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  • View in gallery

    Two very different meteorological stations in terms of siting (e.g., height of sensor, surface cover, distance from obstacles), instrumentation (e.g., type, performance characteristics), and exposure (representativeness would need to be assessed via micro- and local-scale surveys; see main text and supplementary material at http://dx.doi.org/10.1175/BAMS-D-12-00096.2). Both are located within the city boundaries of Birmingham, United Kingdom: (a) a city-center site and (b) an urban park site.

  • View in gallery

    Schematic of the urban climatological network metadata protocol components—a summary of the metadata elements required for each individual component [(a)–(d)] corresponding to Table 2 (Note: colors correspond to the metadata tables). Please refer to main text for more information.

  • View in gallery

    Main approaches taken toward network design, with references (after Robinson 2010).

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Toward a Standardized Metadata Protocol for Urban Meteorological Networks

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  • 1 School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
  • | 2 King's College London, London, United Kingdom
  • | 3 *CURRENT AFFILIATION: Department of Meteorology, University of Reading, Earley Gate, Reading, United Kingdom
  • | 4 School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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With the growing number and significance of urban meteorological networks (UMNs) across the world, it is becoming critical to establish a standard metadata protocol. Indeed, a review of existing UMNs indicate large variations in the quality, quantity, and availability of metadata containing technical information (i.e., equipment, communication methods) and network practices (i.e., quality assurance/quality control and data management procedures). Without such metadata, the utility of UMNs is greatly compromised. There is a need to bring together the currently disparate sets of guidelines to ensure informed and well-documented future deployments. This should significantly improve the quality, and therefore the applicability, of the high-resolution data available from such networks. Here, the first metadata protocol for UMNs is proposed, drawing on current recommendations for urban climate stations and identified best practice in existing networks.

CORRESPONDING AUTHOR: Lee Chapman, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom, E-mail: l.chapman@bham.ac.uk

With the growing number and significance of urban meteorological networks (UMNs) across the world, it is becoming critical to establish a standard metadata protocol. Indeed, a review of existing UMNs indicate large variations in the quality, quantity, and availability of metadata containing technical information (i.e., equipment, communication methods) and network practices (i.e., quality assurance/quality control and data management procedures). Without such metadata, the utility of UMNs is greatly compromised. There is a need to bring together the currently disparate sets of guidelines to ensure informed and well-documented future deployments. This should significantly improve the quality, and therefore the applicability, of the high-resolution data available from such networks. Here, the first metadata protocol for UMNs is proposed, drawing on current recommendations for urban climate stations and identified best practice in existing networks.

CORRESPONDING AUTHOR: Lee Chapman, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom, E-mail: l.chapman@bham.ac.uk

Bringing together the disparate guidelines for best practices in observing and documenting urban stations and existing meteorological networks should improve the quality and applicability of the increasing amount of data gathered by high-resolution urban networks.

The complexity of urban atmospheric processes makes them impossible to measure adequately using traditional surface observation approaches consisting of a few individual monitoring stations. However, in recent years, meteorological observations have benefited from automated monitoring, advancement of sensor technologies (e.g., miniaturization, wider range of sensor types), lower cost of sensors, and improved data transmission to near-real-time communications networks. Once combined, these have enabled the creation of urban meteorological networks (UMNs) with the capability to operate at a range of atmospheric scales (Table 1). Hence, a UMN can be defined as cooperative, spatially distributed meteorological monitoring equipment across an urban environment with autonomous, near-real-time communication capabilities for transmitting data. The specific scale and type of UMN implemented is dependent upon required coverage, the variables observed, and the atmospheric processes being studied, which, along with resource availability, have an impact on the communications system, physical arrangement of sensors, power sources, size, and topology of the network [see Muller et al. (2013) for a detailed review of such networks]. These advances allow urban environments to be monitored at much finer spatial scales over a wider range of temporal scales than was previously possible, furthering our understanding of atmospheric processes and the impacts of climatic changes. As such, this high-resolution information can help to improve decision making, emergency preparation, weather forecasting, urban climate research, and urban planning for critical infrastructure needs (Chapman et al. 2013).

Table 1.

Relations between spatial scales and UMNs, from largest to smallest areal extent [from Muller et al. (2013), with modifications].

