Matthias Thoma

Alexandru-Florian Antonescu

Theano Mintsi

Torsten Braun

Linked Services for Enabling Interoperability in the Sensing Enterprise

Keywords: Linked services, enterprise integration, wireless sensor network, smart items, retail, sensing enterprise

In future, the so called "sensing enterprise", as part of the Future Internet, will play a crucial role in the success or the failure of an enterprise. We present our vision of an enterprise interacting with the physical world based on a retail scenario. One of the main challenges is the interoperability not only between the enterprise IT systems themselves, but also between these systems and the sensing devices. We will argue that semantically enriched service descriptions, the so called linked services will ease interoperability between two or more enterprises IT systems, and between enterprise systems and the physical environment.

Introduction

One of the main challenges of future enterprise IT systems is the usage of data collected from the real world in real time, contextualizing it and providing the user of these systems with the best possible up to date information to base business decisions on. We will call the vision of context-aware and real-world aware enterprise IT systems the "Sensing Enterprise".

The emerging sensing enterprise makes cooperation and interoperability between enterprises, and more specifically, between heterogeneous enterprise IT systems on one hand more important than already today, but on the other hand also more complicated. It is a well known problem, that enterprises today have a need for collaboration, but because of interoperability issues between enterprise IT systems this is often not as successful as it would be desirable [START_REF] Van Sinderen | Challenges and solutions in enterprise computing[END_REF].

Traditionally enterprise IT vendors base their system on service oriented architectures (SOA). Furthermore they mainly use high-level, often proprietary, protocols. Unfortunately, this imposes a specific problem when implementing the sensing enterprise. The integration of sensing devices, which deliver the context information needed on the state of the physical world (like mobile phones, sensor networks, RFID readers and tags), is still performed manually and does not integrate well into SOA architectures of nowadays IT landscapes.

A recent approach to describe services in a SOA environment are semantically enriched service descriptions, based on RDF and semantic web technologies. In this paper we will present our vision of the sensing enterprise based on these next generation Linked Services. We use them to access sensor devices, which are able to describe themselves, thus enabling a sensing enterprise which seamlessly integrates into today's enterprise world. Furthermore, we will discuss an integration middleware which can be used to connect ERP systems to the physical world. We conclude by presenting a case study of applying the integration middleware, a reasoning system and data analysis to a real-world scenario.

Related work

The idea of using Linked Data for service descriptions has received wider attention recently. There are approaches known as linked service (e. g. [START_REF] Leidig | Linked usdl[END_REF], [START_REF] Dietze | Smartlink: A web-based editor and search environment for linked services[END_REF] and [START_REF] Speiser | Taking the lids off data silos[END_REF]), which contribute to the web of data by applying ontologies for service descriptions and discovery. iServe [START_REF] Pedrinaci | iserve: a linked services publishing platform[END_REF], for example, aims to support service publishing and discovery in a better way. Service descriptions, on the other hand, have attracted a lot of attention in the context of the Internet of services. The most well known standard is the WS-* family, which centers around the Webservice Description language (WSDL) [START_REF] Christensen | Web services description language (wsdl)[END_REF] Many existing service description languages focus more or less on the description of technical interfaces, sometimes annotated with quality parameters. We currently use an RDF-based version of USDL (Unified Service Description Language), which addresses -in addition to the technical aspects -businessrelated properties, capabilities and non-functional characteristics [START_REF] Charfi | An overview of the unified service description language[END_REF].

The main difference between our approach and ontology-based linked service approaches, which try to contribute to the web of data, is that we focus purely on services, which do not necessarily have to be part of the web of data. This includes their capabilities and their integration into current enterprise IT systems.

The general problem of integrating smart items and data gathered from sensor networks is well known in the literature. Gomez et. al. [START_REF] Gomez | Design guidelines for integration of wireless sensor networks with enterprise systems[END_REF] propose an additional layer called Enterprise Integration Component (EIC), which is a generic mediation layer between enterprise systems and the WSN middleware.

Two approaches which are also addressing some business aspects are from Glombitza et. al. [START_REF] Glombitza | Integrating wireless sensor networks into web service-based business processes[END_REF] and from Carracas [START_REF] Caracas | On the expressiveness of bpmn for modeling wireless sensor networks applications[END_REF]. Glombitza et. al. propose the usage of standard web service technologies (SOAP, XML). They also target for using SOA principles aiming towards the integration with BPEL, nonetheless their approach is based on WSDL and covers only the pure technical aspects of SOA. Carracas [START_REF] Caracas | On the expressiveness of bpmn for modeling wireless sensor networks applications[END_REF] concentrates on WSN integration via BPMN and compiling corresponding code for running it on the mote.

Sensor description languages are used to model the characteristics, as well as the input and output parameters of sensing services. SensorML [11], for example, is an XML based modelling language, which allows specifying each sensor by its meta-data. It allows to model processes that are linked together through inputs and outputs.

Terminology

In this section we will properly define some terms we will use throughout this paper. Furthermore we provide some brief background informations on technologies used as this paper is combining ideas from different research areas (e. g. linked services, semantic web, business networks, enterprise systems, smart items, wireless sensor networks) and not ever reader might be fluent in all of these fields.

Enterprise System We use the term enterprise system (often called ERP or Enterprise Resource planning system) as follows [START_REF] Gomez | Design guidelines for integration of wireless sensor networks with enterprise systems[END_REF]: An ERP is a set of business applications that allows large enterprises to run all phases of an enterprise's operations to facilitate cooperation and coordination of work across the enterprise. The ideal enterprise system could control all major business processes in real time. Enterprise systems have in general high requirements concerning availability, scalability, reliability as well as security and interoperability.

Wireless Sensor Network A wireless sensor network (WSN) is a network consisting of wirelessly connected small embedded devices (motes). The devices are equipped with one or more sensors, a microcontroller, radio transceiver, some memory and a power supply. Sensor nodes are often battery powered. Usually they are very restricted devices to reduce the cost per unit. They allow an enterprise to perceive the state of its environment in real-time at a low cost.

Interoperability There has been a lot of research on enterprise interoperability in recent years. The term interoperability is defined by IEEE as "the ability of two or more systems or components to exchange information and to use the information that has been changed." [START_REF] Electrical | Ieee standard computer dictionary: A compilation of ieee standard computer glossaries[END_REF]. More information on the theory behind enterprise interoperability can be found, for example, in [START_REF] Van Sinderen | Challenges and solutions in enterprise computing[END_REF] and [START_REF] Chen | Architectures for enterprise integration and interoperability: Past, present and future[END_REF].

OLTP (On Line Transaction Processing) and OLAP (On Line Analytic Processing) are two complementing technologies, which are used to facilitate business processes and enable business intelligence respectively. While OLTP focuses on serving the maximum possible number of short transactions as fast as possible and on maintaining vast, multi-access warehouses consistent, OLAP concentrates on complex, low-volume transactions over multi-dimensional data in order to support decision support and problem solving.

RDF and SPARQL In search of a general method for interchanging data over the Web, W3C introduced Resource Description Framework (RDF). This simple semantic data model uses URIs to name things and the relationships between them, creating this way triples which can be conceptualized as directed, labeled graphs. RDF enables the easier integration of data in the web, an extension of existing schemas and a data consumption through the use of SPARQL queries. A more in-detail presentation can be found in [START_REF] Klyne | Resource Description Framework (RDF):Concepts and Abstract Syntax[END_REF].

Sensing Enterprise

The sensing enterprise is a catch phrase for the ongoing integration of physical data captured by sensors into ERP systems and the on-demand utilization (sometimes refereed to as sense making) of this data. This will allow a tight integration of events and processes in the physical world to be used in business processes. In the following two sections we will first present the key drivers of our current research, and then go on with explaining how semantically enriched linked services help solving the problems we identified.

