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in initial and boundary conditions, and other reasons. Therefore, various dynamical model postprocessing strategies are developed to remove forecast biases and errors, and to nudge model predictions toward observations, before forecasts are issued to the public. Linear statistical postprocessing methods show some success in improving direct model prediction skill. One of those techniques is the model output statistics (MOS), which relates observed weather elements (predictands) to appropriate
in initial and boundary conditions, and other reasons. Therefore, various dynamical model postprocessing strategies are developed to remove forecast biases and errors, and to nudge model predictions toward observations, before forecasts are issued to the public. Linear statistical postprocessing methods show some success in improving direct model prediction skill. One of those techniques is the model output statistics (MOS), which relates observed weather elements (predictands) to appropriate
Nonlinear Wave Ensemble Averaging in the Gulf of Mexico Using Neural Networkspotential. Berbić et al. (2017) applied ANNs and support vector machines for short forecasts of significant wave height. Dixit and Londhe (2016) developed a neuro wavelet technique, combining discrete wavelet transform and ANNs, to explore the predictability of extreme events for five major hurricanes at four locations in the Gulf of Mexico. Deo et al. (2001) , Deo and Sridhar Naidu (1998) , and Mandal and Prabaharan (2006) developed ANN systems to predict significant wave heights in India, and
potential. Berbić et al. (2017) applied ANNs and support vector machines for short forecasts of significant wave height. Dixit and Londhe (2016) developed a neuro wavelet technique, combining discrete wavelet transform and ANNs, to explore the predictability of extreme events for five major hurricanes at four locations in the Gulf of Mexico. Deo et al. (2001) , Deo and Sridhar Naidu (1998) , and Mandal and Prabaharan (2006) developed ANN systems to predict significant wave heights in India, and
, multiple-physics CAEs is an artificial inflation of ensemble spread due to the existence of systematic biases between ensemble members ( Eckel and Mass 2005 ; Clark et al. 2010b ; Loken et al. 2019 ). These shortcomings are typically resolved using one or more postprocessing techniques, including isotropic (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ; Roberts et al. 2019 ) or anisotropic (e.g., Marsh et al. 2012 ) spatial smoothing of the raw forecast probability field
, multiple-physics CAEs is an artificial inflation of ensemble spread due to the existence of systematic biases between ensemble members ( Eckel and Mass 2005 ; Clark et al. 2010b ; Loken et al. 2019 ). These shortcomings are typically resolved using one or more postprocessing techniques, including isotropic (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ; Roberts et al. 2019 ) or anisotropic (e.g., Marsh et al. 2012 ) spatial smoothing of the raw forecast probability field
applied techniques known as supervised learning methods, where the user oversees many aspects of the study, including the case selection, the predictor selection, the classification criteria and method, and traditional cross-validation techniques ( Russell and Norvig 2010 ). Techniques such as regression and classification fall under the broader heading of supervised learning methods. These methods have value as evidenced by the improvements seen in RI forecasts in recent years ( Kaplan et al. 2015
applied techniques known as supervised learning methods, where the user oversees many aspects of the study, including the case selection, the predictor selection, the classification criteria and method, and traditional cross-validation techniques ( Russell and Norvig 2010 ). Techniques such as regression and classification fall under the broader heading of supervised learning methods. These methods have value as evidenced by the improvements seen in RI forecasts in recent years ( Kaplan et al. 2015
the model resolution, to extend the forecast horizon, and to increase ensemble size). Flexibility. ML techniques can accommodate (i) variables that have not been (and sometimes cannot be) included in physically based models, (ii) physical constrains (like conservation laws or balance equations), (iii) processes that are nonlinear, (iv) non-Gaussian observation errors, and (v) empirical data for processes for which the true physics is poorly understood ( Krasnopolsky 2013 ). Ease of use. Modern
the model resolution, to extend the forecast horizon, and to increase ensemble size). Flexibility. ML techniques can accommodate (i) variables that have not been (and sometimes cannot be) included in physically based models, (ii) physical constrains (like conservation laws or balance equations), (iii) processes that are nonlinear, (iv) non-Gaussian observation errors, and (v) empirical data for processes for which the true physics is poorly understood ( Krasnopolsky 2013 ). Ease of use. Modern
1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites
1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites
specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
specific hydrologic processes. For example, the Flash Flood Guidance (FFG) system used worldwide estimates runoff generation ( Sweeney 1992 ), However, FFG only addresses parts of the flood’s characteristics and does not focus on water propagation overland or along streams. It misses any occurrence of flooding downstream of the rainfall, especially the delay, magnitude, and duration of the flood. Because a flood forecasting system needs to describe these characteristics ahead of time, modern instances
techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study
techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study
observations and satellite applications, including the use of neural networks for NWP model parameterization ( Krasnopolsky et al. 2010 ) and using deep learning to infer missing data ( Boukabara et al. 2019a ). NCAR, a federally funded research and development center, has a long history of developing AI techniques for weather applications. Haupt et al. (2019) highlighted the Dynamic Integrated Forecast (DICast) system, a 20-year effort at NCAR that forms the “weather engine” of many applications, as
observations and satellite applications, including the use of neural networks for NWP model parameterization ( Krasnopolsky et al. 2010 ) and using deep learning to infer missing data ( Boukabara et al. 2019a ). NCAR, a federally funded research and development center, has a long history of developing AI techniques for weather applications. Haupt et al. (2019) highlighted the Dynamic Integrated Forecast (DICast) system, a 20-year effort at NCAR that forms the “weather engine” of many applications, as
. Furthermore, a new generation of infrared imagers ( Schmit et al. 2005 , 2017 ) and emerging cubesat sensors ( Blackwell et al. 2012 ; Cahoy et al. 2015 ; Reising et al. 2016 ) require new, robust techniques for TC nowcasting and forecasting that can assimilate a spectrally diverse variety of images all at once. It is well known that the microwave imaging frequencies available on polar-orbiting meteorological satellites since the late 1980s offer unique perspectives for observing TCs. Uses of the
. Furthermore, a new generation of infrared imagers ( Schmit et al. 2005 , 2017 ) and emerging cubesat sensors ( Blackwell et al. 2012 ; Cahoy et al. 2015 ; Reising et al. 2016 ) require new, robust techniques for TC nowcasting and forecasting that can assimilate a spectrally diverse variety of images all at once. It is well known that the microwave imaging frequencies available on polar-orbiting meteorological satellites since the late 1980s offer unique perspectives for observing TCs. Uses of the