Table 1.
Because of the growing usage of urban meteorological data, it is imperative that UMNs are implemented and managed to a high standard, using common guidelines where possible. However, existing guidelines and recommendations are for synopticscale national networks or for individual urban monitoring stations (e.g., Oke 2004, 2006a; WMO 2008), rather than for UMNs. The divergent requirements, implementation, and management of UMNs suggest that there is an equivalent need for recommendations or standards for UMNs. This would benefit developers and data users by increasing confidence in data representativeness and quality. Indeed, technical information about UMNs is frequently difficult to ascertain because of insufficient reporting and documentation of methodologies and procedures. As data quality may therefore be questionable (NRC 2012; Muller et al. 2013), it makes the ability to cross reference networks difficult. For example, the need to standardize approaches has been identified as critical from the Word Climate Conference-3 (WCC-3, in 2009) for urban areas (Grimmond et al. 2010) and in the United States (NRC 2009, 3–4):

The status of US surface meteorological observations capabilities is energetic and chaotic, driven mainly by local needs without adequate coordination. . . An over-arching national strategy is needed to integrate disparate systems. . . . Increased coordination amongst existing surface networks would provide a significant step forward and would serve to achieve improved quality checking, more complete metadata, increased access to observations, and broader usage of data serving multiple locally driven needs.

Similarly, the NRC (2012, p. 94) report on urban meteorology prioritizes the need for “regularly updated metadata of the urban observations using standardised urban protocols” as a key short-term need for the advancement of urban meteorology. Furthermore, they note that the value of observational data is maximized only when accompanied by comprehensive metadata, including information on site selection, quality assurance, and management procedures, which are often lacking for urban sites and networks.

Frequently, urban meteorological studies have been critiqued because of poor metadata and/or siting (e.g., Grimmond and Oke 1999; Roth 2000). Most recently Stewart's (2011) review of urban heat island (UHI) studies found a large number failed to adequately describe experimental design, choice of sites, exposure of instruments, and contained a lack of sufficient instrument metadata. To ensure highquality usage of the data for applications and urban research, recommendations and guidelines must be followed and adequate information reported.

ESTABLISHED GUIDELINES AND RECOMMENDATIONS.

The term metadata is commonly used for any scheme of resource description for any type of object, digital or nondigital (NISO 2004). It provides the key aspect in any protocol and is essential to effective integration of diverse data sources (NRC 2009). The importance of documenting detailed metadata is highlighted in the Global Climate Observing System (GCOS) climate monitoring principles document (WMO 2003), which states that metadata should be “treated with the same care as the data themselves.” Metadata ensure that the end user has “has no doubt about the conditions in which data have been recorded, gathered and transmitted” (Aguilar et al. 2003, p. 2) in order to ensure accurate interpretation, manipulation, and evaluation of results with minimal assumptions regarding data quality or homogeneity (WMO 2008). If detailed metadata are collected, then data can be interpreted accurately, and anomalies or patterns adequately explained and accounted for, whereas if insufficient metadata are collected, then it is difficult or impossible to assess site representativeness and therefore perform reliable data analyses (Stewart 2011). Hence, for meteorological datasets (from in situ monitoring equipment or networks), this includes all supplementary information, characteristics, and descriptions of the monitoring equipment (instrument, sensor, and variable metadata), the monitoring site itself (site, station, and enclosure metadata), the network (network or subnetwork metadata), and the network management procedures and communications methods (cyberinfrastructure or network operations metadata). For example, Fig. 1 shows two different meteorological stations, both located within the same city—detailed metadata are clearly essential for data interpretation at these very different locations.

Fig. 1.
Fig. 1.

Two very different meteorological stations in terms of siting (e.g., height of sensor, surface cover, distance from obstacles), instrumentation (e.g., type, performance characteristics), and exposure (representativeness would need to be assessed via micro- and local-scale surveys; see main text and supplementary material at http://dx.doi.org/10.1175/BAMS-D-12-00096.2). Both are located within the city boundaries of Birmingham, United Kingdom: (a) a city-center site and (b) an urban park site.

Citation: Bulletin of the American Meteorological Society 94, 8; 10.1175/BAMS-D-12-00096.1

Existing World Meteorological Organization (WMO) guidelines for the measurement of meteorological variables and climatological practices (e.g., WMO 2008e.g., WMO 2011) are mainly concerned with national and/or global instrument networks whose objective is to collect regionally representative data (i.e., not within urban areas). These standard guidelines contain essential and detailed information relevant to making meteorological observations, including details on requirements for each variable, siting and exposure, instrument calibrations, operating practices, data management and quality assurance/quality control (QA/QC) techniques. However, it is difficult and often inappropriate to conform to standard WMO guidelines when siting equipment in cities, since there are numerous obstructions to airflow and radiation exchange caused by anthropogenic surfaces, objects, and activities (Oke 2004).

