Application of Statistical Models to the Prediction of Seasonal Rainfall Anomalies over the Sahel

2014 ◽  
Vol 53 (3) ◽  
pp. 614-636 ◽  
Author(s):  
Hamada S. Badr ◽  
Benjamin F. Zaitchik ◽  
Seth D. Guikema
Keyword(s):  
Statistical Models ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Surface Air Temperature ◽  
Principal Component ◽  
Seasonal Precipitation ◽  
Precipitation Prediction ◽  
Rainfall Anomalies ◽  
Sahel Region

AbstractRainfall in the Sahel region of Africa is prone to large interannual variability, and it has exhibited a recent multidecadal drying trend. The well-documented social impacts of this variability have motivated numerous efforts at seasonal precipitation prediction, many of which employ statistical techniques that forecast Sahelian precipitation as a function of large-scale indices of surface air temperature (SAT) anomalies, sea surface temperature (SST), surface pressure, and other variables. These statistical models have demonstrated some skill, but nearly all have adopted conventional statistical modeling techniques—most commonly generalized linear models—to associate predictor fields with precipitation anomalies. Here, the results of an artificial neural network (ANN) machine-learning algorithm applied to predict summertime (July–September) Sahel rainfall anomalies using indices of springtime (April–June) SST and SAT anomalies for the period 1900–2011 are presented. Principal component analysis was used to remove multicollinearity between predictor variables. Predictive accuracy was assessed using repeated k-fold random holdout and leave-one-out cross-validation methods. It was found that the ANN achieved predictive accuracy superior to that of eight alternative statistical methods tested in this study, and it was also superior to that of previously published predictive models of summertime Sahel precipitation. Analysis of partial dependence plots indicates that ANN skill is derived primarily from the ability to capture nonlinear influences that multiple major modes of large-scale variability have on Sahelian precipitation. These results point to the value of ANN techniques for seasonal precipitation prediction in the Sahel.

scholarly journals Variations in the Summertime Atmospheric Hydrologic Cycle Associated with Seasonal Precipitation Anomalies over the Southwestern United States

2006 ◽  
Vol 7 (4) ◽  
pp. 788-807 ◽  
Author(s):  
Bruce T. Anderson ◽  
Hideki Kanamaru ◽  
John O. Roads
Keyword(s):  
New Mexico ◽  
Large Scale ◽  
Moisture Flux ◽  
Hydrologic Cycle ◽  
Seasonal Precipitation ◽  
Model Simulations ◽  
Moisture Flux Convergence ◽  
Rainfall Season ◽  
Rainfall Anomalies ◽  
The Mean

Abstract This paper examines year-to-year variations in the large-scale summertime hydrologic cycle over the southwestern United States using a suite of regional model simulations and surface- and upper-air-based observations. In agreement with previous results, it is found that observed interannual precipitation variations in this region can be subdivided into two spatiotemporal regimes—one associated with rainfall variability over the southwestern portion of the domain centered on Arizona and the other associated with variations over the southeastern portion centered on western Texas and eastern New Mexico. Because of the limited duration of the model simulation data, it is possible to only investigate one positive rainfall season over the Arizona region and one negative rainfall season over the New Mexico region. From these investigations it appears that for the positive rainfall anomalies over Arizona excess seasonal precipitation is balanced by both enhanced evaporation and vertically integrated large-scale moisture flux convergence. Vertical profiles of these terms indicate that the anomalous large-scale moisture flux convergence is actually related to a decrease in the mean large-scale moisture flux divergence aloft; below 800 mb there is a decrease in the mean moisture flux convergence typically found at these levels, which in turn produces anomalous moisture divergence from the region. For the negative rainfall anomalies over New Mexico similar results, but of opposite sign, are found; one exception is that at the lowest levels there is an additional (negative) contribution to the vertically integrated moisture flux convergence anomaly related to a weakening of the mean low-level moisture flux convergence during the low-rainfall year. Further studies using two different model simulations with the same large-scale dynamic forcing but differing initial soil moisture values indicate that similar balances are also found for rainfall anomalies related to surface soil moisture changes within the domain, suggesting that the changes in large-scale moisture flux convergence described above can be attributed to both year-to-year variations in the regional land–atmosphere interactions as well as variations in the large-scale circulation patterns.

scholarly journals The Dynamical Influence of the Atlantic Multidecadal Oscillation on Continental Climate

2017 ◽  
Vol 30 (18) ◽  
pp. 7213-7230 ◽  
Author(s):  
Christopher H. O’Reilly ◽  
Tim Woollings ◽  
Laure Zanna
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Atlantic Multidecadal Oscillation ◽  
The North ◽  
Sst Variability ◽  
Western Sahel ◽  
Prediction Systems ◽  
Sahel Region

Abstract The Atlantic multidecadal oscillation (AMO) in sea surface temperature (SST) has been shown to influence the climate of the surrounding continents. However, it is unclear to what extent the observed impact of the AMO is related to the thermodynamical influence of the SST variability or the changes in large-scale atmospheric circulation. Here, an analog method is used to decompose the observed impact of the AMO into dynamical and residual components of surface air temperature (SAT) and precipitation over the adjacent continents. Over Europe the influence of the AMO is clearest during the summer, when the warm SAT anomalies are interpreted to be primarily thermodynamically driven by warm upstream SST anomalies but also amplified by the anomalous atmospheric circulation. The overall precipitation response to the AMO in summer is generally less significant than the SAT but is mostly dynamically driven. The decomposition is also applied to the North American summer and the Sahel rainy season. Both dynamical and residual influences on the anomalous precipitation over the Sahel are substantial, with the former dominating over the western Sahel region and the latter being largest over the eastern Sahel region. The results have potential implications for understanding the spread in AMO variability in coupled climate models and decadal prediction systems.

scholarly journals A Comparison of Statistical Approaches for Seasonal Precipitation Prediction in Pakistan

2013 ◽  
Vol 28 (5) ◽  
pp. 1116-1132 ◽  
Author(s):  
Ting Ding ◽  
Zongjian Ke
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Consistent Pattern ◽  
Seasonal Precipitation ◽  
Model Output ◽  
Statistical Regression ◽  
Precipitation Prediction ◽  
Statistical Approaches

Abstract The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

scholarly journals Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

2021 ◽  
Vol 13 (10) ◽  
pp. 5359
Author(s):  
Afrika Onguko Okello ◽  
Jonathan Makau Nzuma ◽  
David Jakinda Otieno ◽  
Michael Kidoido ◽  
Chrysantus Mbi Tanga
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Principal Component ◽  
Farm Income ◽  
Purchase Decisions ◽  
Wealth Status ◽  
Feed Formulation ◽  
Alternative Protein ◽  
Livestock Products ◽  
The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

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