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paper introduces the state-of-the-art parametric approach called natural gradient boosting (NGB; Duan et al. 2020 ). This method estimates the distributional parameters iteratively in the sequence of several weak base learners (e.g., decision trees). Experiments on several regression datasets have shown that the NGB is more flexible, scalable, and faster than existing methods for probabilistic forecasting ( Ren et al. 2019 ; Duan et al. 2020 ). Although this method is designed for tasks such as
paper introduces the state-of-the-art parametric approach called natural gradient boosting (NGB; Duan et al. 2020 ). This method estimates the distributional parameters iteratively in the sequence of several weak base learners (e.g., decision trees). Experiments on several regression datasets have shown that the NGB is more flexible, scalable, and faster than existing methods for probabilistic forecasting ( Ren et al. 2019 ; Duan et al. 2020 ). Although this method is designed for tasks such as
could greatly enhance our ability to observe it. Decision trees ( Quinlan 1986 ) were chosen as the primary approach for classification for two reasons. One of the biggest factors is the intuitive understanding of the model. Many other machine learning models, such as neural networks, are difficult to interpret, particularly by noncomputer scientists. As Fig. 1 illustrates with its sample decision tree, the relationships between attributes can be easily shown and converted into other forms, such
could greatly enhance our ability to observe it. Decision trees ( Quinlan 1986 ) were chosen as the primary approach for classification for two reasons. One of the biggest factors is the intuitive understanding of the model. Many other machine learning models, such as neural networks, are difficult to interpret, particularly by noncomputer scientists. As Fig. 1 illustrates with its sample decision tree, the relationships between attributes can be easily shown and converted into other forms, such
An Iterative Storm Segmentation and Classification Algorithm for Convection-Allowing Models and Gridded Radar Analyses-Radar/Multi-Sensor (MRMS; Smith et al. 2016 ) suite for observed storms. The classification model is a hand-developed decision tree. The storm modes in our classification scheme were selected to support a breadth of operational and research activities involving intense convection, but especially efforts within the NOAA Warn-on-Forecast (WoF; Stensrud et al. 2009 , 2013 ) program. We developed and tested our technique using the WoF System (WoFS; Wheatley et al. 2015 ; Jones et al. 2016 ; Lawson et al. 2018
-Radar/Multi-Sensor (MRMS; Smith et al. 2016 ) suite for observed storms. The classification model is a hand-developed decision tree. The storm modes in our classification scheme were selected to support a breadth of operational and research activities involving intense convection, but especially efforts within the NOAA Warn-on-Forecast (WoF; Stensrud et al. 2009 , 2013 ) program. We developed and tested our technique using the WoF System (WoFS; Wheatley et al. 2015 ; Jones et al. 2016 ; Lawson et al. 2018
DECEMBER 1987 J.R. COLQUHOUN 337FORECAST TECHNIQUESA Decision Tree Method of Forecasting Thunderstorms, Severe Thunderstorms and Tornadoes J. R. COLQUHOUNBureau of Meteorology, Darlinghurst, 2010, Australia(Manuscript received 20 November 1986, in final form 11 May 1987)ABSTRACT A decision tree approach to forecasting thunderstorms, severe thunderstorms and tornadoes is describedwhich uses only
DECEMBER 1987 J.R. COLQUHOUN 337FORECAST TECHNIQUESA Decision Tree Method of Forecasting Thunderstorms, Severe Thunderstorms and Tornadoes J. R. COLQUHOUNBureau of Meteorology, Darlinghurst, 2010, Australia(Manuscript received 20 November 1986, in final form 11 May 1987)ABSTRACT A decision tree approach to forecasting thunderstorms, severe thunderstorms and tornadoes is describedwhich uses only
ocean basins, though many variables have been suggested to be effective in predicting TC genesis in previous studies. The C4.5 algorithm is a useful machine learning method and a classic decision tree algorithm, which can deal with inherent nonlinear relationships in variables and missing values ( Quinlan 1987 , 1993 ; Fayyad 1997 ; Fayyad and Stolorz 1997 ). Moreover, this algorithm enables the quantification of the relative importance of variables and builds decision rules for prediction
ocean basins, though many variables have been suggested to be effective in predicting TC genesis in previous studies. The C4.5 algorithm is a useful machine learning method and a classic decision tree algorithm, which can deal with inherent nonlinear relationships in variables and missing values ( Quinlan 1987 , 1993 ; Fayyad 1997 ; Fayyad and Stolorz 1997 ). Moreover, this algorithm enables the quantification of the relative importance of variables and builds decision rules for prediction
