A Comparison of Methods to Sample Model Errors for Convection-Allowing Ensemble Forecasts in the Setting of Multiscale Initial Conditions Produced by the GSI-Based EnVar Assimilation System

2020 ◽  
Vol 148 (3) ◽  
pp. 1177-1203 ◽  
Author(s):  
Nicholas A. Gasperoni ◽  
Xuguang Wang ◽  
Yongming Wang
Keyword(s):  
Initial Conditions ◽  
Heavy Precipitation ◽  
Model Error ◽  
Lead Times ◽  
Model Errors ◽  
Ensemble Forecasts ◽  
System A ◽  
Ensemble Mean ◽  
Statistical Interpolation ◽  
Stochastic Physics

Abstract A gridpoint statistical interpolation (GSI)-based hybrid ensemble–variational (EnVar) scheme was extended for convective scales—including radar reflectivity assimilation—and implemented in real-time spring forecasting experiments. This study compares methods to address model error during the forecast under the context of multiscale initial condition error sampling provided by the EnVar system. A total of 10 retrospective cases were used to explore the optimal design of convection-allowing ensemble forecasts. In addition to single-model single-physics (SMSP) configurations, ensemble forecast experiments compared multimodel (MM) and multiphysics (MP) approaches. Stochastic physics was also applied to MP for further comparison. Neighborhood-based verification of precipitation and composite reflectivity showed each of these model error techniques to be superior to SMSP configurations. Comparisons of MM and MP approaches had mixed findings. The MM approach had better overall skill in heavy-precipitation forecasts; however, MP ensembles had better skill for light (2.54 mm) precipitation and reduced ensemble mean error of other diagnostic fields, particularly near the surface. The MM experiment had the largest spread in precipitation, and for most hours in other fields; however, rank histograms and spaghetti contours showed significant clustering of the ensemble distribution. MP plus stochastic physics was able to significantly increase spread with time to be competitive with MM by the end of the forecast. The results generally suggest that an MM approach is best for early forecast lead times up to 6–12 h, while a combination of MP and stochastic physics approaches is preferred for forecasts beyond 6–12 h.

scholarly journals Ensemble Forecasting of Volcanic Emissions in Hawai’i

2015 ◽  
Vol 57 ◽  
Author(s):  
Andre Kristofer Pattantyus ◽  
Steven Businger
Keyword(s):  
Deterministic Model ◽  
Initial Conditions ◽  
Dispersion Model ◽  
Probabilistic Forecast ◽  
Model Forecast ◽  
Volcanic Emissions ◽  
Ensemble Forecasts ◽  
Pollutant Concentrations ◽  
Ensemble Mean ◽  
Initial Comparison

<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p><span>Deterministic model forecasts do not convey to the end users the forecast uncertainty the models possess as a result of physics parameterizations, simplifications in model representation of physical processes, and errors in initial conditions. This lack of understanding leads to a level of uncertainty in the forecasted value when only a single deterministic model forecast is available. Increasing computational power and parallel software architecture allows multiple simulations to be carried out simultaneously that yield useful measures of model uncertainty that can be derived from ensemble model results. The Hybrid Single Particle Lagrangian Integration Trajectory and Dispersion model has the ability to generate ensemble forecasts. A meteorological ensemble was formed to create probabilistic forecast products and an ensemble mean forecast for volcanic emissions from the Kilauea volcano that impacts the state of Hawai’i. The probabilistic forecast products show uncertainty in pollutant concentrations that are especially useful for decision-making regarding public health. Initial comparison of the ensemble mean forecasts with observations and a single model forecast show improvements in event timing for both sulfur dioxide and sulfate aerosol forecasts. </span></p></div></div></div></div><p> </p>

Ensemble Data Assimilation Using a Unified Representation of Model Error

2015 ◽  
Vol 144 (1) ◽  
pp. 213-224 ◽  
Author(s):  
Chiara Piccolo ◽  
Mike Cullen
Keyword(s):  
Data Assimilation ◽  
Covariance Structure ◽  
Model Error ◽  
Model Errors ◽  
Stochastic Forcing ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Physically Based ◽  
Set Up ◽  
Initial Uncertainty

