Error Growth Dynamics within Convection-Allowing Ensemble Forecasts over Central U.S. Regions for Days of Active Convection

2021 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Ming Xue ◽  
Jinzhong Min ◽  
Zhiming Kang ◽  
Naigeng Wu ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Large Scale ◽  
Response Times ◽  
Forecast Error ◽  
Small Scale ◽  
Forecast Errors ◽  
Error Growth ◽  
Ensemble Forecasts ◽  
Scale Error ◽  
Active Convection

AbstractError growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

Dynamics and Structure of Mesoscale Error Covariance of a Winter Cyclone Estimated through Short-Range Ensemble Forecasts

2005 ◽  
Vol 133 (10) ◽  
pp. 2876-2893 ◽  
Author(s):  
Fuqing Zhang
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Small Scale ◽  
Horizontal Distance ◽  
Moist Convection ◽  
Error Growth ◽  
State Variables ◽  
Ensemble Forecasts ◽  
Error Covariance ◽  
Cross Covariance

Abstract Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

Beneficial role of diurnal smoothing for grid integration of wind power

2021 ◽  
Author(s):  
Anasuya Gangopadhyay ◽  
Ashwin K Seshadri ◽  
Ralf Toumi
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Diurnal Cycle ◽  
Power Plants ◽  
Large Scale ◽  
Positive Impact ◽  
Forecast Error ◽  
Wind Generation ◽  
Grid Integration ◽  
Wind Maximum

<p>Smoothing of wind generation variability is important for grid integration of large-scale wind power plants. One approach to achieving smoothing is aggregating wind generation from plants that have uncorrelated or negatively correlated wind speed. It is well known that the wind speed correlation on average decays with increasing distance between plants, but the correlations may not be explained by distance alone. In India, the wind speed diurnal cycle plays a significant role in explaining the hourly correlation of wind speed between location pairs. This creates an opportunity of “diurnal smoothing”. At a given separation distance the hourly wind speeds correlation is reduced for those pairs that have a difference of +/- 12 hours in local time of wind maximum. This effect is more prominent for location pairs separated by 200 km or more and where the amplitude of the diurnal cycle is more than about  0.5 m/s. “Diurnal smoothing” also has a positive impact on the aggregate wind predictability and forecast error. “Diurnal smoothing” could also be important for other regions with diurnal wind speed cycles.</p>

scholarly journals A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

2012 ◽  
Vol 27 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Bin Liu ◽  
Lian Xie
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Regional Model ◽  
Small Scale ◽  
Limited Area ◽  
Track Forecast ◽  
Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

scholarly journals More on the Scale Dependence of the Predictability of Precipitation Patterns: Extension to the 2009–13 CAPS Spring Experiment Ensemble Forecasts

2017 ◽  
Vol 145 (9) ◽  
pp. 3625-3646 ◽  
Author(s):  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
M. K. Yau ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Quasi Equilibrium ◽  
Relative Role ◽  
Ensemble Forecasts ◽  
Adjustment Time ◽  
Convective Adjustment ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

This paper analyzes the scale and case dependence of the predictability of precipitation in the Storm-Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms (CAPS) during the NOAA Hazardous Weather Testbed Spring Experiments of 2008–13. The effect of different types of ensemble perturbation methodologies is quantified as a function of spatial scale. It is found that uncertainties in the large-scale initial and boundary conditions and in the model microphysical parameterization scheme can result in the loss of predictability at scales smaller than 200 km after 24 h. Also, these uncertainties account for most of the forecast error. Other types of ensemble perturbation methodologies were not found to be as important for the quantitative precipitation forecasts (QPFs). The case dependences of predictability and of the sensitivity to the ensemble perturbation methodology were also analyzed. Events were characterized in terms of the extent of the precipitation coverage and of the convective-adjustment time scale [Formula: see text], an indicator of whether convection is in equilibrium with the large-scale forcing. It was found that events characterized by widespread precipitation and small [Formula: see text] values (representative of quasi-equilibrium convection) were usually more predictable than nonequilibrium cases. No significant statistical relationship was found between the relative role of different perturbation methodologies and precipitation coverage or [Formula: see text].

