Micro-climatic temperature measurements in the Finnish city of Helsinki

Contains measurement data of air temperature for the manuscript "Real-time measurements of micro-climatic temperature and relative humidity in the Finnish cities of Tampere, Helsinki and Rovaniemi" by Kühn et al. (in preparation) of 30 measurement stations in Helsinki.

Technical Info:

NB: Update on 13.1.2026: data file Helsinki_2025.dat to include all measurements of year 2025. 

The measurements were conducted at a height of 3 m in the locations listed in Helsinki_Table1_New.txt during 12/2024-12/2025.

Each measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf]. Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.

Quality check The temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.

The Local Climate Zones (LCZs) in Helsinki_Table1_New. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).

Table Of Contents:

The descriptions of the measurement stations are in Helsinki_Table1_New.txt. Columns: 1. Station_code 2. Station_id 3. latitude 4. longitude 5. elevation above mean se alevel (m) 6. LCZ_global_point (LCZ at the grid point nearest to the measurement station) 7. LCZ_global_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)

The data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Helsinki_YEAR.dat, with Celsius as Unit and missing value -999. Columns: 1. Station_code 2. Timestamp(YMDHH24) UTC 3. Mean temperature of the previous hour 4. Minimum temperature of the previous hour 5. Maximum temperature of the previous hour 6. Standard deviation of the temperature measurements during the previous hour 7. Number of measurements during the previous hour (usually 12) References:

Aalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651 Stewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1 Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022. Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. Zenodo. https://doi.org/10.5281/zenodo.6364593.

本数据集包含Kühn等人（待发表）论文《芬兰坦佩雷、赫尔辛基和罗瓦涅米市微气候温度与相对湿度实时测量》中赫尔辛基30个监测站的气温测量数据。

技术说明：
注：2026年1月13日更新：Helsinki_2025.dat数据文件将包含2025年全年所有测量数据。
测量工作于2024年12月至2025年12月期间，在Helsinki_Table1_New.txt所列地点的3米高度处开展。
每个监测站由三部分组成：温湿度传感器、太阳辐射防护罩，以及物联网（Internet of Things, IoT）设备，该设备负责采集测量数据，并通过低功耗广域网（Long Range Wide Area Network, LoRaWAN）将数据传输至服务器。
温湿度由一款集成传感器——Digital Matter I2C温湿度传感器（https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf）——完成测量，该传感器内部搭载Silicon Labs Si7021-A20 I2C温湿度传感芯片。该芯片已完成工厂校准，工作量程为相对湿度0%~100%，温度范围-40℃~+125℃。当环境温度处于-10℃~85℃区间时，温度测量最大精度为±0.4℃；当环境相对湿度处于0%~80%区间时，相对湿度测量最大精度为±3%。为降低直射阳光与热辐射对测量结果的干扰，温湿度传感器配备了辐射防护罩。该防护罩高11.5cm，半径14cm，由白色塑料制成，采用圆柱形堆叠9块通风板的设计，可在屏蔽外部辐射的同时保障充足的空气流通。

质量控制
气温数据通过多步骤流程完成质量控制。首先，基于坦佩雷地区10km×10km分辨率网格化气温数据（Aalto等，2016）得到的长期气候学每日最高、最低气温阈值对测量值进行筛选，剔除明显超出气候学范围的记录。随后，基于测量数据的统计特性，通过短时间区间内的中位数与中位数绝对偏差（median absolute deviation, MAD）对剩余数值进行过滤，识别并移除异常值。最后，基于与本地中位数的偏差设置最终阈值，排除所有剩余的极端值。

赫尔辛基_Table1_New.txt中已根据Stewart与Oke（2012）提出的本地气候区（Local Climate Zone, LCZ）分类体系，结合全球LCZ数据（Demuzere, 2022a; Demuzere等, 2022b），为每个监测站定义了对应的本地气候区。

数据集目录：
监测站的描述信息存储于Helsinki_Table1_New.txt，各列依次为：
1. 站点代码（Station_code）
2. 站点ID（Station_id）
3. 纬度（latitude）
4. 经度（longitude）
5. 平均海平面以上海拔（单位：米）
6. 全球格点本地气候区（LCZ_global_point，即距离监测站最近的格点处的LCZ）
7. 200米半径范围内本地气候区众数（LCZ_global_r200，即监测站周边200米半径内LCZ的众数）

各监测站的逐小时气温数据存储于以制表符为分隔符的ASCII格式文件Helsinki_YEAR.dat中，单位为摄氏度，缺失值记为-999。各列依次为：
1. 站点代码（Station_code）
2. UTC时间戳（格式：YMDHH24）
3. 前一小时平均气温
4. 前一小时最低气温
5. 前一小时最高气温
6. 前一小时气温测量值的标准差
7. 前一小时的测量次数（通常为12次）

参考文献：
Aalto, J., Pirinen, P., & Jylhä, K. (2016). 芬兰全新网格化每日气候学数据集：基于置换法的不确定性估计与气候时间趋势分析. 《地球物理研究杂志：大气》, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651
Stewart ID, Oke TR. 用于城市气温研究的本地气候区划分体系. 《美国气象学会公报》, 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): 支持地球系统模拟与城市尺度环境科学研究的全球本地气候区地图. 《地球系统科学数据》, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): 全球本地气候区地图. Zenodo. https://doi.org/10.5281/zenodo.6364593