The use of crowdsourcing – obtaining large quantities of data through the Internet – has been of great value in urban meteorology. Crowdsourcing has been used to obtain urban air temperature, air ...pressure, and precipitation data from sources such as mobile phones or personal weather stations (PWSs), but so far wind data have not been researched. Urban wind behaviour is highly variable and challenging to measure, since observations strongly depend on the location and instrumental set‐up. Crowdsourcing can provide a dense network of wind observations and may give insight into the spatial pattern of urban wind. In this study, we evaluate the skill of the popular “Netatmo” PWS anemometer against a reference for a rural and an urban site. Subsequently, we use crowdsourced wind speed observations from 60 PWSs in Amsterdam, the Netherlands, to analyse wind speed distributions of different Local Climate Zones (LCZs). The Netatmo PWS anemometer appears to systematically underestimate the wind speed, and episodes with rain or high relative humidity degrade the measurement quality. Therefore, we developed a quality assurance (QA) protocol to correct PWS measurements for these errors. The applied QA protocol strongly improves PWS data to a point where they can be used to infer the probability density distribution of wind speed of a city or neighbourhood. This density distribution consists of a combination of two Weibull distributions, rather than the typical single Weibull distribution used for rural wind speed observations. The limited capability of the Netatmo PWS anemometer to measure near‐zero wind speed causes the QA protocol to perform poorly for periods with very low wind speeds. However, results for a year‐long wind speed climatology of the wind speed are satisfactory, as well as for a shorter period with higher wind speeds.
We research the value of crowdsourced urban wind observations from Personal Weather Stations. From comparison measurements against known wind speeds at two sites, we construct a Quality Assurance protocol to improve data quality. After applying this protocol, the crowdsourced data can successfully be used to calculate the urban wind speed probability distribution.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We present twenty-three years (1993-2016) of automatic weather station (AWS) data, collected along the K-transect near Kangerlussuaq in west Greenland. The transect runs from east to west, roughly ...perpendicular to the ice sheet edge at about 67° N. The K-transect originated from the Greenland Ice Margin Experiments (GIMEX), held in the summers of 1990 and 1991. Until recently, surface mass balance and ice velocity measurements were performed at nine locations along the K-transect, of which four are equipped with AWS: two in the ablation zone at approximately 500 m and 1,000 m asl, one at the approximate equilibrium-line altitude (~1,500 m asl), and one in the lower accumulation zone (~1,850 m asl) at distances of 5, 38, 88, and 140 km from the ice edge, respectively. Here, we present an overview of the various AWS types and their data corrections, quality, and availability, including a preliminary trend analysis. Recent increases in temperature and radiation components are associated with the frequent occurrence of anti-cyclonic conditions in west Greenland, resulting in clear skies and relatively warm summers. Strong melt concurs with a decrease in winter accumulation, lowering the surface albedo of the ice sheet. The AWS situated at 1,500 m asl, the former equilibrium-line altitude (ELA), observed almost a doubling of the summertime net shortwave radiation since 2004; as a result, the ELA along the K-transect has been steadily increasing and is currently situated well above 1,700 m asl.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
The study aims for the development of a wireless weather station composed of two modules, the outdoor module that takes the temperature and humidity from the environment through the DHT22 sensor and ...transmits the information through the n24RFL01+ communication module to the indoor module. The indoor module takes the temperature and humidity from the environment and displays it on a 3.5” TFT display along with the information received from the outdoor module, also the date and time are displayed. The development boards used for the weather station are Arduino Mega 2560 for the indoor module and Arduino Nano for the outdoor module. The n24RFL01+ wireless communication module, depending on the model, can transmit data at a distance of 800+ m and the DHT22 sensor is very accurate. The programming code used for the development of the weather station is made in Arduino IDE. Arduino IDE is an open-source software that is used to write and upload code to the Arduino developing boards.
Urban heat island (UHI) significantly affects the energy demand of buildings. Therefore, it is important to consider UHI effect when evaluating the real energy demand of buildings in urban and rural ...areas. Based on hourly data observed over the past ten years from automatic weather stations and four representative rural weather stations selected by remote sensing method, the impact of the UHI intensity (UHII) on the building loads at fine time scales (i.e. day and hour) were evaluated by simulating the hourly loads during whole year of the typical residential and office buildings in urban and rural areas. UHII was found to be the main climatic factor affecting the variations in heating load in both residential and office buildings. With a heating-period mean UHII of 2.1 °C in the ten years, the daily heating load in urban areas is significantly lower than that in rural areas, which is 10.1 and 7.5% less for residential and office buildings, respectively. For residential and office buildings, the daily heating load has decreased by 34.2 × 10−3 and 27.7 × 10−3 kWh/m2, respectively, for every 1 °C increase in UHII. For both types of buildings, the period of high energy consumption in both urban and rural areas was from late December to late January of the following year. The hourly load in residential buildings was high at night and low during the daytime, and the opposite was founded in office buildings. For residential buildings, the period from 18:00 one day to 07:00 the next day was the high load period in both urban and rural areas, whereas the high load period in office buildings was from 07:00 to 19:00. The peak load in residential and office buildings was founded from 05:00 to 07:00 and from 07:00 to 11:00, respectively. Overall, the daily and hourly heating load variations in residential and office buildings response to UHI should be fully considered to improve the fine-level of heating operation regulations, especially in urban areas, to reduce the energy consumption of buildings.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall ...distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km
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on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose ...owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (
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) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than
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, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.
