Potential satellite mission on atmospheric dynamics for severe weather forecasts (Conference Presentation)

2019 ◽  
Author(s):  
Bing Lin ◽  
Qilong Min ◽  
Steven Harrah ◽  
Yongxiang Hu ◽  
Roland W. Lawrence
Keyword(s):  
Severe Weather ◽  
Atmospheric Dynamics ◽  
Satellite Mission ◽  
Weather Forecasts

Impact of planetary waves on infrasound propagation uncertainties

2021 ◽  
Author(s):  
Elisabeth Blanc ◽  
Patrick Hupe ◽  
Bernd Kaifler ◽  
Natalie Kaifler ◽  
Alexis Le Pichon ◽  
...  
Keyword(s):  
Wave Activity ◽  
Atmospheric Dynamics ◽  
Planetary Waves ◽  
Data Sets ◽  
Atmospheric Models ◽  
Period Range ◽  
Weather Forecasts ◽  
Atmospheric Disturbances ◽  
Medium Range ◽  
Model Improvement

<p>The uncertainties in the infrasound technology arise from the middle atmospheric disturbances, which are partly underrepresented in the atmospheric models such as in the European Centre for Medium-Range Weather Forecasts (ECMWF) products used for infrasound propagation simulations. In the framework of the ARISE (Atmospheric dynamics Research InfraStructure in Europe) project, multi-instrument observations are performed to provide new data sets for model improvement and future assimilations. In an unexpected way, new observations using the autonomous CORAL lidar showed significant differences between ECMWF analysis fields and observations in Argentina in the period range between 0.1 and 10 days. The model underestimates the wave activity, especially in the summer. During the same season, the infrasound bulletins of the IS02 station in Argentina indicate the presence of two prevailing directions of the detections, which are not reflected by the simulations. Observations at the Haute Provence Observatory (OHP) are used for comparison in different geophysical conditions. The origin of the observed anomalies are discussed in term of planetary waves effect on the infrasound propagation.</p>

Intercomparison and Validation of GNSS-IWV Derived with G-Nut and Bernese Software

2017 ◽  
Author(s):  
Pawel Golaszewski ◽  
Pawel Wielgosz ◽  
Katarzyna Stepniak
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Severe Weather ◽  
Final Solution ◽  
Weather Forecasts ◽  
Software Packages ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Permanent Station

GNSS is an important source of meteorological data. GNSS measurements can provide tropospheric Zenith Wet Delays (ZWD) over wide area covered with permanent stations. In addition, when using surface synoptical data, GNSS can provide Integrated Water Vapor (IWV) which is very valuable information utilized in weather forecasts and severe weather monitoring. Hence, there is a need to test and validate various algorithms and software used for ZWD estimation. In this research, the accuracy of the ZWD estimates was tested using two different software packages: Bernese GNSS Software v.5.2 and G-Nut/Tefnut. In addition, their computational load was evaluated. The GNSS data were obtained from POTS permanent station, which is located in Potsdam, Germany. To validate the estimation results, the derived ZWD was transformed into the IWV, and afterwards compared to the reference IWV measured by the collocated Microwave Radiometer. In addition, the ZWD estimates were also compared to the EUREF final solution.

Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

2016 ◽  
Vol 31 (1) ◽  
pp. 255-271 ◽  
Author(s):  
Ryan A. Sobash ◽  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Kathryn R. Fossell ◽  
Morris L. Weisman
Keyword(s):  
Extreme Values ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Smoothing Kernel ◽  
Risk Areas ◽  
Forecasting System ◽  
Computational Resources

