scholarly journals Enlarging the Severe Hail Database in Finland by Using a Radar-Based Hail Detection Algorithm and Email Surveys to Limit Underreporting and Population Biases

2020 ◽  
Vol 35 (2) ◽  
pp. 711-721
Author(s):  
Jari-Petteri Tuovinen ◽  
Harri Hohti ◽  
David M. Schultz
Keyword(s):  
Social Media ◽  
Population Density ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Detection Algorithm ◽  
Convective Cell ◽  
High Likelihood ◽  
New Approach ◽  
Telephone Calls ◽  
Email Survey

Abstract Collecting hail reports to build a climatology is challenging in a sparsely populated country such as Finland. To expand an existing database, a new approach involving daily verification of a radar- and numerical weather prediction–based hail detection algorithm was trialed during late May–August for the 10-yr period, 2008–17. If the algorithm suggested a high likelihood of hail from each identified convective cell in specified locations, then an email survey was sent to people and businesses in these locations. Telephone calls were also used occasionally. Starting from 2010, the experiment was expanded to include trained storm spotters performing the surveys (project called TATSI). All the received hail reports were documented (severe or ≥2 cm, and nonsevere, excluding graupel), giving a more complete depiction of hail occurrence in Finland. In combination with reports from the general public, news, and social media, our hail survey resulted in a 292% increase in recorded severe hail days and a 414% increase in observed severe hail cases compared to a climatological study (1930–2006). More than 2200 email surveys were sent, and responses to these surveys accounted for 53% of Finland’s severe hail cases during 2008–17. Most of the 2200 emails were sent into rural locations with low population density. These additional hail reports allowed problems with the initial radar-based hail detection algorithm to be identified, leading to the introduction of a new hail index in 2009 with improved detection and nowcasting of severe hail. This study shows a way to collect hail reports in a sparsely populated country to mitigate underreporting and population biases.

scholarly journals Improved Nowcasts by Blending Extrapolation and Model Forecasts

2015 ◽  
Vol 30 (5) ◽  
pp. 1201-1217 ◽  
Author(s):  
Yunsung Hwang ◽  
Adam J. Clark ◽  
Valliappa Lakshmanan ◽  
Steven E. Koch
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Processing Method ◽  
Lead Times ◽  
New Approach ◽  
Simple Extrapolation ◽  
Model Initialization ◽  
Better Than

Abstract Planning and managing commercial airplane routes to avoid thunderstorms requires very skillful and frequently updated 0–8-h forecasts of convection. The National Oceanic and Atmospheric Administration’s High-Resolution Rapid Refresh (HRRR) model is well suited for this purpose, being initialized hourly and providing explicit forecasts of convection out to 15 h. However, because of difficulties with depicting convection at the time of model initialization and shortly thereafter (i.e., during model spinup), relatively simple extrapolation techniques, on average, perform better than the HRRR at 0–2-h lead times. Thus, recently developed nowcasting techniques blend extrapolation-based forecasts with numerical weather prediction (NWP)-based forecasts, heavily weighting the extrapolation forecasts at 0–2-h lead times and transitioning emphasis to the NWP-based forecasts at the later lead times. In this study, a new approach to applying different weights to blend extrapolation and model forecasts based on intensities and forecast times is applied and tested. An image-processing method of morphing between extrapolation and model forecasts to create nowcasts is described and the skill is compared to extrapolation forecasts and forecasts from the HRRR. The new approach is called salient cross dissolve (Sal CD), which is compared to a commonly used method called linear cross dissolve (Lin CD). Examinations of forecasts and observations of the maximum altitude of echo-top heights ≥18 dBZ and measurement of forecast skill using neighborhood-based methods shows that Sal CD significantly improves upon Lin CD, as well as the HRRR at 2–5-h lead times.

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 Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

2019 ◽  
Vol 147 (11) ◽  
pp. 4241-4259 ◽  
Author(s):  
Paul J. Roebber ◽  
John Crockett
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Evolutionary Programming ◽  
The United States ◽  
Training Data ◽  
New Approach ◽  
Numerical Weather ◽  
Probabilistic Forecasts

Abstract An evolutionary programming postprocessor, using coevolution in a predator–prey ecosystem model, is developed and applied both to 72-h, 2-m temperature forecasts for the conterminous United States and southern Canada and to 60-min nowcasts of convection occurrence for the United States east of 94°W. The new approach improves deterministic and probabilistic forecasts of surface temperature relative to bias-corrected numerical weather prediction forecasts and to an earlier version of evolutionary programming forecasts for these same data. The new method also improves deterministic performance for an artificial neural network trained and evaluated for these same data. Additionally, the new approach substantially improves these forecasts’ reliability, as evidenced by reductions in the occurrence of excessive outliers in the rank histogram. The coevolutionary postprocessor also improves deterministic nowcasts of convection occurrence when compared to those produced by the National Weather Service’s AutoNowCaster system and to those obtained using multiple logistic regression. Notably, the degree of improvement relative to traditional methods appears to be problem dependent, while the training and implementation of such a system requires additional effort. However, the coevolutionary system is shown to be robust to imbalances between the frequency of positive and null events in the training data, unlike many postprocessing methods; to be implementable and effective in an adaptive mode, removing the need for retraining as inputs (such as numerical weather prediction model data) change; and to provide a useful, alternative perspective on the likelihood of event occurrence when used in combination with other methods.

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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