The transition from conventional weather stations (CWSs) to automated weather stations (AWSs) of the Colombian coffee network has required testing their performance and adjusting the temperature ...measurements to ensure the continuity of the historical CWS series. In this study, the mean (Tmean), minimum (Tmin), and maximum temperature (Tmax) measurements of CWS and AWS operating in parallel at 36 locations between 2014 and 2019 were compared, and the biases of the daily temperature differences (CWS − AWS), the agreement index (d), and the percentage of data within the allowed range (PR05) were calculated. The most consistent method for calculating Tmean and Tmax for CWS was selected for use on the AWS data. With the standard normal homogeneity test and with the metadata, we found that the series of temperature differences between CWS and AWS was not homogeneous, instrument failures and sensor changes being the main causes of the lack of homogeneity. The statistical analyses indicated that the AWS data need to be adjusted to be continuous with the CWS series. To correct the temperature bias, two approaches were applied: quantile mapping and the additive constant. The results suggest that the quantile mapping adjustments improve the average bias at all stations but do not necessarily bring the percentage to within ±0.5°C. In Tmin and Tmean, 12 AWSs can give continuity with the historical series of the CWS, and for the rest of the stations and variables, the series of the AWSs are independent of the CWSs.
The method to calculate the mean and maximum temperature in the automatic meteorological stations was selected to have a better concordance with the records of the conventional stations, and the automatic stations that give continuity to the historical series of the conventional network were specified. In the temperature variable, most of the automatic stations that operated in parallel with the conventional station have an independent series and will not give continuity to the historical series.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
During the last 10 years, the Institute for Environmental Research and Sustainable Development of the National Observatory of Athens has developed and operates a network of automated weather stations ...across Greece. The motivation behind the network development is the monitoring of weather conditions in Greece with the aim to support not only the research needs (weather monitoring and analysis, weather forecast skill evaluation) but also the needs of various communities of the production sector (agriculture, constructions, leisure and tourism, etc.). By the end of 2016, 335 weather stations are in operation, providing real‐time data at 10‐min intervals. This paper provides information about the logistics of this network, including real‐time applications of the collected data as well as information on the quality control protocols, the construction of the station data and metadata repository and the means through which the data are made available to users.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
A one-year data set for the year 2015 of near-surface air temperature (T$T$), crowdsourced from ‘Netatmo’ citizen weather stations (CWS) in Berlin, Germany, and surroundings was analysed. The CWS ...data set, which has been quality-checked and filtered in a previous study, consists of T$T$ measurements from several hundred CWS. It was investigated (1) how CWS are distributed among urban and rural environments, as represented by ‘local climate zones’ (LCZ), (2) how LCZ are characterised in T$T$ along the annual cycle and concerning intra-LCZ T$T$ variability, and (3) if significant T$T$ differences between LCZ (ΔT$\Delta T$) can be detected with CWS data. Further, it was investigated how the results from CWS compare to reference data from standard meteorological measurement stations. It can be shown that all ‘urban’ LCZ are covered by CWS, but only few CWS are located in ‘natural’ LCZ (e.g. forests or urban parks). CWS data along the annual cycle show generally good agreement to reference data, though for some LCZ monthly means between both data sets differ up to 1 K. Intra-LCZ T$T$ variability is particularly large during night-time. Statistically significant ΔT$\Delta T$ can be detected with CWS data between various LCZ pairs, particularly for structurally dissimilar LCZ, and the results are in agreement with existing literature on LCZ or the urban heat island. Furthermore, annual mean ΔT$\Delta T$ in CWS data agree well with reference data, thus showing the potential of CWS data for long-term studies. Several challenges related to crowdsourced CWS data need further investigation, namely missing meta data, the non-standard measurement locations, the imbalanced availability in time and space, and potentials to combine CWS and reference data to benefit from the main advantages of both, i.e., the large number of stations and the high quality of data, respectively.
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