2. Semi-Empirical Model Description

A. Bonaduce (1) , N. Pinardi (2), G. Coppini (1)
(1) Euro-Mediterranean Center for Climate Change, Italy
(2) University of Bologna, Environmental Science, Italy
2nd GODAE OceanView Coastal Oceans and Shelf Seas Task Team
(COSS-TT) International Coordination Workshop (COSS-ICW2)
4 – 7 February 2013, Lecce, Italy
1. Introduction
• Early Warning System (EWS) Definition
• Sea-level (SL) Variability Components: Overview
• Existing System
• Objectives
2. Semi-Empirical Model Description
• SL components considered
• Model Calibration : Italian tide-gauge network
3. Results
• Sea-level extreme events EWS along the Italian coast
4. Conclusions and Future Developments
1. Introduction: Early Warning System Definition
• Early warning (EW) is “the provision of timely and effective
information, that allows individuals exposed to hazard to take action
to avoid or reduce their risk and prepare for effective response”, and is
the integration of four main elements, (from International Strategy for
Disaster Reduction (ISDR), United Nations (UN), 2006):
• Risk Knowledge
• Monitoring and Predicting
• Disseminating Information
• Response
1. Introduction: Early Warning System Definition
What does it mean “Early” ?
It is a temporal and spatial scale dependent concept
The general approach used is design a EWSs capable to resolve the physical
processes involved at the proper scales, complicating the system according to
the monitoring and forecasting activities needed
1. Introduction: Overview
Sea-level variability is characterized by multiple interacting factors that act over
wide spectra of temporal and spatial scales.
Sea-level changes at decadal and interannual time scales are due to density and
water-mass distribution variations in the ocean, driven by wind, atmospheric
pressure and heat and water fluxes.
Atmospheric forcings produce, through mechanical stress, a displacement of the
water mass involving sea-level variations due to barotropic displacemet of the
water column.
Variations of temperature and salinity due to heat and water fluxes tend to
modify the density structure of the water column (the steric effect), which in
turn changes the height of the water column.
Looking at the local relative sea-level, it is crucial to consider also the high
frequency processes, that influence sea-level over a short
temporal scales. Such processes are represented by the periodic sea-level
variations due to the tidal motion and the fast variations associated with waves.
1. Introduction: Existing Systems
CI-FLOW* Project (NOAA, Van Cooten et al.,2011)
Coupled Modelling System for total water level simulations for
flooding in coastal areas (North Carolina: Tar-Pamlico and Neuse
*Coastal and Inland
Flooding Observations
1. Introduction: Objectives
• Develop a semi-empirical model considering all the sea-level
varibility components using TEchnologies for Situational Sea
Awareness projects products
• Design an EWS for SL extreme events along the Italian coasts
• Calibrate the system with available observations
2. Semi-Empirical Model Description: SL components considered
ηM = Sea-level component resolved by OGCM (incompressible and Boussinesq)
Sea-Surface Height (SSH)
• Mediterranean Forecasting System (Oddo et al., 2009)
• horiz resolution: 1/16 ; 72 vertical levels
• Geografical domain:
-18.125 W – 36.25 E ; 30 N – 46 N
ηST = Sea-level steric comp. due to water column
density variations (Mellor and Etzer 1995)
ηIB = Inverse Barometer Effect (Dourandeau and Le Traon, 1999)
• European Center for Medium-Range Weather Forecasting
• Horiz. Resolution: 1/4
2. Semi-Empirical Model Description: SL components considered
ηAT = Sea-level comp. due to astronomichal tides
AT   Ai cos i t  i
• Oregon State University Tidal Prediction Software
(OTPS; Egbert and Erofeeva, 2002)
• 8 tidal constituents
• Mediterranean solution: 1/30
ηWA = sea-level and waves interactions in the coastal zones, due to the
transfer of momentum at the wawe breaking
to the water column (wave-setup)
• Wave Model: i.e. Wave Amplitude Model (WAM)
• Horiz. Res. : 1/16 ;
• Geografical domain: MFS
• Significant Wave Height (Hs) and Wave Peak Period (Tp)
2. Semi-Empirical Model Description: SL components considered
• Storm-surge EWS development:
Phase I
Phase II
Phase III
2. Semi-empirical model description: Calibration
• Coastal areas considered defined choosing Italian tide-gauge stations positions
• Italian tide-gauge stations network (ISPRA):
26 stations – only 6 operational
• Each SL component has a calibration
• In-situ data area used to as reference level
for model data;
• Calibrations parameters are obtained
through multiple regression against
in-situ SL signals filtered accordingly:
i.e: Steric signal obtained from model data as f(T,S,P) againts a climatology
obtained considering 20 years of in-situ data
• Calibrations parameters are spatially and temporarily dependent
3.Results: Storm-surge EWS
Phase I
Target Coastal Areas:
ISPRA tide-gauge stations
Time window: 14 days
Frequency: daily mean
System update: daily
Observations: J – 7
Forecast: J + 7
Skill: < 5 cm OBS – FOR
Web Interface:
3. Results: Storm-surge EWS Phase I
Date: 30 Jan 2013
4. Conclusions
A semi-empirical model for sea-level forecasting has been designed
considering all the sea-level variability components and planning
3 phases of development.
• Italian tide-gauge network has been used to define target
coastal areas and to calibrate the model
• A preliminar version of an Early Warning System for sea-level extreme
events has been set-up for 6 target areas along the italian coasts,
providing data over a 14 days time-window
• The semi-empirical model (phase I) shows skill in reproducing
sea-level insitu signal ( < 5 cm)
4. Future Developments
• PHASE II: Introduce the sea-level component associated to the tidalelevation and consider the entire
italian tide-gauge network
(hourly data)
• PHASE III: Consider waves contr.
(wave set-up) and extend model
coverage to the highest number of
tide-gauge stations (positions)
available in the Med.
• Estimate forecast uncertainty
providing the p confidential
interval considering PDF.
• Implement the system using sub-regional and coastal numerical models
(TESSA project products)