Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam ( ∼ 575 km 2 ) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

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Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data is required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam (~ 575 km2) that measure rainfall over at least 4 months in a 17-month period are evaluated, in addition to a detailed assessment that is made of three Netatmo stations, the largest contributor of the dataset, in an experimental set-up. Although the sensor performance in the experimental set-up and the density of the PWS-network are promising, the method of data transfer to the online platform causes considerable errors in the datasets obtained. These errors are especially large during low intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals, improving the correlation with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the data conversion by the PWS-platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time available (unadjusted) radar product. Averaging nearby raw PWS measurements already improves the match with gauge-adjusted radar data in that area. The results confirm that the growing number of internet-connected PWSs holds a promise for urban rainfall monitoring.

National meteorological services (NMS) are limited by practical and financial boundaries in the number of official meteorological measurements that it can collect. This means that large regions are often unobserved. These gaps can be filled by novel data sources, including measurements from personal weather stations that are owned and operated by amateur citizen scientists, or opportunistic sensing from devices that are not designed to measure meteorological variables, like commercial microwave links (radio connections between mobile phone towers). These types of data are known as “third‐party data” (3PD) as they are not owned or operated by NMS or research institutes (e.g., university, government department). Demonstration of the quality and value of these novel data sources is an active area of research. NMS, like the Royal Netherlands Meteorological Institute (KNMI), are faced with some unique challenges when it comes to transferring research to operations. KNMI is in the early stages of developing an operational pipeline for 3PD. We outline some use cases where we have demonstrated the quality of 3PD. We discuss our experiences with some of these challenges that can occur when transferring between proof‐of‐concepts on 3PD developed in research settings into real operational workflows providing valuable services. Hence, in this work we introduce our third‐party data life cycle, in which we provide an integral overview of this transitioning process considering business and social aspects, technical feasibility assessments, the importance of quality control, and aspects related to data integration and alignment with the existing official data sources. We also reflect on how these potential new applications could fit into KNMIs long‐term strategies and contribute to the high‐resolution weather forecast and early warning issuing. We hope that sharing these experiences will provide some general guidelines to organizations in need of providing new services stemming from 3PD and transform them into “daily business.”

Abstract. Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. This is certainly the case for the pan-European radar precipitation products. The density of (near) real-time rain gauge accumulations from official networks is often relatively low. Crowdsourced rain gauge networks have a much higher density than conventional ones and are a potentially interesting (complementary) source to merge with radar precipitation accumulations. Here, a 1-year personal weather station (PWS) rain gauge dataset of ~5 min accumulations is obtained from the private company Netatmo over the period September 1, 2019–August 31, 2020, which is subjected to quality control using neighbouring PWSs and on 1-h accumulations using unadjusted radar data. The PWS 1-h gauge accumulations are employed to spatially adjust OPERA radar accumulations, covering 78 % of geographical Europe. The performance of the merged dataset is evaluated against daily and disaggregated 1-h gauge accumulations from weather stations in the European Climate Assessment & Dataset (ECA&D). Results are contrasted to those from an unadjusted OPERA-based radar dataset and from EURADCLIM. The severe average underestimation for daily precipitation of ~28 % from the unadjusted radar dataset diminishes to ~3 % for the merged radar-PWS dataset. A station-based spatial verification shows that the relative bias in 1-h precipitation is still quite variable and suggests stronger underestimations for colder climates. A dedicated evaluation with scatter density plots reveals that the performance is indeed less good for lower temperatures, which points to limitations in observing solid precipitation by PWS gauges. The outcome of this study confirms the potential of crowdsourcing to improve radar precipitation products in (near) real-time.

<p>Several opportunistic sensors (private weather stations, commercial microwave links and smartphones) are employed to obtain weather information and successfully monitor urban weather events. The ongoing urbanisation and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using non-traditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: (1) air temperature estimates from smartphone batteries and personal weather stations; (2) rainfall from commercial microwave links and personal weather stations; (3) solar radiation from smartphones; (4) wind speed from personal weather stations; (5) air pressure from smartphones and personal weather stations; (6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First we show that the passage of a front can be successfully monitored using data from several types of non-traditional sensors in a complementary fashion. Also we demonstrate the added value of opportunistic measurements in quantifying the Urban Heat Island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using non-traditional and opportunistic sensing networks.</p>

<p>Traditionally, hydrologists have relied on dedicated measurement equipment to do their business (e.g. rainfall-runoff modeling). Such instruments are typically owned and operated by government agencies and regional or local authorities. Installed and maintained according to (inter)national standards, they offer accurate and reliable information about the state of and fluxes in the hydrological systems we study as scientists or manage as operational agencies. Such standard instruments are often further developments of novel measurement techniques which have their origins in the research community and have been tested during dedicated field campaigns.</p><p>One drawback of the operational measurement networks available to the hydrological community today is that they often lack the required coverage and spatial and/or temporal resolution for high-resolution real-time monitoring or short-term forecasting of rapidly responding hydrological systems (e.g. urban areas). Another drawback is that dedicated networks are often costly to install and maintain, which makes it a challenge for nations in the developing world to operate them on a continuous basis, for instance.</p><p>Yet, our world is nowadays full of sensors, often related to the rapid development in wireless communication networks we are currently witnessing (notably 5G). Let us try to make use of such opportunistic sensors to do our (hydrologic) science and our (water management) operations. They may not be as accurate or reliable as the dedicated measurement equipment we are used to working with, let alone meet official international standards, but they typically come in large numbers and are accessible online. Hence, in combination with smart retrieval algorithms and statistical treatment, opportunistic sensors may provide a valuable complementary source of information regarding the state of our environment.</p><p>The presentation will focus on some recent examples of the potential of opportunistic sensing techniques in hydrology and water resources, from rainfall monitoring using microwave links from cellular communication networks (in Europe, South America, Africa and Asia), via crowdsourcing urban air temperatures using smartphone battery temperatures to high-resolution urban rainfall monitoring using personal weather stations.</p>

REVIEWER: The paper discusses the advantages of crowdsourced weather station data (rainfall measurement) to obtain rainfall information suitable for hydrology studies in urban areas, i.e., rainfall measurements that the need to have high temporal and spatial resolutions. The paper is, to the best of my knowledge, the first attempt to quantify the errors of rainfall data made available from local, distributed and crowdsourced weather stations, which makes it an interesting study. In the paper the crowdsourced rainfall data are compared with dedicated rain gauges and rainfall radar data as these are the common rainfall data sources used in urban hydrology.AUTHORS: We thank the reviewer for the valuable review of this paper. We appreciate C1