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.”
In the Agile Way of Working (AoW), a group of developers jointly work to efficiently realize a project. Here we report on the application of AoW in meteorological research and development (R&D) outside of the software engineering environment. Three projects were formulated, derived from the observations strategy (2015) of the Royal Netherlands Meteorological Institute (KNMI). An initial phase of preparation consisted of breaking down the workload into tasks to be accomplished by individual project members and achievable in two one-week sprints. Sprints consisted of daily stand-ups, where accomplishments, work intentions, and obstacles were discussed, followed by project work in a joint working environment. The three projects identified were 1) flying a drone to detect boundary layer evolution, 2) monitoring the quality of the precipitation measurement system, and 3) realizing a platform for merging third-party data with meteorological observations. The preparation phase proved to be vitally important to each of the projects. The roles of the product owner and Scrum master in streamlining and guiding these projects were essential to the success of the sprint weeks, but the joint group settings worked well for only two of the three projects. While team members were positive about their experience with the AoW, the challenge remains to fuse the traditional individual work practice of researchers with that of software engineers, who are experienced in working in a group setting.
<div> <div> <p>Recent studies indicate that global warming changes the global hydrological cycle and may trigger drought or expand and deepen existing drought conditions at our planet. During the summer of 2018 the Netherlands experienced extreme drought conditions, matching the previous drought record from 1976. This climatic extreme has been monitored using a cumulative metric based on the difference between (potential) evaporation and precipitation. In an effort to provide exhaustive drought monitoring facilities, the Netherlands Meteorological Service (KNMI) developed a drought monitor based on the Standard Precipitation Index (SPI) using 40 years of daily rainfall (1971-2010) from our official network of rain gauges for calibration. The daily SPI maps help decision makers to assess the status of meteorological drought in the Netherlands, thus enabling preventive measures mitigating its negative impacts on different socio-economic sectors.&#160;</p> </div> <div> <p>In the past two decades our global society has witnessed the advent of new technological and scientific advances that have reshaped the way we collect weather observations. Increasing numbers of citizens are joining the effort of monitoring the weather by installing citizen weather stations (CWS) in private spaces (e.g., home, schools), thus conforming novel sources of weather data. In 2015, the KNMI joined as a partner the Weather Observations Website (WOW) consortium, a citizen science initiative promoted by the UK Met Office bringing together weather enthusiasts all around the world. WOW-NL CWS have collected 100+ million observations between 2015-2019. However, it is still unclear how to use this remarkable volume of observations, or what is the added value (e.g., economic, operational, research) they provide with respect to the official network.&#160;</p> </div> <div> <p>In this ongoing work, we combined the newly developed SPI drought monitor with WOW observations from the Netherlands to obtain an &#8216;SPI-WOW&#8217; indicator. Our goal is threefold: 1) illustrating how to turn WOW-NL data into operational value; 2) assessing the possibility of providing higher resolution drought maps including WOW-NL rainfall data; 3) enable the possibility for underrepresented regions to obtain (relevant) local drought metrics.&#160;</p> </div> <div> <p>We extracted 12 million precipitation observations for 2019 and, for each day of the year, we computed the daily rainfall accumulations for the previous 30 days (i.e., SPI-1). Note that the precipitation observations are not quality-controlled (QC). The calibrated model is tested with these newly created rainfall accumulations to obtain the SPI-WOW values. Our preliminary results compare the official vs alternative values of SPI at the location of each WOW-NL CWS. For each month we observe a moderate positive correlation, and there are CWS in the network capable of providing measurements close to the official ones. Further work to achieve the above-mentioned goal should include a) the application of a QC to the rainfall data to remove the outliers beforehand; b) thoroughly comparing the values of both networks in space and time across different scenarios; c) identifying the WOW-NL stations providing the best SPI metrics and its characteristics; d) assess the inclusion of radar data for the hi-res maps.</p> </div> </div>
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