scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

scholarly journals Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction

2008 ◽  
Vol 21 (2) ◽  
pp. 230-247 ◽  
Author(s):  
Youmin Tang ◽  
Richard Kleeman ◽  
Andrew M. Moore
Keyword(s):  
Southern Oscillation ◽  
A Priori ◽  
Prediction Skill ◽  
Coupled Models ◽  
Potential Predictability ◽  
Model Skill ◽  
Enso Predictability ◽  
Predictive Skill ◽  
Triangular Relationship ◽  
The Relationship

Abstract In this study, ensemble predictions of the El Niño–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative entropy (R), predictive information (PI), predictive power (PP), and mutual information (MI), were explored in terms of their ability of estimating a priori the predictive skill of the ENSO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations, and the model prediction skills of correlation and root-mean-square error (RMSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the MI is a good indicator of overall skill. When it is large, the prediction system has high prediction skill, whereas small MI often corresponds to a low prediction skill. This suggests that MI is a good indicator of the actual skill of the models. The R and PI have a nearly identical average (over all predictions) as should be the case in theory. Comparing the different information-based measures reveals that R is a better predictor of prediction skill than PI and PP, especially when correlation-based metrics are used to evaluate model skill. A “triangular relationship” emerges between R and the model skill, namely, that when R is large, the prediction is likely to be reliable, whereas when R is small the prediction skill is quite variable. A small R is often accompanied by relatively weak ENSO variability. The possible reasons why R is superior to PI and PP as a measure of ENSO predictability will also be discussed.

The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5351-5364 ◽  
Author(s):  
Baoqiang Xiang ◽  
Ming Zhao ◽  
Xianan Jiang ◽  
Shian-Jiann Lin ◽  
Tim Li ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Strong Dependence ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Prediction Skill ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Madden Julian Oscillation ◽  
Geophysical Fluid Dynamics ◽  
Potential Predictability ◽  
Anomaly Correlation Coefficient

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

scholarly journals Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

2021 ◽  
pp. 1-55
Author(s):  
Pengfei Shi ◽  
Bin Wang ◽  
Yujun He ◽  
Hui Lu ◽  
Kun Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Potential Predictability ◽  
Climate System Model ◽  
Climate Predictability ◽  
Summer Climate ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

AbstractLand surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Sea ice and atmospheric potential predictability in coupled GCMs

2021 ◽  
Author(s):  
Daniela Flocco ◽  
Ed Hawkins ◽  
Leandro Ponsoni ◽  
François Massonnett ◽  
Daniel Feltham ◽  
...  
Keyword(s):  
Sea Ice ◽  
Initial Conditions ◽  
Complete Information ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Potential Predictability ◽  
Sea Ice Thickness

<p>Assimilation of sea ice concentration satellite products has successfully been used to initialize sea ice models and coupled NWP systems. Sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. We have examined the potential for sea ice thickness observations to improve forecast skill on timescales from days to a year ahead in two state-of-the-art coupled GCMs.</p><p>Here we examine the influence of Arctic sea-ice thickness observations on the potential predictability of the sea-ice and atmospheric circulation using idealised ‘data denial’ experiments. We perform paired sets of ensembles with the HadGEM3 and EC-Earth GCMs using different initial conditions retrieved from present-day control runs.</p><p>One set of ensembles start with complete information about the sea-ice conditions and is treated as “truth”, and one set has degraded sea ice information. We investigate how the pairs of ensembles, all started in January, predict the subsequent evolution of the sea-ice state, sea level pressure and circulation within the Arctic with the aim of quantifying the value of sea-ice observations for improving predictions.</p><p>We show that accurate initialization of sea ice thickness improves the model prediction skill during the first month of simulation and that several sea ice state and atmospheric variables present a re-emergence of skill in September. Prediction skill of several oceanic variables is also observed. The two models present a good agreement in terms of the regions where they show either a skill gain or loss.</p>

scholarly journals Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors?

