scholarly journals Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

2013 ◽  
Vol 20 (5) ◽  
pp. 759-770 ◽  
Author(s):  
T. Haszpra ◽  
I. Lagzi ◽  
T. Tél
Keyword(s):  
Aerosol Particles ◽  
Geographical Area ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Threshold Concentration ◽  
Free Atmosphere ◽  
Ensemble Forecasts ◽  
Centers Of Mass ◽  
Mean Square Distance ◽  
Observation Period

Abstract. The dispersion of aerosol particle pollutants is studied using 50 members of an ensemble forecast in the example of a hypothetical free atmospheric emission above Fukushima over a period of 2.5 days. Considerable differences are found among the dispersion predictions of the different ensemble members, as well as between the ensemble mean and the deterministic result at the end of the observation period. The variance is found to decrease with the particle size. The geographical area where a threshold concentration is exceeded in at least one ensemble member expands to a 5–10 times larger region than the area from the deterministic forecast, both for air column "concentration" and in the "deposition" field. We demonstrate that the root-mean-square distance of any particle from its own clones in the ensemble members can reach values on the order of one thousand kilometers. Even the centers of mass of the particle cloud of the ensemble members deviate considerably from that obtained by the deterministic forecast. All these indicate that an investigation of the dispersion of aerosol particles in the spirit of ensemble forecast contains useful hints for the improvement of risk assessment.

scholarly journals On the predictability of outliers in ensemble forecasts

2012 ◽  
Vol 8 (1) ◽  
pp. 53-57
Author(s):  
S. Siegert ◽  
J. Bröcker ◽  
H. Kantz
Keyword(s):  
Logistic Regression ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Base Rate ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Interpretation

Abstract. In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.

Representing Forecast Error in a Convection-Permitting Ensemble System

2014 ◽  
Vol 142 (12) ◽  
pp. 4519-4541 ◽  
Author(s):  
Glen S. Romine ◽  
Craig S. Schwartz ◽  
Judith Berner ◽  
Kathryn R. Fossell ◽  
Chris Snyder ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Forecast Error ◽  
Stage Iv ◽  
Model Error ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Lateral Boundary Condition ◽  
Initial Condition ◽  
Lateral Boundary ◽  
Ensemble Forecasts

Abstract Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established. Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.

scholarly journals Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields

2020 ◽  
Vol 27 (3) ◽  
pp. 411-427
Author(s):  
Josh Jacobson ◽  
William Kleiber ◽  
Michael Scheuerer ◽  
Joseph Bellier
Keyword(s):  
Spatial Structure ◽  
Weather Conditions ◽  
Heavy Precipitation ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Precipitation Forecast ◽  
Ensemble Forecasts ◽  
Adequate Representation ◽  
Grid Points ◽  
High Winds

Abstract. Most available verification metrics for ensemble forecasts focus on univariate quantities. That is, they assess whether the ensemble provides an adequate representation of the forecast uncertainty about the quantity of interest at a particular location and time. For spatially indexed ensemble forecasts, however, it is also important that forecast fields reproduce the spatial structure of the observed field and represent the uncertainty about spatial properties such as the size of the area for which heavy precipitation, high winds, critical fire weather conditions, etc., are expected. In this article we study the properties of the fraction of threshold exceedance (FTE) histogram, a new diagnostic tool designed for spatially indexed ensemble forecast fields. Defined as the fraction of grid points where a prescribed threshold is exceeded, the FTE is calculated for the verification field and separately for each ensemble member. It yields a projection of a – possibly high-dimensional – multivariate quantity onto a univariate quantity that can be studied with standard tools like verification rank histograms. This projection is appealing since it reflects a spatial property that is intuitive and directly relevant in applications, though it is not obvious whether the FTE is sufficiently sensitive to misrepresentation of spatial structure in the ensemble. In a comprehensive simulation study we find that departures from uniformity of the FTE histograms can indeed be related to forecast ensembles with biased spatial variability and that these histograms detect shortcomings in the spatial structure of ensemble forecast fields that are not obvious by eye. For demonstration, FTE histograms are applied in the context of spatially downscaled ensemble precipitation forecast fields from NOAA's Global Ensemble Forecast System.

scholarly journals Beyond Univariate Calibration: Verifying Spatial Structure in Ensembles of Forecast Fields

2020 ◽  
Author(s):  
Joshuah Jacobson ◽  
William Kleiber ◽  
Michael Scheuerer ◽  
Joseph Bellier
Keyword(s):  
Spatial Structure ◽  
Weather Conditions ◽  
Heavy Precipitation ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Precipitation Forecast ◽  
Ensemble Forecasts ◽  
Spatial Properties ◽  
Adequate Representation ◽  
High Winds