Key Drivers

We see three key drivers in the sensing enterprise context, which motivates our current research. In section 5.1 we will show that the linked service paradigm is an ideal candidate for fulfilling these requirements. The three key drivers are: Interoperability In the past (and even nowadays, but at a lower level) ERP vendors used to base their system on proprietary protocols. Interoperability, as a result, meant implementing custom connectors to these services. To some degree this was caused by historical, technical reasons and the lack of agreed-on standards. Nowadays, in the days of coopetition interoperability has become more important. Thinking further in the future, especially in the sensing enterprise, where we might deal with all kinds of smart items from several vendors, which have to integrate into various backend systems, flexible and smart interoperability is a must. Furthermore, even nowadays, when we talk about enterprise interoperability there is specific knowledge of the used protocol and the data necessary. Future Enterprise systems need to target interoperability at a semantic level as well. Enablement of sense-making Future Enterprise backend systems will have to do reasoning on data coming from various sources. The description of these services should therefore allow semantic annotations, which are to be understood and processed by enterprise IT systems. As we will describe later, we are following a very pragmatic approach here, without the need of being fully reasonable in a theoretical sense. Enablement of Real-time business decision support In many industries the backend systems are still rather disconnected from what is actually happening. Often, the information is gathered a-posteriori and deviations from the planned state are detected late. Integrating real-time decision support into these systems will enable a business to run more efficient, react timely on changes in the business process and allow a proper exception handling. While this closely related to sense-making, real-time business decision support does not only rely on sense-making, it would be even possible without semantic (ontological) reasoning by utilizing domain knowledge and complex event processing, as it is already done today.

There are two recent emerging technologies which enables the sensing enterprise become a reality:

Real time big data analytics A typical enterprise generates very large and diverse data sets coming from its distributed business locations. Besides OLAP data, the enterprise might also record data produced in social networks, surveillance devices or by third party systems owned by business partners. These massive amounts of detailed data can be combined and analyzed by predictive analytics, data mining or statistics. Doing this process in real-time, for example, by using in-memory data processing, creates a business advantage for the company by giving insight into the real-world dynamics of their business. Sensor networks and Near Field Communication Sensor networks are starting to complement the already existing RFID (Auto ID) technologies that are already available on the market.

The Sensing Enterprise from a Retailer's Point of View

As the authors have the most proficient background in retail, we will illustrate the application and usefulness of the above-mentioned concepts in the retail domain.

We will therefore just have a brief look on the transportation of goods from the producer to the retailer and, of course, finally to the consumer. Furthermore, we will focus on the data that can be gathered in the stores. An exemplary integration into enterprise IT systems is then discussed in section 6.

The spirit of the sensing enterprise will be everywhere in the stores of the future. In the retail store and in the supporting supply chain physical and social realities change at a high pace. In the stores themselves this includes different exceptional situations, such as out-of-shelve situations, technical problems or congestions at the point of sell. As an example, monitoring the freeing systems would help to prevent loss. If one freezing machine failed then the ERP would suggest shuffling them to other freezers and/or starting immediate discounts. In such a case the discount could be determined by checking the product validity ranges (e.g. temperature) associated with a product description, located in the manufacturer ERP system. The store's ERP would combine the sensor data with the remote descriptions in order to trigger the discount decision.

Furthermore, there are a magnitude of forecasts possible to avoid out-ofshelve situations or to detect unexpected high or low demand and to react on real-time, including sending the personnel to the right spot or to suggest changes in the product placement. This of course, does not apply to a single store only. Most stores are part of enormous, sometimes multi-national corporations, where predictions and forecasts based on information gathered from all stores contribute to one huge knowledge base.

An important and competitive requirement in the retail world is to deliver products according to a predefined agreement. A Service Level Agreement (SLA) defines the conditions under which a product is expected to get transported and delivered to the stores. Sensing comes into play to detect the conditions, that when deviating from the SLA, cause an internal system change and demand or even bring changes in the whole retail process. Through enriching this part of the retail procedure with sensing technologies and SLAs, every involved enterprise can benefit by reducing risks, saving time, reducing cost and increasing sales and earnings.

In our scenario, a sensor-equipped "intelligent container" is responsible for monitoring the goods. The monitoring process is driven by a device-constrained SLA, which would determine which sensors are used and how the information is processed. Upon arriving at a distribution point, such as a harbor or an airport, edge sensors of the transporter share their SLAs with the ERP of the distributor, while they also communicate their collected information to the central enterprise system of the receiving retailer. As the sensors are resource-constrained, the SLAs are not actually stored on them, but only a link is provided to the complete SLA description which resides in the transporter's ERP. An extra benefit is that this procedure creates an accurate track of the products transportation in terms of localization and transportation conditions. The entire tracking history would be recorded as ERP transactions in the intermediary ERP systems. Such information can prove vital to the retailer in terms of tracking back problems, deciding on its collaborators and defining the transportation costs.

At the next stage of the supply chain goods are at the selling point available for the customers. Following the same logic as in the transportation phase, there are wireless sensors on the shelves measuring all crucial parameters, carrying the SLAs and sending collected data to edge sensors, which in turn forward them to the store ERP. Upon arriving at the store, the SLA of every product is loaded on the local ERP, so that it is aware of the characteristics and maintenance conditions of each of them.

Sensing the conditions, under which products are maintained, and making decisions on the sensor level facilitates and expedites finding a solution. Analyzing the sensed data at real-time serves the retailers as a useful source of information for further investments and re-arranging the supply chain. In the retail world all that translates to time, successful collaborations, building a strong brand name and earnings.

Moving from the actual store, its supply chain and personnel to the consumers: The sensing enterprise will enable new ways of interacting with the consumers. Interactions with the consumer via mobile devices are then possible based on the consumers context. The smartphone use would also help track the user and then combining data from in-store video cameras and location processing would produce some dynamic price changes or voucher generation which would be then sent to the buyers smartphones, published on store's website, advertised in store, etc.

Linked services and the sensing enterprise

As part of an enterprise level SOA platform, service descriptions are used to describe the functional and non-functional properties of a service, including but not limited to its technical interface. We follow the idea of using linked services which we will describe in detail in section 5.1 and then go on showing how they can be used in a sensing enterprise context in section 5.2. In this context we will present an enterprise architecture, which allows interacting with smart items based on linked services.

When talking about service descriptions it is important to distinguish between the actual deployed (and running) service and the description of the service. A linked service therefore describes a technical or non-technical service, it is not the service itself and thus does not have, for example, a technical service endpoint. It can describe one though, if the service described has one. A thorough classification and taxonomy of services and the difference to a service description can be found in [START_REF] Thoma | On iot-services: Survey, classification and enterprise integration[END_REF]. Furthermore, as service is a term which can have many meanings. One wide-spread use of service is, especially in computer science, to use it as a synonym for a technical interface. We are following the approach of Barros et. al., where in [START_REF] Barros | Handbook of service description USDL and its methods[END_REF] a service is defined as a "commercial transaction where one party grants temporary access to the resources of another party (...)".

A more thorough discussion of linked services can be found in [START_REF] Pedrinaci | Toward the next wave of services: Linked services for the web of data[END_REF] and [START_REF] Pedrinaci | iserve: a linked services publishing platform[END_REF]. In this work we do not dive deep into the realm of service science. We present enough background information for the reader to understand our contribution, but we limit ourselves to the sensing enterprise and more specifically the points were linked services contribute to that vision. A good and comprehensive overview can be found in [START_REF] Barros | Handbook of service description USDL and its methods[END_REF].

