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1. Introduction Algorithms that can extract properties of storm cells 1 and track those properties over time provide information that is important to forecasters in assessing storm intensity, growth, and decay ( Wilson et al. 1998 ). However, associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Because storm-tracking algorithms are a key component of nowcasting systems, the problem of how to track storms has received
1. Introduction Algorithms that can extract properties of storm cells 1 and track those properties over time provide information that is important to forecasters in assessing storm intensity, growth, and decay ( Wilson et al. 1998 ). However, associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Because storm-tracking algorithms are a key component of nowcasting systems, the problem of how to track storms has received
maximum wind (RMW), and the center location of a TC from Doppler radar data, and could be applied in an operational environment. However, it was highly affected by the asymmetry of circulations and strong mean flows across the vortex. Marks et al. (1992) used the “simplex” algorithm ( Nelder and Mead 1965 ) to find the center that maximizes the tangential circulations encompassing the observed RMW at different altitudes. They found that the center was 3–6 km to the right of that determined
maximum wind (RMW), and the center location of a TC from Doppler radar data, and could be applied in an operational environment. However, it was highly affected by the asymmetry of circulations and strong mean flows across the vortex. Marks et al. (1992) used the “simplex” algorithm ( Nelder and Mead 1965 ) to find the center that maximizes the tangential circulations encompassing the observed RMW at different altitudes. They found that the center was 3–6 km to the right of that determined
( Bocchieri and Glahn 1972 ; Bocchieri et al. 1974 ). MOS schemes are still applied to many of today’s numerical models. Along with NWP model data, observations are also used to develop MOS regression equations for ceiling and visibility, temperature and dewpoint, wind speed and direction, probability of precipitation, precipitation amount, and cloud cover. MOS techniques, applied to various NWP models, have also been used to develop specific algorithms to forecast the occurrence of Levante wind regimes
( Bocchieri and Glahn 1972 ; Bocchieri et al. 1974 ). MOS schemes are still applied to many of today’s numerical models. Along with NWP model data, observations are also used to develop MOS regression equations for ceiling and visibility, temperature and dewpoint, wind speed and direction, probability of precipitation, precipitation amount, and cloud cover. MOS techniques, applied to various NWP models, have also been used to develop specific algorithms to forecast the occurrence of Levante wind regimes
accretion fields can also be generated using the hybrid approach. When coupled with the electric distribution system models, such data, which are currently lacking, will enable utilities to assess the economic value of management practices such as tree trimming and distribution system maintenance. 2. Precipitation-type models Cortinas and Baldwin (1999) describe and compare the performance of six precipitation-type forecast algorithms. Collectively, the methods were better able to forecast the
accretion fields can also be generated using the hybrid approach. When coupled with the electric distribution system models, such data, which are currently lacking, will enable utilities to assess the economic value of management practices such as tree trimming and distribution system maintenance. 2. Precipitation-type models Cortinas and Baldwin (1999) describe and compare the performance of six precipitation-type forecast algorithms. Collectively, the methods were better able to forecast the
, whereas the Z DR column extends to ~6.3 km AGL. Since the BWER can be characterized as a minimum within a local maximum (i.e., the convective storm) in reflectivity factor, it is not particularly easy to design an algorithm to diagnose this feature, although Lakshmanan and Witt (1997) describe such an algorithm. In addition, merging convective storms or other processes can create local minima in Z H that are not associated with BWERs (at least how the term BWER is typically used). In contrast
, whereas the Z DR column extends to ~6.3 km AGL. Since the BWER can be characterized as a minimum within a local maximum (i.e., the convective storm) in reflectivity factor, it is not particularly easy to design an algorithm to diagnose this feature, although Lakshmanan and Witt (1997) describe such an algorithm. In addition, merging convective storms or other processes can create local minima in Z H that are not associated with BWERs (at least how the term BWER is typically used). In contrast
