scholarly journals SINFONY - the combination of Nowcasting and Numerical Weather Prediction at the convective scale at DWD 

2021 ◽  
Author(s):  
Ulrich Blahak ◽  
Julia Keller ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Value Chain ◽  
Weather Prediction ◽  
Convective Cell ◽  
Interdisciplinary Teams ◽  
Information Value ◽  
Cell Detection ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Pbl Scheme

<p>There are different "optimal" forecast methods for different forecast lead times and different weather phenomena. Focusing on precipitation and convective events up to some hours ahead, radar extrapolation techniques (Nowcasting) show good skill up to about 2 h ahead (depending on the situation), while numerical weather prediction (NWP) outperforms Nowcasting only at later hours. Ensembles of both Nowcasting and NWP help to assess forecast uncertainties.</p><p>DWD's new Seamless INtegrated FOrecastiNg sYstem (SINFONY) combines forecast information from Nowcasting and NWP in an optimized way and as a function of lead time to generate seamless probabilistic precipitation forecasts from minutes to 12 hours. After four years of research and development, SINFONY is about to come to life in the upcoming two years, with an initial focus on the prediction of severe convective events.</p><p>For the development of SINFONY, different interdisciplinary teams work closely together in developing</p><ul><li>Radar Nowcasting ensembles for precipitation, reflectivity and convective cell objects</li> <li>Hourly SINFONY-RUC-EPS NWP on the km-scale with extensive data assimilation of high-resolution remote sensing (radial wind, reflectivity and cell objects from volume radar scans; Meteosat VIS channels; lightning)</li> <li>Optimal combination of Nowcasting and NWP ensemble forecasts in observation space (precipitation, radar reflectivity and cell objects)</li> <li>Systems for common Nowcasting and NWP verification of precipitation, reflectivity and objects.</li> </ul><p>For the SINFONY-RUC-EPS, new innovative and efficient forward operators for volume radar scans and visible satellite data enable direct operational assimilation of these data in an LETKF framework. Advanced model physics (stochastic PBL scheme, 2-moment bulk cloud mircophysics) contribute to an improved forecast of convective clouds.</p><p>As input for the combination of NWP and Nowcasting information, SINFONY-RUC-EPS generates simulated reflectivity volume scan ensembles of the entire German radar network every 5 min online during its forecast runs. Ensembles of composites and cell object tracks are generated by the same compositing and cell detection- and tracking methods/software packages, which are applied to generate the Nowcasting information.</p><p>To help evolve DWD's warning process for convective events towards a flexible "warn-on-objects", our Nowcasting- and NWP cell object ensemble forecasts are then blended into a seamless forecast ("probability objects") in a pragmatic way. Gridded combined precipitation and reflectivity ensembles are also under development, targeted towards hydrological warnings.</p><p>In addition to the development of SINFONY itself, focus is also put on the interaction with users (e.g. from flood forecasting centres) along the weather information value chain for co-designing the development of new forecast products and approaches to improve the prediction and warning process.</p><p>This presentation will introduce the goal and the concept of SINFONY and provide an overview on the ongoing developments as well as on the incipient interaction with users.</p>

scholarly journals EuLerian Identification of Ascending air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part II: Model application to different data sets

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian Michael Grams ◽  
Annika Oertel ◽  
Moritz Pickl
Keyword(s):  
Temporal Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Future Research ◽  
Data Sets ◽  
Ensemble Forecasts ◽  
Numerical Weather ◽  
Spatio Temporal ◽  
Air Streams

Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these air streams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatio-temporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different data sets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart which is most frequently used to objectively identify WCBs but requires data at higher spatio-temporal resolution which is often not available and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases, and opens numerous directions for future research.

scholarly journals Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3587-3603 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

scholarly journals Realizing the Benefits of AI across the Numerical Weather Prediction Value Chain

2020 ◽  
Vol 101 (1) ◽  
pp. 29-33
Author(s):  
Sid-Ahmed Boukabara ◽  
Vladimir Krasnopolsky ◽  
Jebb Q. Stewart ◽  
Eric Maddy ◽  
Narges Shahroudi ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Value Chain ◽  
Weather Prediction ◽  
Numerical Weather

scholarly journals Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting

2013 ◽  
Vol 10 (5) ◽  
pp. 6765-6806 ◽  
Author(s):  
D. E. Robertson ◽  
D. L. Shrestha ◽  
Q. J. Wang
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Probability Distributions ◽  
Weather Prediction ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
New Approach ◽  
Numerical Weather

Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

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