The impact of step orography on flow in the Eta Model: Two contrasting examples

Simulations were performed using the Eta Model with its eta vertical coordinate and stepwise treatment of terrain, and with a substitution of the terrain-following sigma vertical coordinate to investigate the impact of step orography on flow near high mountains. Two different cases were simulated: (i) a downslope windstorm along the Front Range of the Rocky Mountains, and (ii) stably stratified flow blocked by high mountains in Taiwan. Flow separation on the lee side of the mountains, previously shown to occur in idealized two-dimensional Eta simulations, was also apparent in these real data cases, even for the downslope wind event. The step orography resulted in a substantial underestimate of wind speeds to the lee of the Rockies during the windstorm. Near the surface, both the eta and sigma simulations of the Taiwan blocking event were comparable. For both events, the use of step orography resulted in much weaker mountain waves than occurred when the sigma vertical coordinate was used. Localized vertical velocity perturbations associated directly with the step corners were minor for these cases

Eta simulations of three extreme precipitation events: Sensitivity to resolution and convective parameterization

A versatile workstation version of the NCEP Eta Model is used to simulate three excessive precipitation episodes in the central United States. These events all resulted in damaging flash flooding and include 16-17 June 1996 in the upper Midwest, 17 July 1996 in western Iowa, and 27 May 1997 in Texas. The episodes reflect a wide range of meteorological situations: (i) a warm core cyclone in June 1996 generated a meso-β-scale region of excessive rainfall from echo training in its warm sector while producing excessive overrunning rainfall to the north of its warm front, (ii) a mesoscale convective complex in July 1996 produced excessive rainfall, and (iii) tornadic thunderstorms in May 1997 resulted in small-scale excessive rains. Model sensitivity to horizontal resolution is investigated using a range of horizontal resolutions comparable to those used in operational and quasi-operational forecasting models. Sensitivity tests are also performed using both the Betts-Miller-Janjic convective scheme (operational at NCEP in 1998) and the Kain-Fritsch scheme. Variations in predicted peak precipitation as resolution is refined are found to be highly case dependent, suggesting forecaster interpretation of increasingly higher resolution model quantitative precipitation forecast (QPF) information will not be straightforward. In addition, precipitation forecasts and QPF response to changing resolution are both found to vary significantly with choice of convective parameterization

Application of Object-Based Verification Techniques to Ensemble Precipitation Forecasts

Both theMethod for Object-basedDiagnostic Evaluation (MODE) and contiguous rain area (CRA) objectbased verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters. One set consisted of two eight-member Weather Research and Forecasting (WRF) model ensembles: one having mixed physics and dynamics with unperturbed initial and lateral boundary conditions (Phys) and another using common physics and a dynamic core but with perturbed initial and lateral boundary conditions (IC/LBC). Traditional measures found that spread grows much faster in IC/LBC than in Phys so that after roughly 24 h, better skill and spread are found in IC/LBC. These measures also reflected a strong diurnal signal of precipitation. The other set of ensembles included five members of a 4-km grid-spacing WRF ensemble (ENS4) and five members of a 20-km WRF ensemble (ENS20). Traditional measures suggested that the diurnal signal was better in ENS4 and spread increased more rapidly than in ENS20. Standard deviations (SDs) of four object parameters computed for the first set of ensembles using MODE and CRA showed the trend of enhanced spread growth in IC/LBC compared to Phys that had been observed in traditional measures, with the areal coverage of precipitation exhibiting the greatest growth in spread with time. The two techniques did not produce identical results; although, they did show the same general trends.A diurnal signal could be seen in the SDs of all parameters, especially rain rate, volume, and areal coverage. MODE results also found evidence of a diurnal signal and faster growth of spread in object parameters in ENS4 than in ENS20. Some forecasting approaches based onMODEand CRAoutput are also demonstrated. Forecasts based on averages of object parameters from each ensemble member were more skillful than forecasts based on MODE or CRA applied to an ensemble mean computed using the probability matching technique for areal coverage and volume, but differences in the two techniques were less pronounced for rain rate and displacement. The use of a probability threshold to define objects was also shown to be a valid forecasting approach with MODE

Adapting the SAL method to evaluate reflectivity forecasts of summer precipitation in the central United States

The Structure Amplitude Location (SAL)methodwas originally developed to evaluate forecast accumulated-precipitation fields through identification and comparison of objects in both the forecast and the observed fields. This study describes a small modification for use with instantaneous composite-reflectivity forecasts, where objects’ minimum size and reflectivity thresholds are prescribed. Both the original and modified SAL methods are used to evaluate daily 0000UTC 12-km North American Model (NAM) forecasts, against NCEP/EMC 4-km Stage IV accumulated-precipitation estimates, during the summer of 2015 for a central US domain. Results show substantial sensitivity to the reflectivity threshold. This is likely related to sampling more signal from convective cell cores, and progressively ignoring stratiform rain areas, as threshold increases. Setting the threshold too high (40 dBZ) yields only 7% of time periods on which error scores can be computed, as opposed to 94% using a low threshold (5 dBZ). The primary difference between the two methods is a larger structural error in SAL using reflectivity, likely related to the unresolved convective peaks in the 12-kmNAMforecasts; this error is smoothed out when accumulated precipitation is evaluated. SAL using reflectivity also reveals a diurnal cycle of skill, with minimum skill occurring around 1800–2200UTC (early to late afternoon local time, before average convective activity reaches its maximum) and maximum skill occurring around 1000UTC (just before sunrise). We conclude that both methods yield useful results, but results presented herein may not be generalisable to other verification domains or SAL formulations

Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts

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

On Contrasting Ensemble Simulations of Two Great Plains Bow Echoes

Bow echo structures, a subset of mesoscale convective systems (MCSs), are often poorly forecast within deterministic numerical weather prediction model simulations. Among other things, this may be due to the inherent low predictability associated with bow echoes, deficient initial conditions (ICs), and inadequate parameterization schemes. Four different ensemble configurations assessed the sensitivity of the MCSs’ simulated reflectivity and radius of curvature to the following: perturbations in initial and lateral boundary conditions using a global dataset, different microphysical schemes, a stochastic kinetic energy backscatter (SKEB) scheme, and a mix of the previous two. One case is poorly simulated no matter which IC dataset or microphysical parameterization is used. In the other case, almost all simulations reproduce a bow echo. When the IC dataset and microphysical parameterization is fixed within a SKEB ensemble, ensemble uncertainty is smaller. However, while differences in the location and timing of the MCS are reduced, variations in convective mode remain substantial. Results suggest the MCS’s positioning is influenced primarily by ICs, but its mode is most sensitive to the model error uncertainty. Hence, correct estimation of model error uncertainty on the storm scale is crucial for adequate spread and the probabilistic forecast of convective events

Spring and Summer Midwestern Severe Weather Reports in Supercells Compared to Other Morphologies

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

WRF Forecasts of Great Plains Nocturnal Low-Level Jet-Driven MCSs. Part II: Differences between Strongly and Weakly Forced Low-Level Jet Environments

The classic Great Plains southerly low-level jet (LLJ) is a primary factor in sustaining nocturnal convection. This study compares convection-allowing WRF forecasts of LLJ events associated with MCSs in strongly and weakly forced synoptic environments. The depth of the LLJs and magnitude, altitude, and times of the LLJ peak wind were evaluated in observations and WRF forecasts for 31 cases as well as for case subsets of strongly and weakly forced synoptic regimes. LLJs in strongly forced regimes were stronger, deeper, and peaked at higher altitudes and at earlier times compared to weakly forced cases. Mean error MCS-centered composites of WRF forecasts versus RUC analyses were derived at MCS initiation time for the LLJ atmospheric water vapor mixing ratio, LLJ total wind magnitude, convergence, most unstable convective available potential energy (MUCAPE), and most unstable convective inhibition (MUCIN). In most configurations, simulated MCSs in strongly and weakly forced regimes initiated to the north and east of observations, generally in a region where LLJ moisture, MUCAPE, and MUCIN fields were forecast well, with larger errors outside this region. However, WSM6 simulations for strongly forced cases showed a southward displacement in MCS initiation, where a combination of ambient environmental factors and microphysics impacts may simultaneously play a role in the location of forecast MCS initiation. Strongly forced observed and simulated MCSs initiated west of the LLJ axis and moved eastward into the LLJ, while observed and simulated MCSs in weakly forced environments traversed the termini of the LLJ. A northward bias existed for simulated MCS initiation and LLJ termini for weakly forced regimes

WRF Forecasts of Great Plains Nocturnal Low-Level Jet-Driven MCSs. Part I: Correlation between Low-Level Jet Forecast Accuracy and MCS Precipitation Forecast Skill

The Great Plains low-level jet (LLJ) fosters an environment that supports nocturnal mesoscale convective systems (MCSs) across the central United States during the summer months. The current study examines if LLJ forecast accuracy correlates with MCS precipitation forecast skill in 4-km WRF runs. LLJs were classified based on their synoptic background as either strongly forced, cyclonic flow (type C) or weakly forced, anticyclonic flow inertial oscillation driven (type A). Large-scale variables associated with the LLJ were examined. For all LLJs inclusive and the subset of type C LLJs alone, the forecast accuracy of the LLJ total wind direction significantly correlated with MCS precipitation forecast skill. For type C LLJ cases, where predictive skill for MCSs was higher overall, the LLJ ageostrophic wind direction forecast accuracy significantly correlated with MCS precipitation forecast skill during the LLJ and MCS developmental stages, with potential temperature and moisture forecast accuracy correlating well with the forecast skill of mature MCSs. Statistically significant correlations were mainly absent between MCS precipitation forecast skill and LLJ forecast accuracy for type A cases. It is thus suggested that either non-LLJ factors like most unstable convective available potential energy (MUCAPE) or most unstable convective inhibition (MUCIN) fields within close proximity of MCSs, or factors on smaller scales than analyzed (such as gravity waves or bores), may have the greatest potential influence on MCS precipitation forecast skill in LLJ-induced MCS cases in an ambient weakly forced synoptic regime

An Evaluation of QPF from the WRF, NAM, and GFS Models Using Multiple Verification Methods over a Small Domain

The ARW model was run over a small domain centered on Iowa for 9 months with 4-km grid spacing to better understand the limits of predictability of short-term (12 h) quantitative precipitation forecasts (QPFs) that might be used in hydrology models. Radar data assimilation was performed to reduce spinup problems. Three grid-to-grid verification methods, as well as two spatial techniques, neighborhood and object based, were used to compare the QPFs from the high-resolution runs with coarser operational GFS and NAM QPFs to verify QPFs for various precipitation accumulation intervals and on two grid configurations with different resolutions. In general, NAM had the worst performance not only for model skill but also for spatial feature attributes as a result of the existence of large dry bias and location errors. The finer resolution of NAM did not offer any advantage in predicting small-scale storms compared to the coarser GFS. WRF had a large advantage for high precipitation thresholds. A greater improvement in skill was noted when the accumulation time interval was increased, compared to an increase in the spatial neighborhood size. At the same neighborhood scale, the high-resolution WRF Model was less influenced by the grid on which the verification was done than the other two models. All models had the highest skill from midnight to early morning, because the least wet bias, location, and coverage errors were present then. The lowest skill was shown from late morning through afternoon. The main cause of poor skill during this period was large displacement errors