) and quantitative precipitation estimation (e.g., Giangrande and Ryzhkov 2008 ) algorithms. For a detailed overview and discussion regarding commonly used polarimetric radar variables, including radar reflectivity factor at horizontal polarization Z H , copolar cross-correlation coefficient ρ hv , specific differential phase K DP , and differential reflectivity Z DR , the reader is referred to Doviak and Zrnić (1993) , Bringi and Chandrasekar (2001) , and Kumjian (2013a , b , c) . Perhaps
) and quantitative precipitation estimation (e.g., Giangrande and Ryzhkov 2008 ) algorithms. For a detailed overview and discussion regarding commonly used polarimetric radar variables, including radar reflectivity factor at horizontal polarization Z H , copolar cross-correlation coefficient ρ hv , specific differential phase K DP , and differential reflectivity Z DR , the reader is referred to Doviak and Zrnić (1993) , Bringi and Chandrasekar (2001) , and Kumjian (2013a , b , c) . Perhaps
hydrometeor classification algorithm (HCA; Zrnić et al. 2001 ; Straka et al. 2000 ; Park et al. 2009 ) for use in warm-season convective weather; it, along with a melting-layer detection algorithm (MDA) and a rainfall-estimation algorithm, is the only such algorithm currently scheduled for the initial deployment of the dual-polarization (dual pol) WSR-88D network. Yet, a top-ranked expectation recently expressed by the operational forecast community is the ability to “determine the precipitation type
hydrometeor classification algorithm (HCA; Zrnić et al. 2001 ; Straka et al. 2000 ; Park et al. 2009 ) for use in warm-season convective weather; it, along with a melting-layer detection algorithm (MDA) and a rainfall-estimation algorithm, is the only such algorithm currently scheduled for the initial deployment of the dual-polarization (dual pol) WSR-88D network. Yet, a top-ranked expectation recently expressed by the operational forecast community is the ability to “determine the precipitation type
was obtained with greater detail than was possible from traditional operational datasets. In another example, Hyvärinen and Saltikoff (2010) compared hail photos in Finland from the photo-sharing service Flickr to output from algorithms for hail detection from dual-polarimetric radar, showing the possible utility of such nontraditional online data. Finally, crowd-sourcing applications, like mobile weather apps, are the newest approach allowing anyone to send their real-time weather observations
was obtained with greater detail than was possible from traditional operational datasets. In another example, Hyvärinen and Saltikoff (2010) compared hail photos in Finland from the photo-sharing service Flickr to output from algorithms for hail detection from dual-polarimetric radar, showing the possible utility of such nontraditional online data. Finally, crowd-sourcing applications, like mobile weather apps, are the newest approach allowing anyone to send their real-time weather observations
coverage, Doppler capabilities, and a suite of severe weather detection algorithms. A more complete description of the WSR-88D system is given in Crum and Alberty (1993) . Severe weather detection algorithms are a key element of the WSR-88D system for numerous reasons. Such algorithms can provide severe weather potential and detection products to a forecaster during severe weather warning operations. The WSR-88D algorithms used in NWS offices across the country can detect mesocyclone and tornadic
coverage, Doppler capabilities, and a suite of severe weather detection algorithms. A more complete description of the WSR-88D system is given in Crum and Alberty (1993) . Severe weather detection algorithms are a key element of the WSR-88D system for numerous reasons. Such algorithms can provide severe weather potential and detection products to a forecaster during severe weather warning operations. The WSR-88D algorithms used in NWS offices across the country can detect mesocyclone and tornadic
) recommend the development of automated approaches for identifying, tracking, and visualizing drylines simulated in high-resolution models, and recognize that the increasing use of high-resolution ensembles will make automation increasingly valuable. This study aims to address some of the recommendations of Coffer et al. (2013) by developing an automated, multiparameter dryline identification algorithm, which applies image-processing and pattern recognition techniques to various fields (and their
) recommend the development of automated approaches for identifying, tracking, and visualizing drylines simulated in high-resolution models, and recognize that the increasing use of high-resolution ensembles will make automation increasingly valuable. This study aims to address some of the recommendations of Coffer et al. (2013) by developing an automated, multiparameter dryline identification algorithm, which applies image-processing and pattern recognition techniques to various fields (and their
