Higher-order statistics based multifractal predictability measures for anisotropic turbulence and the theoretical limits of aviation weather forecasting

AbstractTheoretical predictability measures of turbulent atmospheric flows are essential in estimating how realistic the current storm-scale strategic forecast skill expectations are. Atmospheric predictability studies in the past have usually neglected intermittency and anisotropy, which are typical features of atmospheric flows, rendering their application to the storm-scale weather regime ineffective. Furthermore, these studies are frequently limited to second-order statistical measures, which do not contain information about the rarer, more severe, and, therefore, more important (from a forecasting and mitigation perspective) weather events. Here we overcome these rather severe limitations by proposing an analytical expression for the theoretical predictability limits of anisotropic multifractal fields based on higher-order autocorrelation functions. The predictability limits are dependent on the order of statistical moment (q) and are smaller for larger q. Since higher-order statistical measures take into account rarer events, such more extreme phenomena are less predictable. While spatial anisotropy of the fields seems to increase their predictability limits (making them larger than the commonly expected eddy turnover times), the ratio of anisotropic to isotropic predictability limits is independent of q. Our results indicate that reliable storm-scale weather forecasting with around 3 to 5 hours lead time is theoretically possible.

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Site-specific analog weather-forecast system for northwest Himalaya, India

Annals of Glaciology ◽

10.3189/172756408787815013 ◽

2008 ◽

Vol 49 ◽

pp. 224-230 ◽

Cited By ~ 1

Author(s):

Dan Singh ◽

Amreek Singh ◽

Ashwagosha Ganju

Keyword(s):

Weather Forecasting ◽

Forecast Accuracy ◽

Weather Forecast ◽

Analog Model ◽

Site Specific ◽

Northwest Himalaya ◽

Weather Forecasts ◽

The Past ◽

Weather Events ◽

Weather Situation

AbstractIn an analog weather-forecasting procedure, recorded weather in the past analogs corresponding to the current weather situation is used to predict future weather. Consistent with the procedure, a theoretical framework is developed to predict weather at a specific site in the Pir Panjal range of the northwest Himalaya, India, using surface weather observations of the past ten winters (1991/92 to 2001/02) 3 days in advance. Weather predictions were made as snow day with quantitative snowfall category or no-snow day, for day1 through day3. As currently deployed, the procedure routinely provides a 3 day point weather forecast as guidance information to a weather and avalanche forecaster. Forecasts by analog model are evaluated by the various accuracy measures achieved for an independent dataset of three winters (2002/03 to 2004/05). The results indicate that weather forecasts by analog model are quite reliable, in that forecast accuracy corresponds closely to the relative frequencies of observed weather events. Moreover, qualitative weather (snow day or no-snow day) and quantitative categorical snowfall forecasts (quantitative snowfall category for snow day) are better than reference forecasts based on persistence and climatology for day1 predictions. Site-specific snowfall forecast guidance may play a major role in assessing avalanche danger, and accordingly formulating an avalanche forecast for a given area in advance.

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Development and Evaluation of Geostatistical Methods for Estimating Weather Related Collisions: A Large-Scale Case Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211020008 ◽

2021 ◽

pp. 036119812110200

Author(s):

Andy H. Wong ◽

Tae J. Kwon

Keyword(s):

Large Scale ◽

Mean Squared Error ◽

Hot Spot ◽

High Ratio ◽

Contributing Factors ◽

Geostatistical Methods ◽

Road Users ◽

Statistical Measures ◽

Weather Events

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

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Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

Weather and Forecasting ◽

10.1175/waf-d-15-0130.1 ◽

2016 ◽

Vol 31 (3) ◽

pp. 1001-1017 ◽

Cited By ~ 3

Author(s):

Omar V. Müller ◽

Miguel A. Lovino ◽

Ernesto H. Berbery

Keyword(s):

