scholarly journals Inter-Comparison of Ensemble Forecasts for Low Level Wind Shear against Local Analyses Data over Jeju Area

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 198
Author(s):  
Young-Gon Lee ◽  
Sang-Boom Ryoo ◽  
Keunhee Han ◽  
Hee Wook Choi ◽  
Chansoo Kim
Keyword(s):  
Local Time ◽  
Wind Shear ◽  
Ensemble Member ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Simple Method ◽  
Low Level ◽  
Reliability Diagram ◽  
Probability Threshold ◽  
Selection Of

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operations and management. In this study, we conducted an accuracy and reliability analysis using a rank histogram, Brier score, and reliability diagram to verify LLWS ensemble member forecasts based on grid points over the Jeju area of the Republic of Korea. Thirteen LLWS ensemble member forecasts derived from a limited area ensemble prediction system (LENS) were obtained between 1 July 2016 and 30 May 2018, and 3-h LLWS forecasts for lead times up to 72 h (three days) were issued twice a day at 0000 UTC (9 am local time) and 1200 UTC (9 pm local time). We found that LLWS ensemble forecasts have a weak negative bias in summer and autumn and a positive bias in the spring and winter; the forecasts also have under-dispersion for all seasons, which implies that the ensemble spread of an ensemble is smaller than that of the corresponding observations. Additionally, the reliability curve in the associated reliability diagram indicates an over-forecasting of LLWS events bias. The selection of a forecast probability threshold from the LLWS ensemble forecast was confirmed to be one of the most important factors for issuing a severe LLWS warning. A simple method to select a forecast probability threshold without economic factors was conducted. The results showed that the selection of threshold is more useful for issuing a severe LLWS warning than none being selected.

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 Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 8 (2) ◽  
pp. 2739-2782 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Criterion Score ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

Probabilistic forecast of low-level wind shear over Jeju international airport using non-homogeneous regression model

2021 ◽  
pp. 002072092199706
Author(s):  
Young-Gon Lee ◽  
Chansoo Kim
Keyword(s):  
Reliability Analysis ◽  
Wind Shear ◽  
Probabilistic Forecast ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
International Airport ◽  
Probabilistic Forecasts ◽  
Exchangeable Model

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operation and management. However, there have been few studies on the probabilistic forecasts of LLWS obtained from ensemble prediction system. In this study, we analyzed a reliability analysis to verify LLWS ensemble member forecasts and observation based on the limited grid points around Jeju International Airport in Jeju. Homogeneous and non-homogeneous regression models were used to reduce the bias and dispersion existing ensemble prediction system and to provide probabilistic forecast. Prior to applying probabilistic forecast model, reliability analysis was conducted by using rank histogram to identify the statistical consistency of LLWS ensemble forecasts and corresponding observations. Based on the results of our study, we found that LLWS ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed for all seasons. To correct such biases, homogeneous regression and non-homogeneous regressions as EMOS (Ensemble Model Output Statistics) and EMOS exchangeable model by assuming exchangeable ensemble members were applied. The prediction skills of the methods were compared by the mean absolute error and continuous ranked probability score. We found that the prediction skills of probabilistic forecasts of EMOS exchangeable model were superior to the bias-corrected forecasts in terms of deterministic prediction.

scholarly journals End-to-end verification of the ensemble precipitation-to-river-flow forecasting chain: how to maximise skill for the user and does the uncertainty propagate?

2021 ◽  
Author(s):  
Marion Mittermaier ◽  
Seonaid Anderson ◽  
Ric Crocker ◽  
Steven Cole ◽  
Robert Moore ◽  
...  
Keyword(s):  
River Flow ◽  
Daily Precipitation ◽  
Time Window ◽  
Ensemble Member ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
End To End ◽  
The Impact

<p>Forecasting the potential for flood-producing precipitation and any subsequent flooding is a challenging task; the process is highly non-linear and inherently uncertain. Acknowledging and accounting for the uncertainty in precipitation and flood forecasts has become increasingly important with the move to risk-based warning and guidance services which combine the likelihood of flooding with the potential impact on society and the environment.</p><p>A standard approach to accounting for uncertainty is to generate ensemble forecasts. Here the national Grid-to-Grid (G2G) model is coupled to a Best Medium Range (BMR) ensemble which consists of three models spanning different time horizons: an ensemble nowcast for the first 6h, which is blended with the short-range 2.2 km Met Office Global Regional Ensemble Prediction System (MOGREPS-UK) ensemble up to 36h and the ~20 km global MOGREPS-G up to day 6. The G2G model is driven by 15-minute accumulations on a 1 km grid.</p><p>16-months of precipitation and river flow ensemble forecasts have been processed to develop and assess a joint verification framework which can facilitate the evaluation of the end-to-end forecasting chain. Analysis concluded the following: (1) daily precipitation accumulations provide the best guidance in terms of rain volume for hydrological impacts. One reason may be because it removes the impact of timing errors at the sub-daily scale. However, sub-daily precipitation can be more closely related to river flow on an ensemble member-by-member basis. (2) Observation uncertainty is important. The same forecasts verified against three different observed precipitation sources (raingauge, radar or merged) can provide markedly different results and interpretations. G2G river flow performance can also be affected, when driven by these datasets rather than forecasts. (3) The change in precipitation-intensity with model is evident and has an impact on downstream modelling and verification. (4) The period used for ensemble verification should be at least two years. The 16-month test period was sufficient for generating enough precipitation threshold-exceedances for the 95th percentile: but insufficient for higher thresholds and for river flow thresholds above half the median annual maximum flood at sub-regional scales. (5) A new method of presenting Time-Window Probabilities (TWPs) has been developed for precipitation thresholds that are hydrologically relevant. Verification of these shows that probabilities are larger, and more reliable so that users can have greater confidence in them. (6) Overall precipitation forecast skill was far more uniform than for river-flow, primarily because the atmosphere is a continuum whilst catchments are finite and subject to external, non-atmospheric factors including antecedent moisture. (7) Though G2G can be sensitive to precipitation outliers, the precipitation ensemble is generally under-spread and spread does not appear to amplify or propagate to enhance the river flow ensemble spread, so spread is reduced rather than increased in the downstream application.</p>