Abstract. Most available verification metrics for ensemble forecasts focus on univariate quantities. That is, they assess whether the ensemble provides an adequate representation of the forecast uncertainty about the quantity of interest at a particular location and time. For spatially-indexed ensemble forecasts, however, it is also important that forecast fields reproduce the spatial structure of the observed field, and represent the uncertainty about spatial properties such as the size of the area for which heavy precipitation, high winds, critical fire weather conditions, etc. are expected. In this article we study the properties of a new diagnostic tool designed for spatially-indexed ensemble forecast fields. The metric is based on a level-crossing statistic that we term the fraction of threshold exceedance (FTE), and is calculated for the verification field, and separately for each ensemble member. The FTE yields a projection of a – possibly high-dimensional – multivariate quantity onto a univariate quantity that can be studied with standard tools like verification rank histograms. This projection is appealing since it reflects a spatial property that is intuitive and directly relevant in applications, though it is not obvious whether the FTE is sufficiently sensitive to misrepresentation of spatial structure in the ensemble. In a comprehensive simulation study we find that departures from uniformity of these so called FTE histograms can be indeed be related to forecast ensembles with biased spatial variability, and that these histograms detect shortcomings in the spatial structure of ensemble forecast fields that are not obvious by eye. For demonstration, FTE histograms are applied in the context of spatially downscaled ensemble precipitation forecast fields from NOAA's Global Ensemble Forecast System.

scholarly journals Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts

2018 ◽  
Vol 57 (4) ◽  
pp. 1011-1019 ◽  
Author(s):  
H. F. Dacre ◽  
N. J. Harvey
Keyword(s):  
Flow Separation ◽  
Volcanic Ash ◽  
Dispersion Model ◽  
Forecast Accuracy ◽  
Large Error ◽  
Ensemble Member ◽  
Forecast Errors ◽  
Atmospheric Conditions ◽  
Wind Fields ◽  
Ensemble Forecasts

ABSTRACTVolcanic ash poses an ongoing risk to safety in the airspace worldwide. The accuracy with which volcanic ash dispersion can be forecast depends on the conditions of the atmosphere into which it is emitted. In this study, meteorological ensemble forecasts are used to drive a volcanic ash transport and dispersion model for the 2010 Eyjafjallajökull eruption in Iceland. From analysis of these simulations, the authors determine why the skill of deterministic-meteorological forecasts decreases with increasing ash residence time and identify the atmospheric conditions in which this drop in skill occurs most rapidly. Large forecast errors are more likely when ash particles encounter regions of large horizontal flow separation in the atmosphere. Nearby ash particle trajectories can rapidly diverge, leading to a reduction in the forecast accuracy of deterministic forecasts that do not represent variability in wind fields at the synoptic scale. The flow‐separation diagnostic identifies where and why large ensemble spread may occur. This diagnostic can be used to alert forecasters to situations in which the ensemble mean is not representative of the individual ensemble‐member volcanic ash distributions. Knowledge of potential ensemble outliers can be used to assess confidence in the forecast and to avoid potentially dangerous situations in which forecasts fail to predict harmful levels of volcanic ash.

scholarly journals Changes in the occurrence of rainfall-induced landslides in Calabria, Southern Italy, in the 20th century

2015 ◽  
Vol 3 (6) ◽  
pp. 3579-3619
Author(s):  
S. L. Gariano ◽  
O. Petrucci ◽  
F. Guzzetti
Keyword(s):  
Southern Italy ◽  
Geographical Area ◽  
Daily Rainfall ◽  
Recent Period ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Entire Observation Period ◽  
Geographical Variations ◽  
The Impact ◽  
Observation Period

Abstract. We exploit a catalogue of 1466 rainfall events with landslides in the 90 year period 1921–2010 to study temporal and geographical variations in the occurrence of landslides in Calabria, Southern Italy. We use daily rainfall records obtained by a network of 318 rain gauges to reconstruct 448 493 rainfall events. Combining the rainfall and the landslide information, we obtain a catalogue of 1466 rainfall events with landslides (REL) in Calabria from 1921 to 2010, where a REL is the occurrence of one or more landslide during or immediately after a rainfall event. We find that the geographical and the temporal distributions of the rainfall-induced landslides have changed in the observation period. The average and the maximum values of the cumulated event rainfall that have resulted in landslides in the recent-most 30 year period 1981–2010 are lower than the values necessary to trigger landslides in previous periods, whereas the duration of the rainfall events that triggered landslides has remained the same. This can be considered evidence of variations in rainfall conditions, but also an increase in the vulnerability of the territory. We further find that the yearly distribution of rainfall-induced landslides has changed in the observation period, analysing the variations in the number of rainfall events with landslides occurred in each month in three 30 year periods. To investigate variations in the impact of REL on the population, we compared the number of REL in each of the 409 municipalities in Calabria, with the size of the population in the municipalities, measured by national Censuses conducted in 1951, 1981, and 2011. For the purpose, we adopted two strategies. The first strategy considered impact as IREL = #REL/P and the second strategy measured impact as RREL = #REL × P, where #REL is the total number of REL in a period, and P is the size of the population in the same period and geographical area. Considering the entire observation period, IREL and RREL have both increased in Calabria. However, considering the changes between the recent period 1981–2010 and the previous period 1951–1980, results are more variegated with a number of municipalities where IREL and RREL have increased, or decreased. Municipalities where IREL has increased are mainly in the mountains, and municipalities where RREL has increased are mainly along the coasts.