Linked Services

In a nutshell the idea of linked services is to base service descriptions on standard technologies known from the semantic web (e. g. Ontologies, OWL and RDF). Furthermore, these semantically enriched service descriptions are following the linked data idea as outlined by Berners-Lee [START_REF] Berners-Lee | Linked data -design issues[END_REF]. He suggested the following four simple rules for publishing data on the web, thus creating one single data space -the web of data:

1. Use URIs as names for things 2. Use HTTP so that people can look up those names 3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL) 4. Include links to other URIs, so that they can discover more things Compared to the very ambitious idea of the web of data our vision is way more focused on the interoperability between enterprise systems, and enterprise systems and end-user devices. We currently see reasoning applicable on a domain level only. In contrast to many research as it is going on today in the semantic web, we do not want to model or understand the whole world. Our mid-term goal is semantic interoperability in very specific domains (for example in retail). For this, we foresee the use of light-weight ontologies. Furthermore, we would even allow manual steps, like hard-coded rules by domain experts, in the processing of this services. Research on interoperability has shown the need of semantic interoperability, this is sometimes complemented by the need of pragmatic interoperability. Our approach does not solve all the problems that can arise from wrong assumptions on either the semantic or the pragmatic level, but the use of semantic technologies and the restriction on a domain-level should reduce the risk of making wrong assumptions on both sides of the communication channel.

Linked Services do not apply only to the description of one single service provided by one service provider. Linking services together will allow an even tighter integration of business partners, thus contributing to the idea of service networks [START_REF] Barros | Handbook of service description USDL and its methods[END_REF]. We use a specific implementation of Linked Services for expressing functional and non-functional properties, namely Linked USDL [START_REF] Leidig | Linked usdl[END_REF]. To maximize interoperability Linked USDL adopts, whenever possible, existing RDF(S) vocabularies such as the Minimal Service Model and FOAF. Linked USDLs objective is to develop an ontology to represent services. It therefore creates explicit ontological links to domain specific ontologies. While this is the origin of the name linked services, the authors see a lot of potential also in actually linking one part of the service description to more detailed information defining the very same service. An excerpt of the Linked USDL ontology is shown in figure 1.

To our knowledge Linked USDL is the only standardization effort driven by large corporations with the goal of expressing not only purely functional aspects of a service, but also the business and operational aspects. A comprehensive introduction into each can be found in [START_REF] Barros | Handbook of service description USDL and its methods[END_REF].

A very concrete service description for a smart item, in this case a simple temperature sensor as it could be used in a shops freezers, looks as follows:

<> rdf : type usdl : S e r v i c e D e s c r i p t i o n ; dcterms : title " USDL service description for Freezer "; dcterms : creator : Mat thias_T homa ; owl : versionInfo "0. : ip_1 a usdl : I n t e r a c t i o n P r o t o c o l ; dcterms : title " Read sensor value "; dcterms : description " Read sensor data " @en ; usdl : h a s T e c h n i c a l I n t e r f a c e : S A P D a t a I n t e r f a c e _ 1 ; usdl : ha sIntera ction [ a usdl : Interaction ; dcterms : title " Get sensor data " @en ; usdl : hasOutput [ a usdl : Parameter ; gr : u n i t O f M e a s u r e m e n t " kelvin "; rdfs : label " temperature " @en ] , ] .

In this simple example one can already see the advantages of Linked Services. The interaction protocol describes how to access the service in a technical way and provides semantics on the parameters. These can be used in a reasoning engine of an ERP system. The whole service description is based upon standard technologies. It can be queried using SPARQL and information that can be found elsewhere can be directly linked to the service description. There is no need for any additional layer.

Linked services is a very powerful concept, which goes way beyond our usage as an ERP interoperability and service exchange platform. For example, there already exists an marketplace for tradeable services, service repositories and registries based on Linked USDL.

Extending Linked Services to sensors and smart items

In this section we will provide an overview of how linked services can be used to enable the sensing enterprise. We will present the architecture of a next-generation linked services enterprise platform and describe how we apply lightweight service descriptions to smart items and sensor networks. We are not going into too many details on how linked services integration is done in regular enterprise networks and service marketplaces, but present our extension to the concept. For more information on that we need refer to the already existing literature (e. g. [START_REF] Pedrinaci | Toward the next wave of services: Linked services for the web of data[END_REF] or [START_REF] Pedrinaci | iserve: a linked services publishing platform[END_REF]).

As shown in [START_REF] Thoma | Service descriptions and linked data for integrating wsns into enterprise it[END_REF] a basic, shrunk to the essentials, version of the service description on the smart item which links to other parts of the service description (like comprehensive SLAs) is enough to enable interactions with sensor motes. Our platform, as shown in Fig. 1 enables an enterprise IT system to access and evaluate linked services. As we consider smart items (sensor motes) as first-class entities in our SOA architecture, they are able to describe themselves through linked services descriptions. The actual services are accessed via the service management unit. Interoperability is achieved via a standardization of the interface to the management unit and of the service description language. Currently, CoAP seems to be a good candidate in addition to linked services. This allows different ERP vendors to implement their own integration platform without loosing interoperability.

These self-description capabilities allow the smart items joining the realm of an ERP backend system to automatically integrate into the system based on the semantic information found in the service description, trigger backend actions and access the services on the smart item.

The Service Handling Layer is taking care of the actual handling of services on smart devices. It is exposed to the Enterprise Layer through a SOA compatible interface and is thus integrating into an enterprise' SOA environment. Within the Service Handling Layer there are several agents which we will describe briefly: The Discovery Agent (DA) is interacting with the Physical Resource Adapter (PRA) and discovers new services available on new smart items. Communication with and monitoring of smart items is done by the PRA. New service descriptions are first completed by the Resolve RDF Agent and (in case of compression) uncompressed by the Service Description Compression unit. The Service Description agent is then responsible for adding the service to the ERPs service registry and repository, thus making it available to the enterprise.

Having a closer look into the enterprise layer, the service descriptions are exposed to other ERP systems via a gateway using a standardized protocol.

In addition to what already exists today in business intelligence (e. g. demand forecasting, campaing management, price calculations), we make use of context acquired by sensors. Based on this information, combined with historical data it is possible to do reasoning and real-time risk analysis. We currently see the short-term to mid-term applicability of this approach at a domain-specific or industry specific level.

The communication between the enterprise backend system and the SOA integration platform is commonly done via a gateway component (for example SAP Netweaver Gateway separating the ERP and auxiliary services. As the integration platform is for the ERP system also just a service, communication is done through a standard interface known to the enterprise SOA environment. Nowadays, ERP systems still use a lot of heavy-weight proprietary protocols, e. g. BAPI or SAP RFC, while the communication with the SOA platform can be done via a standardized protocol. The industry is currently moving towards the Open Data Protocol (OData) [START_REF]OData Technical Committee: Open data protocol[END_REF].

Applying Linked Services to the Sensing Enterprise

In this section we will present how our integration platform as described in the previous section and the concept of linked services enable realizing the sensing retailer as presented in section 4.2. We will assume that all enterprises involved are already linked services enabled.

Looking at the ERP systems of collaborating enterprises or cooperating ERPs withing one enterprise, there are usually many service invocations involved. They grant each other (restricted) access to their service repositories. The fact that they are semantically enriched allows reasoning even between systems. The retail sector is an ideal candidate for being used as pioneering industry, as there are already exchange formats (e. g. EDI) and identification systems for goods (e. g. EPCglobal) available. Furthermore, the retail industry already uses some sensing technologies like RFID tags and AutoID quite heavily. On the business side, existing business analytics are already working with these information, which allows incremental, non-disruptive, innovations to be performed.

We foresee a wide-spread use of sensing technologies used in the retail industry, e. g. within the supply chain as intelligent containers, for tracking of goods and their conditions or for tracking customers and personnel. Thanks to the selfdescription capabilities of these smart items the ERP system can communicate with them and exchange, for example, the SLAs and the corresponding sensed condition of goods.

In the following we will present a SLA document, which could be used for monitoring the temperature of goods. The TransportationProfile would then be referred from the sensor as residing in a remote ERP system.

< http :// www . sap . com / research /2012/10/ retail . owl > rdf : type owl : Ontology ; owl : imports < http :// www . linked -usdl . org / ns / usdl -sla > .