Oke (2006b, 2009) was among the first to call for common urban climate protocols (particularly paying attention to issues related to scales, experimental design, site classification, instrument exposure, and metadata collection), suggesting it would be valuable to have a “manual” for workers in urban climate to aid with the design of observational networks (Oke 2006b). Specific recommendations do exist for siting and exposure of equipment in urban areas (e.g., Aguilar et al. 2003; Manfredi et al. 2005; NOAA 2004; Oke 2006a; WMO 2008, 2011) and outline the type of information that needs to be included as urban station metadata in order to obtain representative measurements (e.g., Oke 2004, 2006a, b; WMO 2008). Within these guidelines and others (e.g., Aguilar et al. 2003; NOAA 2004; Manfredi et al. 2005), specific concepts, definitions, approaches, and recommendations relevant to urban stations are discussed. Furthermore, these guidelines also provide general recommendations for collecting and documenting additional instrumentation, network, and operations metadata that are not intrinsic to a particular station but are equally important (Grimmond 2006; WMO 2011). These additional metadata are essential for anyone utilizing network data, comparing data from different networks, or setting up a new network. For example, Aguilar et al. (2003) and WMO (2011) include comprehensive recommendations for instrument metadata, including sensor type, manufacturer, serial number, method of measurement and observation, units, resolution, accuracy, response time, time constant, time resolution, date of installation, corrections and calibrations, and comparison results. These guidelines also call for information on operational procedures, such as data processing methods and algorithms, resolution, input source, parameter values, QA/QC, constants, storage procedures, access and processing methods, and communications and transmission methods. McGuirk and May (2003) include similar recommendations but further distinguish between station and network metadata (comprising instrument, research, software, and network procedures). However, such recommendations are often specific to the application (e.g., road weather monitoring, large-scale measurement networks and facilities, individual sites), meaning that certain aspects that are important for UMNs (as discussed in the “Proposed UMN protocol” section) are lacking in these guidelines.

Although metadata and technical information are difficult to ascertain for many established UMNs, there are some for which the complete technical details of their network and the protocols employed have been documented [e.g., Oklahoma City Micronet (Basara et al. 2010); Oklahoma Mesonet (Brock et al. 1995; Shafer et al. 2000; McPherson et al. 2007); West Texas Mesonet (Schroeder et al. 2005); Helsinki Testbed (Poutiainen et al. 2006)]. As such, these may also be used as a source of guidance for implementing other UMNs. For example, technical information for both the Oklahoma Mesonet and the Oklahoma City Micronet is published and available online. These include information about the station and network architecture and design, site selection and classifications, sensors (including type, accuracy, etc.), sensor locations, communication infrastructure, instrumentation, monitoring, and network operations (e.g., QA/QC, calibration, and maintenance procedures). Additionally, Basara et al. (2010) and Schroeder et al. (2010) outline the land classification procedures used for the Oklahoma Micronet. However, as acknowledged by the NRC (2009), such a level of technical information is very disparate for the majority of UMNs.

By reviewing these existing guidelines, collating recommendations and best practices and establishing where information is missing, this paper endeavors to produce a comprehensive, standardized protocol for assisting those involved in implementing and/or utilizing UMNs.

PROPOSED UMN PROTOCOL.

Metadata are required to cover the instrumentation, site, network, and operations; therefore, a number of factors need to be considered in developing an urban meteorological network protocol (UMNP). Figure 2 and Table 2 summarize the proposed UMNP components, from whole network operations metadata to individual sensor metadata. The elements are derived from urban network literature (e.g., Mikami et al. 2003; Basara et al. 2010; Koskinen et al. 2011; Muller et al. 2013), recommendations available for urban stations (e.g., Oke 2004, 2006a), and larger-scale meteorological monitoring networks (e.g., Aguilar et al. 2003; WMO 2008 WMO 2011), as well as the authors' experiences of setting up urban networks. The following sections provide an overview of each metadata component of the proposed UMNP (from the whole network scale to the individual sensor scale, concluding with the network operations- scale metadata), outlining and explaining the individual elements and their necessity for inclusion.

Fig. 2.
Fig. 2.

Schematic of the urban climatological network metadata protocol components—a summary of the metadata elements required for each individual component [(a)–(d)] corresponding to Table 2 (Note: colors correspond to the metadata tables). Please refer to main text for more information.

Citation: Bulletin of the American Meteorological Society 94, 8; 10.1175/BAMS-D-12-00096.1

Table 2.

Summary of minimum metadata required. More complete details in Tables 3, 4, 6, and 8. Letters correspond to those in Fig. 2.

Table 2.

