=Paper=
{{Paper
|id=Vol-2728/short3
|storemode=property
|title=NWPOntology: A Well-Founded Ontology for the Numerical Weather Prediction Data Domain
|pdfUrl=https://ceur-ws.org/Vol-2728/short3.pdf
|volume=Vol-2728
|authors=Tiago Duarte Neves,Ivanildo Barbosa,Kelli de Faria Cordeiro
|dblpUrl=https://dblp.org/rec/conf/ontobras/NevesBC20
}}
==NWPOntology: A Well-Founded Ontology for the Numerical Weather Prediction Data Domain==
https://ceur-ws.org/Vol-2728/short3.pdf
NWPOntology: A well-founded Ontology for the Numerical
Weather Prediction Data Domain
Tiago Duarte Neves , Ivanildo Babosa , Kelli de Faria Cordeiro
1 1 1,2
1
Military Institute of Engineering (IME), Rio de Janeiro – RJ 22290-270, Brazil
2
Center of Naval System Analysis of Brazilian Navy, Rio de Janeiro – RJ 20091-000,
Brazil
tiago.duarte@ime.eb.br, ivanildo@ime.eb.br, kelli@marinha.mil.br
Abstract. Weather forecasting is an important application of Science to plan
daily activities, being used in agriculture, air traffic, sea, forestry, severe
weather alerts, military operations and enterprise systems. The weather
forecast information is provided by several systems with different formats,
parameters and with a large number of heterogeneous concepts used in
different organizations. To minimize this problem, the number of these
heterogeneous concepts must be reduced in order to provide uniform access
for better decision making. This article describes the construction of a well-
founded ontology for the Numerical Weather Prediction data domain
(NWPOntology), developed from philosophically based foundations of the
Unified Foundational Ontology as a solution to promote the semantic
enrichment of data from meteorological observations, aiming to reduce
conceptual ambiguities.
1. Introduction
Existing weather forecast centers focus on presenting data to the forecaster supporting
some important decisions. Most of the data analysis and assembling key decisions in a
forecasting policy takes place in the minds of analysts. This is usually transformed into
forecast products finished in a simple text editor with large amounts of complex, semi-
structured and multivariate data, having to interpret them in a short time. Therefore, one
of the biggest challenges in geospatial research is to generate accurate data.
In Meteorology, it is necessary ensure data consistency in the most efficient
way. Inconsistencies and variations in the data forecasting series may occur due to
several reasons, such as, human error in data acquisition, inadequate maintenance and
calibration of the measuring instrument, erroneous data processing, random variations
expected for any weather phenomenon and changes in the surrounding environment. In
this context, the weather forecasting centers may use different syntaxes and
terminologies for describing information as well as the same term may have different
meanings and interpretations in different organizations.
For example, in the sentence "Maps of daily temperature and precipitation are
produced," an expert would recognize that the observation is "temperature" but could
not determine the details related to the temperature concept (atmospheric temperature,
sea surface temperature, etc.). He needs to ask the data provider to get more details
[Masmoudi et al. 2020]. Additionally, in different disciplines, the same term may
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
correspond to various meanings, in one hand. For example, the term “environment” is
defined as “the biological and abiotic elements surrounding an individual organism,” in
the biological domain. However, it refers to “all the natural components of the Earth
(air, water, oils, etc.)” [Sauvé et al. 2016].
On the other hand, various terms may correspond to the same meaning. For
instance, the Observatory of Sahara and Sahel (OSS) may use the word “rainfall” for the
same real-world feature that usually refers to “precipitation” in other sources. The
variety of terms complicates the work of emergency responders who should be
familiar with the terms used in each discipline [Masmoudi et al. 2020]. Another
situation that occurs in an organization might use the term “river”, while another might
use the term “lake” to describe the concept “water area” [Khantong and Ahmad 2019].
Consequently, although the meanings of the concepts for describing information from
these organizations are similar, stakeholders and computational systems in different
organizations cannot understand the meanings.
Semantic Sensor Web (SSW), SWEET and Next Generation Network Enabled
Weather (NNEW) are ontologies designed to represent the domain of weather data (to
be detailed in the Section2), but they did not emphasize numerical weather prediction,
which is essential for an area that is constantly evolving. To provide conceptual
methodological support for the exchange of knowledge on Numerical Weather
Prediction Data, NWPOntology is being developed, a domain ontology for numerical
weather prediction data based on Ontology of Unified Foundational Ontology (UFO)
[Guizzardi 2005] to support a future decision-making system. The adoption of
NWPOntology could improve the safety of marine navigation by users as well as the
semantic accuracy of that data by Command and Control applications, e.g., Search and
Rescue operations.
This document introduces NWPOntology as follows: Section 2 reviews some
related work from ontology in weather data domain; Section 3 details NWPOntology;
and finally, Section 4 presents final observations and topics for further investigation.