: T e m p e r a t u r e S e r v i c e L e v e l E x p r e s s i o n rdf : type < http :// www . linked -usdl . org / ns / usdl -sla # ServiceLevelExpression > , owl : N am ed In d iv id ua l ; owl : to p Da ta Pr o pe rt y " temperature > 4 && temperature < 10" ; < http :// www . linked -usdl . org / ns / usdl -sla # hasVariable > : temperature . < http :// www . linked -usdl . org / ns / usdl -sla # hasUnit > : Celsius . : T r a n s p o r t a t i o n P r o f i l e rdf : type < http :// www . linked -usdl . org / ns / usdl -sla # ServiceLevelProfile > , owl : N am ed I nd iv id u al ; < http :// www . linked -usdl . org / ns / usdl -sla # hasServiceLevel > : t e m e r a t u r e S e r v i c e L e v e l .

: t e m e r a t u r e S e r v i c e L e v e l rdf : type < http :// www . linked -usdl . org / ns / usdl -sla # GuaranteedState > , owl : N a me dI n di vi du a l ; < http :// www . linked -usdl . org / ns / usdl -sla # serviceLevelExpression > :

T e m p e r a t u r e S e r v i c e L e v e l E x p r e s s i o n .

: temperature rdf : type < http :// www . linked -usdl . org / ns / usdl -sla # Variable > , owl : Na m ed In di v id ua l ; < http :// purl . oclc . org / NET / ssnx / qu / qu # referenceUnit > < http :// purl . oclc . org / NET / ssnx / qu / unit # degreeCelsius > .

The temperate here would have to be within an temperate range of [START_REF] Speiser | Taking the lids off data silos[END_REF][START_REF] Caracas | On the expressiveness of bpmn for modeling wireless sensor networks applications[END_REF] degrees Celsius.

Within business analytics there are already many solutions available which work with near-realtime information. This can be transformed to real-time by leveraging big data technologies. The granularity of the results can be increased by using sensing technologies. Exception management, today a manual tasks as the ERP systems are often unaware of the real conditions the goods are in, will become easier. The store itself collects data about goods and customers, contextuilzes it and thus improves the business analytics process. Furthermore, a direct interaction with personel (e. g. sending them directly to out-of-shelve situations, or to supreme cusomers) and customers will be possible.

Further work

In this paper we focused on the interoperability part and the vision of semanticaly enriched service descriptions for business intelligence systems. We will continue our work and integrate mobile devices also as first class entities in our platform and utilize data gathered from them. Furthermore, we are working on extending real systems, which are already using some kind of structured data, to use semantic technologies and to improve the quality of the results by using context information.

Conclusion

In this paper we extended the scope of Linked Services to the Sensing Enterprise. We specifically addressed the integration of semantically enriched Service Descriptions, which additionally follow the Linked Data principles, and of real-time sensed data into distributed Enterprise Systems and the involved business processes. The final goal is on one hand to enable decision-making at the low-level embedded devices supporting business processes and reduce thereby the time response in critical situations, while on the other hand to allow both low-level devices and backend of different enterprises to communicate independently and efficiently empowering the inter-operation of the involved enterprises. We presented all necessary concepts and the way we make use and elaborate on them, a motivating scenario in the retail world, whereas we also illustrated our suggested integration platform for applying the Linked Services in the Sensing Enterprise.

Fig. 1 .

 1 Fig. 1. Excerpt from the Linked USDL ontology.

Fig. 2 .

 2 Fig. 2. High level block diagram of the linked service enterprise platform. (We are following the TAM standard for modeling the architecture)

Acknowledgment

This research received funding from the European Commission under grant 257521 (IoT-A) and grant 285248 (FIWARE). The authors want to thank the Mote Runner team at IBM Research for their support.


Vincent Noël
email: vincent.noel@lmd.polytechnique.fr

Martial Haeffelin

Midlatitude Cirrus Clouds and Multiple Tropopauses from a 2002-2006 Climatology over the SIRTA Observatory

This study present a comparison of lidar observations of midlatitude cirrus clouds over the SIRTA observatory between 2002 and 2006 with multiple tropopauses (MT) retrieved from radiosounding temperature profiles. The temporal variability of MT properties (frequency, thickness) are discussed. Results show a marked annual cycle, with MT frequency reaching its lowest point in May (~18% occurrence of MT) and slowly rising to more than 40% in DJF. The average thickness of the MT also follows an annual cycle, going from less than 1 km in spring to 1.5 km in late autumn. Comparison with lidar observations show that cirrus clouds show a preference for being located close below the 1 st tropopause. When the cloud top is above the 1 st tropopause (7% of observations), in 20% of cases the cloud base is above it as well, resulting in a cirrus cloud "sandwiched" between the two tropopauses. Compared to the general distribution of cirrus, cross-tropopause cirrus show a higher frequency of large optical depths, while inter-tropopause cirrus show almost exclusively low optical depths (τ < 0.03 in 90% of cases) typical of subvisible clouds. Results suggest the occurrence of intertropopause cirrus clouds is correlated with the frequency of multiple tropopauses.

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I. Introduction

Clouds have been identified by the IPCC as one of the most important problems to tackle regarding climate prediction (IPCC 2001), as their radiative and dynamic influence on the Earth's climate is complex and not yet fully understood. Exercises in climate model comparison reveal divergences even in the sign of cloud feedback to CO2 doubling scenarios (e.g. [START_REF] Cess | Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models[END_REF][START_REF] Bony | On dynamic and thermodynamic components of cloud change[END_REF]). The role of high altitude ice clouds, herein after referred to as cirrus clouds, is particularly difficult to characterize. As they are optically thin (i.e. semi-transparent), they often lie below the detection threshold of most passive remote sensing instruments such as those present on meteorological and research satellite systems. Recent studies suggest the existence of an entire population of subvisible ice clouds (Nee et al. 1998[START_REF] Goldfarb | Cirrus climatological results from lidar measurements at OHP (44N, 6E)[END_REF]) whose radiative influence (Prabhakara et al. 1993) is totally overlooked in current radiative budget calculations. Additionally, the optical and microphysical properties of cirrus clouds are extremely variable [START_REF] Cantrell | Production of Ice in Tropospheric Clouds: A Review[END_REF]. Hence, it is difficult to ascertain if their properties can be correctly parameterized at small or large scales, as they strongly depend on local phenomenonas -even when only considering latitude-constrained areas [START_REF] Heymsfield | Parameterizations for the Cross-Sectional Area and Extinction of Cirrus and Stratiform Ice Cloud Particles[END_REF]. Since recent results suggest that cirrus cloud cover may be increasing due to human activities (Zerefos et al. 2003, Minnis et al. 2004), improving our knowledge of these clouds is a current and important issue.

The conditions in terms of thermodynamics, dynamics and ice forming nuclei are keyelements that influence the formation of high-altitude tropospheric clouds (Sassen 1992[START_REF] Mace | The Composite Characteristics of Cirrus Clouds: Bulk Properties Revealed by One Year of Continuous Cloud Radar Data[END_REF][START_REF] Korolev | Microphysical characterization of mixed-phase clouds[END_REF]. Since cirrus clouds are found at high altitudes, their formation process and further evolution can be sensitive to atmospheric conditions in the Upper Tropos-phere and Lower Stratosphere (UTLS). Studying UTLS conditions could give hints on what drives cirrus cloud formation and how to better predict their properties. The tropopause temperature inversion has a constraining effect on cloud altitude, and several studies have focused on the properties of cirrus clouds near the tropopause, especially at tropical latitudes (McFarquhar et al. 2000;Peter et al. 2003;[START_REF] Garrett | Evolution of a Florida Cirrus Anvil[END_REF]. Hence variability in the thermodynamic structure of the tropopause could affect the properties of cirrus clouds near the UTLS transition. One such structure is the occurrence of multiple inversions in the UTLS area resulting in the formation of Multiple Tropopauses (MT). More than just a theoretical concept, multiple tropopauses are symptoms of actual physical phenomena (Sect. III) that can help detect and analyze specific atmospheric conditions of cloud formation.

