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performance of current operational systems with respect to tropical rainfall calls for alternative approaches reaching from convection-permitting resolution ( Pante and Knippertz 2019 ) to methods from statistics and machine learning ( Shi et al. 2015 ; Rasp et al. 2020 ; Vogel et al. 2021 ). Before developing and evaluating new models and approaches, it is essential to establish benchmark forecasts in order to systematically assess forecast improvement. Rasp et al. (2020) recently proposed
performance of current operational systems with respect to tropical rainfall calls for alternative approaches reaching from convection-permitting resolution ( Pante and Knippertz 2019 ) to methods from statistics and machine learning ( Shi et al. 2015 ; Rasp et al. 2020 ; Vogel et al. 2021 ). Before developing and evaluating new models and approaches, it is essential to establish benchmark forecasts in order to systematically assess forecast improvement. Rasp et al. (2020) recently proposed
). Conventional observations such as surface stations and weather balloons are scarce at low latitudes, particularly over the vast tropical oceans. Consequently, the observing system is dominated by satellite data, which are heavily skewed toward measuring atmospheric mass variables rather than wind (e.g., Baker et al. 2014 ). However, data denial experiments for periods with a much enhanced radiosonde network during field campaigns over West Africa have shown a relatively small impact on model performance
). Conventional observations such as surface stations and weather balloons are scarce at low latitudes, particularly over the vast tropical oceans. Consequently, the observing system is dominated by satellite data, which are heavily skewed toward measuring atmospheric mass variables rather than wind (e.g., Baker et al. 2014 ). However, data denial experiments for periods with a much enhanced radiosonde network during field campaigns over West Africa have shown a relatively small impact on model performance
NWP forecasts for TC activity in many oceans (e.g., Vitart 2009 ; Belanger et al. 2010 ; Camp et al. 2018 ). Several studies have systematically evaluated these models in terms of predictive skill for different TC occurrence measures ( Lee et al. 2018 , 2020 ; Gregory et al. 2019 ). Lee et al. (2018) found that the Subseasonal to Seasonal (S2S; Vitart et al. 2017 ) models generally have little to zero skill in predicting TC occurrence from week 2 on for all basins relative to
NWP forecasts for TC activity in many oceans (e.g., Vitart 2009 ; Belanger et al. 2010 ; Camp et al. 2018 ). Several studies have systematically evaluated these models in terms of predictive skill for different TC occurrence measures ( Lee et al. 2018 , 2020 ; Gregory et al. 2019 ). Lee et al. (2018) found that the Subseasonal to Seasonal (S2S; Vitart et al. 2017 ) models generally have little to zero skill in predicting TC occurrence from week 2 on for all basins relative to
of the form with nonnegative weights , , and that sum to 1, and reflects the members’ performance during the training period. 4 Each of the component distributions, , , and , contains a point mass at zero and a density for positive accumulations. The point mass at zero specifies the probability of no precipitation and is estimated in a logistic regression model, where the cube root of the member forecast and a binary indicator of the member forecast being zero are used as predictor
of the form with nonnegative weights , , and that sum to 1, and reflects the members’ performance during the training period. 4 Each of the component distributions, , , and , contains a point mass at zero and a density for positive accumulations. The point mass at zero specifies the probability of no precipitation and is estimated in a logistic regression model, where the cube root of the member forecast and a binary indicator of the member forecast being zero are used as predictor
“extreme” compared with the model climatology, the observed rainfall amount shall be considered extreme when compared against the real climatology ( ECMWF 2015 ). This approach also has advantages in that problems with rain gauge densities and errors in satellite-derived rainfall estimates are circumvented. In other words, for the case under study it shall be evaluated at which lead times, if any, extreme 24-h precipitation totals were forecasted in the Gulf of Tonkin area with respect to the EPS model
“extreme” compared with the model climatology, the observed rainfall amount shall be considered extreme when compared against the real climatology ( ECMWF 2015 ). This approach also has advantages in that problems with rain gauge densities and errors in satellite-derived rainfall estimates are circumvented. In other words, for the case under study it shall be evaluated at which lead times, if any, extreme 24-h precipitation totals were forecasted in the Gulf of Tonkin area with respect to the EPS model
