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1. Introduction Hydrological forecasts for floods and landslides often require precipitation information at finer space and time scales than those available from spaceborne microwave observations. Statistical approaches have been used commonly to merge and downscale precipitation observations ( Huffman et al. 2007 ). There is an emerging interest in using data assimilation techniques to extract information from multiple data sources, combining with high-resolution modeling to downscale
1. Introduction Hydrological forecasts for floods and landslides often require precipitation information at finer space and time scales than those available from spaceborne microwave observations. Statistical approaches have been used commonly to merge and downscale precipitation observations ( Huffman et al. 2007 ). There is an emerging interest in using data assimilation techniques to extract information from multiple data sources, combining with high-resolution modeling to downscale
1. Introduction One of the greatest challenges in meteorology is the prediction of precipitation, particularly the accurate prediction of extreme precipitation events (i.e., events with large precipitation amounts). Recent surveys of public use of forecast information ( Lazo et al. 2009 ) have documented that precipitation prediction (e.g., the location, timing, and amount of precipitation) is the most heavily utilized part of standard forecasts. This general public demand for precipitation
1. Introduction One of the greatest challenges in meteorology is the prediction of precipitation, particularly the accurate prediction of extreme precipitation events (i.e., events with large precipitation amounts). Recent surveys of public use of forecast information ( Lazo et al. 2009 ) have documented that precipitation prediction (e.g., the location, timing, and amount of precipitation) is the most heavily utilized part of standard forecasts. This general public demand for precipitation
, and this approach leaves some artifacts that result from the disparity between satellite-based estimates and isolated gauge reports ( Tian et al. 2009 ). Recently Xiong et al. (2008) and Janowiak et al. (2009) proposed a procedure to correct the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Morphing technique (CMORPH; Joyce et al. 2004 ) in real time. This procedure first performs bias correction for CMORPH with the (probability density function) PDF
, and this approach leaves some artifacts that result from the disparity between satellite-based estimates and isolated gauge reports ( Tian et al. 2009 ). Recently Xiong et al. (2008) and Janowiak et al. (2009) proposed a procedure to correct the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) Morphing technique (CMORPH; Joyce et al. 2004 ) in real time. This procedure first performs bias correction for CMORPH with the (probability density function) PDF
heavy rainfall events correspond with the intensified baiu front that causes flooding and serious damages to human life and properties. Early prediction and warning for heavy rainfall events are among the most important elements for minimizing damages. However, it has always been difficult to accurately forecast rainfall owing to imperfection of global models and observational errors. Development of a forecasting system for extreme events continues to be a challenging task in spite of recent rapid
heavy rainfall events correspond with the intensified baiu front that causes flooding and serious damages to human life and properties. Early prediction and warning for heavy rainfall events are among the most important elements for minimizing damages. However, it has always been difficult to accurately forecast rainfall owing to imperfection of global models and observational errors. Development of a forecasting system for extreme events continues to be a challenging task in spite of recent rapid
1. Introduction High-quality precipitation data at fine time and space resolution have many hydrometeorological applications including flood forecasting, drought monitoring, disaster management, and initialization of numerical weather prediction models, among others. The current constellation of earth observing satellites allows global retrieval of precipitation data that complement ground precipitation observations from relatively sparse radar/gauge networks. While high
1. Introduction High-quality precipitation data at fine time and space resolution have many hydrometeorological applications including flood forecasting, drought monitoring, disaster management, and initialization of numerical weather prediction models, among others. The current constellation of earth observing satellites allows global retrieval of precipitation data that complement ground precipitation observations from relatively sparse radar/gauge networks. While high