Abstract A natural way to set up an ensemble forecasting system is to use a model with additional stochastic forcing representing the model error and to derive the initial uncertainty by using an ensemble of analyses generated with this model. Current operational practice has tended to separate the problems of generating initial uncertainty and forecast uncertainty. Thus, in ensemble forecasts, it is normal to use physically based stochastic forcing terms to represent model errors, while in generating analysis uncertainties, artificial inflation methods are used to ensure that the analysis spread is sufficient given the observations. In this paper a more unified approach is tested that uses the same stochastic forcing in the analyses and forecasts and estimates the model error forcing from data assimilation diagnostics. This is shown to be successful if there are sufficient observations. Ensembles used in data assimilation have to be reliable in a broader sense than the usual forecast verification methods; in particular, they need to have the correct covariance structure, which is demonstrated.

scholarly journals Analysis of Ensemble Mean Forecasts: The Blessings of High Dimensionality

2019 ◽  
Vol 147 (5) ◽  
pp. 1699-1712 ◽  
Author(s):  
Bo Christiansen
Keyword(s):  
High Dimensional ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Analytical Results ◽  
Weather And Climate ◽  
Ensemble Mean ◽  
The Individual ◽  
Almost All ◽  
Community Standard ◽  
Better Than

Abstract In weather and climate sciences ensemble forecasts have become an acknowledged community standard. It is often found that the ensemble mean not only has a low error relative to the typical error of the ensemble members but also that it outperforms all the individual ensemble members. We analyze ensemble simulations based on a simple statistical model that allows for bias and that has different variances for observations and the model ensemble. Using generic simplifying geometric properties of high-dimensional spaces we obtain analytical results for the error of the ensemble mean. These results include a closed form for the rank of the ensemble mean among the ensemble members and depend on two quantities: the ensemble variance and the bias both normalized with the variance of observations. The analytical results are used to analyze the GEFS reforecast where the variances and bias depend on lead time. For intermediate lead times between 20 and 100 h the two terms are both around 0.5 and the ensemble mean is only slightly better than individual ensemble members. For lead times larger than 240 h the variance term is close to 1 and the bias term is near 0.5. For these lead times the ensemble mean outperforms almost all individual ensemble members and its relative error comes close to −30%. These results are in excellent agreement with the theory. The simplifying properties of high-dimensional spaces can be applied not only to the ensemble mean but also to, for example, the ensemble spread.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals A Comparison of the Hybrid and EnSRF Analysis Schemes in the Presence of Model Errors due to Unresolved Scales

2009 ◽  
Vol 137 (10) ◽  
pp. 3219-3232 ◽  
Author(s):  
Xuguang Wang ◽  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Craig H. Bishop
Keyword(s):  
Model Comparison ◽  
Additive Noise ◽  
Initial Conditions ◽  
Random Noise ◽  
Model Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Hybrid Analysis ◽  
Error Covariance ◽  
Analysis Scheme

Abstract A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a follow-up to a previous perfect-model comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ensemble mean is updated with a hybrid ensemble and static background-error covariance. The experiments were conducted with a two-layer primitive equation model. The true state was a T127 simulation. Data assimilation experiments were conducted at T31 resolution (3168 complex spectral coefficients), assimilating imperfect observations drawn from the T127 nature run. By design, the magnitude of the truncation error was large, which provided a test on the ability of both schemes to deal with model error. Additive noise was used to parameterize model errors in the background ensemble for both schemes. In the first set of experiments, additive noise was drawn from a large inventory of historical forecast errors; in the second set of experiments, additive noise was drawn from a large inventory of differences between forecasts and analyses. The static covariance was computed correspondingly from the two inventories. The hybrid analysis was statistically significantly more accurate than the EnSRF analysis. The improvement of the hybrid over the EnSRF was smaller when differences of forecasts and analyses were used to form the random noise and the static covariance. The EnSRF analysis was more sensitive to the size of the ensemble than the hybrid. A series of tests was conducted to understand why the EnSRF performed worse than the hybrid. It was shown that the inferior performance of the EnSRF was likely due to the sampling error in the estimation of the model-error covariance in the mean update and the less-balanced EnSRF initial conditions resulting from the extra localizations used in the EnSRF.

Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble Clustering over the Whole Experiment Period

2011 ◽  
Vol 139 (12) ◽  
pp. 3694-3710 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Cluster Analysis ◽  
Wind Speed ◽  
Data Assimilation ◽  
Hierarchical Cluster Analysis ◽  
Initial Conditions ◽  
Hierarchical Cluster ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Pbl Scheme ◽  
Hazardous Weather

Abstract Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa. At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members without DA in a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 11 (1) ◽  
pp. 919-944 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
The World ◽  
The Standardized Precipitation Index

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two datasets as initial conditions: the Global Precipitation Climatology Center (GPCC) and the ECMWF ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation: the most current ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast skill is concentrated on verification months where precipitation deficits are likely to have higher drought impacts and grouped over different regions in the world. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. Our results also support recent questions whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Bimodality in ensemble forecasts of 2 m temperature: identification

2021 ◽  
Vol 2 (4) ◽  
pp. 1209-1224
Author(s):  
Cameron Bertossa ◽  
Peter Hitchcock ◽  
Arthur DeGaetano ◽  
Riwal Plougonven
Keyword(s):  
False Positive Rate ◽  
Equatorial Region ◽  
Decision Makers ◽  
Lead Times ◽  
Scoring Methods ◽  
Ensemble Forecasts ◽  
Ensemble Mean ◽  
Positive Rate ◽  
Non Gaussian ◽  
Winter Hemisphere

Abstract. Bimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of nonlinear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2 m temperature are found to exhibit widespread bimodality well over a derived false-positive rate. In some regions bimodality occurs in excess of 30 % of forecasts, with the largest rates occurring during lead times of 2 to 3 weeks. Bimodality occurs more frequently in the winter hemisphere with indications of baroclinicity being a factor to its development. Additionally, bimodality is more common over the ocean, especially the polar oceans, which may indicate development caused by boundary conditions (such as sea ice). Near the equatorial region, bimodality remains common during either season and follows similar patterns to the Intertropical Convergence Zone (ITCZ), suggesting convection as a possible source for its development. Over some continental regions the modes of the forecasts are separated by up to 15 °C. The probability density for the modes can be up to 4 times greater than at the minimum between the modes, which lies near the ensemble mean. The widespread presence of such bimodality has potentially important implications for decision makers acting on these forecasts. Bimodality also has implications for assessing forecast skill and for statistical postprocessing: several commonly used skill-scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality, suggesting the need for improvements in how non-Gaussian ensemble forecasts are evaluated.

scholarly journals Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze–Huai River Valley in Summer 2016

2018 ◽  
Vol 33 (1) ◽  
pp. 221-238 ◽  
Author(s):  
Baiquan Zhou ◽  
Panmao Zhai ◽  
Ruoyun Niu
Keyword(s):  
Statistical Downscaling ◽  
Extreme Precipitation ◽  
Heavy Precipitation ◽  
Lead Times ◽  
Extreme Precipitation Events ◽  
Huai River ◽  
Ensemble Mean ◽  
Medium Range ◽  
Forecast Models ◽  
Precipitation Events

Abstract Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze–Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (≥25 mm day−1) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models’ performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4–10 days.

scholarly journals Comparison of Probabilistic Quantitative Precipitation Forecasts from Two Postprocessing Mechanisms

2017 ◽  
Vol 18 (11) ◽  
pp. 2873-2891 ◽  
Author(s):  
Yu Zhang ◽  
Limin Wu ◽  
Michael Scheuerer ◽  
John Schaake ◽  
Cezar Kongoli
Keyword(s):  
The United States ◽  
Storm Event ◽  
Lead Times ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Ensemble Mean ◽  
Medium Range ◽  
Quantitative Precipitation Forecasts ◽  
The Mean ◽  
Probability Of Precipitation

Abstract This article compares the skill of medium-range probabilistic quantitative precipitation forecasts (PQPFs) generated via two postprocessing mechanisms: 1) the mixed-type meta-Gaussian distribution (MMGD) model and 2) the censored shifted Gamma distribution (CSGD) model. MMGD derives the PQPF by conditioning on the mean of raw ensemble forecasts. CSGD, on the other hand, is a regression-based mechanism that estimates PQPF from a prescribed distribution by adjusting the climatological distribution according to the mean, spread, and probability of precipitation (POP) of raw ensemble forecasts. Each mechanism is applied to the reforecast of the Global Ensemble Forecast System (GEFS) to yield a postprocessed PQPF over lead times between 24 and 72 h. The outcome of an evaluation experiment over the mid-Atlantic region of the United States indicates that the CSGD approach broadly outperforms the MMGD in terms of both the ensemble mean and the reliability of distribution, although the performance gap tends to be narrow, and at times mixed, at higher precipitation thresholds (&gt;5 mm). Analysis of a rare storm event demonstrates the superior reliability and sharpness of the CSGD PQPF and underscores the issue of overforecasting by the MMGD PQPF. This work suggests that the CSGD’s incorporation of ensemble spread and POP does help enhance its skill, particularly for light forecast amounts, but CSGD’s model structure and its use of optimization in parameter estimation likely play a more determining role in its outperformance.

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