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

scholarly journals Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

2019 ◽  
Vol 147 (8) ◽  
pp. 2997-3023 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Ensemble Forecast ◽  
The Other ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Variable Resolution ◽  
Cell Spacing ◽  
Medium Range ◽  
Potential Benefits ◽  
Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

scholarly journals Ensemble Regression

2009 ◽  
Vol 137 (7) ◽  
pp. 2365-2379 ◽  
Author(s):  
David A. Unger ◽  
Huug van den Dool ◽  
Edward O’Lenic ◽  
Dan Collins
Keyword(s):  
Linear Regression ◽  
Error Variance ◽  
Ensemble Member ◽  
Ensemble Prediction ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Ensemble Mean ◽  
Standard Linear ◽  
Environmental Prediction ◽  
Ensemble Regression

A regression model was developed for use with ensemble forecasts. Ensemble members are assumed to represent a set of equally likely solutions, one of which will best fit the observation. If standard linear regression assumptions apply to the best member, then a regression relationship can be derived between the full ensemble and the observation without explicitly identifying the best member for each case. The ensemble regression equation is equivalent to linear regression between the ensemble mean and the observation, but is applied to each member of the ensemble. The “best member” error variance is defined in terms of the correlation between the ensemble mean and the observations, their respective variances, and the ensemble spread. A probability density function representing the ensemble prediction is obtained from the normalized sum of the best-member error distribution applied to the regression forecast from each ensemble member. Ensemble regression was applied to National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) forecasts of seasonal mean Niño-3.4 SSTs on historical forecasts for the years 1981–2005. The skill of the ensemble regression was about the same as that of the linear regression on the ensemble mean when measured by the continuous ranked probability score (CRPS), and both methods produced reliable probabilities. The CFS spread appears slightly too high for its skill, and the CRPS of the CFS predictions can be slightly improved by reducing its ensemble spread to about 0.8 of its original value prior to regression calibration.

scholarly journals Diagnosis and Optimization of Ensemble Forecasts

2008 ◽  
Vol 136 (3) ◽  
pp. 1054-1074 ◽  
Author(s):  
Tomislava Vukicevic ◽  
Isidora Jankov ◽  
John McGinley
Keyword(s):  
Initial Conditions ◽  
Forecast Error ◽  
Absolute Error ◽  
Ensemble Forecast ◽  
Stochastic Representation ◽  
Ensemble Forecasts ◽  
Ensemble Technique ◽  
Consensus Forecasts ◽  
Almost All ◽  
The Impact

Abstract In the current study, a technique that offers a way to evaluate ensemble forecast uncertainties produced either by initial conditions or different model versions, or both, is presented. The technique consists of first diagnosing the performance of the forecast ensemble and then optimizing the ensemble forecast using results of the diagnosis. The technique is based on the explicit evaluation of probabilities that are associated with the Gaussian stochastic representation of the weather analysis and forecast. It combines an ensemble technique for evaluating the analysis error covariance and the standard Monte Carlo approach for computing samples from a known Gaussian distribution. The technique was demonstrated in a tutorial manner on two relatively simple examples to illustrate the impact of ensemble characteristics including ensemble size, various observation strategies, and configurations including different model versions and varying initial conditions. In addition, the authors assessed improvements in the consensus forecasts gained by optimal weighting of the ensemble members based on time-varying, prior-probabilistic skill measures. The results with different observation configurations indicate that, as observations become denser, there is a need for larger-sized ensembles and/or more accuracy among individual members for the ensemble forecast to exhibit prediction skill. The main conclusions relative to ensembles built up with different physics configurations were, first, that almost all members typically exhibited some skill at some point in the model run, suggesting that all should be retained to acquire the best consensus forecast; and, second, that the normalized probability metric can be used to determine what sets of weights or physics configurations are performing best. A comparison of forecasts derived from a simple ensemble mean to forecasts from a mean developed from variably weighting the ensemble members based on prior performance by the probabilistic measure showed that the latter had substantially reduced mean absolute error. The study also indicates that a weighting scheme that utilized more prior cycles showed additional reduction in forecast error.

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