It should be noted at this stage that this protocol is designed as a guideline document to assist with collecting and documenting meaningful metadata, for use by the end user and those implementing and managing UMNs. UMNs are often designed for a specific purpose, and therefore have specific siting requirements depending on a number of aspects, including required network density, available equipment, applications, partners involved, site access, etc. (Muller et al. 2013). The metadata protocol is one of many tools needed to assist in UMN implementation. Others include, for example, instrumentation selection, communications selection, data protocols, network design, and management approach—each of which have extensive literatures that are rapidly evolving. For example, Fig. 3 summarizes some of the main approaches toward network design; however, this also needs to take into account the land cover characteristics in the urban area when determining the appropriate number of stations and their location. Thus, how to classify urban areas—such as Stewart and Oke's (2012) local climate zones (LCZs) driven by urban heat island characteristics or Loridan and Grimmond's (2012a,b) urban zones for energy partitioning (UZE) developed for characterizing observations and for numerical modeling (Loridan et al. 2013)—needs to be part of the process of the overall UMN design. Similarly, how a UMN is managed depends on such things as the requirements of network owners, partners, number of staff employed, and resources.

Fig. 3.
Fig. 3.

Main approaches taken toward network design, with references (after Robinson 2010).

Citation: Bulletin of the American Meteorological Society 94, 8; 10.1175/BAMS-D-12-00096.1

Network metadata.

First and foremost, details are required about the network itself (Table 3). Such network information would include the type/purpose of the network (e.g., meteorological, air pollution), a description of the network (e.g., objectives, partners), operating authority, contact details, and information regarding the operational time frame (e.g., implementation date, periods offline). Additional geomorphological, orographic, geographic, and socioeconomic data that may characterize the overall setting are also necessary (e.g., digital elevation models; census data; GIS data such as percent built, percent vegetation cover, satellite imagery, thermal imagery). Such metadata are useful for end users to appreciate the network setting and for determining land classifications, but they are also useful during the network design stages, for assisting with source area calculations (see “Site metadata” section), and for interpreting results.

Table 3.

Network level [(a) in Fig. 2] and subnetwork(s) (when the subnetworks can also standalone, the information differs) metadata directory [based on established metadata guidelines from WMO (Oke 2004, 2006a; WMO 2008 WMO 2011), other recommendations (e.g., Aguilar et al. 2003; NRC 2009; Manfredi et al. 2005; Muller et al. 2012), individual UMN guidelines (e.g., McPherson et al. 2007; Shafer et al. 2000; Koskinen et al. 2011) plus additional elements]. Information will need to be recorded at different time intervals (R = as required, O = once).

Table 3.

Metadata management requires not only the protection of the data itself but also regular updating. Table 3 and subsequent metadata tables provide an indication of the recommended frequency to ensure that updates or changes are documented. For example, changes to the number of sites or areal extent of the network [including updated map(s)], dates when the network is offline, changes to the morphology of the area (major redevelopment, changes to specific boundaries, etc.), and vegetation characteristics (e.g., growth, planting, removal) all need to be documented.

Second, the network architecture needs documenting (e.g., number of subnetworks and individual sites, network maps, and size of the network), which will include the areal extent of the networks and the density of the array (e.g., number of sensors per area or distance between sensors). The specific size of the network will depend on its objectives, such as the atmospheric processes to be observed and the temporal and spatial resolutions required (Grimmond 2006).

Site metadata.

Next, the schema includes established guidelines for individual urban meteorological stations (e.g., Oke 2004, 2006a) that are used as the basis for recommendations (Table 4). Measurements from individual sensors observe atmospheric processes from a particular source area or field of view that is representative of a specific scale. The scales of interest across and within an urban area are mesoscale (i.e., regional climate, covering urban, peri-urban, and rural areas), local scale (i.e., distinct neighborhoods), and microscale (i.e., urban canyons or lots) (Oke 1982, 1984, 2004, 2006b, 2009, 2006a; WMO 2008, 2011). The representativeness of individual measurements (i.e., the surrounding area an instrument “observes”) or “exposure” is a function of the area influencing a measurement (“source area” or “footprint”). Source areas for many instruments and/or variables over urban areas are often difficult to calculate. They depend on the location of the instrument (e.g., height, distance to obstacles); the specific variable and temporal scale being observed; the measurement method of the instrument; the morphology of the area and the nature of the underlying surface; and in some cases, the meteorological conditions (Oke 2004; Grimmond 2006). Therefore, thorough metadata collection is paramount to inform estimates of source areas, particularly for instrumentation located within the urban canopy layer (UCL). Metadata provide additional important understanding, both about the site and the local surface characteristics that influence the measurements that are crucial to the interpretation of observations from a particular instrument.