been under real-time evaluations at several river forecast centers. One of the issues found with the NMQ radar-based precipitation product is the overestimation of precipitation associated with bright band (BB). The bright band is a layer of enhanced reflectivity due to melting of aggregated snow ( Fig. 1 ). The phenomenon has been recognized near the beginning of radar meteorology (e.g., Ryde 1947 ; Austin and Bemis 1950 ; Wexler and Atlas 1956 ; Lhermitte and Atlas 1963 ), and many recent
been under real-time evaluations at several river forecast centers. One of the issues found with the NMQ radar-based precipitation product is the overestimation of precipitation associated with bright band (BB). The bright band is a layer of enhanced reflectivity due to melting of aggregated snow ( Fig. 1 ). The phenomenon has been recognized near the beginning of radar meteorology (e.g., Ryde 1947 ; Austin and Bemis 1950 ; Wexler and Atlas 1956 ; Lhermitte and Atlas 1963 ), and many recent
; http://hmt.noaa.gov ) in the mountainous area of northern California and develops an S-PROF–based VPR correction methodology (S-PROF VPR) that takes into account orographic processes and radar beam broadenings with range. The S-PROF-VPR correction technique was tested using three heavy rain events in northern California during the period from 21 December 2005 to 1 January 2006. The S-PROF-VPR corrected radar QPE is compared with ZQKH2012 . The new technique was found to provide consistent
; http://hmt.noaa.gov ) in the mountainous area of northern California and develops an S-PROF–based VPR correction methodology (S-PROF VPR) that takes into account orographic processes and radar beam broadenings with range. The S-PROF-VPR correction technique was tested using three heavy rain events in northern California during the period from 21 December 2005 to 1 January 2006. The S-PROF-VPR corrected radar QPE is compared with ZQKH2012 . The new technique was found to provide consistent
to preview the data by quick looks, which are stored with the data files in the database. The data are all stored in network common data form (netCDF) format (available online at http://www.unidata.ucar.edu/software/netcdf ) and comply with the Climate and Forecast (CF) Metadata Convention version 1.4 (available online at http://cf-pcmdi.llnl.gov ). The CDS has become operational in July 2009 and will be filled with many datasets from the operational continuous measurement program, measurements
to preview the data by quick looks, which are stored with the data files in the database. The data are all stored in network common data form (netCDF) format (available online at http://www.unidata.ucar.edu/software/netcdf ) and comply with the Climate and Forecast (CF) Metadata Convention version 1.4 (available online at http://cf-pcmdi.llnl.gov ). The CDS has become operational in July 2009 and will be filled with many datasets from the operational continuous measurement program, measurements
temporal stability on hydrologic simulation are subjects addressed in the following section. 5. Hydrologic evaluation The second component of this study is an evaluation of KOUN precipitation estimators in the context as inputs to a distributed parameter hydrologic model. The primary focus is to answer: will polarimetric radar upgrades to the WSR-88D network improve hydrologic simulation and flash-flood forecasting? As in the previous section, R ( Z ) is the least biased when considering all events
temporal stability on hydrologic simulation are subjects addressed in the following section. 5. Hydrologic evaluation The second component of this study is an evaluation of KOUN precipitation estimators in the context as inputs to a distributed parameter hydrologic model. The primary focus is to answer: will polarimetric radar upgrades to the WSR-88D network improve hydrologic simulation and flash-flood forecasting? As in the previous section, R ( Z ) is the least biased when considering all events
1. Introduction Much progress has been made in the last decade in the study of orographic precipitation using high-resolution idealized and forecast models, case studies from field projects, and the analysis of radar and precipitation-gauge characteristics from multiseason datasets. Mountains more commonly modify and amplify precipitation associated with preexisting weather disturbances rather than solely initiating all the precipitation ( Smith 2006 ). For unblocked flow, the strength and
1. Introduction Much progress has been made in the last decade in the study of orographic precipitation using high-resolution idealized and forecast models, case studies from field projects, and the analysis of radar and precipitation-gauge characteristics from multiseason datasets. Mountains more commonly modify and amplify precipitation associated with preexisting weather disturbances rather than solely initiating all the precipitation ( Smith 2006 ). For unblocked flow, the strength and