South America ◽

Lead Time ◽

Weather Forecasting ◽

Wrf Model ◽

Probability Of Detection ◽

Southern South America ◽

Climate Conditions ◽

Forecast Lead Time ◽

Highly Correlated ◽

Precipitation Season

Abstract Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

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SELEKSI KINERJA SAHAM TERCATAT DI BURSA EFEK JAKARTA MENGGUNAKAN MODEL JENSEN PADA MASA TRANSISI SIKLUS EKONOMI

Journal of Management and Business ◽

10.24123/jmb.v5i1.189 ◽

2006 ◽

Vol 5 (1) ◽

Author(s):

Mohamad Samsul

Keyword(s):

Lead Time ◽

Stock Return ◽

Stock Selection ◽

Training Set ◽

Expected Return ◽

Stock Performance ◽

The Past ◽

The Difference ◽

Actual Return ◽

Model Approach

The purpose of this research is to select the undervalued stock based on the Jensen's model approach. Data used is monthly stock return for 24 months during period of the year 2004 to 2005. Population used is 314 kind of stocks. The result of stock selection shows average undervalued stock of 53 or 17% of stock available for the year 2005. By experiment of 6 kind of initial set, 1 month lead time and 12 time training set, the result shows that the Jensen's model is not enough accurate to estimate the return for the next one month. The average monthly expected return of 13,7% and actual return of 2,9% shows the difference statistically significant. Eventhough, the actual return 2,9% still higher than market return IHSG BEJ of 1,6% on the monthly average. The correlation between the past stock performance and the future stock return is negative and not significant. The difference between return of high past stock performance and low past stock performance is not significant. The correlation between past beta and stock return is not significant. The difference between return of high past beta and low past beta is not significant.

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Optimizing cloud cover prediction by the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS)

10.5194/egusphere-egu21-9299 ◽

2021 ◽

Author(s):

Yen-Sen Lu ◽

Philipp Franke ◽

Dorit Jerger

Keyword(s):

Boundary Layer ◽

Solar Radiation ◽

Planetary Boundary Layer ◽

Cloud Cover ◽

Weather Forecasting ◽

Extreme Weather ◽

Atmospheric Modeling ◽

The Other ◽

Extreme Weather Events ◽

Weather Events

ESIAS is an atmospheric modeling system including the ensemble version of the Weather Forecasting and Research Model (WRF V3.7.1) and the ensemble version of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM), the latter uses the output of the WRF model to calculate, amongst others, the transportation of aerosols. To capture extreme weather events causing the uncertainty in the solar radiation and wind speed for the renewable energy industry, we employ ESIAS by using stochastic schemes, such as Stochastically Perturbed Parameterization Tendency (SPPT) and Stochastic Kinetic Energy Backscatter (SKEBS) schemes, to generate the random fields for ensembles of up to 4096 members.&#160;&#160;&#160;&#160; Our first goal is to produce 48 hourly weather predictions for the European domain with a 20 KM horizontal resolution to capture extreme weather events affecting wind, solar radiation, and cloud cover forecasts. We use the ensemble capability of ESIAS to optimize the physics configuration of WRF to have a more precise weather prediction. A total of 672 ensemble members are generated to study the effect of different microphysical schemes, cumulus schemes, and planetary boundary layer parameterization schemes. We examine our simulation outputs with 288 simulation hours in 2015 using model input from the Global Ensemble Forecast System (GEFS). Our results are validated by the cloud cover data from EUMETSAT CMSAF. Besides the precision of weather forecasting, we also determine the greatest spread by generating total 768 ensemble members: 16 stochastic members for each different configurations of physical parameterizations (48 combinations). The optimization of WRF will help for improving the air quality prediction by EURAD-IM, which will be demonstrated on a test case basis.&#160;&#160;&#160;&#160; Our results show that for the performed analysis the Community Atmosphere Model (CAM) 5.1, WRF Single-Moment 6-class scheme (WSM6), and the Goddard microphysics outstand the other 11 microphysics parameterizations, where the highest daily average matching rate is 64.2%. The Mellor&#8211;Yamada Nakanishi Niino (MYNN) 2 and MYNN3 schemes give better results compared to the other 8 planetary boundary layer schemes, and Grell 3D (Grell-3) works generally well with the above mentioned physical schemes. Overall, the combination of Goddard and MYNN3 produces the greatest spread comparing to the lowest spread (Morrison 2-moment & GFS) by 40%.

