power (kW
633
m−1).
634 635
Fig. 3. QATWaSS-TC database structure, where lon = longitude, lat = latitude, Vm =
636
maximum sustained wind speed, Rmw = radius of maximum winds, Pc = central
637
pressure, and Pn = neutral pressure.
638 639
Fig. 4. QATWaSS-TC flow diagram, where Hs = significant wave height, WLs = water
640
level, and Ws = wind speed.
641 642
Fig. 5. Criteria for synthetic events, including: a) fitting search criteria for tropical
643
cyclones; b) fitting search criteria for hurricanes; and c) events selected by the user.
644
Figures are screenshots of the QATWaSS-TC graphic interface; therefore, text is in
645
Spanish. Please see Appendix A for a list of translated terms.
646 647
Fig. 6. Maximum envelop maps of significant wave height (m) for: a) individual event
648
1605; b) maxima of maximum envelop for events 1940, 2044, 2029, 1734, and 1605; and
649
c) the normalized maxima of maximum envelop for the same events. Figures are
31 650
screenshots of the QATWaSS-TC graphic interface; therefore, text is in Spanish. Please
651
see Appendix A for a list of translated terms.
652 653
Fig. 7. Wind speed category at each track location for the 3100 synthetic events, where
654
blue corresponds to tropical depressions, green to tropical cyclones, orange to minor
655
hurricanes, and fuchsia to major hurricanes.
656 657
Fig. 8. QATWaSS-TC database post-storm assessment of waves generated by hurricane
658
Patricia (2015), showing: a) the best track (larger dots), synthetic events 1605 and 2029
659
(smaller dots), the forecast track with the 5-day uncertainty cone during National
660
Hurricane Center (NHC) Advisory #14, and the location of Manzanillo and San Blas;
661
significant wave height maximum envelop maps for synthetic events b) 1605 and c)
662
2029, and for d) the north track, e) the south track, f) the central track of advisory #14
663
uncertainty cone, and g) for the best track data.
664
32 665
666 667 668
Fig. 1. Flow diagram used for the generation of the system database.
33
669 670 671 672 673 674
Fig. 2. Examples of maximum envelops for synthetic event 903 in the Gulf of Mexico/Caribbean Sea, showing: a) maximum wind speed (m s−1); b) maximum water level (m); c) maximum significant wave height (m); and d) maximum wave power (kW m−1).
34
675 676 677 678 679 680
Fig. 3. QATWaSS-TC database structure, where lon = longitude, lat = latitude, Vm = maximum sustained wind speed, Rmw = radius of maximum winds, Pc = central pressure, and Pn = neutral pressure.
35
681 682 683 684 685
Fig. 4. QATWaSS-TC flow diagram, where Hs = significant wave height, WLs = water level, and Ws = wind speed.
36
686 687 688 689 690 691 692
Fig. 5. Criteria for synthetic events, including: a) fitting search criteria for tropical cyclones; b) fitting search criteria for hurricanes; and c) events selected by the user. Figures are screenshots of the QATWaSS-TC graphic interface; therefore, text is in Spanish. Please see Appendix A for a list of translated terms.
37
693 694 695 696 697 698 699
Fig. 6. Maximum envelop maps of significant wave height (m) for: a) individual event 1605; b) maxima of maximum envelop for events 1940, 2044, 2029, 1734, and 1605; and c) the normalized maxima of maximum envelop for the same events. Figures are screenshots of the QATWaSS-TC graphic interface; therefore, text is in Spanish. Please see Appendix A for a list of translated terms.
38
700 701 702 703 704
Fig. 7. Wind speed category at each track location for the 3100 synthetic events, where blue corresponds to tropical depressions, green to tropical cyclones, orange to minor hurricanes, and fuchsia to major hurricanes.
39
705 706 707 708 709 710 711 712 713 714 715
Fig. 8. QATWaSS-TC database post-storm assessment of waves generated by hurricane Patricia (2015), showing: a) the best track (larger dots), synthetic events 1605 and 2029 (smaller dots), the forecast track with the 5-day uncertainty cone during National Hurricane Center (NHC) Advisory #14, and the location of Manzanillo and San Blas; significant wave height maximum envelop maps for synthetic events b) 1605 and c) 2029, and for d) the north track, e) the south track, f) the central track of advisory #14 uncertainty cone, and g) for the best track data.