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 15 (11) ◽  
pp. 3307-3325 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue real-time forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Probabilistic Forecast of Low Level Wind Shear of Gimpo, Gimhae, Incheon and Jeju International Airports Using Ensemble Model Output Statistics

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1643
Author(s):  
Hee-Wook Choi ◽  
Yeon-Hee Kim ◽  
Keunhee Han ◽  
Chansoo Kim
Keyword(s):  
Wind Shear ◽  
Ensemble Prediction ◽  
Economic Damage ◽  
Ensemble Model ◽  
Model Output ◽  
Low Level ◽  
Probabilistic Forecasts ◽  
Truncated Normal ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Wind shear can occur at all flight levels; however, it is particularly dangerous at low levels, from the ground up to approximately 2000 feet. If this phenomenon can occur during the take-off and landing of an aircraft, it may interfere with the normal altitude change of the aircraft, causing delay and cancellation of the aircraft, as well as economic damage. In this paper, to estimate the probabilistic forecasts of low-level wind shear at Gimpo, Gimhae, Incheon and Jeju International Airports, an Ensemble Model Output Statistics (EMOS) model based on a left-truncated normal distribution with a cutoff zero was applied. Observations were obtained from Gimpo, Gimhae, Incheon and Jeju International Airports and 13 ensemble member forecasts generated from the Limited-Area Ensemble Prediction System (LENS), for the period December 2018 to February 2020. Prior to applying to EMOS models, statistical consistency was analyzed by using a rank histogram and kernel density estimation to identify the uniformity of ensembles with corresponding observations. Performances were evaluated by mean absolute error, continuous ranked probability score and probability integral transform. The results showed that probabilistic forecasts obtained from the EMOS model exhibited better prediction skills when compared to the raw ensembles.

Identification of Possible High Integrity Containers for Low-Level Nuclear Waste Disposal

1981 ◽  
Vol 6 ◽  
Author(s):  
Jeffrey D. Williams
Keyword(s):  
South Carolina ◽  
Structural Integrity ◽  
Exchange Resin ◽  
Ion Exchange Resin ◽  
Economic Factors ◽  
Burial Site ◽  
Low Level ◽  
High Integrity ◽  
Low Level Radioactive Waste ◽  
Selection Of

ABSTRACTIncreased concern by the State of South Carolina over the condition and capacity of the low-level radioactive waste burial site at Barnwell has prompted them to promulgate new regulations on waste burial containers. As of September 30, 1981, ion exchange resin and filter media waste with an activity of 1 μCi/cc or greater and with isotopes with halflives greater than five years disposed at Barnwell shall be solidified or confined in a “high integrity container”. The materials and designs of these containers are required to provide waste isolation from the environment for a period of 300 years and provide the structural integrity specified in 49 CFR 173.398(b). HITTMAN has been active in the design and development of containers suitable for this purpose with this paper detailing the analyses involved. Material selections were limited to stainless steel, fiberglass, and polyethylenes. Structural concerns focused on overpressure requirements, drop-testing requirements, and lifting capabilities. With a lifetime dose of up to 108 rads, the possibilities of radiation damage were considered. Preliminary selection of polyethylene was based on satisfactory resolution of these issues and economic factors.

Airborne infrared low level wind shear predictor

1984 ◽  
Author(s):  
P. KUHN ◽  
R. KURKOWSKI
Keyword(s):  
Wind Shear ◽  
Low Level

scholarly journals Geographic Shift and Environment Change of U.S. Tornado Activities in a Warming Climate

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 567
Author(s):  
Zuohao Cao ◽  
Huaqing Cai ◽  
Guang J. Zhang
Keyword(s):  
Wind Shear ◽  
Convective Available Potential Energy ◽  
Geographic Location ◽  
Reanalysis Data ◽  
Cyclonic Circulation ◽  
Upward Motion ◽  
Environment Change ◽  
Low Level ◽  
High Event ◽  
The U.S

Even with ever-increasing societal interest in tornado activities engendering catastrophes of loss of life and property damage, the long-term change in the geographic location and environment of tornado activity centers over the last six decades (1954–2018), and its relationship with climate warming in the U.S., is still unknown or not robustly proved scientifically. Utilizing discriminant analysis, we show a statistically significant geographic shift of U.S. tornado activity center (i.e., Tornado Alley) under warming conditions, and we identify five major areas of tornado activity in the new Tornado Alley that were not identified previously. By contrasting warm versus cold years, we demonstrate that the shift of relative warm centers is coupled with the shifts in low pressure and tornado activity centers. The warm and moist air carried by low-level flow from the Gulf of Mexico combined with upward motion acts to fuel convection over the tornado activity centers. Employing composite analyses using high resolution reanalysis data, we further demonstrate that high tornado activities in the U.S. are associated with stronger cyclonic circulation and baroclinicity than low tornado activities, and the high tornado activities are coupled with stronger low-level wind shear, stronger upward motion, and higher convective available potential energy (CAPE) than low tornado activities. The composite differences between high-event and low-event years of tornado activity are identified for the first time in terms of wind shear, upward motion, CAPE, cyclonic circulation and baroclinicity, although some of these environmental variables favorable for tornado development have been discussed in previous studies.

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