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Propositional encodings are a subset of organization theory

Behavioral and Brain Sciences ◽

10.1017/s0140525x09001034 ◽

2009 ◽

Vol 32 (2) ◽

pp. 214-215

Author(s):

George Mandler

Keyword(s):

Associative Learning ◽

Organization Theory ◽

Organizational Structures ◽

Higher Order ◽

Past Century ◽

Order Process ◽

Human Associative Learning ◽

The Past

AbstractThe notion that human associative learning is a usually conscious, higher-order process is one of the tenets of organization theory, developed over the past century. Propositional/sequential encoding is one of the possible types of organizational structures, but learning may also involve other structures.

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The benefit of pre- and postprocessing streamflow forecasts for 119 Norwegian catchments, evaluated within the frame of an operational flood-forecasting system

10.5194/egusphere-egu2020-5036 ◽

2020 ◽

Author(s):

Trine Jahr Hegdahl ◽

Kolbjørn Engeland ◽

Ingelin Steinsland ◽

Andrew Singleton

Keyword(s):

Lead Time ◽

Bayesian Model Averaging ◽

Weather Forecasting ◽

False Alarms ◽

Flood Events ◽

Ensemble Forecasts ◽

Flood Warning ◽

Temperature And Precipitation ◽

Streamflow Forecasts ◽

Forecasting System

In this work the performance of different pre- and postprocessing methods and schemes for ensemble forecasts were compared for a flood warning system.&#160; The ECMWF ensemble forecasts of temperature (T) and precipitation (P) were used to force the operational hydrological HBV model, and we estimated 2 years (2014 and 2015) of daily retrospect streamflow forecasts for 119 Norwegian catchments. Two approaches were used to preprocess the temperature and precipitation forecasts: 1) the preprocessing provided by the operational weather forecasting service, that includes a quantile mapping method for temperature and a zero-adjusted gamma distribution for precipitation, applied to the gridded forecasts, 2)&#160; Bayesian model averaging (BMA) applied to the catchment average values of temperature and precipitation. For the postprocessing of catchment streamflow forecasts, BMA was used. Streamflow forecasts were generated for fourteen schemes with different combinations of the raw, pre- and postprocessing approaches for the two-year period for lead-time 1-9 days.The forecasts were evaluated for two datasets: i) all streamflow and ii) flood events. The median flood represents the lowest flood warning level in Norway, and all streamflow observations above median flood are included in the flood event evaluation dataset. We used the continuous ranked probability score (CRPS) to evaluate the pre- and postprocessing schemes. Evaluation based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of 2 days, while preprocessing T and P using BMA improved the forecasts for 50% - 90% of the catchments beyond 2 days lead-time. However, with respect to flood events, no clear pattern was found, although the preprocessing of P and T gave better CRPS to marginally more catchments compared to the other schemes.In an operational forecasting system, warnings are issued when forecasts exceed defined thresholds, and confidence in warnings depends on the hit and false alarm ratio. By analyzing the hit ratio adjusted for false alarms, we found that many of the forecasts seemed to perform equally well. Further, we found that there were large differences in the ability to issue correct warning levels between spring and autumn floods. There was almost no ability to predict autumn floods beyond 2 days, whereas the spring floods had predictability up to 9 days for many events and catchments.The results underline differences in the predictability of floods depending on season and the flood generating processes, i.e. snowmelt affected spring floods versus rain induced autumn floods. The results moreover indicate that the ensemble forecasts are less good at predicting correct autumn precipitation, and more emphasis could be put on finding a better method to optimize autumn flood predictions. To summarize we find that the flood forecasts will benefit from pre-/postprocessing, the optimal processing approaches do, however, depend on region, catchment and season.

