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involvement. Another component that could be improved in the current GTG algorithm is the selection of the diagnostics that constitute the final ensemble combination. The current method employs a forward-selection optimization technique that maximizes the skill of the ensemble prediction for a given statistical metric of relevance. As the forecasting skill metric, the area under the receiving operating characteristic curve (AUC) is typically used, which represents the degree or measure of separability of
involvement. Another component that could be improved in the current GTG algorithm is the selection of the diagnostics that constitute the final ensemble combination. The current method employs a forward-selection optimization technique that maximizes the skill of the ensemble prediction for a given statistical metric of relevance. As the forecasting skill metric, the area under the receiving operating characteristic curve (AUC) is typically used, which represents the degree or measure of separability of
extensively in hydrology: input uncertainty, parameter uncertainty, and model uncertainty have been examined to provide ensemble streamflow simulations. For example, Carpenter and Georgakakos (2004) used a Monte Carlo technique to examine the impact of input uncertainty on ensemble streamflow simulations. Carbone and Dow (2005) applied an ensemble technique for drought index forecasts by resampling historical data. This work demonstrates the benefit of an ensemble approach for drought forecasting
extensively in hydrology: input uncertainty, parameter uncertainty, and model uncertainty have been examined to provide ensemble streamflow simulations. For example, Carpenter and Georgakakos (2004) used a Monte Carlo technique to examine the impact of input uncertainty on ensemble streamflow simulations. Carbone and Dow (2005) applied an ensemble technique for drought index forecasts by resampling historical data. This work demonstrates the benefit of an ensemble approach for drought forecasting
techniques with piecewise polynomials to estimate the probabilities of interest as functions of explanatory variables (predictors; see appendices A and B for further details). The explanatory variables used were monthly average temperature, (forecast from previous months), PDSI value in the previous month, maximum PDSI in the last 12 months, and values of Niño and PDO in addition to location (latitude, longitude) and month. The use of piecewise polynomials, rather than logistic regression with linear
techniques with piecewise polynomials to estimate the probabilities of interest as functions of explanatory variables (predictors; see appendices A and B for further details). The explanatory variables used were monthly average temperature, (forecast from previous months), PDSI value in the previous month, maximum PDSI in the last 12 months, and values of Niño and PDO in addition to location (latitude, longitude) and month. The use of piecewise polynomials, rather than logistic regression with linear
( Breiman 2001 ) is the most popular machine learning technique for classification, prediction, studying variable importance, and so on ( Yu et al. 2017 ). This model is known as the ensemble machine learning model because RF combines forecasting results from each sample to get the final prediction (the general description of the RF model is provided in section 2g ). RF has emerged as an alternative forecasting technique in many fields, especially for hydroclimatologic variables, for example, regional
( Breiman 2001 ) is the most popular machine learning technique for classification, prediction, studying variable importance, and so on ( Yu et al. 2017 ). This model is known as the ensemble machine learning model because RF combines forecasting results from each sample to get the final prediction (the general description of the RF model is provided in section 2g ). RF has emerged as an alternative forecasting technique in many fields, especially for hydroclimatologic variables, for example, regional
, and low-level turbulence (LLT) owing to turbulence events within the atmospheric boundary layer. These turbulence forecasting techniques typically relate the large-scale atmospheric turbulence production mechanisms that are explicitly resolved with horizontal grid spacings of NWP models (~10 km) to aircraft-scale turbulence (~10–100 m), through the assumption of a downscale energy-cascade process (e.g., Lindborg 1999 ). An example of turbulence forecasting algorithm is the Graphical Turbulence