2018 ◽  
Vol 31 (2) ◽  
pp. 613-621 ◽  
Author(s):  
Arun Kumar ◽  
Jieshun Zhu
Keyword(s):  
Spatial Variability ◽  
Time Scales ◽  
Seasonal Prediction ◽  
Spatial Variations ◽  
Climate System ◽  
Prediction Skill ◽  
Forecast Errors ◽  
Bjerknes Feedback ◽  
Coupled Models ◽  
Ocean Atmosphere

Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean–atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air–sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air–sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean–atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean–atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases.

Transplantation of coral fragment, Acropora formosa (Scleractinia)

2014 ◽  
pp. 1
Author(s):  
Hanny Tioho ◽  
Maykel A.J Karauwan
Keyword(s):  
Fragment Size ◽  
Observation Time ◽  
Minimum Size ◽  
Average Growth Rate ◽  
Average Growth ◽  
Significant Difference ◽  
Acropora Formosa ◽  
Survival And Growth ◽  
One Way Anova ◽  
Better Than

The minimum size of coral transplants, Acropora formosa, was assessed to support their survival and growth. For this, 150 coral fragments of different sizes (5, 10, 15 cm) were transplanted close to the donor colony. Their survivorship and growth were observed for 12 months. At the end of the observation time, 90% of 15 cm-transplanted coral fragments survived, while the others (10cm and 5 cm) did 86% and 82% respectively. The average growth rate of 5 cm-coral fragments was 0.860 cm/month, while 10 and 15 cm-fragments were 0.984 cm/month and 1.108 cm/month respectively. One-way ANOVA showed that there was significant difference (p<0.05) among the three (5, 10, 15 cm) transplant initial sizes in which the longest fragment size tended to survive longer than the smaller one.  However, the smaller transplants grew better than the bigger one, 10.318 cm/year (206%) for 5 cm-transplant, 11.803 cm/year (118%) for 10 cm-transplant, and 13.299 cm/year (89%) for 15 cm-transplant, respectively. Ukuran minimal fragmen karang Acropora formosa yang ditransplantasi diduga untuk mendukung ketahanan hidup dan pertumbuhannya. Untuk itu, 150 fragmen karang ditransplantasi ke lokasi yang berdekatan dengan koloni induknya.  Ketahanan hidup dan pertumbuhan semua fragmen karang yang ditransplantasi diamati selama 12 bulan.  Pada akhir pengamatan, 90% dari fragmen karang berukuran 15 cm yang ditransplantasi dapat bertahan hidup, sedangkan yang lainnya (ukuran 10 cm dan 5 cm) masing-masing sebesar 86% dan 82%.  Rata-rata laju pertumbuhan fragmen karang dengan ukuran awal 5 cm adalah 0,860 cm/bulan, sedangkan ukuran fragmen 10 dan 15 cm masing-masing adalah 0,984 cm/bulan and 1,108 cm/bulan. ANOVA satu arah menunjukkan adanya perbedaan yang nyata (p<0.05) antara ketiga ukuran fragmen yang berbeda, di mana ukuran fragmen karang yang lebih panjang cenderung mempunyai ketahanan hidup yang lebih baik. Namun demikian, ukuran transplant yang lebih kecil memiliki pertumbuhan lebih baik dibandingkan dengan ukuran yang lebih besar, yakni10,318 cm/tahun (206%) untuk transplant berukuran 5 cm, 11,803 cm/tahun (118%) untuk 10 cm, dan 13,299 cm/tahun (89%) untuk ukuran 15 cm.

Isolation of an Anti-fatigue Preparation from Cellulase-treated Maca by Hydroalcoholic Extraction

2017 ◽  
Vol 17 (1) ◽  
pp. 93-98
Author(s):  
Zheng Yue ◽  
Zhang Wen-Cheng ◽  
Wu Ze-Yu ◽  
Fu Chuan-Xiang ◽  
Gao Han ◽  
...  
Keyword(s):  
Positive Control ◽  
Glycogen Storage ◽  
Hepatic Glycogen ◽  
Time To Exhaustion ◽  
High Dose ◽  
Hydroalcoholic Extract ◽  
Swimming Time ◽  
Significant Difference ◽  
Pre Treatment ◽  
Better Than

