Ensemble mean
On the choice of ensemble mean for estimating the forced signal in the presence of internal variability
... the ensemble mean of the climate models so as to minimize ...multimodel ensemble mean (MMEM), con- structed as the average of all the available CMIP5 models, as well as an MMEM from a subset ...
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Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland
... of ensemble mean density here is the same as for mean bulk density in ...population mean effective density is determined from ice water ...estimated ensemble mean snow density is ...
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Evaluation of drought propagation in an ensemble mean of large scale hydrological models
... wetlands. Of the grid cells covering the catchment, the one closest to the outlet of the catchment representing 14 % of the catchment was used (Table 2). A number of other grid cells from this catchment were also studied ...
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Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean
... the ensemble median has the greatest magnitude, the IQR is also large, indicating high variability among the models in the effect of BC on the precipitation ...in mean precipitation between the two periods ...
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The Effects of Imputing Missing Data on Ensemble Temperature Forecasts
... including ensemble consensus forecasting schemes. In addition, when ensemble forecast data are missing, the real-time use of the consensus forecast weighting scheme becomes difficult and the quality of ...
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The efficient global primitive equation climate model SPEEDO V2.0
... The ensemble mean Atlantic meridional overturning circulation (AMOC) averaged from 1960 to 2000 is shown in the left panel of ...the ensemble mean of the monthly mean mixed layer depth ...
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Multiphysics superensemble forecast applied to Mediterranean heavy precipitation situations
... corrected ensemble mean performs better than the other forecasts followed by the ensemble mean, the control member and finally by the superensemble, nevertheless all forecasts present ROC ...
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Climate of the last millennium: ensemble consistency of simulations and reconstructions
... the ensemble- mean relative standard deviations in ...and ensemble are rather small-scale over the ...absolute mean anomalies in the set of SIM ensemble and ...
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Verification against perturbed analyses and observations
... how ensemble size can affect the results, we need to return to estimates of the analysis error and ...analysis ensemble spread with the error of the ensemble ...size ensemble this cancellation ...
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Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations
... CMIP5 ensemble mean can generally capture the spatial pattern of soil ...CMIP5 ensemble varies significantly from subregion to sub- ...CMIP5 ensemble can accurately simulate warm season sur- ...
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A Novel Method for Partially Adaptive Broadband Beamforming
... ensemble mean square residual error / [dB].. 30 CCD method SVD method subband select...[r] ...
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Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products
... NASA’s Global Land Data Assimilation System (GLDAS) is an uncoupled land-surface modelling system that drives multiple land surface models (GLDAS LSMs: CLM, NOAH, VIC and MOSAIC) globally at high spatial and temporal ...
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Estimation of the Greenland ice sheet surface mass balance for the 20th and 21st centuries
... The ensemble mean of the 24 models used in the 20C3M experiment (see Table 4) gives a mean surface JJA temperature (resp. no significant precipitation change) in Region 1 (resp. These re[r] ...
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Tropical cyclone genesis potential across palaeoclimates
... The ensemble mean is obtained by first bilinearly interpolating the individual model fields onto the coarsest- resolution grid (HadCM3 in this case) and then averaging ...
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A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and the Fourier transform framework
... performing an inverse 3-D Fourier transform on a matrix of simulated Fourier coefficients with amplitude consistent with observed 1-D spectra. Then random phases are generated for the coefficient allowing multiple cloud ...
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Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria
... – The variability is low at least for the first three days of predictions (MDCV < 0.12), many models showing no variability (i.e. the same response for all members). As shown by Vel´azquez et al. (2011), part of this ...
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Flood event attribution and damage estimation using national scale grid based modelling:Winter 2013/2014 in Great Britain
... Re On ly Figure 8 Maps of FAR values for 1-day mean flows in the Thames@Kingston catchment black outline and dot, using the pooled Natural ensemble ‘e-o’ and each Natural ensemble ‘e’ to[r] ...
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An intercomparison of approaches for improving operational seasonal streamflow forecasts
... Despite generally promising findings from this body of work and from a number of agency development efforts (We- ber et al., 2012; Demargne et al., 2014), the use of large- scale climate information for real-time ...
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TAMSAT-ALERT v1: a new framework for agricultural decision support
... To accomplish this, the system converts the daily time series of driving data into multiple files, each contain- ing driving data for one ensemble member. The user is allowed to set the period over which the ...
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PubMedCentral-PMC5131881.pdf
... a mean in- and out-degree of 〈k〉 = 2; however, in network (a) the links are made randomly so the in-and out-degrees at each node are not necessarily equal, while network (b) is balanced so that the links are made ...
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