This paper presents a study of MT at midlatitudes, and investigates their possible influence on the occurrence, persistence and optical properties of high-altitude tropospheric clouds. The observation dataset and the retrieval techniques is presented in Sect. II. Statistical properties of the retrieved MT are presented in Sect. III and compared to existing litterature. The frequency of occurrence and properties of cirrus clouds in case of MT are then compared to the rest of the database in Sect. IV. Results are discussed and conclusion is given in Sect. V.

II. Observations

To investigate a possible relationship between multiple tropopauses and ice clouds, the present study uses collocated in-situ and remote sensing observations made between 2002 and 2006. Tropopause levels were retrieved from temperature profiles, obtained through a dataset of radiosoundings launched twice daily (00 and 12 UT) from the Meteo France Trappes regional center (48.8°N, 2.0°E), 20 km to the West of Paris. The dataset contains over 3000 radiosonde profiles gathered over the same period. The simultaneous study of ice clouds involved finding a data source suited to the detection and analysis of optically thin clouds.

During the past decades, numerous techniques have been developed to detect cloud occurrences from e.g. passive remote sensing observations by spaceborne radiometers (Rossow and Garder 1993). These methods are generally successful in estimating the spatial and temporal coverage of clouds with moderate to high optical depths τ. However, due to their limited optical thickness, ice clouds are usually difficult to detect via these conventional means.

Especially in the lower end of optical thickness, subvisible cirrus (τ < 0.03) account for more than 20% of cirrus clouds [START_REF] Goldfarb | Cirrus climatological results from lidar measurements at OHP (44N, 6E)[END_REF] but are in effect invisible to passive remote sensing, typically limited to optical depths above 0.1 (Stordal et al. 2005). In addition, several studies have shown that retrievals of high-altitude cloud properties using passive cloud imager data, such as cloud top height (e.g. Naud et al. 2004;[START_REF] Daloze | Validation of SAFNWC/MSG Cloud Top Height using groundbased lidar and radar measurements[END_REF] or microphysical properties (e.g. [START_REF] Chiriaco | Improving Retrievals of Cirrus Cloud Particle Size Coupling Lidar and Three-Channel Radiometric Techniques[END_REF], can include significant biases due to the semi-transparent nature of those clouds. This precludes the use of passive remote sensing data to study interactions between the thermodynamic structure near the UTLS and cirrus cloud properties. Other observations are therefore required to obtain a realistic estimate of the ice cloud cover, and lidar measurements have become the de facto standard when considering this particular problem, thanks to their high sensitivity to optically thin atmospheric layers.

Moreover, most modern lidar systems are equipped with the ability to read the polarization state of the backscattered light (Sassen 1991), providing insights into the cloud phase (Wang and Sassen 2001) and microphysical properties (e.g. ice crystal shapes, [START_REF] Chiriaco | Improving Retrievals of Cirrus Cloud Particle Size Coupling Lidar and Three-Channel Radiometric Techniques[END_REF]).

In the present paper, lidar observations are provided by the 532 nm LNA (Lidar Nuages Aerosols) lidar system, located at the SIRTA Observatory (Site Instrumental de Recherche par Télédétection Atmosphérique, 48.71°N 2.21°E, [START_REF] Haeffelin | SIRTA, a ground-based atmospheric observatory for cloud and aerosol research[END_REF], 15 km east of Trappes. This lidar features a narrow field-of-view telescope (0.5 mrad) dedicated to the observation of high-altitude tropospheric clouds, and is equipped to retrieve 20 vertical profiles of backscattered light and depolarization ratio δ by second, with a 15 meters resolution. The first lidar observations stored in the SIRTA database date back to 2002(Matthias et al. 2004)), and routine operation (5 days a week) was initiated in 2004, totaling today more than 500 days of observations. The STRAT algorithm (Morille et al. 2006) was applied 1) on the backscattered light profiles to separate cloud layers from the molecular background (using wavelet transform analysis), and 2) on the depolarization ratio to unequivocally flag ice clouds (only clouds producing values of δ>0.2 were considered). Optical depths were then retrieved for ice cloud layers using a standard transmission-loss algorithm (e.g. Platt, 1973;Platt et al. 1998). This algorithm derives the extinction produced by a given cloud layer (hence, its optical depth) by comparing the molecular backscatter in the free troposphere above and below the layer. This requires a significant signal-to-noise ratio above the given layer -i.e. only layers that were penetrated all the way through by the lidar were considered.

This criteria means that a lot of cloud layers were discarded in the process; it is assumed that the volume of observation is still sufficient to paint an accurate representation of cirrus clouds.

III. Multiple Tropopauses over the SIRTA

Despite its central role in atmospheric sciences, the tropopause can still behave in unusual, surprising ways. For instance, depressions at midlatitudes (e.g. on the Eastern U.S. coast) can drag cold air masses from the arctic, resulting on occasion in tropopauses as low as 6 km. In the Tropics, the separation between the troposphere and the stratosphere is often defined as the point of temperature minimum, but in midlatitude regions the thermal structure of the tropopause layer can be quite complex. The thermal or lapse-rate tropopause definition is based on the variability in lapse rate (-dT/dZ) in an atmospheric temperature profile. In the most commonly used definition, the conventional tropopause is defined by the World Meteorological Organization (WMO) as the "lowest level at which the lapse rate decreases to 2°C/ km or less, provided also the averaged lapse rate between this level and all higher levels within 2 km does not exceed 2°C/km" (WMO 1957). However, the WMO definition also allows additional tropopauses above the first one, if the average lapse rate between any level above the first tropopause and all higher levels within 1 km exceeds 3°C/km. This technical definition, which is used in the rest of the present study, actually reflects dynamical disturbances to the temperature profile (e.g. jet streams or upper level fronts, Nadstrom et al. 1989) resulting in multiple temperature inversions in the UTLS (Shapiro 1980), that can lead to tropopause foldings (Sprenger et al, 2003) and mixing of stratospheric and tropospheric air [START_REF] Hoinka | Statistics of the Global Tropopause Pressure[END_REF]. According to a recent climatology of MT using Global Positioning System radio occultation data between 2001and 2004(Schmidt et al. 2006)), MT frequency and thickness (defined as the altitude difference between the lowest and the highest tropopauses) exhibit a strong dependence on latitude and season. Their results show the occurrence of MT is almost null near the equator (20°S-20°N), while it varies between 80% (DJF) and 45% (JJA) at 40°N. The seasonal dependency is less intense in the southern hemisphere, but occurrences of ~70% are still reached in the southern hemisphere winter (JJA). MT thickness is correlated to MT occurrence frequency, reaching maxima of 5.2 km at 40°N (DJF) and 4.0 km at 40°S (JJA).

Analysis of temperature profiles from the Trappes radiosoundings (49°N) on the [2002][2003][2004][2005][2006] period shows that Multiple Tropopauses occur in 31.4% of cases, with a 3 rd tropopause in 10.1% of cases and a 4 th in 4.3% of cases (additional tropopauses were considered to be negligible). Figure 1 The minimum and maximum altitudes are reached in January-April and June-September respectively. The monthly mean altitude of the first tropopause varies 1.6 km between winter and summer (from 10.2 to 11.8 km), while that of the second tropopause varies almost 2.7 km (from 10.25 to 12.9 km). The annual variations of additional tropopauses (3 rd and 4 th ) exceed 3 km, with the maximum variation showed by the 3rd tropopause: from 10.75 km in April to more than 14 km in August.