2. Related Work
There exists a huge number of sensor networks around the globe designed to monitor a
large set of different phenomena, creating a vast amount of data. Making them available
demands to structure such data as well as to allow the interoperability between different
sensor networks. Semantic Sensor Web (SSW) is an approach that builds upon Sensor
Web Enablement (SWE) [Reed et al. 2007] and Semantic Web activities by the W3C
[W3C 2020] which aims at annotating sensor data with semantic metadata to increase
interoperability and to provide contextual information essential for situational
knowledge [Sheth et al. 2008]. Semantic metadata includes spatial, temporal, and
thematic data.
The SSW ontology does not support forecast values. However, it would be
possible to extend the SSW ontology to implement them. In the context of
NWPOntology, the concept Observations, as well as its subconcepts, could be reused;
however, some additional subconcepts should to be added. The Basic (WGS84 lat/long)
for location [Brickley 2004] and OWLTime [Hobbs and Pan 2006] can be used by the
NWPOntology without the use of the SSW ontology.
Another approach towards a semantically-enriched sensor network, based on
SWE, is an OWL 2 ontology created by the W3C Semantic Sensor Network Incubator
group (SSN-XG) which is referred to as the SSN Ontology [Compton et al. 2012]. The
goal of this ontology is to simplify managing, querying, and combining sensors and
observation data from different sensor networks. Different perspectives can be used to
view the knowledge base, including: the sensor perspective (which sensors are
available; what and how do they sense), the observation perspective (focusing on
observations and related metadata), the system perspective (systems of sensors), and the
property perspective (properties of physical phenomena and how they are sensed).
SWEET is a set of more than 200 ontologies comprising about 6000 concepts
[Savić et al. 2019]. The initial version dates back to 2003 and based on DAML+OIL
[Connolly et al. 2001, Raskin and Pan 2003]. Works regarding environmental
applications include integrating volcanic and atmospheric data in the context of volcanic
eruptions [Fox et al. 2007], an ontology of earth's crust fractures [Zhong et al. 2009] and
an extension of SWEET by climate and forecast terms [Ramachandran and Raskin
2006].
In the context of weather forecasting, SWEET ontologies are a well-qualified
approach for reusing them in a climate ontology. Time definitions, and geographical
position specifications are supported. However, SWEET comes with some
disadvantages, which ultimately led to the decision not to use them in the
implementation of NWPOntology to support a future decision-making system: it does
not currently cover future events, as well as does not cover all weather elements
relevant to numerical weather prediction data, e.g., numerical weather prediction
models.
The Next Generation Network Enabled Weather (NNEW) ontology is designed
to provide a comprehensive view on the weather across the country, built from
thousands of single weather observations. One of the tasks foreseen for the system was
the development of ontologies for the meteorological domain, mainly for the reduction
of delays related to the climate in the airspace of the USA to half of its current
magnitude. Recently, approximately 70 percent of all air traffic delays are attributable to
weather [Blasch et al. 2019].
This ontology was built with respect to SWEET ontologies to map weather
phenomena. Extensions to SWEET include additional weather phenomena and concepts
and relations that lead to the 4-D Wx Data Cube (Four Dimensional Weather Data
Cube) which uses time as the fourth dimension for the “location” of weather
observations. Just like SWEET, NNEW ontology does not address the context of
weather predictions. This was the reason for not using this ontology.
The Section 2 discusses no existing foundational ontology covers the domain of
numerical weather prediction data, in a way suitable for using it as a starting point for
NWPOntology. Thus, the creation of NWPOntology was based on UFO conceptual
requirements, reusing of existing meteorological ontology concepts (see Section3). The
ability to clearly express concepts of the real-world, reducing conceptual ambiguities,
leads the scientific community, as well as conceptual modeling professionals, to
consider UFO as an important resource to model the domain ontologies.
3. NWPOntology: A well-founded Ontology for the Numerical Weather
Prediction Data Domain
Detailed surveys on ontology development methodologies can be found in the works of
[Corcho et al. 2003, Iqbal et al. 2013]. To construct NWPOntology the methodology
used was the Systematic Approach to Build Ontologies (SABiO) [Falbo 2014]. SABiO
methodology having been created to support the development of domain reference
ontologies and assumes that domain ontologies must be developed based on
foundational ontology. The ontology development process comprises five main phases:
Purpose Identification and Requirements Specification; Capture and Formalization of
Ontology; Project and Test.
In the Purpose Identification and Requirements Specification phase, it was
defined that NWPOntology aims to serve as a shared conceptualization to resolve the
semantic heterogeneity caused by divergent interpretations of data according to the
different contexts in which they are used, and the green part of the model will support
an analytical environment of historical events. Its main objective is the modeling and
implementation of a domain ontology for numerical weather prediction data
(NWPOntology), as a solution to reduce the conceptual ambiguities of the weather
forecast data existing in the various forecast centers, promoting semantic enrichment
meteorological observation data. In this way, the information can be observed, analyzed
and disseminated by weather reports, assisting in the decision making process.
