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probability distributions allows a point mass for zero precipitation and flexible modeling in positive precipitation accumulations, depending on the specifics of the ensemble forecast at hand. For mathematical details we refer to the original paper by Scheuerer (2014) . Postprocessing techniques rely on statistical parameters that need to be estimated from training data, comprising forecast–observation-pairs from the TRMM pixel at hand and typically from a rolling training period consisting of the n
probability distributions allows a point mass for zero precipitation and flexible modeling in positive precipitation accumulations, depending on the specifics of the ensemble forecast at hand. For mathematical details we refer to the original paper by Scheuerer (2014) . Postprocessing techniques rely on statistical parameters that need to be estimated from training data, comprising forecast–observation-pairs from the TRMM pixel at hand and typically from a rolling training period consisting of the n
dispersion errors and biases. Statistical postprocessing addresses these deficiencies and realizes the full potential of ensemble forecasts ( Gneiting and Raftery 2005 ). Additionally, it performs implicit downscaling from the model grid resolution to finer resolutions or station locations. The correction of systematic forecast errors is based on (distributional) regression techniques and, depending on the need of the user, several approaches are at hand ( Schefzik et al. 2013 ; Gneiting 2014 ). Hamill
dispersion errors and biases. Statistical postprocessing addresses these deficiencies and realizes the full potential of ensemble forecasts ( Gneiting and Raftery 2005 ). Additionally, it performs implicit downscaling from the model grid resolution to finer resolutions or station locations. The correction of systematic forecast errors is based on (distributional) regression techniques and, depending on the need of the user, several approaches are at hand ( Schefzik et al. 2013 ; Gneiting 2014 ). Hamill
the mean value and standard deviation of the 2-m temperature forecasts. 3. Benchmark postprocessing techniques a. Ensemble model output statistics Within the general EMOS framework proposed by Gneiting et al. (2005) , the conditional distribution of the weather variable of interest, , given ensemble predictions , is modeled by a single parametric forecast distribution with parameters : The parameters vary over space and time, and depend on the ensemble predictions through suitable link
the mean value and standard deviation of the 2-m temperature forecasts. 3. Benchmark postprocessing techniques a. Ensemble model output statistics Within the general EMOS framework proposed by Gneiting et al. (2005) , the conditional distribution of the weather variable of interest, , given ensemble predictions , is modeled by a single parametric forecast distribution with parameters : The parameters vary over space and time, and depend on the ensemble predictions through suitable link
; Joos and Forbes 2016 ). For example, a rather simple set of variables and appropriate thresholds can be used to identify warm conveyor belts as ensembles of trajectories that behave very similarly and that densely populate a compact region in 3D physical space (sometimes referred to as coherent bundles of trajectories; Wernli and Davies 1997 ). Clustering techniques have been employed more recently in an attempt to identify conveyor-belt trajectories more objectively (e.g., Hart et al. 2015 ). If
; Joos and Forbes 2016 ). For example, a rather simple set of variables and appropriate thresholds can be used to identify warm conveyor belts as ensembles of trajectories that behave very similarly and that densely populate a compact region in 3D physical space (sometimes referred to as coherent bundles of trajectories; Wernli and Davies 1997 ). Clustering techniques have been employed more recently in an attempt to identify conveyor-belt trajectories more objectively (e.g., Hart et al. 2015 ). If
this study is split into “cyclone” and “no-cyclone” groups to elucidate dynamic causes limiting predictability of the pre-Chris cyclone’s formation. The group memberships are determined based on similarity between forecast tracks and the analysis track using a dynamic time warping technique (see section 2c for details). This track-based approach for the identification of equivalent cyclones in the forecast members ensures that those are excluded that are of substantially different origin from the
this study is split into “cyclone” and “no-cyclone” groups to elucidate dynamic causes limiting predictability of the pre-Chris cyclone’s formation. The group memberships are determined based on similarity between forecast tracks and the analysis track using a dynamic time warping technique (see section 2c for details). This track-based approach for the identification of equivalent cyclones in the forecast members ensures that those are excluded that are of substantially different origin from the
Palmer 2011 ). Second, those ensembles require refined initial conditions, which can only be obtained by data assimilation (DA) of spatially dense observations on kilometer scales ( Johnson and Wang 2016 ). And third, novel techniques are necessary to verify the forecasts and assess their skill with observations of high spatial and temporal resolution ( Cintineo and Stensrud 2013 ). Estimates of the forecast horizon of storm-scale features remain rather pessimistic, being on the order of only a few
Palmer 2011 ). Second, those ensembles require refined initial conditions, which can only be obtained by data assimilation (DA) of spatially dense observations on kilometer scales ( Johnson and Wang 2016 ). And third, novel techniques are necessary to verify the forecasts and assess their skill with observations of high spatial and temporal resolution ( Cintineo and Stensrud 2013 ). Estimates of the forecast horizon of storm-scale features remain rather pessimistic, being on the order of only a few
challenging circumstances in weather situations with reduced predictability (i.e., in situations with large changes between subsequent forecasts). Therefore, NAWDEX combined modern forecasting tools, including ensemble and adjoint-based diagnostics, and new visualization techniques to incorporate forecast uncertainty in the planning process (see the “Forecast products for investigating forecast uncertainty” sidebar). Fig . 2. Tracks of consecutively numbered RFs of (a) HALO (97 flight hours during 13 RFs
challenging circumstances in weather situations with reduced predictability (i.e., in situations with large changes between subsequent forecasts). Therefore, NAWDEX combined modern forecasting tools, including ensemble and adjoint-based diagnostics, and new visualization techniques to incorporate forecast uncertainty in the planning process (see the “Forecast products for investigating forecast uncertainty” sidebar). Fig . 2. Tracks of consecutively numbered RFs of (a) HALO (97 flight hours during 13 RFs
research on the representation of model errors arising from diabatic processes using techniques such as stochastic physics. The research summarized in this review primarily focused on assessing the impact of ET on the short-to-medium-range forecast horizon. Preliminary results reveal a statistically significant correlation between monthly mean values of selected teleconnection indices and ET event counts, as well as significant departures from climatology on the subseasonal to seasonal time scale in
research on the representation of model errors arising from diabatic processes using techniques such as stochastic physics. The research summarized in this review primarily focused on assessing the impact of ET on the short-to-medium-range forecast horizon. Preliminary results reveal a statistically significant correlation between monthly mean values of selected teleconnection indices and ET event counts, as well as significant departures from climatology on the subseasonal to seasonal time scale in
to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over
to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over
, and R. L. Elsberry , 2000 : Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage . Wea. Forecasting , 15 , 373 – 395 , https://doi.org/10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 . 10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 Kossin , J. P. , and C. S. Velden , 2004 : A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique . Mon. Wea. Rev. , 132
, and R. L. Elsberry , 2000 : Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage . Wea. Forecasting , 15 , 373 – 395 , https://doi.org/10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 . 10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2 Kossin , J. P. , and C. S. Velden , 2004 : A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique . Mon. Wea. Rev. , 132