Gaussian process algorithms, achieving over 98% accuracy in fog classification. More recently, Castillo-Botón et al. (2022) have comprehensively showed the ability of ensemble models based on decision trees, neural networks, support vector machines, and other techniques in capturing local atmospheric patterns associated with fog formation. In a case study by Miao et al. (2020) , a long short-term memory network was also found to be effective in predicting fog with a classification approach. Bari
Gaussian process algorithms, achieving over 98% accuracy in fog classification. More recently, Castillo-Botón et al. (2022) have comprehensively showed the ability of ensemble models based on decision trees, neural networks, support vector machines, and other techniques in capturing local atmospheric patterns associated with fog formation. In a case study by Miao et al. (2020) , a long short-term memory network was also found to be effective in predicting fog with a classification approach. Bari
, stationarity of the underlying processes, and normality). Data mining methods can be employed to unravel classification, clusters, association rules, decision rules, and other patterns from archived databases ( Han and Kamber 2006 ; Leung 2010 ). For TC research, association rule mining has been successfully used to discover association rules for the rapid intensification of Atlantic hurricanes ( Yang et al. 2007 , 2008 , 2011 ). In addition, a decision tree approach [e.g., the C4.5 algorithm; Quinlan
, stationarity of the underlying processes, and normality). Data mining methods can be employed to unravel classification, clusters, association rules, decision rules, and other patterns from archived databases ( Han and Kamber 2006 ; Leung 2010 ). For TC research, association rule mining has been successfully used to discover association rules for the rapid intensification of Atlantic hurricanes ( Yang et al. 2007 , 2008 , 2011 ). In addition, a decision tree approach [e.g., the C4.5 algorithm; Quinlan
procedures were available to alert forecasters to the likelihood of particular atmospheric environments conducive to significant weather events. It is the purpose of this paper to describe one such system, where the output of a regional NWP model is coupled with a decision tree to predict areas of likely thunderstorms and the most intense type of thunderstorm. Colquhoun (1987) developed a decision tree for forecasting thunderstorm occurrence and type, using as input environmental wind and thermodynamic
procedures were available to alert forecasters to the likelihood of particular atmospheric environments conducive to significant weather events. It is the purpose of this paper to describe one such system, where the output of a regional NWP model is coupled with a decision tree to predict areas of likely thunderstorms and the most intense type of thunderstorm. Colquhoun (1987) developed a decision tree for forecasting thunderstorm occurrence and type, using as input environmental wind and thermodynamic
Alternating Decision Trees for Cloud Masking in MODIS and VIIRS NASA Sea Surface Temperature Productsrecursive binary decision trees (BDtrees) ( Kilpatrick et al. 2015 , 2001 ). These trees were based on the classification algorithm of Breiman et al. (1984) , a statistical classifier that uses known characteristics of an object (referred to as “attributes” or “features”) to classify that object into one or more classes. In the MODIS cloud mask, several observed or derived variables were used to estimate the probability of a pixel belonging to one of two possible classes: “clear” or “potentially cloud
recursive binary decision trees (BDtrees) ( Kilpatrick et al. 2015 , 2001 ). These trees were based on the classification algorithm of Breiman et al. (1984) , a statistical classifier that uses known characteristics of an object (referred to as “attributes” or “features”) to classify that object into one or more classes. In the MODIS cloud mask, several observed or derived variables were used to estimate the probability of a pixel belonging to one of two possible classes: “clear” or “potentially cloud
contained in the cluster centroids. Table 2 Time of year corresponding to the data in each section of the online supplemental material (SM). To assist users in identifying an appropriate AOD 550 to use for atmospheric transmission modeling in support of quick-turn analysis and decision-making, we have provided several tools that help identify a suitable cluster. First, each of the MERRA-2 cluster code books has a unique decision tree that assists users in narrowing the set of clusters under
contained in the cluster centroids. Table 2 Time of year corresponding to the data in each section of the online supplemental material (SM). To assist users in identifying an appropriate AOD 550 to use for atmospheric transmission modeling in support of quick-turn analysis and decision-making, we have provided several tools that help identify a suitable cluster. First, each of the MERRA-2 cluster code books has a unique decision tree that assists users in narrowing the set of clusters under
