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@ARTICLE{Schnepper:1014782,
      author       = {Schnepper, Tobias and Groh, Jannis and Gerke, Horst H. and
                      Reichert, Barbara and Pütz, Thomas},
      title        = {{E}valuation of precipitation measurement methods using
                      data from a precision lysimeter network},
      journal      = {Hydrology and earth system sciences},
      volume       = {27},
      number       = {17},
      issn         = {1027-5606},
      address      = {Munich},
      publisher    = {EGU},
      reportid     = {FZJ-2023-03466},
      pages        = {3265 - 3292},
      year         = {2023},
      abstract     = {Accurate precipitation data are essential for assessing the
                      water balance of ecosystems. Methods for point precipitation
                      determination are influenced by wind, precipitation type and
                      intensity and/or technical issues. High-precision weighable
                      lysimeters provide precipitation measurements at ground
                      level that are less affected by wind disturbances and are
                      assumed to be relatively close to actual precipitation. The
                      problem in previous studies was that the biases in
                      precipitation data introduced by different precipitation
                      measurement methods were not comprehensively compared with
                      and quantified on the basis of those obtained by lysimeters
                      in different regions in Germany.The aim was to quantify
                      measurement errors in standard precipitation gauges as
                      compared to the lysimeter reference and to analyze the
                      effect of precipitation correction algorithms on the gauge
                      data quality. Both correction methods rely on empirical
                      constants to account for known external influences on the
                      measurements, following a generic and a site-specific
                      approach. Reference precipitation data were obtained from
                      high-precision weighable lysimeters of the TERrestrial
                      ENvironmental Observatories (TERENO)-SOILCan lysimeter
                      network. Gauge types included tipping bucket gauges (TBs),
                      weighable gauges (WGs), acoustic sensors (ASs) and optical
                      laser disdrometers (LDs). From 2015-2018, data were
                      collected at three locations in Germany, and 1 h
                      aggregated values for precipitation above a threshold of
                      0.1 mm h−1 were compared.The results show that all
                      investigated measurement methods underestimated the
                      precipitation amounts relative to the lysimeter references
                      for long-term precipitation totals with catch ratios (CRs)
                      of between $33 \%–92 \%.$ Data from ASs had overall
                      biases of −0.25 to −0.07 mm h−1, while data from
                      WGs and LDs showed the lowest measurement bias (−0.14 to
                      −0.06 mm h−1 and −0.01 to −0.02 mm h−1).
                      Two TBs showed systematic deviations with biases of −0.69
                      to −0.61 mm h−1, while other TBs were in the
                      previously reported range with biases of
                      −0.2 mm h−1. The site-specific and generic
                      correction schemes reduced the hourly measurement bias by
                      0.13 and 0.08 mm h−1 for the TBs and by 0.09 and
                      0.07 mm h−1 for the WGs and increased long-term CRs by
                      $14 \%$ and $9 \%$ and by $10 \%$ and $11 \%,$
                      respectively.It could be shown that the lysimeter reference
                      operated with minor uncertainties in long-term measurements
                      under different site and weather conditions. The results
                      indicate that considerable precipitation measurement errors
                      can occur even at well-maintained and professionally
                      operated stations equipped with standard precipitation
                      gauges. This generally leads to an underestimation of the
                      actual precipitation amounts. The results suggest that the
                      application of relatively simple correction schemes, manual
                      or automated data quality checks, instrument calibrations,
                      and/or an adequate choice of observation period can help
                      improve the data quality of gauge-based measurements for
                      water balance calculations, ecosystem modeling, water
                      management, assessment of agricultural irrigation needs, or
                      radar-based precipitation analyses.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001161824700001},
      doi          = {10.5194/hess-27-3265-2023},
      url          = {https://juser.fz-juelich.de/record/1014782},
}