Ensemble Variance Calibration for Representing Meteorological Uncertainty for Atmospheric Transport and Dispersion Modeling

Abstract

In the event of the release of a dangerous atmospheric contaminant, an atmospheric transport and dispersion (ATD) model is often used to provide forecasts of the resulting contaminant dispersion affecting the population. These forecasts should also be accompanied by accurate estimates of the forecast uncertainty to allow for more informed decisions about the potential hazardous area. This study examines the calculation of uncertainty in the meteorological data as derived from an ensemble, and its effects when used as additional input to drive an ATD model. The first part of the study examines the capability of a linear function to relate ensemble spread to error variance of the ensemble mean given ensemble spread from 24 days of forecasts from the National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF). This linear function can then be used to calibrate the ensemble spread to produce a more accurate estimate of the meteorological uncertainty. Results for the linear relationship of wind variance are very good, with values of the coefficient of determination R2 generally exceeding 0.94 for forecast lengths of 12 h and greater. The calibration is shown to be more sensitive to forecast hour than vertical level within the lower troposphere. The second part presents a 24-h case study to assess the impact of meteorological uncertainty calculations on Second-Order Closure Integrated Puff (SCIPUFF) ATD model predictions. Both uncalibrated ensemble wind variances and wind variances calibrated based on the results of the first part show improvement in mean concentration forecasts relative to a control experiment using the default hazard mode uncertainty when compared with a baseline SCIPUFF integration based on a high-resolution dynamic analysis of the meteorological conditions. The SCIPUFF experiments that use a wind variance calibration show both qualitative and quantitative improvement in most of the mean concentrations and patterns over the control experiment and the SCIPUFF experiment using uncalibrated wind variances. The SCIPUFF experiments using meteorological ensemble uncertainty information also produce mean concentrations and patterns that compare favorably to those of an explicit SCIPUFF ensemble based on each SREF member. Use of the uncalibrated variance information within a single ATD prediction produces mean ATD predictions most similar to those of the explicit ATD ensemble, and use of calibrated ensemble variance is shown to have some advantages over the explicit ATD ensemble.

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part II: application in a shallow-water model

“…Again we emphasise that many more cases are needed to truly assess the spreadÁerror relationship for an ensemble and its effect on the ensemble-based data assimilation methods, and calibration of the spreadÁerror relationship may also be needed (e.g. Kolczynski et al, 2009Kolczynski et al, , 2011.…”

Investigation of Ensemble Variance as a Measure of True Forecast Variance

“…The method, dubbed linear variance calibration (LVC), is applied to real data from the National Centers for Environmental Protection (NCEP) Short-Range Ensemble Forecast (SREF). Kolczynski et al (2009) finds that the LVC has high correlation for low-level wind components, but produces positive y intercepts and slopes less than 1. In this current paper we show that the biases in the slopes and intercepts are a statistical artifact that can be corrected using population statistics that should be readily available from real ensemble data.…”

“…Since the mean squared error is related to the error variance, this can also be viewed as a spread-to-spread comparison. Kolczynski et al (2009) proposes the use of a method similar to that of Grimit and Mass (2007) to calibrate ensemble variances for ensembles that are not perfectly reliable. The method, dubbed linear variance calibration (LVC), is applied to real data from the National Centers for Environmental Protection (NCEP) Short-Range Ensemble Forecast (SREF).…”

Spread Calibration of Ensemble MOS Forecasts

“…They suggested a linear function could be fit to the data and used to calibrate future forecast. Other studies have applied similar spread-dependent calibration techniques to a variety of weather elements including 2-m temperature (Eckel et al 2012) and upper-level winds (Kolczynski et al 2009). …”