Abstract Probabilistic severe weather forecasts for days 1 and 2 were produced using 30-member convection-allowing ensemble forecasts initialized by an ensemble Kalman filter data assimilation system during a 32-day period coinciding with the Mesoscale Predictability Experiment. The forecasts were generated by smoothing the locations where model output indicated extreme values of updraft helicity, a surrogate for rotating thunderstorms in model output. The day 1 surrogate severe probability forecasts (SSPFs) produced skillful and reliable predictions of severe weather during this period, after an appropriate calibration of the smoothing kernel. The ensemble SSPFs exceeded the skill of SSPFs derived from two benchmark deterministic forecasts, with the largest differences occurring on the mesoscale, while all SSPFs produced similar forecasts on synoptic scales. While the deterministic SSPFs often overforecasted high probabilities, the ensemble improved the reliability of these probabilities, at the expense of producing fewer high-probability values. For the day 2 period, the SSPFs provided competitive guidance compared to the day 1 forecasts, although additional smoothing was needed to produce the same level of skill, reducing the forecast sharpness. Results were similar using 10 ensemble members, suggesting value exists when running a smaller ensemble if computational resources are limited. Finally, the SSPFs were compared to severe weather risk areas identified in Storm Prediction Center (SPC) convective outlooks. The SSPF skill was comparable to the SPC outlook skill in identifying regions where severe weather would occur, although performance varied on a day-to-day basis.

scholarly journals Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

2020 ◽  
Vol 35 (4) ◽  
pp. 1605-1631
Author(s):  
Eric D. Loken ◽  
Adam J. Clark ◽  
Christopher D. Karstens
Keyword(s):  
Random Forests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Decision Makers ◽  
Limited Information ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Weather Forecasters ◽  
Forecast Data ◽  
Better Than

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

scholarly journals A Binary Logistic Regression Model for Severe Convective Weather with Numerical Model Data

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Guqian Pang ◽  
Jian He ◽  
Yuming Huang ◽  
Liuhong Zhang
Keyword(s):  
Binary Logistic Regression ◽  
Severe Weather ◽  
Radiosonde Data ◽  
Model Data ◽  
Binary Logistic Regression Model ◽  
Weather Forecasts ◽  
Logistic Regressions ◽  
Meteorological Observations ◽  
Convective Weather ◽  
Ecmwf Model

Based on meteorological observations and products of a GRAPES and an ECMWF model from March to April 2014, some indexes and parameters with good relevancy were selected as predictors. Through analyzing the spatial distributions and the binary logistic regressions of the indexes, estimated values of the predictors and severe convective weather diagnostic prediction equations were established to get a severe weather predictor P for forecasting severe convective weather for the next 12 hours in Guangdong province. The equations were tested and analyzed, respectively, with the two models as well as the radiosonde data. The results indicated that the severe weather forecasts’ CSI by the predictor P was obviously higher than by any single index. The TT error between the models and the soundings was small, while the K index of the models was more discrete than the soundings. The index MDPIs were 1 greater than the soundings, but their trends of change were consistent with the soundings.

Advanced MUlti-GNSS Array for Monitoring Severe Weather Events (AMUSE): Project overview

2020 ◽  
Author(s):  
Karina Wilgan ◽  
Jens Wickert ◽  
Galina Dick ◽  
Florian Zus ◽  
Torsten Schmidt ◽  
...  
Keyword(s):  
Water Vapor ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Spatiotemporal Resolution ◽  
Weather Forecasts ◽  
Precise Knowledge ◽  
Weather Events ◽  
Data Products ◽  
New Generation ◽  
The Impact

<p>Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is currently established as a powerful and versatile observation tool for geosciences. An outstanding application in this context is the operational monitoring of atmospheric water vapor with high spatiotemporal resolution. The water vapor is the most abundant greenhouse gas, which accounts for about 70% of atmospheric warming and plays a key role in the atmospheric energy exchange. The precise knowledge of its highly variable spatial and temporal distribution is a precondition for precise modeling of the atmospheric state as a base for numerical weather forecasts especially with focus to the strong precipitation and severe weather events.</p><p>The data from European GNSS networks are widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the exclusively focusing on GPS-only based data products; to the limited atmospheric information content, which is provided mostly in the zenith direction and to the time delay between measurement and providing the data products, which is currently about one hour.</p><p>AMUSE is a recent research project, funded by the DFG (German Research Council) and performed in close cooperation of TUB, GFZ and DWD during 2020-2022. The project foci are the major limitations of currently operationally used generation of GNSS-based water vapor data. AMUSE will pioneer the development of next generation data products. Main addressed innovations are:  1) Developments to provide multi-GNSS instead of GPS-only data, including GLONASS, Galileo and BeiDou; 2) Developments to provide high quality slant observations, containing water vapor information along the line-of-sight from the respective ground stations; 3) Developments to shorten the delay between measurements and the provision of the products to the meteorological services.</p><p>This GNSS-focused work of AMUSE will be complemented by the contribution of German Weather Service DWD to investigate in detail and to quantify the forecast improvement, which can be reached by the new generation GNSS-based meteorology data. Several dedicated forecast experiments will be conducted with focus on one of the most challenging issues, the precipitation forecast in case of severe weather events. These studies will support the future assimilation of the new generation data to the regional forecast system of DWD and potentially also to other European weather services.</p>