Table 4

Site and enclosure(s)-level [(b) in Fig. 2] metadata directory [based on established metadata guidelines from WMO (Oke 2004, 2006a; WMO 2008, 2011), other recommendations (e.g., Aguilar et al. 2003; NRC 2009; Manfredi et al. 2005; Muller et al. 2013), individual UMN guidelines (e.g., McPherson et al. 2007; Shafer et al. 2000; Koskinen et al. 2011) plus additional elements]. Information will need to be recorded at different time intervals (H = hourly, D = daily, W = weekly, S = seasonal, A = annual, R = as required, O = once).

Table 4
Table 4

Continued.

Table 4
Table 4

Continued.

Table 4
Table 4

Continued.

Table 4

Frequently, the siting of instrumentation in urban areas causes difficulties with respect to the representativeness of measurements. For example, it may be necessary to locate equipment over a range of surfaces (e.g., asphalt, concrete, grass) at variable heights, to split instruments over different locations, or to locate instruments nearer to buildings or anthropogenic heat/moisture sources than would otherwise be recommended by standard WMO guidelines (Oke 2004). With the impact of the urban morphology being a key aspect of the environment to be observed (Stewart and Oke 2012), the standardization of the sensor location explicitly has to relate to its 3D characteristics (height and density/spacing), rather than to the more traditional objective of being a set distance away from the roughness elements. Oke (2006b) provides a detailed recommendation for locating instruments, primarily for those within the UCL, and for calculating source areas. There continues to be a need for more developments in source area modeling for use within the UCL and above that are applicable beyond neutral conditions.

Given the dynamic nature of urban areas, the site metadata should also include maps, photographs, aerial photography, sketches, geographic information, site descriptions, and maintenance logs at regular intervals. Site or station metadata require local scale and microscale site surveys. Currently, approximate and arbitrary areas of 500 m × 500 m and 50 m × 50 m, centered on the sensor site, are designated for conducting the local-scale and microscale surveys, respectively, since it has been found that on average the source area for a screenheight temperature sensor in neutrally stable atmosphere is no more than a few hundred meters (Tanner 1963; Mizuno et al. 1990/1991; Runnalls and Oke 2006; Stewart and Oke 2012). However, since the precise domain (size, shape, orientation) of these source areas does vary with meteorological conditions, stability, and the temporal resolution being investigated, conducting source-area analyses using a footprint model (e.g., Kljun et al. 2002; Schmid 2002) would be ideal and may be required for certain UMN applications. Stewart and Oke (2012) discuss this in more detail and provide a good illustration in Fig. 5 of their paper.

Site surveys will examine the structure of the area (building types, materials and mean heights, roof types, mean tree heights, distance between buildings, etc.), urban cover (e.g., built up, vegetated, water, soil), urban fabric (e.g., road, wall materials), and urban metabolism (e.g., anthropogenic activities, anomalous and typical heat, water and pollutants, traffic density) at the respective scales (Oke 2006a). Tracking disturbances in the area (e.g., from roadwork and construction) is important but may be difficult at many sites. With the increasing availability of lidar datasets, digital surface models (DSMs), and aerial imagery, the local and microscale 3D morphological influences can now be readily identified (e.g., Kidd and Chapman 2012). Additional site surveys provide key additional information about vegetation, materials, and nearby activities (e.g., vehicle parking, vent locations) relative to the instruments. The microenvironmental factors (building types, materials, heights, distance between buildings, roof types, tree heights, surface material, traffic density, heat/moisture vents, etc.) include creating sketch maps (radiation horizon, site sketch map), taking numerous photographs of the site (e.g., location, cardinal directions, panoramic, and hemispherical), documenting location information (e.g., latitude, longitude, elevation), and other factors [sky view factor (SVF), aspect ratio, heights of sensors, etc.]. Since instruments can be placed at different locations within a site (e.g., on masts and rooftops, at more open locations, in different enclosures), different microscale surveys are required for each instrument enclosure.

