a spatial data infrastructure to support interpretation of interacting

A SPATIAL DATA INFRASTRUCTURE TO SUPPORT INTERPRETATION OF
INTERACTING ATMOSPHERIC AND OCEANOGRAPHIC FEATURES IN THE
LIGURIAN AND NORTH TYRRHENIAN SEA
C. Lapucci(1, 2), L. Bottai(1,2), C. Brandini(1, 2), B. Doronzo(1, 2), M. Fattorini(1, 2), L. Fibbi(1, 2), R. Mari(1, 2), A.
Orlandi(2), A. Ortolani(1, 2), S. Taddei(2), B. Gozzini(2)
(1)Institute of BioMeteorology, National Council of Research (IBIMET-CNR), Florence,Via G. Caproni 8, 50145 Florence, Italy Email: [email protected]
(2)Laboratory for Meteorology and Environmental Modelling (LaMMA Consortium), Via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy, Email:
[email protected]
ABSTRACT
A large number of applications and practical problems
concerning the services for the sea, supporting also the
Blue Growth, require a growing number of marine data
on the sea state and on some characteristics representing
the marine ecosystem basis. This work describes an SDI
(Spatial Data Infrastructure) focused on oceanographic
features implemented at the LaMMA Consortium
http://159.213.57.108/esa_demo/index.html. This tool
can help to understand the events concerning
interactions between biogeochemical parameters and the
ocean - atmosphere components. This need is also
connected to the current growth of the monitoring
network that LaMMA implements with other
institutions, within the oceanographic activity on the
Ligurian and North Tyrrhenian sea. Besides modelling
and satellite components, current meters and wave
buoys, one X-band radar, drifters and floats, new
instruments are going to be included: two HF radars,
two FerryBoxes and a Wave Glider, managed by
different cooperating institutions. An example of SDI
use in the case of an anomalous event is reported.
1. INTRODUCTION
The interpretation of events concerning strong oceanatmosphere interactions and affecting both the physical
and biogeochemical components of the marine systems,
needs overlapping information concerning different
layers such as remotely sensed and in situ observations,
and data from physical and biogeochemical models.
In this work we describe the use of a SDI (Spatial Data
Infrastructure)
implemented
at
the
LaMMA
Consortium, initially developed with a meteorological
focus and then upgraded to manage oceanographic
features. This need comes out because the monitoring
network that LaMMA carries out within the
oceanographic activity on the Ligurian and North
Tyrrhenian Sea is growing in complexity. Next to the
oceanographic components such as the modelling and
satellite ones, current meters and wave buoys, one Xband radar, drifters and floats, new instruments are
going to be included such as two HF radars, two
FerryBoxes, and a Wave Glider. This ocean observing
and forecasting system at a coastal scale represents a
facility that can be used for multipurpose applications
such as performing numerical and physical experiments
in a defined area. The SDI together with the monitoring
network are also designed as an instrument exploitable
for the different ESA Sentinel satellites data validation.
LaMMA is a public Consortium between the Tuscan
Regional Government and the Italian National Research
Council (CNR) that joins the scientific expertise of
CNR with the public utility of the regional
administration. It develops research and provides
advanced services mainly for the Tuscan territory, but
also for other areas, depending on demand and
opportunities. The Consortium, created by a project
started in 1997, is now the regional meteorological
service and more generally it works in the fields of
meteorology,
oceanography,
climatology
and
geomatics, with a main focus on the scales of regional
interest.
The SDI aim is to disseminate different data in nearreal-time, and for sharing and viewing the selected
geospatial data that are processed daily and used
operationally in many different applications. In the first
release the available parameters were data concerning
atmospheric research and operational forecasting needs
obtained by weather models, satellites, precipitation
radar and weather stations. The new oceanographic
version is including in situ observations, marine
parameters
from
model,
satellite-derived
biogeochemical and physical data. The SDI was
developed by open source technologies. However, the
SDI is able to respond to different needs, such as
allowing users to obtain a better understanding of an
event, test the reliability of a satellite parameter up to
evaluate estimation error, and vice versa also the
dynamics of satellite biogeochemical features can be
profitably used to verify the reliability of model outputs
especially at regional scales.
As a case study, it is shown how this tool can be helpful
to overlap different kinds of data in the study of an
anomalous event, such as an unusual algal growth
detected form satellite data in a time of the year when it
is not expected, as it is possible to compare different
image layers and corresponding numerical data with the
analysis of the related atmospheric and oceanographic
parameters.