Assimilating synthetic hyperspectral sounder temperature and humidity retrievals to improve severe weather forecasts

2017 ◽  
Vol 186 ◽  
pp. 9-25 ◽  
Author(s):  
Thomas A. Jones ◽  
Steven Koch ◽  
Zhenglong Li
Keyword(s):  
Severe Weather ◽  
Weather Forecasts ◽  
Temperature And Humidity

scholarly journals Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

2021 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Shawn Handler ◽  
Amy McGovern
Keyword(s):  
Machine Learning ◽  
Storm Track ◽  
Severe Weather ◽  
Storm Tracks ◽  
State Variables ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Environment Variables

AbstractA primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Post-processing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for post-processing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output.Our dataset includes WoFS ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based post-processing of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance.

When Weather Forecasters Are Wrong: Image Repair and Public Rhetoric After Severe Weather

2018 ◽  
Vol 40 (6) ◽  
pp. 778-788 ◽  
Author(s):  
Josh Compton
Keyword(s):  
Science Communication ◽  
Rhetorical Analysis ◽  
Severe Weather ◽  
Winter Storm ◽  
Image Repair ◽  
Unique Type ◽  
Public Rhetoric ◽  
Weather Forecasts ◽  
Weather Forecasters ◽  
Insight Into

Weather forecasts are a unique type of prediction rhetoric—science communication with inherent uncertainty and multiple potential interpretations from diverse audiences. When forecasts are wrong, audiences often turn their ire toward the weather forecaster. This rhetorical analysis considers image repair efforts of a meteorologist following a botched winter storm forecast. Implications for communication efforts of weather forecasters are offered, in addition to consideration of insight into the larger realm of the rhetoric of science.

scholarly journals Intra-annual variability of Antarctic precipitation from weather forecasts and high-resolution climate models

1998 ◽  
Vol 27 ◽  
pp. 488-494 ◽  
Author(s):  
Christophe Genthon ◽  
Gerhard Krinner ◽  
Michel Déqué
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Atmospheric Dynamics ◽  
Small Scale ◽  
Weather Forecasts ◽  
Annual Variability ◽  
Climate Simulations ◽  
Antarctic Precipitation ◽  
The Antarctic ◽  
The Impact

The intra-annual variability of Antarctic precipitation from the European Centre for Medium-range Weather Forecasts short-term meteorological forecasts and from climate simulations by the ARPÈGE and LMD-Zoom general circulation models is presented and discussed. The spatial resolution of forecasts and simulations is high over the Antarctic region, about 100 km, so that the impact of topography and small-scale atmospheric dynamics are better resolved than with more conventional model grids (about 300 km). All the models and forecasts show that the seasonality of precipitation is spatially very variable. Meridional coast-to-interior contrasts are marked, but equally strong variations are unexpectedly found where more homogeneity might be expected because of the homogeneity of the environment, e.g. on the high Antarctic plateau. Neither the forecasts nor the simulations confirm that precipitation is mostly maximum in winter over much of East Antarctica as suggested by scarce and potentially unreliable observations (Bromwich, 1988). Spring and fall maxima are quite frequent too, though summer maxima are rare. Daily precipitation statistics show more spatial pattern, with increasingly infrequent precipitation as distance from the coast toward the interior of the ice sheet increases Several aspects of the intra-annual variability of precipitation can be interpreted in terms of atmospheric dynamics, but at both daily and seasonal time-scales the different forecasts and climate simulations often locally and regionally disagree with each other. Discrimination between models and their ability to reproduce the dynamics of Antarctic hydrology, and progress on simulating such aspects of the Antarctic climate, is limited by the lack of reliable observation of precipitation variability.

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