NCAR Auto-Nowcast System

Mueller, Cynthia K.; Saxen, Thomas R.; Roberts, Rita D.; Wilson, James W.; Betancourt, Terri; Dettling, Susan; Oien, Niles; Yee, Jurng-Jae

doi:10.1175/1520-0434(2003)018<0545:nas>2.0.co;2

Cited by 165 publications

(118 citation statements)

References 24 publications

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“…Rainfall with ≥35 dBZ precipitation intensity measured by weather radar is known to have significant correlation with the eventual development of cumulonimbus clouds (Mecikalski and Bedka, 2006;Mueller et al, 2003). Therefore, the threshold of ≥35 dBZ precipitation intensity has been widely used as the definition of convective events (Mecikalski and Bedka, 2006;Mecikalski et al, 2008Mecikalski et al, , 2010Roberts and Rutledge, 2003;Walker et al, 2012).…”

Section: Weather Radar Echo and Lightning Datamentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

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Section: Weather Radar Echo and Lightning Datamentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

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“…Some studies have shown that the extrapolation-based algorithms are reasonable nowcasting methods for precipitation (Dixon and Wiener, 1993;Johnson et al, 1998;Germann andZawadzki, 2002, 2004;Germann et al, 2006;Mueller et al, 2003).…”

Section: Sqpf With Extrapolation-based Modelsmentioning

confidence: 99%

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Zahraei

Hsu

Sorooshian

et al. 2012

Atmospheric Research

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“…The ability to observe convergence lines tens of minutes before convection is initiated has applications for nowcasting thunderstorms. The Auto-Nowcast System (ANC), developed by the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, produces short-period forecasts of convective storm initiation, intensity and dissipation (Mueller et al 2003). A large component of the system is the algorithms that detect boundarylayer convergence lines and determine their likelihood of triggering storms.…”

Section: (C) the Nature And Significance Of Boundary-layer Convergencmentioning

confidence: 99%

A review of the initiation of precipitating convection in the United Kingdom

Bennett

Browning

Blyth

et al. 2006

Quart J Royal Meteoro Soc

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SUMMARYRecent severe weather events have prompted the European scientific community to assess the current understanding of convective processes with a view to more detailed and accurate forecasting. The initial development of convective cells remains one of the least understood aspects and one in which limited research has taken place. The important processes can be split into three main areas: boundary-layer forcing, upper-level forcing and secondary generation. This paper is a review of the mechanisms responsible for the initiation of precipitating convection in the United Kingdom; i.e. why convective clouds form and develop into precipitating clouds in a particular location.The topography of the United Kingdom has a large influence on the initiation of convection. Boundary-layer forcings determine the specific location where convection is triggered within larger regions of potential instability. These latter regions are created by mesoscale or synoptic-scale features at a higher level such as dry intrusions and mesoscale vortices. Second-generation cells are those formed by the interaction of outflow from convective clouds with the surrounding environmental air. Large, long-lived thunderstorm complexes can develop when new cells are repeatedly triggered on one side of the system. Current and future field campaigns along with the development of high-resolution modelling will enable these processes to be investigated in more detail than has previously been achieved.

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NCAR Auto-Nowcast System

Cited by 165 publications

References 24 publications

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

A review of the initiation of precipitating convection in the United Kingdom

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