2. THE SDI
The SDI (Spatial Data Infrastructure) initially developed
at the LaMMA Consortium with a meteorological focus
http://geoportale.lamma.rete.toscana.it/MapStore/public/
?locale=en was upgraded with oceanographic features,
as it is shown in the demo described, accessible at the
web
page:
http://159.213.57.108/esa_demo/index.html.
The SDI aim is to disseminate different data in nearreal-time, and for sharing and viewing the selected
geospatial data that are daily processed and used
operationally in many different applications. In the first
release the available parameters were data concerning
atmospheric research and operational forecasting needs
obtained by weather global models such as GFS (Global
Forecast System), ECMWF (European Centre for
Medium-Range Weather Forecasts), limited area model
simulations from WRF (Weather Research and
Forecasting), observations from satellites (MSG2 and
MSG 3), precipitation radars and weather stations.
The new oceanographic version is including marine
parameters from wave – WW3 and ocean – ROMSmodels, and from in situ and satellite observations of
biogeochemical and physical data . The SDI was
developed by open source technologies, and the
geospatial data have been implemented via protocols
based on OGC (Open Geospatial Consortium) standards
such as WMS (Web Map Service), WFS (Web Feature
Service).
Figure 1. SDI deploy diagram .
More in detail, Geoserver (http://geoserver.org/)
produces maps and geospatial data available through the
OGC WMS, WFS protocols, while GeoNetwork
(http://geonetwork-opensource.org/) provides catalogue
and discovery services through the OGC CSW protocol;
eventually MapStore (https://github.com/geosolutionsit/MapStore) is responsible mapping mash-ups and
front-end functionalities, as described in [1].
The most innovative aspect of this portal is related to its
capacity to ingest, fuse and disseminate in near realtime geospatial data related to the MetOc
(Meteorological and Oceanographic) field from various
sources in a comprehensive manner, that allows users to
create added value visualizations for supporting
operational use cases as well as for accessing and
downloading the underlying data (where applicable).
Key point of this kind of tool is the ability to overlap the
variables of the weather models developed at LaMMA,
either with themselves, and with information from a
database, which is also managed and implemented by
LaMMA. The database contains meteorological data
measured and updated as quickly as possible by Italian
and international observational networks. Such
information, although equipped with a spatial
component, has not been used in a geospatial context,
and then displayed in a GIS environment until now.
Instead it was prepared for the simple distribution to end
users only in textual form aimed at specific elaborations
or used for the production of graphs. In addition to
weather
models,
images
from
geostationary
meteorological satellites Meteosat MSG (Meteosat
Second Generation) managed by EUMETSAT
(European Organisation for the Exploitation of
Meteorological Satellites) and radar images from the
National Civil Protection are produced by LaMMA
from raw data. To support the Geoportal development,
and to work with the existing proprietary software (e.g.
the Oracle DBMS) the infrastructure design was
customized with Open Source components . The funds
saved on licenses could then be invested in staff training
and in professional support for development, bug-fixes
and customizations. It is important to underline that
some of these developments have become part of the
codebase of its Open Source products (e.g. improved
support for the TIME dimension in GeoServer). Focal
point of all the work was the implementation of an
infrastructure for automatic preprocessing and ingestion,
able to process, classify and ingest in an unsupervised
way the enormous amount of data acquired in real-time
from LaMMA, in order to create highly informative and
up to date mash-ups. In order to reduce the hardware
and software necessary for the infrastructure, the time
window of the data available online was limited, relying
on automatic procedures running during the night, when
the accesses are less frequent, removing the obsolete
content (e.g. the output of weather models older than 3
days). Figure 1 shows a simple deploy diagram of the
infrastructure. Figure 2 shows a display interface of data
visualization
Figure 2. Display interface of data visualization
3. THE DATA IN SDI
Data included in this demo cover the period of
November and December 2012, on the area -10° – 25°
E, 35° – 50° N.
3.1. In situ data
Daily river flow rates are taken from the Centro
Funzionale della Regione Toscana.
Average sea temperature is taken from Giglio Island
tide gauge (data are available every 4 hours).
For the layer rain, data are obtained by the spatialisation
of daily data from weather stations located in Tuscany,
by
DAYMET
algorithm
described
in
[2]
(http://www.daymet.org/) implemented by LaMMA
Consortium. Space resolution of the cell is 1 Km. The
final result is obtained cumulating the data concerning
the daily spatializations.
3.2. Satellite data
Satellite-derived chlorophyll a concentrations were
estimated by OC5, an empirical ocean color algorithm
applied
to
MODIS
AQUA
data
(http://modis.gsfc.nasa.gov/).
