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Need an account? Click here to sign up. Download Free PDF. Joel C Gaydos. Jean-paul Chretien. Dylan George. Julie Pavlin. Kevin Russell. Jose Sanchez. A short summary of this paper. Download Download PDF. Translate PDF. Department of Health and Human Services DoD lacks formal procedures for coordinating across these HHS , formed a smallpox modeling working group in programs. As a result, DoD decision-makers sometimes have to guide national bioterrorism planning. In the United Kingdom, authorities used modeling As a step toward improved epidemiologic modeling capa- results in establishing control measures during the bilities and coordination in the DoD, the AFHSC convened foot-and-mouth disease outbreak.
HIV treatment as a prevention strategy. Table I. Government USG agencies. This definition includes predictions of disease Silver Spring, MD Room G, Washington, DC The views expressed are those of the authors, and do not necessarily represent those of the Department of Defense or Department of Health The specific objectives of the WG were to and Human Services.
IP: Copyright c Association of Military Surgeons of the U. All rights reserved. Government, and first responders. WG members identified relevant DoD epidemiologic modeling systems based on their knowledge, review of pro- To make a preliminary assessment of the usefulness of gramming within their components or offices, and queries to modeling systems, the WG determined whether the system DoD- and Service-level research and public health organiza- is used routinely in military operations, and reviewed assess- tions about activities they support or participate in which may ments of its performance characteristics in real-world or meet the WG definition of epidemiologic modeling.
The WG simulated environments in sponsor evaluations, project described systems based on the following characteristics: reports, and published manuscripts. The WG made these sub- — Purpose jective judgments based on their knowledge of the systems — Sponsor and consultation with others external to the WG who were — Modeling Approach familiar with the systems and their applications.
It reviewed previous assessments of DoD epidemi- medical personnel. Four of — Military: The system currently incorporates data on these focus specifically on vector-borne diseases, one focuses specific or generic military populations to provide on influenza-like illness, and each of the other eight includes results for those populations. DoD sponsors of the systems results for military populations.
Remotely sensed data on environmental conditions and vector biology-based models VectorMap AFHSC Provide risk maps for various vector-borne diseases various projects.
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Get This Book. Visit NAP. Looking for other ways to read this? No thanks. Suggested Citation: "Appendix B: Agendas. B Agendas. Page Share Cite. Moderator: John Ahearne, Committee Chair. Fred A. These values are Variances and covariances are used to then sorted into bins of a specified number of represent the uncertainty of a meteorological points and averaged.
For this study, all of our forecast. We use the standard definition of bins represent points, so that the these quantities, so that the ensemble variance points with the lowest value of the predictor EVar of a scalar quantity s is given by: would go into the first bin, and the next 1 N 2 points into the second bin, etc. Thus EVar s ij is the ensemble U and V, respectively, in three space variance of scalar s at point i,j , and sm ij is the dimensions, and new arrays are read every value of scalar s at point i,j in ensemble meteorological model output time.
The ensemble variance for each of these variables is calculated readily from the model ensemble outputs. Because, as we discuss above, this estimate is not necessarily the same as the actual variance, we would like a way to calibrate our ensemble variances to represent more accurately the actual variances. Toward this end, we apply the bootstrap sampling and binning technique described in Section 2, with ensemble variance as the predictor quantity and actual error variance as the predicted quantity.
Figure 1 shows the result when this is applied to the 15hPa AGL U component of the wind field of the hour forecast, and Figures 2- 5 shows the binned scatterplot for progressively longer forecasts, every 12 hours from 24 hours to 60 hours. All of the plots show a strong functional relationship between ensemble variance and actual error variance. This means that, not only is ensemble uncertainty a good Figure 1 - Relationship of ensemble variance predictor of the actual uncertainty, but also that abscissa to the actual error variance ordinate we can often apply a simple, computationally for hour SREF forecasts of 15hPa AGL U inexpensive, linear calibration based on the made during the study period 25 August — 15 least-squares fit line to the ensemble variances September Horizontal lines represent the to derive expected actual variances.
While width of the point bin. Another important thing to note from the figures is that the slope of the line, which changes depending on the duration of the forecast, becomes steeper as the forecast period gets longer.
This line represents the variance of the model compared to that of the actual atmosphere, with a line representing an ideal ensemble, steeper lines an under- dispersive ensemble and shallower lines an over-dispersive ensemble. The steepening is indicative of the ensemble becoming less over- dispersive with increased integration time. This steepening is consistent with other studies of ensemble spread, which show that ensembles are start over-dispersive at short forecast times and gradually become under-dispersive at longer forecast times e.
Du et al , Whitaker and Loughe This result is important to our calibration, because it means we must apply a different calibration to the Figure 2 — As in Figure 1, except for hour ensemble at each forecast time for which we forecasts. Figure 3 - As in Figure 1, except for hour Figure 5 - As in Figure 1, except for hour forecasts.
Figure 6 demonstrates the relationship between ensemble covariance and actual error covariance for hour forecasts. Because two points separated in space can have a negative correlation, the covariances in this plot are both positive and negative, as opposed to the variance plots, which are positive definite.
The linear relationship does not look as good as that of the variance relations, but this is not surprising, as there is no reason to expect errors in the ensemble mean field to be spatially correlated, as they may be in individual model runs. Additionally, note the smaller scale for the actual error covariance compared to the ensemble covariance. Figure 4 — As in Figure 1, except for hour Recognizing the problem with using the forecasts.
These results for the uncorrelated errors, it would also be useful to same variable and forecast time as Figure 6 are have information about the covariance of errors shown in Figure 7. The linear relationship in the wind field at two separate points. We appears much stronger for this formulation of easily can apply the same technique that we covariance. Instead, information about the spatial error correlation is provided via the parameter SLE, which is a length scale related to the Lagrangian time scale Peltier et al.
However, this Lagrangian length scale is likely related to the length scale of the Eulerian covariance in some way. Using this reasoning, we compare the correlation of ensemble spread between two points, which is just a normalized form of the covariance, as a function of the distance separating the two points. This relationship between distance and correlation of ensemble spread is shown in Figure 8, which demonstrates that there is a significant correlation between that at two points close Figure 6 - The relationship of ensemble together but almost no correlation for points at covariance abscissa to actual error covariance much greater separation distances.
Note the weighting observations in a data assimilation much smaller scale along the ordinate axis scheme based on background model error compared to the abscissa axis.
This error correlation distance scale generally increases for observations located above the surface and boundary layer where the atmosphere contains much larger scale energy e. The exact length at which ensemble spread or errors are uncorrelated depends on many parameters including the variable type, forecast length, vertical level and the choice of threshold.
Clearly, beyond about km the correlations for low-level wind in this case are all within the noise level of the plot and are thus uncorrelated. Choosing 0.
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