Cluster Analysis of Multimodel Ensemble Data from SAMEX

Alhamed, Ahmad A.; Lakshmivarahan, S.; Stensrud, David J.

doi:10.1175/1520-0493(2002)130<0226:caomed>2.0.co;2

Cited by 47 publications

(46 citation statements)

References 31 publications

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“…A CA was then applied to the original group of variables to group parameters with similar characteristics thereby further reducing the number of parameters (Peña, 2000). An agglomerative hierarchical CA was applied to group parameters, and the similarity-dissimilarity measure was used for the correlation coefficient (Alhamed et al, 2002;Unal et al, 2003). Single linkage was used to link clusters, which is based on the shortest distance between objects.…”

Section: Discussionmentioning

confidence: 99%

A non-destructive method for estimating onion leaf area

Córcoles¹,

Domı́nguez²,

Moreno³

et al. 2015

Irish Journal of Agricultural and Food Research

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Leaf area is one of the most important parameters for characterizing crop growth and development, and its measurement is useful for examining the effects of agronomic management on crop production. It is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies. Several direct and indirect methods have been developed for determining leaf area. The aim of this study is to develop an indirect method, based on the use of a mathematical model, to compute leaf area in an onion crop using non-destructive measurements with the condition that the model must be practical and useful as a Decision Support System tool to improve crop management. were used and the leaf area for individual leaves was computed using two indirect methods, one based on the use of an automated infrared imaging system, LI-COR-3100C, and the other using a digital scanner EPSON GT-8000, obtaining several images that were processed using Image J v 1.43 software. A total of 1146 leaves were used. Before measuring the leaf area, 25 parameters related to leaf length and width were determined for each leaf. The combined application of principal components analysis and cluster analysis for grouping leaf parameters was used to reduce the number of variables from 25 to 12. The parameter derived from the product of the total leaf length (L) and the leaf diameter at a distance of 25% of the total leaf length (A25) gave the best results for estimating leaf area using a simple linear regression model. The model obtained was useful for computing leaf area using a non-destructive method. Keywords leaf area • onion • non-destructive method • mathematical model.

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Section: Discussionmentioning

confidence: 99%

A non-destructive method for estimating onion leaf area

Córcoles¹,

Domı́nguez²,

Moreno³

et al. 2015

Irish Journal of Agricultural and Food Research

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“…The present study extends the results of Alhamed et al (2002) to much finer grid spacing (8-10 km) and examines if changes in dynamic core within the same model [e.g., the Weather Research and Forecasting (WRF) model] result in similar impacts to those present from changes in models, physical parameterizations, and initial conditions. The 8-km grid spacing is more refined than that used in previously discussed ensembles, so that the findings of the present study may help influence design of future ensembles that can use finer grid spacings as computer power increases.…”

Section: Introductionmentioning

confidence: 86%

“…Therefore, ensemble techniques are increasingly being investigated for short-term rainfall forecasting (e.g., Wandishin et al 2001;Alhamed et al 2002).…”

Section: Introductionmentioning

confidence: 99%

“…This type of ensemble was found, however, to suffer from insufficient spread when applied to shorter-range forecasts, and it was found that spread could be increased by varying model physics (e.g., Buizza et al 1999;Stensrud et al 2000) or using multiple models (Alhamed et al 2002). Alhamed et al (2002) found in a study using 25 model members run with roughly 30-km grid spacing during the Storm and Mesoscale Ensemble Experiment (SAMEX) that members tend to cluster first by model, next by physics, and last by initial conditions in a mixed model, physics, and initial condition ensemble.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts

