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Dive into the research topics where Youcun Qi is active.

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Featured researches published by Youcun Qi.


Bulletin of the American Meteorological Society | 2016

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

Jian Zhang; Kenneth W. Howard; Carrie Langston; Brian Kaney; Youcun Qi; Lin Tang; Heather M. Grams; Yadong Wang; Stephen B. Cocks; Steven M. Martinaitis; Ami Arthur; Karen Cooper; Jeff Brogden; David Kitzmiller

AbstractRapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.


Journal of Applied Meteorology and Climatology | 2013

Impact of Low-Level Jets on the Nocturnal Urban Heat Island Intensity in Oklahoma City

Xiao-Ming Hu; Petra M. Klein; Ming Xue; Julie K. Lundquist; Fuqing Zhang; Youcun Qi

AbstractPrevious analysis of Oklahoma City (OKC), Oklahoma, temperature data indicated that urban heat islands (UHIs) frequently formed at night and the observed UHI intensity was variable (1°–4°C). The current study focuses on identifying meteorological phenomena that contributed to the variability of nocturnal UHI intensity in OKC during July 2003. Two episodes, one with a strong UHI signature and one with a weak signature, were studied in detail using observations along with simulations with the Weather Research and Forecasting model. Mechanical mixing associated with low-level jets (LLJs) played a critical role in moderating the nocturnal UHI intensity. During nights with weak LLJs or in the absence of LLJs, vertical mixing weakened at night and strong temperature inversions developed in the rural surface layer as a result of radiative cooling. The shallow stable boundary layer (SBL < 200 m) observed under such conditions was strongly altered inside the city because rougher and warmer surface characte...


Journal of Hydrometeorology | 2013

Evaluation and Uncertainty Estimation of NOAA/NSSL Next-Generation National Mosaic Quantitative Precipitation Estimation Product (Q2) over the Continental United States

Sheng Chen; Jonathan J. Gourley; Yang Hong; Pierre Kirstetter; Jian Zhang; Kenneth W. Howard; Zachary L. Flamig; Junjun Hu; Youcun Qi

AbstractQuantitative precipitation estimation (QPE) products from the next-generation National Mosaic and QPE system (Q2) are cross-compared to the operational, radar-only product of the National Weather Service (Stage II) using the gauge-adjusted and manual quality-controlled product (Stage IV) as a reference. The evaluation takes place over the entire conterminous United States (CONUS) from December 2009 to November 2010. The annual comparison of daily Stage II precipitation to the radar-only Q2Rad product indicates that both have small systematic biases (absolute values > 8%), but the random errors with Stage II are much greater, as noted with a root-mean-squared difference of 4.5 mm day−1 compared to 1.1 mm day−1 with Q2Rad and a lower correlation coefficient (0.20 compared to 0.73). The Q2 logic of identifying precipitation types as being convective, stratiform, or tropical at each grid point and applying differential Z–R equations has been successful in removing regional biases (i.e., overestimated ...


Journal of Hydrometeorology | 2012

Radar-Based Quantitative Precipitation Estimation for the Cool Season in Complex Terrain: Case Studies from the NOAA Hydrometeorology Testbed

Jian Zhang; Youcun Qi; David E. Kingsmill; Kenneth W. Howard

AbstractThis study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed signifi...


Journal of Hydrometeorology | 2013

Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS

Sheng Chen; Pierre Kirstetter; Yang Hong; Jonathan J. Gourley; Yudong Tian; Youcun Qi; Qing Cao; Jian Zhang; Kenneth W. Howard; Junjun Hu; Xianwu Xue

AbstractIn this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agencys (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratorys (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering...


Journal of Applied Meteorology and Climatology | 2013

Statistical and Physical Analysis of the Vertical Structure of Precipitation in the Mountainous West Region of the United States Using 11+ Years of Spaceborne Observations from TRMM Precipitation Radar

Qing Cao; Yang Hong; Jonathan J. Gourley; Youcun Qi; Jian Zhang; Yixin Wen; Pierre-Emmanuel Kirstetter

AbstractThis study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the charac...


Journal of Applied Meteorology and Climatology | 2014

Characterizing Spatiotemporal Variations of Hourly Rainfall by Gauge and Radar in the Mountainous Three Gorges Region

Zhe Li; Dawen Yang; Yang Hong; Jian Zhang; Youcun Qi

Understanding spatiotemporal rainfall patterns in mountainous areas is of great importance for prevention of natural disasters such as flash floods and landslides. There is little knowledge about rainfall variability over historically underobserved complex terrains, however, and especially about the variations of hourly rainfall. In this study, the spatiotemporal variations of hourly rainfall in the Three Gorges region (TGR) of China are investigated with gauge and newly available radar data. The spatial pattern of hourly rainfall has been examined by a number of statistics, and they all show that the rainfall variations are time-scale and location dependent. In general, the northern TGR receives more-intense and longer-duration rainfall than do other parts of the TGR, and short-duration storms could occur in most of the TGR. For temporal variations, the summerdiurnal cycle shiftsfrom a morningpeakin the west toa late-afternoon peak in the eastwhile a mixed pattern of two peaks exists in the middle. In statistical terms, empirical model‐basedestimation indicates that the correlation scale of hourly rainfall is about 40km. Further investigation shows that the correlation distance varies with season, from 30km in the warm season to 60km in the cold season. In addition, summer rainstorms extracted from radar rainfall data are characterized by short duration (6‐8h) and highly localized patterns (5‐17 and 13‐36km in the minor and major directions, respectively). Overall, this research provides quantitative information about the rainfall regime in the TGR and shows that the combination of gauge and radar data is useful for characterizing the spatiotemporal pattern of storm rainfall over complex terrain.


Journal of Hydrometeorology | 2014

A Real-Time Algorithm for Merging Radar QPEs with Rain Gauge Observations and Orographic Precipitation Climatology

Jian Zhang; Youcun Qi; Carrie Langston; Brian Kaney; Kenneth W. Howard

AbstractHigh-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (Z–R) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a ...


Water Resources Research | 2014

The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long‐term spaceborne observations with TRMM precipitation radar

Qing Cao; Youcun Qi

The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with ∼15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin.


Journal of Hydrometeorology | 2013

Correction of Radar QPE Errors for Nonuniform VPRs in Mesoscale Convective Systems Using TRMM Observations

Youcun Qi; Jian Zhang; Qing Cao; Yang Hong; Xiao-Ming Hu

AbstractMesoscale convective systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) is often found in the stratiform region. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE). A vertical profile of reflectivity (VPR) correction is necessary to reduce such biases. However, existing VPR correction methods for ground-based radars often perform poorly for MCSs owing to their coarse resolution and poor coverage in the vertical direction, especially at far ranges. Spaceborne radars such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), on the other hand, can provide high resolution VPRs. The current study explores a new approach of incorporating the TRMM VPRs into the VPR correction for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar QPE. High-resolution VPRs derived from the Ku-band TRMM PR data are converted into equivalent S-band VPRs using an empiri...

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Jian Zhang

National Oceanic and Atmospheric Administration

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Yang Hong

University of Oklahoma

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Kenneth W. Howard

National Oceanic and Atmospheric Administration

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Qing Cao

University of Oklahoma

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Brian Kaney

University of Oklahoma

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Jonathan J. Gourley

National Oceanic and Atmospheric Administration

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Stephen B. Cocks

National Oceanic and Atmospheric Administration

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Junjun Hu

University of Oklahoma

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Sheng Chen

University of Oklahoma

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