Standardized site information is needed so data users are aware of site variations, since they rarely have the luxury of being able to visit each station across a network (Oke 2006b). If adequate metadata are available, then this should not create limitations for end users. Indeed, the majority of urban heat island studies fail to communicate the physical nature of the surfaces surrounding the instruments (Stewart 2011). To characterize urban locations for meteorological and climatological purposes, a number of schemes have been proposed (e.g., Table 5). However, no standard presently exists (Basara et al. 2010) and the current schemes may not be internationally applicable or definitive, as sites may fall into more than one category. It is therefore important that generalized and/or customized classification techniques implemented for interpreting results be documented and the assigned type reported for all sites. Critical details that should be documented include the area used for classification (e.g., 100 m2, 500 m2, 2 km2), the source of data (e.g., year, aerial photos, ground surveys), and the assumptions (e.g., dominant, weighted average) for repeatability and consistency. The complete station history (maintenance logs, metadata updates) is also essential, so instrumental and site changes can be distinguished, and will include dates and details of any changes; interruptions; inspection visits; and comments about the exposure, quality of observations, changes to the site, and operations (WMO 2011).

Table 5.

Examples of urban site classification schemes.

Table 5.
Table 5.

Continued.

Table 5.
Table 5.

Continued.

Table 5.

While many aspects of this UMNP are designed to aid with the collection of high-quality data and to assist the end user with data analysis (QA/QC, station metadata, representativeness, etc.), there are other aspects specifically to assist network owners, managers, and technicians, since it is also important to provide guidance for the implementation and running of an UMN to ensure that networks are efficiently established. Therefore, additional elements are required for sites that form part of an UMN—for example, information about the local communications network or local node that is being used to transmit the data [this will vary for each UMN and depend on the type of information required; however; e.g., it may include network type, encryptions, passwords, etc., which are also part of the “network operations” component (see “Network operations metadata” section)] and the relevant contact details [e.g., if a school site is used, then it might be useful to have liaison details for information and communications technology (ICT) staff]. Furthermore, since access to different elements of the metadata will vary, it is expected that some of the metadata are stored in an encrypted format and not released to most end users (e.g., passwords, network information, personal details, and other details to comply with data protection laws). Thus, only the portion of the metadata regarded as useful to the end user would be initially provided with the data. This would be managed by the UMN data manager or technician.

Aguilar et al. (2003) and Oke (2004, 2006a) provide templates for collecting the minimum information necessary for individual urban stations. Based on these, an adaptable UMNP station metadata template (see supplementary material at http://dx.doi.org/10.1175/BAMS-D-12-00096.2, along with a completed example) has been developed with additional elements included (e.g., information on the communication network, contacts, instrumentation, type of site). Collection of these metadata in the field should typically take no more than 30 min, with some additional time required (prior to and postfield collection) using Internet-based resources (such as Google Earth, GIS, satellite datasets, etc.) to explore the local area (to determine land classifications, Davenport roughness class, land cover, etc.) and to collate additional logistical and instrumental data. The aim of this template is to facilitate the regular update of station metadata in order to assess any changes occurring at the sites, which can then be used in conjunction with the detailed account of the station history (whether equipment has been moved, replaced, etc.). It is expected that individual UMNs will need to adapt the form for their specific needs—for example, not all fields may be required and/or additional fields may be necessary. However, we highlight those elements considered “mandatory” (e.g., latitude, longitude, elevation, local-scale sketch map and information, microscale sketch map and information, and station and cardinal photographs). Once the metadata have been collected during the initial installation, they can be input electronically (into a form and/or a database), used for subsequent visits, and quickly updated. Indeed, with the recent proliferation of smart devices/tablets, updating can now be done quickly and directly in the field. Overall, this would form part of the network QA/QC procedures (see the “Network operations metadata” section) and is an essential part of the station metadata to ensure homogeneity (Aguilar et al. 2003).

Instrumentation metadata.

As with existing site protocols, separate information is mandatory for each piece of equipment at each site (Table 6), including information about the instrument itself (e.g., manufacturer, model, serial number, installation and calibration dates, and calibration and testing results; see also the “Network operations metadata” section). Operational instrument-specific information about the communication system will need to be safely documented (passwords, IP address, MAC address, etc.—see Table 6 for detailed list).

Table 6.

Instrumentation-level [(c) in Fig. 2] metadata directory [based on established metadata guidelines from WMO (Oke 2004, 2006a; WMO 2008, 2011), other recommendations (e.g., Aguilar et al. 2003; NRC 2009; Manfredi et al. 2005; Muller et al. 2013), individual UMN guidelines (e.g., McPherson et al. 2007; Shafer et al. 2000; Koskinen et al. 2011) plus additional elements]. Information will need to be recorded at different time intervals (Se = seconds, M = minute, H = hour, D = daily, W = weekly, S = seasonal, A = annual, R = as required, O = once)

Table 6.
Table 6.

Continued

Table 6.

