Jeffrey Dean Duda
@colorado.edu
Research Scientist at Cooperative Institute for Research in Environmental Sciences
University of Colorado at Boulder
Hometown: Marion, IA
EDUCATION
Ph.D., Meteorology, Univ. Oklahoma - 2016
M.S., Meteorology, Iowa St. Univ. - 2011
B.S., Meteorology & Mathematics, Iowa St. Univ. - 2009
Univ. of Iowa (2003-2006)
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jeffrey D. Duda and David D. Turner
American Meteorological Society
Abstract The object-based verification procedure described in a recent paper by Duda and Turner was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast–observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced overforecasting bias for medium- and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely overforecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts. Significance Statement This work builds upon the authors’ prior work in assessing model forecast quality using an alternative verification method—object-based verification. In this paper we verified two versions of the same model (one an upgrade from the other) that were making forecasts covering the same time window, using the object-based verification method. We found that the updated model was not statistically significantly better, although there were indications it performed better in certain aspects such as capturing the change in the number of storms during the daytime. We were able to identify specific problem areas in the models, which helps us direct model developers in their efforts to further improve the model.
David C. Dowell, Curtis R. Alexander, Eric P. James, Stephen S. Weygandt, Stanley G. Benjamin, Geoffrey S. Manikin, Benjamin T. Blake, John M. Brown, Joseph B. Olson, Ming Hu,et al.
American Meteorological Society
Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development. Significance Statement NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.
Jeffrey D. Duda and David D. Turner
American Meteorological Society
AbstractThe Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.
Jeffrey D. Duda, Xuguang Wang, Yongming Wang, and Jacob R. Carley
American Meteorological Society
Abstract Two methods for assimilating radar reflectivity into deterministic convection-allowing forecasts were compared: an operationally used, computationally less expensive cloud analysis (CA) scheme and a relatively more expensive, but rigorous, ensemble Kalman filter–variational hybrid method (EnVar). These methods were implemented in the Nonhydrostatic Multiscale Model on the B-grid and were tested on 10 cases featuring high-impact deep convective storms and heavy precipitation. A variety of traditional, neighborhood-based, and features-based verification metrics support that the EnVar produced superior free forecasts compared to the CA procedure, with statistically significant differences extending up to 9 h into the forecast. Despite being inferior, the CA scheme was able to provide benefit compared to not assimilating radar reflectivity at all, but limited to the first few forecast hours. While the EnVar is able to partially suppress spurious convection by assimilating 0-dBZ reflectivity observations directly, the CA is not designed to reduce or remove hydrometeors. As a result, the CA struggles more with suppression of spurious convection in the first-guess field, which resulted in high-frequency biases and poor forecast evolution, as illustrated in a few case studies. Additionally, while the EnVar uses flow-dependent ensemble covariances to update hydrometers, thermodynamic, and dynamic variables simultaneously when the reflectivity is assimilated, the CA relies on a radar reflectivity-derived latent heating rate that is applied during a separate digital filter initialization (DFI) procedure to introduce deep convective storms into the model, and the results of CA are shown to be sensitive to the window length used in the DFI.
Jeffrey D. Duda, Xuguang Wang, and Ming Xue
American Meteorological Society
Abstract In this exploratory study, a series of perturbations to the land surface model (LSM) component of the Weather Research and Forecasting (WRF) Model was developed to investigate the sensitivity of forecasts of severe thunderstorms and heavy precipitation at 4-km grid spacing and whether such perturbations could improve ensemble forecasts at this scale. The perturbations (generated using a combination of perturbing fixed parameters and using separate schemes, one of which—Noah-MP—is new among the WRF modeling community) were applied to a 10-member ensemble including other mixed physics parameterizations and compared against an identically configured ensemble that did not include the LSM perturbations to determine their impact on probabilistic forecasts. A third ensemble using only the LSM perturbations was also configured. The results from 14 (in total) 36-h ensemble forecasts suggested the LSM perturbations resulted in systematic improvement in ensemble dispersion and error characteristics. Lower-tropospheric temperature, moisture, and wind fields were all improved, as were probabilistic precipitation forecasts. Biases were not systematically altered, although some outlier members are present. Examination of near-surface temperature and mixing ratio fields, surface energy fluxes, and soil fields revealed tendencies caused by certain perturbations. A case study featuring tornadic supercells illustrated the physical causes of some of these tendencies. The results of this study suggest LSM perturbations can sample a dimension of model error not yet sampled systematically in most ensembles and should be included in convection-allowing ensembles.