The OC5 algorithm, proposed by Ifremer (Institut
Français de Recherche pour l’Exploitation durable de la
Mer), described in [3], was developed to give correct
Chlorophyll a concentration estimations on the Bay of
Biscay and the English Channel oceanic and coastal
areas. OC5, recently updated by data from the French
Mediterranean area, shows an intrinsic robustness both
in oceanic and coastal waters, as shown in [4].
Suspended Particulate Matter (SPM) is non-algal SPM
provided by Ifremer algorithm described in [5], in
which it is assumed that the absorption by yellow
substances can be neglected at wavelengths longer than
550 nm and a simple equation is proposed to express the
reflectance (or the water-leaving radiance) from the
absorption and backscattering coefficients of pure sea
water, phytoplankton and non-algal Particles (NaP).
All the MODIS-AQUA Level-2 files were acquired
from the online OBPG Data Processing System. All
images have about 1km spatial resolution at nadir and
are already corrected for the atmospheric effect.
SST METOPA AVHRR Level-2 files, processed by
GHRSST, were acquired from NOAA. All images have
about 1.1km spatial resolution at nadir and are already
corrected for the atmospheric effect.
3.3. Atmosphere and wave data
The LaMMA Consortium meteo-marine forecasting
WRF-WW3 chain described in [6] is active since June
2006; this chain is operating on a cluster of Linux HPC
nodes hosted in the LaMMA Consortium facilities.
In the SDI described here, wind 10m. total precipitation,
2m temperature and wind gust are obtained from WRF
model running at LaMMA Consortium. The version
2.1.2 of the WRF-ARW model is used in the
meteomarine forecasting chain both for scientific
purposes and for the regional weather service. The
acronym ARW refers to the dynamical non-hydrostatic
solver of the Weather Research and Forecasting system
that is developed and maintained by the Mesoscale and
Microscale Meteorology Division of NCAR. In the
LaMMA configuration, the model has a horizontal grid
with a resolution of 0.1 degrees (about 10 km) over a
domain covering the whole Mediterranean area with
vertical levels from ground to 100 hPa. Finally, initial
and boundary conditions are given by ECMWF. (2 daily
updates) and GFS (4 daily updates).
In the SDI described here, mean wave direction,
significant wave height and mean wave period are taken
from WW3 running at LaMMA Consortium. The
WaveWatch III full-spectral third generation ocean
wind-wave model used in the meteo-marine forecasting
chain has been developed at NOAA/NCEP.
The horizontal resolution of the grid over the whole
Mediterranean sea is 0.1 degrees along both the
meridian and zonal directions, while the resolution of
the nested run is 0.02 degrees along both directions.
Wind forcing data are taken from the output of the
WRF-ARW model. A scaling factor of the WRF 10
metres wind speed is used as tuning parameter in order
to optimize the WRF-WW3 (one-way) coupling. Each
WW3 run generates a set of restart files with an interval
of 12 hours. The restart file contains the wave spectra
for the whole computational domain at a given time.
The proper (i.e. at the right time) restart file is then used
to initialize the wave spectra of the next operational run
of WW3. This procedure permits to minimise model
spin-up (“hotstart”). The availability of several restart
files allows to overcome the lack of one or more runs in
an operational environment.
3.4. Tyrreno-ROMS model
The Tyrreno-ROMS model described in [7 and 8]
running in operational mode at LaMMA is configured
within the area defined by the coordinates 7.20 – 16.25°
E and 36.67 – 44.45° N. The model produces a fivedays forecast. The boundary and initial conditions are
taken from the Mediterranean Forecasting System
(MFS) model available in the MyOcean site, while the
model circulation is forced by the WRF-LaMMA
atmospheric 3km model, for the first three days, and by
the WRF-LaMMA 12km model, for the last two days.
Both atmospheric models are initialised by the ECMWF
data.
The bathymetric metadata and Digital Terrain Model
products were derived from the EMODnet Bathymetry
portal - http://www.emodnet-bathymetry.eu.
4. THE LaMMA MONITORING NETWORK
The monitoring network used by LaMMA within the
oceanographic activity on the Ligurian and North
Tyrrhenian sea is growing in complexity (Figure 3): in
addition to the modelling and satellite components, as
well as current meters, wave buoys, one X-band radar
and to a few drifters and floats, new instruments are
going to be included, namely two HF radars, two
FerryBoxes and a Wave Glider, in the framework of the
SICOMAR project (PO maritime Italy-France), leaded
by the Tuscan Regional Administration.