Some instruments incorporate multiple sensors (e.g., temperature and humidity) or multiple variables are obtained (e.g., wind components, virtual temperature), so additional metadata are required for each sensor and/or variable. This includes information specific to the sensor (height of gauge rim above ground for precipitation, type and size of screen for temperature, etc.) in addition to performance characteristics of the sensor (sensitivity, range, etc.) and data and/or measurement characteristics (sampling time, averaging periods, etc.).

The representativeness of each measurement or “instrument exposure” (see “Site metadata” section) will also need documentation. As highlighted in Muller et al. (2013), the use of “scale”-related terms can cause confusion when applied to networks, as information on network and station scales is often difficult to establish since it is not explicitly stated, is unclear, or uses inconsistent terminology to define urban sensor networks. Specifically, this relates to the distinction between spatial or areal extent of the network, which is often reported as the “network scale” or “network size” (see “Network metadata” section), spatial resolution or density of the network (which is dependent upon the density of individual sensor sites), and spatial representativeness or scale length of the individual measurements (which is dependent on the actual location of the instrumentation, measurement interval, and exposure; Oke 2006a). This “confusion of scales” has recently been highlighted as a common flaw in urban climate investigations (Stewart 2011) and is particularly true of urban networks. For example, a sensor network may be classified as a “mesoscale network” since it covers hundreds of square kilometers consisting of urban, suburban, and rural areas. However, the representativeness of the individual sensors or monitoring stations, and the number of sensors in the network could be classified on very different scale. Hence, a network areal extent may be “mesoscale” but the individual measurements could be more representative of mesoscale, local scale, or microscale processes. Presently, and in most cases, UMNs have been defined solely by their spatial extent. Although information about the number of sensors and location of sensors is often given, information about “network density” and “representativeness” of measurements should be explicitly defined, since it affects both the application of the network and what is appropriate with cross network comparisons (Oke 2004, 2006a).

The proposed network-scales UMN classification scheme is given in Table 7. The areal extent or size, the spatial density, and the representativeness of individual monitoring stations within the network are the key descriptors.

Table 7.

Overall UMN scale classification requires all three components to be specified.

Table 7.

Network operations metadata.

Details of network operation (Table 8) can be broken down into hardware components and cyberinfrastructure, which includes the data flow from the sensor to initial analysis, data management, data display, and usage (Hart and Martinez 2006; Muller et al. 2013). This consists of computer systems, instrumentation, data acquisition, data storage systems and repositories, visualization systems, management services, and technicians, linked by software and communications networks (Estrin et al. 2003; Brunt et al. 2007; Muller et al. 2013).

Table 8.

Network-operations-level [(d) in Fig. 2] metadata directory [based on established metadata guidelines from WMO (Oke 2004, 2006a; WMO 2008, 2011), other recommendations (e.g., Aguilar et al. 2003; NRC 2009; Manfredi et al. 2005; Muller et al. 2013), individual UMN guidelines (e.g., McPherson et al. 2007; Shafer et al. 2000; Koskinen et al. 2011) plus additional elements]. Information will need to be recorded at different time intervals (refer to Fig. 6). BADC = British Atmospheric Data Centre. NASA = National Aeronautics and Space Administration. NERC = Natural Environment Research Council.

Table 8.
Table 8.

Continued

Table 8.

Hardware and cyberinfrastructure

Documentation of the hardware assets of a network (e.g., sensors, loggers, communications, and computers) is important not only for reporting purposes but also for keeping track of equipment, especially important for wide-area deployments. Recorded information should be as extensive as possible, with make, model, manufacturer, serial number, purchase date, and current location being minimum requirements for hardware in storage. However, for equipment deployed in the field (i.e., sensors), much more detailed information is required. This includes a description of what the sensor observes [variable(s)], observation method (e.g., direct observation or sampling), observation frequency/period, performance characteristics (e.g., resolution, precision, range, and accuracy), calibration information (e.g., date since last calibration, method, and calibration coefficients), and deployment dates (see Table 8 for a complete list). Records should be kept throughout the sensor deployment of any site visits, problems encountered, changes around the location, and routine/nonroutine maintenance. These records not only provide a history that can be referred to to highlighting issues encountered, but also form an important part of network QA/QC procedures (Fiebrich et al. 2010).

Dense sensor networks may be installed to explore spatiotemporal variability across heterogeneous urban terrain. However, the reliability of the observed variability across a network may be significantly compromised by observation errors, and instrument drift and failure. To minimize these impacts and to ensure any observed fluctuations are credible, a proactive approach to instrument calibration is recommended as part of QA/QC procedures. This approach requires predeployment (both in the laboratory and field), routine site visits (including onsite testing) and postdeployment calibration of each sensor across the network with the methods utilized and frequency clearly stated within the metadata (Shafer et al. 2000; McPherson et al. 2007; Fiebrich et al. 2010).