The focus of the Capture and Formalization phase is to capture the
conceptualization of the discourse universe. Relevant concepts and relationships must
be identified and organized. This phase begins with conceptual modeling using highly
expressive languages to create strongly axiomatized ontologies that are as close as
possible to the domain's ideal ontology. The focus of these languages should be on the
adequacy of the representation of the main concepts and relationships in the domain
using taxonomies.
The NWPOntology fragment presented in this article is based mainly on the
taxonomy of meteorological data terms proposed in [Da Cruz et al. 2015]. Other
concepts found in NWPOntology were obtained by reusing the concepts of previous
knowledge resources, such as GeoSPARQL [OGC 2020], INSPIRE [INSPIRE 2019],
ISO 19156:2011 [ISO 2011], GEneral Multilingual Environmental Thesaurus [GEMET
2020] and World Meteorological Organization Glossary [WMO 2020]. In formalization,
what is intended is to explicitly represent the conceptualization captured in the previous
stage with the use of informal axioms written in a formal language, with OntoUML
being chosen to play this role.
NWPOntology model illustrated in Figure 1, making the distinction between
different types of entities explicit in the ontological sense. The UFO-B stereotypes
present in our modeling (the green part) were adapted from [Botti Benevides et al.
2019].
Figure 1. NWPOntology model based on UFO-A and UFO-B
The meteorological parameters, modeled as the stereotype, is used to
represent rigid concepts, that provide an identity principle for their instances and do not
require a relational dependency. They are basically divided into variables, e.g., values of
relative humidity, precipitation, surface wind, air temperature and sea surface
temperature, atmospheric pressure, and cloudiness. Such concepts are modeled by the
stereotype, so they have their own identities and inherit the properties of the
meteorological parameters class. In this case, the data subtypes were mapped as a
sortal, precisely because they are unique, but at the same time inherit
characteristics in common.
Furthermore, according to our model, they are observed by the Global
Observation Systems sensors, that was modeled by the stereotype, which is
mapped by the formal relationship created between the Global Observation System and
the observed meteorological parameters. These data are received at the National
Meteorological Services (modeled by the stereotype) and the data is transformed
into a graphical visualization, through the Local Data Managers (LDM). The LDM was
modeled by the stereotype, because is a construct used to represent anti-rigid
specializations of identity providers, in this case the Software Agent, that was modeled
by the stereotype.
Meteorological data transformed into a graphical visualization is observed by the
Weather Forecasters and compared with the data generated by the outputs of the
Regional Numerical Models. Weather forecasters, modeled by the stereotype,
which has its identity principle inherit by the National Meteorological Services class.
Regional Numerical Models, modeled by the stereotype, for they have their
own identities and inherit the properties of the Numerical Weather Prediction Models
class, modeled by the stereotype.
Global Numerical Models, modeled by the stereotype, for they have
their own identities and inherit the properties of the Numerical Weather Prediction
models class, input the meteorological parameters data for the Regional Numerical
models. If there are no discrepancies between the outputs of the numerical models and
the meteorological data of the LDM, the Weather Forecaster produces the Weather
Forecast, modeled by the stereotype to be disseminated as the Weather Reports,
modeled by the stereotype. This stereotype represents data whose members
are numerical values belonging to a given set of possible values. A concept represented
as a is a theoretical representation of a conceptual space and the restrictions
imposed by its respective geometric structures [Guizzardi and Wagner et al. 2004]. A
value does not have an identity, so two occurrences of the same value cannot be
differentiated [Guizzardi 2005], that occurred in a certain location and during a certain
time.
Severe Weather Events refers to any dangerous meteorological phenomena with
the potential to cause damage, serious social disruption, or loss of human life. Types of
Severe Weather Events vary depending on the latitude, altitude, topography and
atmospheric conditions. High winds, hail, excessive precipitation and wildfires are
forms and effects of severe weather, as are thunderstorms, downbursts, tornadoes,
waterspouts, tropical cyclones, and extratropical cyclones, and should be released
through weather reports as soon as possible to users [WMO 2020].
Severe Weather Events consist of a complex event that brings about the victim
or property to a damage situation, represented at the time (Severe Weather Event
Moment), when the victim or property suffers that damage. A Severe Weather Events
only occurs if it is triggered by a situation of significant change in atmospheric
parameters (Severe Weather Indicators). An example of a Severe Weather Event would
be a Tropical Cyclone occurring in a given location, causing material damage and death
to people.
4. Final considerations
The need to reduce the conceptual ambiguities of meteorological data, which make up
the Global Observation System, motivated the construction of NWPOntology, aiming at
an open and generic approach to interoperability between the different systems for the
correct decision making. To complete the development of the ontology it is still
necessary to have a dynamic verification and validation of the behavior of the
operational ontology, in a finite set of test cases against the expected behavior in
relation to Competence Questions (CQs). Investigations and experiments in
NWPOntology are still needed, mainly with regard to studies on UFO-B event
stereotypes, as the incorporation of new entities is necessary, such as material damage
caused by serious climatic events in regions of the world potentially affected. Therefore,
future research can benefit from the ontological concepts identified in this research.
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