The purpose of this study was to evaluate the anti-fatigue activity of maca hydroalcoholic extract (ME), which mainly contains macamides and polysaccharides. ME was prepared by circumfluence extraction with enzymatic pre-treatment. Anti-fatigue activity of ME was investigated in weight-loaded forced swimming mice, with pure macamides and commercially available maca tablet as positive control. Compared with normal group, pure macamides treatment group could prolong the swimming time to exhaustion, but there was no statistically significant difference (P > 0.05); while ME (middle-dose and high-dose groups) could effectively prolong the swimming durations (P < 0.05). Supplementation with pure macamides significantly decreased blood lactic acid (BLA), whereas ME significantly increased hepatic glycogen (HG), decreased BLA, and blood urea nitrogen (BUN) compared with those in normal control (P < 0.05). The results suggested that the anti-fatigue effect of ME was better than that of pure macamides, which can be explained by the increase of glycogen storage and the reduction of metabolites accumulation.

EMOTIONAL INTELLIGENCE AND ACADEMIC ACHIEVEMENT OF ADOLESCENTS WITH REFERENCE TO GENDER

2017 ◽  
Vol 4 (24) ◽  
Author(s):  
Nisha Chandel ◽  
Seema Chopra
Keyword(s):  
Academic Achievement ◽  
Emotional Intelligence ◽  
Female Adolescents ◽  
Intelligence Scale ◽  
Male And Female ◽  
Male Adolescents ◽  
Significant Difference ◽  
Understanding Emotions ◽  
School Records ◽  
Better Than

The present study was undertaken to find out emotional intelligence and academic achievement of male and female adolescents. The sample consists of 82 students( 41 male and 41 female adolescents) from different schools in Hamirpur district of Himachal Pradesh. Emotional intelligence was assessed with the help of Emotional Intelligence Scale developed by Singh and Narain (2014) and academic achievement score were taken from the school records. The results revealed that there exists a significant difference in emotional intelligence of male and female adolescents. It was found that there existed significant difference in academic achievement of female adolescents and male adolescents. The mean emotional intelligence of female adolescents was better than of male adolescents. On the dimensions of emotional intelligence, it was found that there was no significant difference between male and female adolescents on understanding emotions, empathy and handling relations dimensions of emotional intelligence; while it was reported that there was significant difference between male and female adolescents on understanding motivation dimension of emotional intelligence On the other hand, it was found that there existed significant difference in academic achievement of female adolescents and male adolescents.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Integrated Bimodal Fitting for Unilateral CI Users with Residual Contralateral Hearing

2021 ◽  
Vol 11 (2) ◽  
pp. 200-206
Author(s):  
Gennaro Auletta ◽  
Annamaria Franzè ◽  
Carla Laria ◽  
Carmine Piccolo ◽  
Carmine Papa ◽  
...  
Keyword(s):  
Cochlear Implants ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Performance Difference ◽  
Test Conditions ◽  
Significant Difference ◽  
Noisy Conditions ◽  
New Formula ◽  
Better Than

Background: The aim of this study was to compare, in users of bimodal cochlear implants, the performance obtained using their own hearing aids (adjusted with the standard NAL-NL1 fitting formula) with the performance using the Phonak Naìda Link Ultra Power hearing aid adjusted with both NAL-NL1 and a new bimodal system (Adaptive Phonak Digital Bimodal (APDB)) developed by Advanced Bionics and Phonak Corporations. Methods: Eleven bimodal users (Naìda CI Q70 + contralateral hearing aid) were enrolled in our study. The users’ own hearing aids were replaced with the Phonak Naìda Link Ultra Power and fitted following the new formula. Speech intelligibility was assessed in quiet and noisy conditions, and comparisons were made with the results obtained with the users’ previous hearing aids and with the Naída Link hearing aids fitted with the NAL-NL1 generic prescription formula. Results: Using Phonak Naìda Link Ultra Power hearing aids with the Adaptive Phonak Digital Bimodal fitting formula, performance was significantly better than that with the users’ own rehabilitation systems, especially in challenging hearing situations for all analyzed subjects. Conclusions: Speech intelligibility tests in quiet settings did not reveal a significant difference in performance between the new fitting formula and NAL-NL1 fittings (using the Naída Link hearing aids), whereas the performance difference between the two fittings was very significant in noisy test conditions.

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