Gallus

Bresch

2006

Monthly Weather Review

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A series of simulations for 15 events occurring during August 2002 were performed using the Weather Research and Forecasting (WRF) model over a domain encompassing most of the central United States to compare the sensitivity of warm season rainfall forecasts with changes in model physics, dynamics, and initial conditions. Most simulations were run with 8-km grid spacing. The Advanced Research WRF (ARW) and the nonhydrostatic mesoscale model (NMM) dynamic cores were used. One physics package (denoted NCEP) used the Betts-Miller-Janjic convective scheme with the Mellor-Yamada-Janjic planetary boundary layer (PBL) scheme and GFDL radiation package; the other package (denoted NCAR) used the Kain-Fritsch convective scheme with the Yonsei University PBL scheme and the Dudhia rapid radiative transfer model radiation. Other physical schemes were the same (e.g., the Noah land surface model, Ferrier et al. microphysics) in all runs. Simulations suggest that the sensitivity of the model to changes in physics is a function of which the dynamic core is used, and the sensitivity to the dynamic core is a function of the physics used. The greatest sensitivity in general is associated with a change in physics packages when the NMM core is used. Sensitivity to a change in physics when the ARW core is used is noticeably less. For light rainfall, the spread in the rainfall forecasts when physics are changed under the ARW core is actually less at most times than when the dynamic core is changed while NCAR physics are used. For light rainfall, the WRF model using NCAR physics is much more sensitive to a change in dynamic core than the WRF model using NCEP physics. For heavier rainfall, the opposite is true with a greater sensitivity occurring when NCEP physics is used. Sensitivity to initial conditions (Eta versus the Rapid Update Cycle with an accompanying small change in grid spacing) is generally less substantial than the sensitivity to changes in dynamic core or physics, except in the first 6-12 h of the forecast when it is comparable. As might be expected for warm season rainfall, the finescale structure of rainfall forecasts is more affected by the physics used than the dynamic core used. Surprisingly, however, the overall areal coverage and rain volume within the domain may be more influenced by the dynamic core choice than the physics used. and the nonhydrostatic mesoscale model (NMM) dynamic cores were used. One physics package (denoted NCEP) used the Betts-Miller-Janjic convective scheme with the Mellor-Yamada-Janjic planetary boundary layer (PBL) scheme and GFDL radiation package; the other package (denoted NCAR) used the Kain-Fritsch convective scheme with the Yonsei University PBL scheme and the Dudhia rapid radiative transfer model radiation. Other physical schemes were the same (e.g., the Noah land surface model, Ferrier et al. microphysics) in all runs. Simulations suggest that the sensitivity of the model to changes in physics is a function of which the dynamic core is used, and the sensitivity to the dynamic...

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“…The challenge of creating meaningful ensemble forecast members is to perturb the initial conditions in skilful ways reflecting the range of observation uncertainty and select a variety of model configurations to maximise forecast sensitivity (e.g. Alhamed et al, 2002;Bright and Mullen, 2002;Roebber et al, 2004;Martin and Xue, 2006;Aligo et al, 2007;Kong et al, 2009). Recently, a first step towards a convection-permitting EPS was done at Deutscher Wetterdienst (DWD) by following a multi-boundary and multi-parameter approach taking into account uncertainties in the lateral boundary conditions and model physics (Gebhardt et al, 2009).…”

Section: High-resolution Epsmentioning

confidence: 99%

Propagation of uncertainty from observing systems into NWP: COST‐731 Working Group 1

Rossa¹,

Haase²,

Keil³

et al. 2010

Atmospheric Science Letters

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The COST-731 Action is focused on uncertainty propagation in hydrometeorological forecasting chains. The goals and activities of the Action Working Group 1 can be subdivided by (1) describing and studying the impact of imperfect observations, mostly from radar, (2) exploiting radar data assimilation as a promising avenue for improved short-range precipitation forecasts and (3) high-resolution ensemble forecasting. Activities of Working Group 1 are presented along with their possible significance for hydrological applications.

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Cluster Analysis of Multimodel Ensemble Data from SAMEX

Cited by 47 publications

References 31 publications

A non-destructive method for estimating onion leaf area

A non-destructive method for estimating onion leaf area

Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts

Propagation of uncertainty from observing systems into NWP: COST‐731 Working Group 1

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