scholarly journals Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression

2015 ◽  
Vol 143 (3) ◽  
pp. 955-971 ◽  
Author(s):  
Kira Feldmann ◽  
Michael Scheuerer ◽  
Thordis L. Thorarinsdottir
Keyword(s):  
Bayesian Model Averaging ◽  
Forecast Error ◽  
Gaussian Random Field ◽  
Rank Correlation ◽  
Model Averaging ◽  
Small Scale ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Scale Modelling ◽  
Gaussian Regression

Abstract Statistical postprocessing techniques are commonly used to improve the skill of ensembles from numerical weather forecasts. This paper considers spatial extensions of the well-established nonhomogeneous Gaussian regression (NGR) postprocessing technique for surface temperature and a recent modification thereof in which the local climatology is included in the regression model to permit locally adaptive postprocessing. In a comparative study employing 21-h forecasts from the Consortium for Small Scale Modelling ensemble predictive system over Germany (COSMO-DE), two approaches for modeling spatial forecast error correlations are considered: a parametric Gaussian random field model and the ensemble copula coupling (ECC) approach, which utilizes the spatial rank correlation structure of the raw ensemble. Additionally, the NGR methods are compared to both univariate and spatial versions of the ensemble Bayesian model averaging (BMA) postprocessing technique.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

scholarly journals Ensemble-Based Error and Predictability Metrics Associated with Tropical Cyclogenesis. Part I: Basinwide Perspective

2014 ◽  
Vol 142 (8) ◽  
pp. 2879-2898 ◽  
Author(s):  
William A. Komaromi ◽  
Sharanya J. Majumdar
Keyword(s):  
Standard Deviation ◽  
Wind Shear ◽  
Large Scale ◽  
Predictive Power ◽  
Forecast Error ◽  
Ensemble Prediction ◽  
Tropical Cyclogenesis ◽  
Small Scale ◽  
Ensemble Prediction System ◽  
Error Standard Deviation

Abstract Several metrics are employed to evaluate predictive skill and attempt to quantify predictability using the ECMWF Ensemble Prediction System during the 2010 Atlantic hurricane season, with an emphasis on large-scale variables relevant to tropical cyclogenesis. These metrics include the following: 1) growth and saturation of error, 2) errors versus climatology, 3) predicted forecast error standard deviation, and 4) predictive power. Overall, variables that are more directly related to large-scale, slowly varying phenomena are found to be much more predictable than variables that are inherently related to small-scale convective processes, regardless of the metric. For example, 850–200-hPa wind shear and 200-hPa velocity potential are found to be predictable beyond one week, while 200-hPa divergence and 850-hPa relative vorticity are only predictable to about one day. Similarly, area-averaged quantities such as circulation are much more predictable than nonaveraged quantities such as vorticity. Significant day-to-day and month-to-month variability of predictability for a given metric also exists, likely due to the flow regime. For wind shear, more amplified flow regimes are associated with lower predictive power (and thereby lower predictability) than less amplified regimes. Relative humidity is found to be less predictable in the early and late season when there exists greater uncertainty of the timing and location of dry air. Last, the ensemble demonstrates the potential to predict error standard deviation of variables averaged in 10° × 10° boxes, in that forecasts with greater ensemble standard deviation are on average associated with greater mean error. However, the ensemble tends to be underdispersive.

scholarly journals Multiple-scale error growth in a convection-resolving model

2015 ◽  
Vol 22 (1) ◽  
pp. 1-13 ◽  
Author(s):  
F. Uboldi ◽  
A. Trevisan
Keyword(s):  
Forecast Error ◽  
Multiple Scale ◽  
Forecast Model ◽  
Dimensional Subspace ◽  
Error Growth ◽  
Breeding Technique ◽  
Initial Errors ◽  
Bred Vectors ◽  
Scale Error ◽  
Analysis Error