, and low-level turbulence (LLT) owing to turbulence events within the atmospheric boundary layer. These turbulence forecasting techniques typically relate the large-scale atmospheric turbulence production mechanisms that are explicitly resolved with horizontal grid spacings of NWP models (~10 km) to aircraft-scale turbulence (~10–100 m), through the assumption of a downscale energy-cascade process (e.g., Lindborg 1999 ). An example of turbulence forecasting algorithm is the Graphical Turbulence
et al. 2016 ), and to provide computational efficiency for regional climate modeling ( DuchÊne et al. 2020 ). At the same time, studies such as Cheng et al. (2014) and Hu and Ayyub (2018) have utilized particular data distribution theories and statistical techniques to model and project regional temperature and precipitation (especially extremes). A statistical forecasting model—the autoregressive integrated moving average (ARIMA) model—was used in Lai and Dzombak (2020) to obtain city
et al. 2016 ), and to provide computational efficiency for regional climate modeling ( DuchÊne et al. 2020 ). At the same time, studies such as Cheng et al. (2014) and Hu and Ayyub (2018) have utilized particular data distribution theories and statistical techniques to model and project regional temperature and precipitation (especially extremes). A statistical forecasting model—the autoregressive integrated moving average (ARIMA) model—was used in Lai and Dzombak (2020) to obtain city
over a limited domain. The key difference in the current work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. Thus, the forecast skill of the system depends in large measure on the underlying NWP model rather than on the subsequent application of analogs. In this paper, a method is presented that leverages the existence of limited-area analogs ( Van den Dool 1994 ) and distinct weather patterns or
over a limited domain. The key difference in the current work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. Thus, the forecast skill of the system depends in large measure on the underlying NWP model rather than on the subsequent application of analogs. In this paper, a method is presented that leverages the existence of limited-area analogs ( Van den Dool 1994 ) and distinct weather patterns or
1284 JOURNAl, OF APPI, IED METEOROLOGY Vo~.uM~,llAn Operational Technique of Forecasting Thunderstorms Along the Lee Slopes of a Mountain Range JoH~ F. HEsz Dept. of Atmospheric Science, Colorado State University, Fort Collins 80521 (Manuscript received 19 May 1972, in revised form 3 Au~st 1972) ABSTRACT The lee slopes of the Rocky Mouut~ius from Montana
1284 JOURNAl, OF APPI, IED METEOROLOGY Vo~.uM~,llAn Operational Technique of Forecasting Thunderstorms Along the Lee Slopes of a Mountain Range JoH~ F. HEsz Dept. of Atmospheric Science, Colorado State University, Fort Collins 80521 (Manuscript received 19 May 1972, in revised form 3 Au~st 1972) ABSTRACT The lee slopes of the Rocky Mouut~ius from Montana
prediction (NWP) forecasts are generally used ( Lorenz et al. 2012 ; Kleissl 2013 ). Intraday irradiance forecasts are used by utility companies and ISOs for load following and planning for dispatch. At these lead times, a combination of methods—empirical models, satellite-based techniques, statistical methods, and NWP models—works best ( Bouzerdoum et al. 2013 ; Voyant et al. 2012 , 2013 ), with the combination producing the lowest forecast error depending on the specific lead time and available
prediction (NWP) forecasts are generally used ( Lorenz et al. 2012 ; Kleissl 2013 ). Intraday irradiance forecasts are used by utility companies and ISOs for load following and planning for dispatch. At these lead times, a combination of methods—empirical models, satellite-based techniques, statistical methods, and NWP models—works best ( Bouzerdoum et al. 2013 ; Voyant et al. 2012 , 2013 ), with the combination producing the lowest forecast error depending on the specific lead time and available
://www.aibn.no/Aviation/Reports/2009-02-eng . Bernstein , B. C. , 2000 : Regional and local influences on freezing drizzle, freezing rain, and ice pellet events . Wea. Forecasting , 15 , 485 – 508 , https://doi.org/10.1175/1520-0434(2000)015<0485:RALIOF>2.0.CO;2 . 10.1175/1520-0434(2000)015<0485:RALIOF>2.0.CO;2 Bernstein , B. C. , 2001 : Evaluation of NCAR Icing/SLD forecasts, tools and techniques used during the 1998 NASA SLD flight season. NASA CR–2001-210954, 52 pp., https
://www.aibn.no/Aviation/Reports/2009-02-eng . Bernstein , B. C. , 2000 : Regional and local influences on freezing drizzle, freezing rain, and ice pellet events . Wea. Forecasting , 15 , 485 – 508 , https://doi.org/10.1175/1520-0434(2000)015<0485:RALIOF>2.0.CO;2 . 10.1175/1520-0434(2000)015<0485:RALIOF>2.0.CO;2 Bernstein , B. C. , 2001 : Evaluation of NCAR Icing/SLD forecasts, tools and techniques used during the 1998 NASA SLD flight season. NASA CR–2001-210954, 52 pp., https