The network, includes three floats which belong to the
DRIVE-floats project (CNR-Ibimet), a component of
the ARGO-Italy program managed by OGS (Istituto
Nazionale di Oceanografia e Geofisica Sperimentale).
ARGO-Italy represents the Italian component of a
program monitoring global ocean, based on the use of
state of the art technologies such as drifters, floats,
autonomous vehicles and measurements made from
ships of opportunity.
placed on the coast, the remote sensing of surfaces
located at a distance much greater then the optical
horizon, up to 80-90km. The surface waves propagate
on the surface of the sea following the curvature of the
earth, using the sea-water electrical conductivity
property. It is because of this fundamental property that
the HF surface-wave radar is used for remote sensing in
oceanography, primarily for the determination of the
current direction and intensity, but also for the
estimation of the wave spectrum.
The FerryBox is an autonomous system to be installed
onboard a ship, for continuous sea water analyses along
the ship route. It consists of a sea-water sampling unit, a
sensor management unit for measurement acquisition
and storage and a communication unit for data
transmission to a ground station. Two FerryBoxes are
going to be part of the monitoring network, equipped
with sensors such as for measuring pH, temperature,
salinity, fluorometer - chlorophyll a, turbidity.
The Wave Glider is an autonomous, wave-powered
ocean-going platform, for gathering and remotely
transmitting information about ocean surface and subsurface parameters,, and atmospheric conditions. It is
composed by a floating part which houses the main
computer and solar panels, connected via a 6 meter
cable to the submerged part which has wings for
motion. It will have onboard instruments such as water
speed sensor, weather station, directional wave sensor,
CTD-DO Sensor - conductivity, temperature, pressure,
and dissolved oxygen, ADCP Acoustic Doppler Current
Profiler, Fluorometer for in vivo Chlorophyll, Crude oil
and Fine oil measurements.
Figure 3. The LaMMA Consortium monitoring network
that is going to be established by 2015; in the future the
radar component south Elba Island is also planned.
5. A CASE STUDY
The X-band marine radar is part of a system for the
detection of the sea state in a marine area that has a 6
km radius, and is centred around the point of installation
located in Giglio Porto. The radar only observes the sea
(surface), as it is equipped with the sector banking (no
electromagnetic signal is sent towards inland areas). The
parameters of sea state observed by the radar are: wavespectra and related integrated parameters (significant
wave height, mean wave direction, wave period), sea
surface currents. All instruments at Giglio island
allowed to support the activities connected to the
management of the emergency following the Costa
Concordia disaster, as for instance the planning of the
removal activities, that required a precise assessment of
waves and wind in real time and over a given area
around the site of the wreck removal.
Two HF radars are going to be installed on the Tuscan
coast. The HF radar is an increasingly important
technique for monitoring the sea surface. There is a
growing interest in HF radars as they allow, although
The SDI can be useful for a large number of
applications. It is used here to observe an anomalous
event and its development, regarding an algal growth
detected by satellite observations in time of the year and
an area when it is not expected, as it gives the
opportunity to overlap different kinds of data. Different
image layers and corresponding numerical data are
compared together with the atmospheric and
oceanographic parameters concerned.
The SDI is used here to simultaneously analyse different
satellite products, i.e. MODIS OC5 chlorophyll a,
Ifremer SPM, and AVHRR SST, in order to compare
the physical and biogeochemical parameters during the
2 months including the extreme event (Nov.-Dec. 2012).
On the 12th November 2012 an extreme rain event
occurred in southern Tuscany, causing the flooding of
Ombrone and Bruna rivers that can be clearly observed
in the graphic of Ombrone river flow (Figure 4).
Figure 6. MODIS OC5 chlorophyll a and MODIS
Ifremer SPM (contour plot) on October 15, 2012.
Figure 4. the rain map and the Ombrone river flow rate
(graphic) of November 12, 2012, showing the extreme
event.
According to satellite observations, this event was
followed by an anomalous algal growth and a high SPM
level detected by satellite chlorophyll a and SPM
algorithms in the sea area near the river estuaries.
Under normal seasonal conditions in November the
chlorophyll a and SPM concentrations estimated from
satellite are the one observable on Figure 5.
Apparently the presence of a larger amount of SPM is
due to the strong river discharge that followed the
Ombrone and Bruna rivers, that has possibly brought
nutrients so that a stronger phytoplankton growth
occurred.
In this case, the availability of the data that could have
been collected by the Wave Glider and the FerryBoxes
during the event, would have been important for a
comparison with the data provided by the SDI. The
availability of all the different data obtainable both from
the SDI and from the monitoring network can be an
important tool to understand the different aspects of an
event like the one reported here as a case study.
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