A simple review of the manufacturer calibration certificate should aid assessment of what was used as the reference instrument and its associated quality. Calibration against international standards is preferred but a traceable reference/working standard is acceptable (WMO 2008). Similarly, in-house calibrations need to consider the quality of the reference instrument. In-house interinstrument comparisons are critical prior to and after network deployment. The results of performance tests, QA/QC procedures, calibration, intercomparisons, research, processing, management techniques, and technical specifications should be made available and easily obtainable through peer-reviewed literature, conference papers, presentation, technical notes, and end-user guides using standardized terms (e.g., metadata, scales) (McGuirk and May 2003; Oke 2006b; Stewart 2011).

Cyberinfrastructure elements include network communications (e.g., wireless sensor node topology, mode of transmission, frequency of transmission), equipment and data processing techniques (which are critical prior to and after data collection), dataset information (e.g., data formats, measurement units, time formats, processing levels, calibration coefficients, constants), data management (e.g., QA/QC, error reporting, missing data flags, filtering, algorithms, programming language/software), and data storage (e.g., servers and storage media used, data backup, where archived, how to access).

Reporting, and communication and information dissemination

Entire network-level metadata will require regular updates and need to be easily obtainable in electronic form via appropriate inventories and catalogues (WMO 2011). The entire network metadata will be encoded, stored, and distributed: with the data itself as an accompanying text file [e.g., comma-separated values (CSV) file] or database {e.g., online using My Sequel (MySQL), Oracle, PostgreSQL, or as attribute data contained within the data file itself [e.g., network Common Data Form (netCDF), hierarchical data format (HDF), gridded-binary (GRIB), and binary universal form for the representation of meteorological data (BUFR) to list but a few but many other acceptable formats]}. Encoding methods such as extensible markup language (XML) provide a logical choice of format for this purpose and are already recommended by WMO as the standard method. However, XML variants are perhaps better suited to documenting the geographic component of UMN's because of their capabilities of providing a visualization of the metadata [e.g., geography markup language (GML) or keyhole markup language (KML); Open Geospatial Consortium 2012].

Overall, dataset metadata need to adhere to the relevant “schemas” for the chosen encoding method(s) that provide(s) the “structure” for describing digital geographic datasets (e.g., WMO Core Metadata Profile and the ISO19100 series, especially the ISO19115 geographical metadata standard and/or the ISO19136 GML metadata standard). These schemas explicitly define metadata elements and structures while establishing a common set of metadata terminology, definitions, and extension procedures for reporting. The network metadata directory (Table 8) includes the universal information required for inclusion in any of the aforementioned metadata-encoding mechanisms.

CONCLUSIONS.

This first effort to create a standardized metadata protocol for UMNs draws upon recommendations from a range of sources to regularize UMN data (and improve compatibility with other nonmeteorological UMNs). The goal is to standardize UMN metadata based on best practices, personal experiences, and official recommendations. It is particularly clear that standardized terms, specific site classification techniques, and an urban network classification scheme would be of benefit to network implementers and end users alike. With implementations and discussion, the urban meteorological community will hopefully arrive at a consensus that is appropriate for current technologies, including more detailed requirements (e.g., variable-specific QA/QC procedures). The intent of this paper is to promote further discussions to facilitate this process.

Long-term, baseline datasets obtained from UMNs are required for a broad spectrum of applications, but the datasets need to be high quality and reliable in order to ensure accurate usage, thus furthering our understanding of increasingly important urban environments. It is acknowledged that it is difficult to ensure guidelines are universally adhered to (Stewart 2011); however, the publication of such protocols significantly increases the likelihood of adoption and is essential to further the understanding of the urban climate.

ACKNOWLEDGMENTS

This work is funded by the U.K. Natural Environmental Research Council (Research Grant NE/I006915/1), which is primarily funding the deployment of the HiTemp network, consisting of 250 wireless air temperature sensors and 25 weather stations across Birmingham, United Kingdom. This network is part of the wider urban meteorological research undertaken by the Birmingham Urban Climate Laboratory (www.bucl.org.uk). The authors would like to thank Prof. Tim Oke and Dr. Iain Stewart for their valuable comments during the preparation of this manuscript, in addition to delegates at ICUC-8, who provided valuable suggestions via formal (and informal) discussions during the “Urban Weather Networks” session when this protocol was first introduced (Muller et al. 2012). Finally, thank you to the anonymous reviewers for their comments, which were integrated into the manuscript.

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