Many cities have sensor network for heat island measurement and it is possible to observe the spatial pattern of meteorological data in detail. In the design and operation of a meteorological measurement network, a larger number of measurement points are better. But considering the labor and cost, a smaller number of them are better if the same result can be obtained. The effect of reducing observation points from an existing meteorological measurement network is considered in this study.

We consider the example of temperature data of the Tokyo metropolitan area (Extended METROS) which has about 200 measurement points. The effect of reducing observation points from an existing meteorological measurement network is considered, using sampling with clustering. The data obtained from May 2007 to October 2008 (18 months, every hour) were used for the analysis. 10% to 90% of the data were reduced and sampled from the original data in 10% interval. The sampled data and the original data were interpolated by using Inverse Distance Weighing method and the original and the sampled 2D images were made. The correlation between the original and the sampled 2D images are calculated and the correlation is used as an index of the similarity of the images.

The results indicated that the data with up to 30% reduction in measurement sites yield more than 0.9 correlations, which means the similar temperature patterns with the original data. The methods presented in this study can be applied in other meteorological measurement networks in evaluating the reduction of existing points of the networks.

Provision of atmospheric data from observational networks at high spatial resolution and over long time periods remains a challenge in urban climate research. Classical observational networks are designed for the detection of synoptic atmospheric conditions and rarely suitable for city-specific and intra-urban analysis. Therefore, using citizens as data provider offers huge potentials, especially in urban areas due to the high population density.

The concept of citizen science is not new, especially in the field of ecology (Dickinson et al. 2012). This concept relies on the active participation of citizens to contribute to research. A number of efforts have been made in recent years concerning atmospheric applications, e.g. mapping of atmospheric aerosols with smartphones (Snik et al., 2014) or involving citizens in observational networks such as “CoCoRaHS” (Community Collaborative Rain, Hail and Snow Network, http://www.cocorahs.org/) or the “Citizen Weather Observer Program” (http://wxqa.com). Another approach to acquire huge amount of data is the concept of crowd-sourcing, defined by Dickinson et al. (2012) as “getting an undefined public to do work, usually directed by designated individuals or professionals.” For instance, Overeem et al. (2013) took battery-temperature measurements from smartphones to derive urban air temperatures by using data from the smartphone application ‘OpenSignal’ (opensignal.com), while Mass and Madaus (2014) exploited air-pressure measurements from another application called ‘pressureNET’ (pressurenet.cumulonimbus.ca) to simulate an active convection event in the United States.

The netatmo system (www.netatmo.com) acts as an intermediate between active citizen science and crowd sourcing of passively acquired data. Netatmo is a private enterprise developing and distributing weather stations around the world for interested citizens to monitor their indoor and outdoor atmospheric conditions and sharing their records publicly. The netatmo weather station is cost-efficient and WiFi connection serves for data transfer, storage and visualisation via application software. Air temperature and relative humidity are measured both indoors and outdoors, the indoor device also records air pressure, CO2 concentration and noise level. While netatmo offers huge potentials due to the dense spatial coverage in many urban areas, the question remains if and how crowd-sourced data from this source can be suitable for urban climate research.

In a test phase during November 2014, we acquired public data (air temperature, relative humidity and air pressure) from more than 700 stations in Berlin and surroundings with a temporal resolution of one hour. Comprehensive analyses of quality and suitability of these crowd-sourced records includes the identification of problems that are user-specific, related to sensor accuracy, radiation shielding, and sensor set-up. This work will be based on meta-data analyses and own observations carried out with netatmo outdoor devices in comparison with standard scientific measurement equipment as well as statistical analyses and tests. If suitable, crowd-sourced atmospheric data can be used for evaluation of urban canopy models or spatio-temporal analysis of atmospheric characteristics of Local Climate Zones.

References:

Overeem, A., J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, R. Uijlenhoet, 2013: Crowdsourcing urban air temperatures from smartphone battery temperatures. Geophys. Res. Lett., 40: 4081-4085.

Dickinson, J. L., B. Zuckerberg, D. N. Bonter, 2010: Citizen Science as an Ecological Research Tool: Challenges and Benefits. Annu. Rev. Ecol. Evol. Syst., 41: 149-172.

Mass, C. F., L. E. Madaus, 2014: Surface Pressure Observations from Smartphones: A Potential Revolution for High-Resolution Weather Prediction? Bull. Amer. Meteor. Soc., 95: 1343-1349.

Snik F., J.H.H. Rietjens, A. Apituley, H. Volten, B. Mijling, A. Di Noia, S. Heikamp, R.C. Heinsbroek, O.P. Hasekamp, J.M. Smit, J. Vonk, D.M. Stam, G. van Harten, J. de Boer, C.U. Keller, 3187 iSPEX citizen scientists, 2014: Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters. Geophys. Res. Lett., 41, 1-8.

Urban climate conditions affect how cities will develop in the future, not only because of the impact on the energy consumption of buildings or on the environment, but also on human thermal comfort. For growing cities in tropical climates, urban microclimate becomes a relevant aspect for the design and planning of future developments. The aim of the overall research project is to establish the relationship between, microclimate data and building geometries using outdoor thermal comfort as an indicator, and to translate this knowledge into a parameterized design-feedback tool in order to explore ‘design spaces’ of urban forms in tropical climates [Tapias & Schmitt, 2014]. In this context, the goal of the first stage is a methodology to acquire local microclimate data with sensor technology for a period of one year in the tropical savanna climate of Barranquilla in Colombia.

The campus of the ‘Universidad del Norte’ is selected as the specific case-study area in Barranquilla. Within this urban settlement, four outdoor locations are identified for the data acquisition procedure. The selection is based on having four different orientations of pedestrian paths on a human scale. Additionally, one final location on a rooftop is selected in order to acquire general global data from the surrounding. For the acquisition of microclimate measurements, sensor technology in form of portable weather stations is used. One portable weathers station is installed for each of the preselected location. For the pedestrian scale locations, four of these mini portable weather stations are mounted at 1.2 meters high from the ground. These stations record measures of; temperature, humidity, rain fall, wind direction, wind speed (anemometer), and atmospheric pressure (barometer). For the location on the rooftop, an advanced portable weather station is mounted, which measures the same parameters plus solar radiation (pyranometer). The data are transferred permanently via internet. The data is being send to the web server (barranquilla.arch.ethz.ch/station1 - station5) and visualised through the WeeWx weather software.

The mini portable weather stations are successfully mounted and installed, recording data from different microclimate parameters. The wireless structure enables the long distance monitoring of the measurements from the weather stations. This measurement campaign is intended to last for one year, in order to acquire microclimate data for different weather periods. When the acquisition of the require data is finalised, the purpose of the next stage of the research project is to proceed with data processing and calculation of the outdoor thermal comfort based on the PET (Physiologically Equivalent Temperature) index using the RayMan model [Matzarakis: Rutz; Mayer, 2006].

References:

- Tapias, E., Schmitt, G. Climate-sensitive urban growth: Outdoor thermal comfort as an indicator for the design of urban spaces. The sustainable city IX : urban regeneration and sustainability. WIT Press (1): 623- 634, 2014.

- Matzarakis, A., Rutz, F., Mayer, H. Modelling radiation fluxes in simple and complex environments - application of the RayMan model. Int J Biometeorol 51:323- 334, 2006.