The Inter-Tropopause Thickness shows a similar annual evolution (thick curve in Fig. 2), from a 900 m minimum in May to a 1500 m maximum in October-November. These values are slightly lower than the averages found in the Schmidt et al. ( 2006) study over France (1.1-1.5 km for JJA and 2.2-2.8 km for DJF), but the general trend is consistent. The ITT increase between May and October is correlated with the increase in multi-tropopause frequency over the same period (Fig. 1), however the sharp drop in ITT between November and December is not found in multi-tropopause frequency (where the decrease happens slowly between December and May). It should also be noted that in Fig. 2 the ITT should not necessarily follow distance between the 1 st and 4 th tropopauses -for its calculation the highest tropopause is used on a case-by-case basis, and a 4 th Tropopause was only present in 4.3% of profiles. Partly because of this, the ITT is not directly correlated with the tropopause altitudes: for instance the period between June and September is marked with stable high tropopauses, but meanwhile the ITT keeps increasing. This can be explained by an increase in the absolute number of tropopauses during the same period. The ITT reaches its peak in October as the MT frequency reaches stability, and begins decreasing simultaneously with tropopause altitudes. The average ITT is constrained between 0.6 and 1.5 km (Fig. 2, more than 50% of all observations), however the individual values spread out over a very large dis-tribution following a gamma shape with a major mode centered on 750 m. The absolute minimum for an ITT is 215 meters, but values larger than 3 or 4 km are not uncommon.

IV. Ice Clouds and Multiple Tropopauses

The SIRTA Cirrus cloud 2002-2004 dataset (322 cases) has been described extensively in Noël et al. 2006, including a presentation of cirrus occurrence statistics. After a review of the 2005 observations, it appears the conclusions reached in this paper still hold true for the extended dataset. The number of cirrus sightings increases smoothly with decreasing temperature between -10°C and -45°C, to reach its maximum in the (-55°C, -45°C) temperature range, followed by a quick drop to zero occurrence at -75°C and colder. More than half of the cirrus clouds are observed in areas where the relative humidity ranges between 80 and 120%.

The frequency of cirrus detection is variable over the course of each year, but it is not possible to ascertain if this is due to an actual change in the population of cirrus clouds or to sampling effects (Protat, 2006).

Because the lidar is not operating continuously, and cirrus are sometimes hidden from it by lower clouds, the frequency of cirrus sightings (from lidar) compared to the frequency of multiple tropopauses (from radiosoundings) is a pointless exercise which will not be attempted here. On the other hand, comparing the properties of multiple tropopauses and cirrus clouds on days when both are detected and fully visible could expose correlations between the two phenomena.

IV. 1. Cirrus Tops and Bases

This study focuses on the relationship between the tropopause(s) and cirrus clouds, thus only clouds whose top altitudes are located higher than 7 km will be considered -this amounts to For such clouds, we find no impact of the occurrence of single or multiple tropopauses on the distribution of cloud top and base altitudes. Not surprizingly, the distribution (Fig. 3a) reveals a large frequency of occurrence of cloud top altitudes close to the tropopause -i.e. cirrus clouds are very likely stuck right under the tropopause.

Cloud with tops above the first tropopause occur in more than 5000 profiles (2.5% of the observations, 40 hours of operating time), out of which a strong 86% happen in a multitropopause situation. For those clouds, a study of distance between cloud top altitude and the 2nd tropopause should naturally come next -however, after review this distance does not yield very meaningful results, as it is itself strongly dependent on the ITT which varies widely on a scale of several kilometers, i.e. in the same range as cloud top altitudes (Fig. 3a). Instead, we use the ratio of the distance between cloud top and first tropopause to the distance between the 1 st and 2 nd tropopauses ( ) to evaluate how much of this region is filled by clouds. This ratio is 0 (resp. 1) for cloud tops stuck to the 1 st (resp. 2 nd ) tropopause.

Its distribution (Fig. 3b) is found to be bimodal and in 50% of cases the ratio is below 0.3, meaning the related clouds occupy a limited region between the 1 st and 2 nd tropopauses, with their top in the lower 30% of the inter-tropopause zone. However, in 33% of cases the ratio is above 0.7, meaning cloud tops are in the higher 30% of the same zone (i.e. these clouds span the entire region). In only 16% of cases was the ratio between 0.3 and 0.7. Almost no cirrus cloud was detected with a top altitude above the 2 nd tropopause. Therefore, it appears that clouds that cross the lowest tropopause live in an unstable temperature profile, which leads a large fraction of them to rise or expand vertically until they reach the next tropopause.

Next we study the distribution of cloud base altitude for clouds that extend above the first tropopause. Fig. 4 shows that tropopause-crossing cirrus clouds have most frequently their base altitude less than 2 km below the 1 st tropopause, with a maximum frequency of occurrence between -1.

IV. 2. Geometrical and Optical Thickness

The distribution of cloud geometrical thickness for CT and IT cirrus clouds (Fig. 6) is multimodal, with a major mode centered on 0.5 -1 km, and a minor mode centered on 3 -3.5 km.

Study showed that these two modes cannot be independently attributed to CT and IT clouds; however they do not appear when considering all cirrus clouds, and therefore could be related to the tropopause behavior and properties. However, relationships between these modes and the distance between cloud and tropopause altitude, or the inter-tropopause thickness could not be established. These two modes are in fact related to seasonal variations: clouds observed between May and November (dark) have a distribution strongly biased towards low values of geometrical thickness (0 -1.5 km) -i.e. they are thinner; clouds observed between December and April (light) equally include thin clouds (with a similar mode centered on small values), but a new mode appears between 2 and 4 km, centered on 3.25 km. This mode is totally absent of the May to November observations; thus it follows that geometrically thick cirrus clouds close to the tropopause are only observed between December and April, i.e. a period marked by a high frequency of multiple tropopauses (DJF, Fig. 1) and a simultaneous decrease in the inter-tropopause thickness (Fig. 2). This is consistent with the observation that tropopause-crossing cirrus clouds are most frequently observed in conjunction with multiple tropopause conditions (Sect. 4.1).

Next we study the optical depth distributions for the four different cirrus cloud classes defined in Sect. 4.1. Figure 7 represents the cumulative density functions of optical depth τ for all cirrus clouds (thick green line) and for the four different cirrus clouds classes (Sect. IV 1).

In this graphical representation, a steep increase signals a high density of the related optical depth, and conversely, low slopes and horizontal sections convey low density. Thus the absence of any extreme slope in the all-cirrus curve shows optical depths are generally evenly distributed between 10 -3 and 1, with the most frequent being found between 0.05 and 0.2. The distributions are analyzed in terms of three optical depths classes, namely thick (τ > 0.3), semi-transparent (0.03 < τ < 0.3) and subvisible (τ < 0.03) using the definition provided by Sassen et al. (1989). The all-cirrus distribution reveals 40% subvisible, 50% semi-transparent and 10% thick cirrus clouds. Free-tropopause and "ceiling" cirrus (i.e. clouds with tops below the first tropopause, Sect. IV.1), shown in black and red lines, follow very closely the general distribution, which is consistent considering they represent respectively 83% and 10% of all cirrus clouds. CT cirrus (blue line) are found to be optically thicker than the general distribution, with 25% subvisible, 65% semi-transparent and 10% thick cirrus clouds. The horizontal section for τ < 10 -3 on the blue curve signals the nearly total absence of very low optical de-pths that exist, although in small quantities, in the general dataset (green line) ; on the other hand, the steep slope of the blue curve around τ ~1 suggests the distribution peaks around these values, while the general dataset is more evenly distributed. IT cirrus clouds (purple line) have evidently far lower optical depths than the other groups, with 75% subvisible, 25% semi-transparent and virtually no thick cirrus clouds. Half of the clouds in that group show optical depths below 0.007. The purple curve is rugged compared to the others, due to the smaller number of observations in that dataset.

Finally we study the relationship between the optical and geometrical thickness of the different cloud classes. The density of cirrus clouds as a function of optical depth τ and geometrical thickness h (Fig. 8) shows a high concentration of optical depths between 0.005 and 0.02 (consistent with Fig. 7) but in addition highlights a high correlation between the two properties. Low optical depths (τ < 10 -3 ) are exclusively found for clouds thinner than 0.5 km, but as optical depths increase the range of possible geometrical thickness rises as well. Subvisible cirrus clouds (τ ≤ 0.03) can bear any geometrical thickness between 0 and 2.5 km, with highest frequencies for 0.2 < h < 0.8 km ; the widest range of cloud thickness (0 < h < 4 km) is found for τ ~0.3. For τ > 0.3 a minimum geometrical thickness appears, which increases with optical depth: for instance, cirrus clouds with τ ~1 are always thicker than 0.5 km.

