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Summary The multisensor Quantitative Precipitation Estimates (MQPEs) created by the US National Weather Service (NWS) are subject to a non-stationary bias. This paper quantifies the impacts of climatological adjustment of MQPEs alone, as well as the compound impacts of adjustment and model calibration, on the accuracy of simulated flood peak magnitude and that in detecting flood events. Our investigation is based on 19 watersheds in the mid-Atlantic region of US, which are grouped into small (<500 km 2 ) and large (>500 km 2 ) watersheds. NWS archival MQPEs over 1997–2013 for this region are adjusted to match concurrent gauge-based monthly precipitation accumulations. Then raw and adjusted MQPEs serve as inputs to the NWS distributed hydrologic model-threshold frequency framework (DHM-TF). Two experiments via DHM-TF are performed. The first one examines the impacts of adjustment alone through uncalibrated model simulations, whereas the second one focuses on the compound effects of adjustment and calibration on the detection of flood events. Uncalibrated model simulations show broad underestimation of flood peaks for small watersheds and overestimation those for large watersheds. Prior to calibration, adjustment alone tends to reduce the magnitude of simulated flood peaks for small and large basins alike, with 95% of all watersheds experienced decline over 2004–2013. A consequence is that a majority of small watersheds experience no improvement, or deterioration in bias (0% of basins experiencing improvement). By contrast, most (73%) of larger ones exhibit improved bias. Outcomes of the detection experiment show that the role of adjustment is not diminished by calibration for small watersheds, with only 25% of which exhibiting reduced bias after adjustment with calibrated parameters. Furthermore, it is shown that calibration is relatively effective in reducing false alarms (e.g., false alarm rate is down from 0.28 to 0.19 after calibration for small watersheds with calibrated parameters); but its impacts on detection rate are mixed. As an example, the detection rate of 2-Y events in fact declines for small watersheds after calibration is performed (from 0.4 to 0.28, and from 0.28 to 0.19 with raw and adjusted MQPE, respectively). These mixed outcomes underscore the complex interplays between errors in MQPEs, conditional bias in the reference gauge-based analysis, and structural deficiencies of the hydrologic model. [ABSTRACT FROM AUTHOR]

Systematic high quality oceanic in-situ precipitation measurements are requested on an international science level and are essential for improved understanding and validation of hydrological processes in satellite, re-analysis and model data. OceanRAIN, the shipboard “Ocean Rainfall And Ice-phase precipitation measurement Network” for surface validation is, to date, the only systematic long-term disdrometer-based oceanic shipboard precipitation data collection effort to establish a comprehensive statistical basis of precipitation for all climate related hotspots over the global oceans. OceanRAIN utilizes automated disdrometer systems (ODM470) capable of measuring precipitation occurrence, intensity and accumulation and discriminates for rain, snow and mixed-phase precipitation through minute-based particle size distributions. The ODM470 was especially designed for shipboard operation under high and frequently varying wind speeds and rough sea states. This paper provides an overview on the OceanRAIN project, the instrumentation, algorithms, methodology, and data products. The procedure of the data processing chain is outlined, including calibration, shipboard operation, data ingest and quality control. The selected research ships do not circumvent high impact weather, allowing for a collection of the full precipitation spectrum including extremes. By October 2014 the fast growing OceanRAIN database comprised more than 3.7 million minutes of precipitation measurements (including true zeros) since its start in 2010. OceanRAIN aims at increasing knowledge about oceanic precipitation, improving error characterization of GPM (Global Precipitation Measurement) era precipitation retrievals, adding to the continual improvement of the satellite retrieval algorithms, as well as benchmarking existing satellite-based climatologies, re-analysis and model data. The accumulating data volume can be utilized for statistical and process study applications on different temporal and spatial scales, microphysical studies of rain and snow formation, and yields insight to the point-to-area problem of precipitation. Information on OceanRAIN including data products is available via http://www.oceanrain.org . [ABSTRACT FROM AUTHOR]