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Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018

Weather and Forecasting ◽

10.1175/waf-d-20-0096.1 ◽

2021 ◽

Vol 36 (1) ◽

pp. 39-51

Author(s):

Shoupeng Zhu ◽

Xiefei Zhi ◽

Fei Ge ◽

Yi Fan ◽

Ling Zhang ◽

...

Keyword(s):

Air Temperature ◽

Lead Time ◽

Weather Forecasting ◽

Heat Waves ◽

Northeast Asia ◽

Surface Air Temperature ◽

Poor Quality ◽

Spatiotemporal Patterns ◽

Lead Times ◽

Ensemble Mean

AbstractBridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluates products derived from four operational centers of CMA, KMA, NCEP, and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP), and Kalman filter superensemble (KF), in forecasts of surface air temperature with lead times of 6–30 days over northeast Asia in 2018. Validations after the preprocessing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The nonequal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after a lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2°–0.5°C relative to EMN. Forecast experiments of the 2018 northeast Asia heat wave reveal that the superensembles remarkably improve the raw forecasts featuring biases of >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at a lead time of 22 days. The superensemble techniques, particularly the KF method of dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal time scale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

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Past of a particle in an entangled state

International Journal of Quantum Information ◽

10.1142/s0219749917400196 ◽

2017 ◽

Vol 15 (08) ◽

pp. 1740019 ◽

Cited By ~ 5

Author(s):

Dilip Paneru ◽

Eliahu Cohen

Keyword(s):

Time Evolution ◽

Single Particle ◽

Quantum Particle ◽

Higher Order ◽

Entangled State ◽

Entire System ◽

Single Quantum ◽

Single Particles ◽

Top Down ◽

The Past

Vaidman has proposed a controversial criterion for determining the past of a single quantum particle based on the “weak trace” it leaves. We here consider more general examples of entangled systems and analyze the past of single, as well as pairs of entangled pre- and postselected particles. Systems with nontrivial time evolution are also analyzed. We argue that in these cases, examining only the single-particle weak trace provides information which is insufficient for understanding the system as a whole. We therefore suggest to examine, alongside with the past of single particles, also the past of pairs, triplets and eventually the entire system, including higher-order, multipartite traces in the analysis. This resonates with a recently proposed top-down approach by Aharonov, Cohen and Tollaksen for understanding the structure of correlations in pre- and postselected systems.

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Personality Attributes as Discriminating Factors in Distinguishing Religio-Mystical from Paranormal Experients

Imagination Cognition and Personality ◽

10.2190/8gt9-quem-ja1m-7f86 ◽

1992 ◽

Vol 11 (4) ◽

pp. 389-406 ◽

Cited By ~ 5

Author(s):

Peter L. Nelson

Keyword(s):

Discriminant Function ◽

Discriminant Function Analysis ◽

Exploratory Study ◽

Function Analysis ◽

Higher Order ◽

Validity Scales ◽

Personality Questionnaire ◽

Personality Measures ◽

The Past

In the first section of this article, an operationalized notion of preternatural experience is described which includes two general classes of experience: religio-mystical (Ontic) and paranormal (Perceptual). The exploratory study which follows uses the personality measures of the complete Tellegen Differential Personality Questionnaire taken from 120 subjects who reported having had spontaneous religio-mystical and/or paranormal experiences at some time in the past. The scores on all eleven primary dimensions, three higher order affect factors, and two validity scales were used individually, in univariate ANOVAs, and together, in a Direct Discriminant Function Analysis, to successfully separate two classes of preternatural experients from non-experients and from each other.

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