Abstract. The properties of the multiple-scale instabilities present in a non-hydrostatic forecast model are investigated. The model simulates intense convection episodes occurring in northern Italy. A breeding technique is used to construct ensembles of perturbations of the model trajectories aimed at representing the instabilities that are responsible for error growth on various timescales and space scales. By means of perfect model twin experiments it is found that, for initial errors of the order of present-day analysis error, a non-negligible fraction of the forecast error can be explained by a bred vector ensemble of reasonable size representing the growth of errors on intermediate scales. In contrast, when the initial error is much smaller, the spectrum of bred vectors representing the fast convective-scale instabilities becomes flat, and the number of ensemble members needed to explain even a small fraction of the forecast error becomes extremely large. The conclusion is that as the analysis error is decreased, it becomes more and more computationally demanding to construct an ensemble that can describe the high-dimensional subspace of convective instabilities and that can thus be potentially useful for controlling the error growth.

scholarly journals Changing Seasonal Predictability of Arctic Summer Sea Ice Area in a Warming Climate

2019 ◽  
Vol 32 (16) ◽  
pp. 4963-4979 ◽  
Author(s):  
Marika M. Holland ◽  
Laura Landrum ◽  
David Bailey ◽  
Steve Vavrus
Keyword(s):  
Sea Ice ◽  
Forecast Error ◽  
Early Summer ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Forecast Errors ◽  
Error Growth ◽  
Seasonal Predictability ◽  
Warming Climate ◽  
Late Twentieth

Abstract We use a large ensemble set of simulations and initialized model forecasts to assess changes in the initial-value seasonal predictability of summer Arctic sea ice area from the late-twentieth to the mid-twenty-first century. Ice thickness is an important seasonal predictor of September ice area because early summer thickness anomalies affect how much melt out occurs. We find that the role of this predictor changes in a warming climate, leading to decadal changes in September ice area predictability. In January-initialized prediction experiments, initialization errors grow over time leading to forecast errors in ice thickness at the beginning of the melt season. The magnitude of this ice thickness forecast error growth for regions important to summer melt out decreases in a warming climate, contributing to enhanced predictability. On the other hand, the influence of early summer thickness anomalies on summer melt out and resulting September ice area increases as the climate warms. Given this, for the same magnitude ice thickness forecast error in early summer, a larger September ice area anomaly results in the warming climate, contributing to reduced predictability. The net result of these competing factors is that a sweet spot for predictability exists when the ice thickness forecast error growth is modest and the influence of these errors on melt out is modest. This occurs at about 2010 in our simulations. The predictability of summer ice area is lower for earlier decades, because of higher ice thickness forecast error growth, and for later decades because of a stronger influence of ice thickness forecast errors on summer melt out.

scholarly journals Comparing different response time threshold setting methods to detect low effort on a large-scale assessment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
James Soland ◽  
Megan Kuhfeld ◽  
Joseph Rios
Keyword(s):  
Response Time ◽  
Test Scores ◽  
Large Scale ◽  
Response Times ◽  
The United States ◽  
Small Scale ◽  
Large Scale Assessment ◽  
Different Response ◽  
Threshold Setting ◽  
Scale Data

AbstractLow examinee effort is a major threat to valid uses of many test scores. Fortunately, several methods have been developed to detect noneffortful item responses, most of which use response times. To accurately identify noneffortful responses, one must set response time thresholds separating those responses from effortful ones. While other studies have compared the efficacy of different threshold-setting methods, they typically do so using simulated or small-scale data. When large-scale data are used in such studies, they often are not from a computer-adaptive test (CAT), use only a handful of items, or do not comprehensively examine different threshold-setting methods. In this study, we use reading test scores from over 728,923 3rd–8th-grade students in 2056 schools across the United States taking a CAT consisting of nearly 12,000 items to compare threshold-setting methods. In so doing, we help provide guidance to developers and administrators of large-scale assessments on the tradeoffs involved in using a given method to identify noneffortful responses.

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