When considering densities from the other axis, very thin clouds (h < 0.5 km) can bear any optical depth between 10 -4 and 0.3 (with a maximum frequency for 0.001 < τ < 0.03, i.e. and IT cirrus clouds, they follow the general distribution quite well, with a relatively higher dispersion of optical depths for cloud thickness above 3 km that might just be due to the relative sparseness of data.

V. Discussion and Conclusion

In this study, multiple tropopause altitudes were retrieved over the SIRTA observatory from radiosoundings (Sect. III), producing results that are consistent with a previous GPS study (Schmidt et al 2006). Using lidar observations it was possible to compare these retrievals with cirrus cloud top and base altitudes (Sect. IV.1). Distributions of the distance between cloud boundaries and the tropopause layer were presented, and used to classify the clouds in 4 categories. Clouds with tops above the first tropopause show distinctively different optical depths distributions (Sect. IV. 2, Fig. 7), but the optical-to-geometrical thickness relationship is not affected (Fig. 8). A study of the distribution of wind speed and direction showed similar distributions for all 4 categories of cirrus clouds (mostly winds coming from the Atlantic Ocean, see Noël et al. 2006), with no specific values for CT and IT clouds -hence, related figures were not presented.

The study of cloud top and base altitudes with respect to tropopause altitudes yields several conclusions. Firstly, most cirrus clouds are totally contained below the first tropopause (93%), but a non-negligible part either cross the tropopause (5%) or is totally contained between the first and second tropopause (2%). For the first group, the presence of multiple tropopauses has close to no effect on their altitude and thickness. However, the frequency of occurrence of the clouds increases with decreasing distance between the cloud top and the first tropopause -with almost 30% cloud tops being located less than 2 km below the first tropopause (Fig. 3a). In a similar fashion, clouds become more frequent as cloud bases get closer to the tropopause, however the maximum frequency is found 0.2-1.5 km below the tropopause. This is consistent with cirrus clouds stuck the tropopause considering this is also the most frequent cloud thickness range (Fig. 8). For CT and IT clouds, the effect is much stronger: tops are either very close to the first tropopause, or stuck all the way to the second tropopause (Fig. 3b), while IT cloud bases are all within 0.5 km above the first tropopause (Fig. 4).

Cloud categories are even more distinct when considering optical depth distributions (Fig. 7).

While clouds located entirely below the first tropopause are very similar to the general distribution (they mostly define it), clouds that cross or are above the tropopause differ significantly: CT clouds have a very low density of subvisible clouds (τ < 0.03) and a large concentration of semi-transparent (τ > 0.03) and thick (τ > 0.3) clouds, while the picture is reversed for IT clouds which are predominantly in the subvisible category and present no thick cloud.

A simple explanation for the relatively high optical depth of CT clouds is that by definition, CT clouds can extend further up than BT clouds. Since the optical-to-geometrical thickness relationship is not affected by the cloud location (Fig. 8), it follows that CT clouds should be optically thicker than BT clouds. Regarding the cloud origin, the simplest hypothesis is that CT clouds originate from within the free troposphere, where they have room to expand, shrink and move vertically (either upwards or downwards), crossing the first tropopause at some point during their life cycle, in particular when multiple tropopauses occur resulting in a weak thermal inversion at the first tropopause level. Looking at IT clouds, their low optical depths cannot be solely explained by their low geometrical extent (constrained by the double Fig. 8 shows that even thin cirrus layers (i.e. less than 1 km thick) can adopt a wide spectrum of optical depths, up to 1. Other explanations will have to be investigated. Regarding their origin, the simplest hypothesis is that IT clouds originate from within the intertropopause zone, where they are constrained by the double temperature inversion (Fig. 3 and4) and thus keep a very low optical depth. 82% of IT and CT clouds with a ratio of (Fig. 3b) have their base within 0.5 km of the first tropopause -in other words, clouds that extend up to the second tropopause have, in most cases, their bases stuck to the first tropopause. This suggests that IT clouds very rarely cross the tropopause to extend downward in the troposphere, confirming the theory that CT clouds originated in the free troposphere and later extended upwards in the inter-tropopause zone. Of course, these are simple considerations given the actual observations; cirrus generation mechanisms involves complex processes and further studies will be required to reliably assess the origin and formation of each cirrus category.

Considering these results, it seems at first glance that cirrus clouds created in situ in the intertropopause zone develop to become almost exclusively subvisible cirrus, and never extend down to develop into optically thicker cloud systems: their development seems hindered by the two temperature inversions found in multi-tropopause situations. Since IT clouds are so high and cold, they have a significant impact in terms of climate change due to their influence on the net radiation budget at the top of the atmosphere [START_REF] Brown | Improved measurements of the ice water content in cirrus using a total water probe[END_REF]. These clouds seems quite rare, at least over the SIRTA (2% of all cirrus occurrences), but since their formation seems correlated with multiple tropopause events, their frequency could in-crease at other latitudes that show higher occurrences of multiple tropopauses (e.g. up to 80% in the Mediterranean region or the United States, Schmidt et al. 2006).

Finally, it is important to remember that ground-based lidars alone cannot provide a exhaustive coverage of cirrus clouds near the tropopause, as (1) some of the cirrus clouds cannot be penetrated all the way through due to their high optical depth, and were not considered in the ;.66+,-../0 ,6-*-<=+,-../0 ,.>00!?.>@>@5/06+,-../0 A<26.!?.>@>@5/06+,-../0

  shows the annual cycle of MT occurrences, averaged over the 2002-2006 period. The vertical bars represent the 2002-2006 interannual variability (one standard deviation), confirming the significance of the annual cycle. The tropopause temperature and pressure are known to exhibit quasi-biennial oscillation and long-term fluctuations (Angell and Korshover 1973) than can affect both the annual cycle and the interannual variability. The annual MT occurrence cycle begins with high occurrences (~35%) in January, followed by a steep decline during spring (reaching a ~18% minimum in May), then finally rising slowly during summer and autumn to reach again high occurrences (~43%) in December. High MT occurrences in winter (DJF) coincide with more frequent occurrences of fronts. These values are consistent with results from Schmidt et al. 2006, who found occurrences over France in the 45-55% range in winter (DJF) and 20-35% range in summer (JJA).

  Figure 2 shows the annual cycle of tropopause altitudes and the mean Inter-Tropopause Thickness (ITT, i.e. the distance between the lowest and highest tropopauses) based on 2002-2006 data.

  198794 lidar profiles, or 1656 hours of cirrus observation. The distance between cloud top altitudes and the first (lowest) tropopause is shown in Fig. 3a for all clouds with top altitudes above 7 km. Negative (resp. positive) values indicate cloud top altitudes below (resp. above) the 1 st Tropopause. Clearly most cirrus clouds are entirely located below the 1 st tropopause.

