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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]

Abstract: Cascades have been used as models of precipitation for nearly 25years yet many basic questions remain unanswered and most applications have been to small or to regional scales. In this paper we revisit some of these issues and present an inter comparison of four global scale data sets each with exceptional characteristics: the hourly (and ≈200km) resolution Climate Prediction Center (CPC) gridded precipitation over the continental US, the three hourly global ECMWF reanalysis stratiform precipitation product at 1.5° resolution, the six hourly Twentieth Century reanalysis at 2° (1871–2008) and an analysis of 5300 orbits (1year) of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall over ±40° latitude. The data were analysed zonally, meridionally and in time. Each showed cascade structures; in space up to planetary scales and in time up to 5–10days. For each we estimated the moment scaling exponent (K(q)) as well as its characterisation near the mean (C 1) and the effective outer cascade scales. The comparison of the cascade structures in different directions indicate that although anomalies remain, they are relatively isotropic in (horizontal) space–time. For any given direction, the comparison of the different products indicates very similar but not identical scaling properties. In order to be properly inter calibrated at more than a unique resolution, the different products must have the same exponents and outer scales so that - while the similarities are encouraging – the remaining anomalies point to needed improvements in techniques for estimating areal rainfall. Our main conclusion is that the rain rate biases introduced by the measurement techniques are larger than the deviations from perfect log–log linearity (scaling) so that multifractal models will be needed for improving space–time precipitation measurements. Our analyses clarify various fundamental issues. For example, the CPC data show that at “weather” scales smaller than ≈2days in time; H =0.17±0.11 so that rain is apparently not the direct product of a cascade process (which would have H =0). Similarly, for the low frequency weather regime (scales >≈2weeks) we find H ≈−0.42 so that fluctuations tend to decrease rather than increase with scale and display long range statistical dependencies. Finally, we find power law probability tails with exponent q D ≈3 so that the orders of singularity are apparently not bounded, ruling out several model types including microcanonical and log-Poisson models. [Copyright &y& Elsevier]

Abstract: This paper introduces a new model for regional climate simulation-Star model, and explains the scheme, the usage and the application of the Star model. Taking the “Temperature of 2m” and “Precipitation” as the characteristic variables of climate, the model is calculated and tested by taking Huailai climate station of China as practical example. [Copyright &y& Elsevier]

Summary: Investigation of links between large-scale climate and river basin-scale precipitation and discharge is necessary to improve understanding of the hydroclimatological process chain. This paper aims to identify the large-scale circulation variables that yield significant statistical relationships with precipitation and river flow for ten British basins, and evaluate the dependence of the strength and location of centres of climate–hydrology correlation on basin situation. Using quasi-geostrophic theory as a framework to select appropriate explanatory variables, this study undertakes ranked correlation analysis between gridded ERA-40 atmospheric data and precipitation and river flow observations at a monthly resolution over a 26year period. Relationships between large-scale atmospheric circulation and precipitation and river flow are found to vary spatially and seasonally across Britain, with strongest associations evident in western Britain and in winter. Monthly precipitation totals and river flow demonstrate highly significant negative (positive) correlation with mean sea level pressure MSLP (Zonal Wind U), but the location of high correlation centres vary for different basins. There are weaker atmospheric links with river flow compared to precipitation, reflecting the non-linearity of the rainfall–runoff transformation and the importance of basin properties as a modifier of climate inputs. The North Atlantic Oscillation Index (NAOI) cannot capture the seasonal movement of climate–hydrology connections due to the fixed station locations used for the index calculation, and thus has weaker monthly correlations with precipitation and river flow compared to gridded ERA-40 circulation variables (and in particular the comparable MSLP). [ABSTRACT FROM AUTHOR]

Abstract: This review paper deals with four aspects of precipitation: measurement, remote sensing, climatology and modeling. The measurement of precipitation is summarized in terms of the instruments that count and measure drop sizes (defined as disdrometers) and the instruments that measure an average quantity proportional to the integrated volume of an ensemble of raindrops (these instruments are normally called rain gauges). Remote sensing of precipitation is accomplished with ground based radar and from satellite retrievals and these two approaches are separately discussed. The climatology of precipitation has evolved through the years from the traditional rain gauge data analyses to the more sophisticated data bases that result from a coalescence of data and information on precipitation that is available from several sources into amalgamated products. Recently, rain observations from both ground and space have been assimilated into regional and global numerical weather prediction models aiming at improved moisture analysis and better forecasts of extreme weather events. The current status and the main outstanding issues related to precipitation forecasting are discussed, providing a basic structure for research coordination aimed at the improvement of modeling, observation and data assimilation applicable to global and regional scales. [Copyright &y& Elsevier]

Abstract: Human activity is known to have an important impact both in the radiation budget and the hydrological cycle. One particular aspect is the increase of anthropogenic aerosols due to changes in land use and industrial activity. However the relation between aerosols and precipitation is still far from being fully understood due to the complexity of the mechanisms involved and the lack of sound data. A multi-sensor approach, combining instruments meant to study aerosols and others meant at the study of precipitation, is required to get a better insight on this phenomenon. The International Research Centre for Telecommunications and Radar (IRCTR) has developed a meteorological radar called IDRA (IRCTR Drizzle Radar) aimed at obtaining high resolution temporal and spatial measurements of all sorts of precipitation including those non-detectable by standard meteorological radar like drizzle. The measurements obtained by the system, coupled with data obtained by other sensors placed in the Cabauw Experimental Site for Atmospheric Research (CESAR) observatory (Cabauw, The Netherlands), are expected to provide crucial information for a better characterization of these mechanisms. The information obtained can be used for a better spatial planning in order to reduce the impact of human activity in the climate. This paper describes the relevant characteristics of the system, its signal processing and the products obtained. [Copyright &y& Elsevier]

Summary: Although weather generators have been used for a long time, recent years have seen a surge in interest in them as potential downscaling tools for climate change impacts studies. Weather generators can only generate weather data at a single point, or independently at several points, whereas many climate change impact studies require information at the basin scale. Such studies would require the coherent generation of weather data at several locations over a basin. The literature on multi-site weather data generation is very thin, with most of the work based on the approach described by Wilks (Wilks, D.S., 1998. Multi-site generalization of a daily stochastic precipitation generation model. J. Hydrol. 210, 178–191). This approach demands significant work to set up, and may suffer from ill-defined correlation matrices due to noise in the observed data. In addition, multi-site generation must address the complex problem of spatial intermittence, in which precipitation amounts depend on neighbouring stations being wet or dry. This likely explains why multi-site generation, despite its obvious advantages, has not been widely used. This paper presents an algorithm for the efficient stochastic generation of multi-site precipitation data following the Wilks approach. The effect of noise on the performance of the algorithm is examined, and the results indicate that it performs very well, even with excessive noise added to the data. The algorithm is fast, simple, easy to implement, and significantly simplifies the generation of multi-site precipitation data for impact studies of climate scenarios. The spatial intermittence problem is dealt with by linking average precipitation to an index that describes the distribution of precipitation occurrence at the basin scale. [Copyright &y& Elsevier]