Jeffrey D. Duda, Xuguang Wang, Fanyou Kong, Ming Xue, and Judith Berner
American Meteorological Society
The efficacy of a stochastic kinetic energy backscatter (SKEB) scheme to improve convection-allowing probabilistic forecasts was studied. While SKEB has been explored for coarse, convection-parameterizing models, studies of SKEB for convective scales are limited. Three ensembles were compared. The SKMP ensemble used mixed physics with the SKEB scheme, whereas the MP ensemble was configured identically but without using the SKEB scheme. The SK ensemble used the SKEB scheme with no physics diversity. The experiment covered May 2013 over the central United States on a 4-km Weather Research and Forecasting (WRF) Model domain. The SKEB scheme was successful in increasing the spread in all fields verified, especially mid- and upper-tropospheric fields. Additionally, the rmse of the ensemble mean was maintained or reduced, in some cases significantly. Rank histograms in the SKMP ensemble were flatter than those in the MP ensemble, indicating the SKEB scheme produces a less underdispersive forecast distribution. Some improvement was seen in probabilistic precipitation forecasts, particularly when examining Brier scores. Verification against surface observations agree with verification against Rapid Refresh (RAP) model analyses, showing that probabilistic forecasts for 2-m temperature, 2-m dewpoint, and 10-m winds were also improved using the SKEB scheme. The SK ensemble gave competitive forecasts for some fields. The SK ensemble had reduced spread compared to the MP ensemble at the surface due to the lack of physics diversity. These results suggest the potential utility of mixed physics plus the SKEB scheme in the design of convection-allowing ensemble forecasts.
Jeffrey D. Duda, Xuguang Wang, Fanyou Kong, and Ming Xue
American Meteorological Society
Abstract Two approaches for accounting for errors in quantitative precipitation forecasts (QPFs) due to uncertainty in the microphysics (MP) parameterization in a convection-allowing ensemble are examined. They include mixed MP (MMP) composed mostly of double-moment schemes and perturbing parameters within the Weather Research and Forecasting single-moment 6-class microphysics scheme (WSM6) MP scheme (PPMP). Thirty-five cases of real-time storm-scale ensemble forecasts produced by the Center for Analysis and Prediction of Storms during the NOAA Hazardous Weather Testbed 2011 Spring Experiment were examined. The MMP ensemble had better fractions Brier scores (FBSs) for most lead times and thresholds, but the PPMP ensemble had better relative operating characteristic (ROC) scores for higher precipitation thresholds. The pooled ensemble formed by randomly drawing five members from the MMP and PPMP ensembles was no more skillful than the more accurate of the MMP and PPMP ensembles. Significant positive impact was found when the two were combined to form a larger ensemble. The QPF and the systematic behaviors of derived microphysical variables were also examined. The skill of the QPF among different members depended on the thresholds, verification metrics, and forecast lead times. The profiles of microphysics variables from the double-moment schemes contained more variation in the vertical than those from the single-moment members. Among the double-moment schemes, WDM6 produced the smallest raindrops and very large number concentrations. Among the PPMP members, the behaviors were found to be consistent with the prescribed intercept parameters. The perturbed intercept parameters used in the PPMP ensemble fell within the range of values retrieved from the double-moment schemes.
Jeffrey D. Duda and William A. Gallus
American Meteorological Society
Abstract A set of mesoscale convective systems (MCSs) was simulated using the Weather Research and Forecasting model with 3-km grid spacing to investigate the skill at predicting convective initiation and upscale evolution into an MCS. Precipitation was verified using equitable threat scores (ETSs), the neighborhood-based fractions skill score (FSS), and the Method of Object-Based Diagnostic Evaluation. An illustrative case study more closely examines the strong influence that smaller-scale forcing features had on convective initiation. Initiation errors for the 36 cases were in the south-southwest direction on average, with a mean absolute displacement error of 105 km. No systematic temporal error existed, as the errors were approximately normally distributed. Despite earlier findings that quantitative precipitation forecast skill in convection-parameterizing simulations is a function of the strength of large-scale forcing, this relationship was not present in the present study for convective initiation. However, upscale evolution was better predicted for more strongly forced events according to ETSs and FSSs. For the upscale evolution, the relationship between ETSs and object-based ratings was poor. There was also little correspondence between object-based ratings and the skill at convective initiation. The lack of a relationship between the strength of large-scale forcing and model skill at forecasting initiation is likely due to a combination of factors, including the strong role of small-scale features that exert an influence on initiation, and potential errors in the analyses used to represent observations. The limit of predictability of individual convective storms on a 3-km grid must also be considered.
Jeffrey D. Duda and William A. Gallus
American Meteorological Society
Abstract This study compares severe weather reports associated with the nine convective system morphologies used in a recent study by Gallus et al. to an additional morphology, supercell storms. As in that previous study, all convective systems occurring in a 10-state region covering parts of the Midwestern United States and central plains were classified according to their dominant morphology, and severe weather reports associated with each morphology were then analyzed. Unlike the previous study, which examined systems from 2002, the time period over which the climatology was performed was shifted to 2007 to allow access to radar algorithm information needed to classify a storm as a supercell. Archived radar imagery was used to classify systems as nonlinear convective events, isolated cells, clusters of cells, broken lines of cells, squall lines with no stratiform precipitation, trailing stratiform precipitation, parallel stratiform precipitation, and leading stratiform precipitation, and bow echoes. In addition, the three cellular classifications were subdivided to allow an analysis of severe weather reports for events in which supercells were present and those in which they were not. As in the earlier study, all morphologies were found to pose some risk of severe weather, and differences in the two datasets were generally small. The 2007 climatology confirmed the theory that supercellular systems produce severe weather more frequently than other morphologies, and also produce more intense severe weather. Supercell systems were especially prolific producers of tornadoes and hail relative to all other morphologies, but also produced severe wind and flooding much more often than nonsupercell cellular morphologies. These results suggest that it is important to differentiate between cellular morphologies containing rotation and those that do not when associating severe weather reports with convective morphology.