@article{SaltikoffFriedrichSoderholmetal.2019,
  author    = {Saltikoff, Elena and Friedrich, Katja and Soderholm, Joshua and Lengfeld, Katharina and Nelson, Brian and Becker, Andreas and Hollmann, Rainer and Urban, Bernard and Heistermann, Maik and Tassone, Caterina},
  title     = {An Overview of Using Weather Radar for Climatological Studies: Successes, Challenges, and Potential},
  series = {Bulletin of the American Meteorological Society},
  volume    = {100},
  journal   = {Bulletin of the American Meteorological Society},
  number    = {9},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {0003-0007},
  doi       = {10.1175/BAMS-D-18-0166.1},
  pages     = {1739 -- 1751},
  year      = {2019},
  abstract  = {Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.},
  language  = {en}
}
@article{AyzelHeistermannWinterrath2019,
  author    = {Ayzel, Georgy and Heistermann, Maik and Winterrath, Tanja},
  title     = {Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)},
  series = {Geoscientific model development},
  journal   = {Geoscientific model development},
  number    = {12},
  publisher = {Copernicus Publications},
  address   = {G{\"o}ttingen},
  issn      = {1991-9603},
  doi       = {10.5194/gmd-12-1387-2019},
  pages     = {1387 -- 1402},
  year      = {2019},
  abstract  = {Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant ("Lagrangian persistence"). In that context, "optical flow" has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library ("rainymotion") for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing - a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.},
  language  = {en}
}