  5 and -1 km. The distribution of cloud base altitudes is wide, with some tropopause-crossing cloud bases as low as 4 km below the 1 st tropopause. The cloud base altitude distribution is much narrower for clouds that are entirely contained in the intertropopause zone (positive values in the Fig. 4 histogram), with almost all cloud base altitudes within 1 km above the 1 st tropopause. These clouds are, in effect, "sandwiched" between the two lowest tropopauses. Only a very small, negligible number of clouds (not shown) have their bases above the second tropopause.By considering Fig.3 and 4, it is possible to classify cirrus clouds in 4 categories (Table1) defined by the position of their boundaries with respect to the tropopause(s): (1) tropospheric cirrus, with cloud top altitude more than 500 m below the 1 st tropopause (83 %); (2) "ceiling" cirrus, with cloud top altitude less than 500 m below the 1 st tropopause (10 %); (3) crosstropopause (CT) cirrus (5%) and (4) inter-tropopause (IT) "sandwich" cirrus contained in the inter-tropopause zone (2%). As this paper focuses on the relationships between multiple tropopauses and cirrus clouds, the following sections are devoted to the last two categories, trying to highlight their specific properties compared to the rest of the cirrus population. Even if, comparatively, they represent only a small part of the entire SIRTA dataset, the absolute population of these clouds is significant enough -for instance, 16 cases of inter-tropopause clouds were identified for the single year 2005. As an example, a cloud observed on March 17 th 2005 is shown in Fig.5a. The first and second tropopauses are marked with dashed lines, at 11.5 and 12.5 km respectively, and are clearly apparent on the relevant temperature profile from the 12 UT radiosounding (Fig.5b). Note that temperature inversion of the first tropopause is significantly weaker than that of the second tropopause. During the 3.5 hours of observation, the cloud remains inside the limits defined by the 1 st and 2 nd Tropopauses. The 2 nd Tropopause acts as a cloud top ceiling.

  subvisible cirrus). The possible minimum optical depth then gradually increases from 10 -4 to 0.05 for cloud geometrical thickness between 0.5 and 4 km. No subvisible cirrus cloud is thicker than 2 km. The relationship between optical and geometrical thickness is consistent for all clouds considered in the present study. Dots in Fig 8 show observations relative to CT

  present study; and (2) an unknown fraction of cirrus clouds is hidden by optically thick, lowlevel clouds. This shouldn't modify the optical depth distribution found for cirrus categories in the present study (as cirrus clouds have generally low opacity), however an unknown quantity of cirrus clouds were not present in the observations, and could sway the importance of a given category one way or another. A possible way to study the entire population of cirrus clouds would be to use combined lidar and radar profiles from spaceborne instruments, i.e. viewing the atmosphere from above. Such observations will soon be available from the recently launched CALIPSO (Winker et al. 2003) and CloudSat (Stephens et al. 2002) spaceborne platforms. In addition to the use of these observations, future work include (1) considering a definition of tropopause based on potential vorticity, which might allow a better understanding of the dynamic features associated with multiple tropopauses and the occurrence of cirrus clouds, and (2) investigating if cirrus in each category share a common origin and formation mechanisms, by correlating back-trajectories of air masses in observed clouds with spatial imagery and models.

Figure 1 :

 1 Figure 1: Annual frequencies of multiple tropopauses over the SIRTA observatory, averaged

Figure 2 :

 2 Figure 2: Annual evolution of the mean tropopause altitudes (thin line) and the mean inter-

Figure 3 :

 3 Figure 3: a) Distribution of distance between the first tropopause and cross-tropopause cirrus

Figure 4 :

 4 Figure 4: Distribution of distance between cloud base and the first tropopause for cross-and

Figure 5 :

 5 Figure 5: a) Backscattering coefficients observed by the LNA lidar on March 17 2005 as a

Figure 6 :

 6 Figure 6: Distribution of cloud Geometrical Thickness, for observations in the May to

Figure 7 :

 7 Figure 7: Cumulative density functions of optical depth frequencies for 4 categories of cirrus,

Figure 8 :

 8 Figure 8: Density Contour plots of the frequency of cirrus clouds as a function of cloud

Figure 1 :Figure 2 :Figure 3 Figure 4 :Figure 5 Figure 6 :

 123456 Figure 1: Annual frequencies of multiple over the SIRTA observatory, averaged on the 2002-2006 period.

Figure 7 :Figure 8 :

 78 Figure 7: Cumulative density functions of optical depth frequencies for 4 categories of cirrus, together and individually.

•

  Peter Th. et al., 2003 Ultrathin Tropical Tropopause Clouds (UTTCs): I. Cloud morphology and occurrence, Atmos. Chem. Phys. 3, 1083-1091 • Platt C. M. R. 1973 : Lidar and Radiometric Observations of Cirrus Clouds. J. Atmos. Sci. Sassen K., G. G. Mace, J. Hallett, and M. R. Poellot, 1998: Corona-producing ice clouds: A case study of a cold mid-latitude cirrus layer. Appl. Opt. 37 1477-1485.

	Tables
	• Schmidt T., G. Beyerle, S. Heise, J. Wickert and M. Rothacher 2006: A climatology of Cirrus Category Frequency in observed (sum=100%)
	Tropospheric Cirrus	83%
	30 1191-1204. multiple tropopauses derived from GPS radio occultations with CHAMP and SAC-C. Geo-
	Ceiling Cirrus	10%
	• Platt C. M. R., S. A. Young, P. J. Manson, G. R. Patterson, S. C. Mardsen and R. T. Austin, phys. Res. Let. 33 L04808, doi:10.1029/2005GL024600. Cross-Tropopause Cirrus 5%
	1998: The Optical Properties of Equatorial Cirrus from Observations in the ARM Pilot Ra-• Sprenger M., et al. 2003: Tropopause folds and cross-tropopause exchange: A global inves-Inter-Tropopause Cirrus 2%
	diation Observation Experiment. J. Atmos. Sci. 55 1977-1996. tigation based upon ECMWF analyses for the time period March 2000 to February 2001, J.
	• Prabhakara C., R. S. Fraser, G. Dalu, M.-L. C. Wu and R. J. Curran, 1988: Thin cirrus Geophys. Res., 108(D12), 8518, doi:10.1029/2002JD002587.
	clouds: Seasonal distribution over oceans deduced from Nimbus-4 IRIS. J. Atmos. Sci. 55
	1977-1996.
	• Protat, A., M. Haeffelin, A. Armstrong, Y. Morille, J. Pelon, J. Delanoë, and D. Bouniol,
	2006: Impact of conditional sampling and instrumental limitations on the statistics of cloud
	properties derived from cloud radar and lidar at SIRTA. Geophys. Res. Let. 33, L11805,
	doi:10.1029/2005GL025340.
	• Rossow W. B. and L. C. Garder 1993: Cloud detection using satellite measurements of in-Phys. 5 2155-2162.
	frared and visible radiances for ISCCP. J. Climate 6 2341-2369.
	• Sassen K. , M. K. Griffin, and G. C. Dodd, 1989: Optical scattering and microphysical
	properties of subvisible cirrus clouds, and climatic implications. J. Appl. Meteor. 28, 91-• Winker D. M., J. Pelon, and M. P. McCormick, 2003 : The CALIPSO mission: Spaceborne
	98. lidar for observation of aerosols and clouds, Proc. SPIE Int. Soc. Opt. Eng., 4893, 1-11.
	• Sassen K. 1991: The polarization lidar technique for cloud research: A review and current • WMO 1957: Definition of the tropopause, WMO Bull. 6 136.
	assessment. Bull. Amer. Meteor. Soc. 72 1848-1866. • Zerefos C. S., K. Eleftheratos, D. S. Balis, P. Zanis, G. Tselioudis and C. Meleti 2003: Evi-
	• Sassen K. 1992: Evidence for liquid-phase cirrus cloud formation from volcanic aerosols: dence of impact of aviation on cirrus cloud formation, Atmos. Chem. Phys. 3 1633-1644.
	Climatic implications. Science 257 516-519.

• Stephens G. L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O'Connor, W. B. Rossow, S. L. Durden, S. D. Miller, R. T. Austin, A. Benedetti, C. Mitrescu, 2002: The CloudSat Mission and the A-Train. B. A. M. S. 83, 1771-1790, doi:10.1175/BAMS-83-12-1771. • Stordal F., G. Myrhe, E. J. G. Stordal, W. B. Rossow, D. S. Lee, D. W. Arlander and T. Svendby 2005: Is there a trend in cirrus cloud cover due to aircraft traffic? Atmos. Chem.

• Wang Z. and K. Sassen 2001: Cloud type and macrophysical property retrieval using multiple remote sensors. J.

Appl. Met. 40 1665-1682.

Table 1 :

 1 Frequencies of occurrence in observed clouds for the 4 cirrus categories.

A. Konguem, L., Kreipl, S., Matthey, R., Rizi, V., Rodrigues, J. A., Wandinger, U., and Wang, X. J. of Appl. Met. 63 (11) 2978-2991.