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

Sensitivity of forecast rainfall in a Texas convective system to soil moisture and convective parameterization

The impact of soil moisture on the forecast of a small-scale convective system, and sensitivity of results to the convective parameterization used, are investigated through Eta Model simulations (run in an operational-like setting) of a convective system occurring on 27 May 1997 in Texas. The event was influenced by a southwestward-propagating gravity wave from early morning convection in Arkansas that intersected a slow-moving cold front, releasing extreme conditional static instability. Isolated heavy rainfall, over 100 mm, occurred in some regions. A control simulation with 22-km horizontal resolution reasonably simulated the event, even though mesoscale influences such as the gravity wave important to this event are often poorly captured by numerical models. A series of sensitivity tests were performed to examine the impact of soil moisture on the simulations. Two different convective parameterizations were used for the tests. Although domain average precipitation is found to generally vary in a straightforward way with soil moisture, peak precipitation in the regions of intense convection shows more complex behavior. Sensitivity of precipitation amounts to soil moisture differs significantly among runs having different convective parameterizations. For instance, with the Kain-Fritsch convective scheme, relatively dry soil is found to result in stronger convective outflows that converge with stronger ambient flow to greatly enhance the precipitation in the region where heaviest rainfall occurs. With the Betts-Miller-Janjic scheme, drier soil generally results in less precipitation than in the control run, although some enhancement in peak amount does occur within a narrow range of drying. The differences between the peak quantitative precipitation forecasts in the runs is primarily due to the inclusion of a convective downdraft in the Kain-Fritsch parameterization, and its impact on secondary convective development. Additional sensitivity tests find limited impact from prescribed vegetation coverage. A final sensitivity test shows that precipitation amounts are even more strongly affected by the vertical resolution of the data used to initialize the shallow but moist boundary layer than by variations in the soil moisture or vegetation fraction

Does increased predicted warm-season rainfall indicate enhanced likelihood of rain occurrence?

The likelihood of simulated rainfall above a specified threshold being observed is evaluated as a function of the amounts predicted by two mesoscale models. Evaluations are performed for 20 warm-season mesoscale convective system events over the upper Midwest of the United States. Simulations were performed using 10-km versions of the National Centers for Environmental Prediction Eta Model and the Weather Research and Forecasting (WRF) model, with two different convective parameterizations tested in both models. It was found that, despite large differences in the biases of these different models and configurations, a robust relationship was present whereby a substantial increase in the likelihood of observed rainfall exceeding a specified threshold occurred in areas where the model runs forecast higher rainfall amounts. Rainfall was found to be less likely to occur in those areas where the models indicated no rainfall than it was elsewhere in the domain; it was more likely to occur in those regions where rainfall was predicted, especially where the predicted rainfall amounts were largest. The probability of rainfall relative-operating-characteristic and relative-operating-level curves showed that probabilistic forecasts determined from quantitative precipitation forecast values had skill comparable to the skill obtained using more traditional methods in which probabilities are based on the fraction of ensemble members experiencing rainfall. When the entire sample of cases was broken into training and test sets, the probability forecasts of the test sets evidenced good reliability. The relationship noted should provide some additional guidelines to operational forecasters. The results imply that the tested models are better able to indicate the regions where atmospheric processes are most favorable for convective rainfall (where the models generate enhanced amounts) than they are able to predict accurately the rainfall amounts that will be observed

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

Cold front acceleration over Lake Michigan

High-resolution Eta Model simulations of a strong but relatively dry late winter surface cold front that occurred during the STORM-FEST project depicted a pronounced acceleration of the front during the afternoon hours over the southern end of Lake Michigan. In this note, the impact of the lake on the front is examined. Reduced lower atmosphere turbulence due to both thermal stabilization and diminished surface roughness acting on postfrontal northerly winds increased frontogenesis strongly over the lake. The enhanced frontal circulation increased the front speed so that a noticeable frontal bulge occurred over the southern end of Lake Michigan. Some observational evidence is available to support the simulated frontal acceleration

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

Impact of improved initialization of mesoscale features on convective system rainfall in 10-km Eta simulations

A 10-km version of the NCEP Eta Model has been run over a roughly 1000 km x 1000 km domain centered over the upper Midwest for 20 cases where heavy warm season rainfall occurred from mesoscale convective systems to investigate the response of the precipitation forecasts to improvements in the depiction of mesoscale features at initialization time. Modifications to the initial conditions included (i) use of a cold pool initialization scheme, (ii) inclusion of mesonetwork surface observations using the model’s own vertical diffusion formulation to allow the surface data to be assimilated into a deeper layer through a simulated initialization period, and (iii) addition of water vapor at points covered by radar echo to ensure relative humidities greater than 80%. All of these modifications were implemented in runs using both the operational Betts–Miller–Janjic (BMJ) and Kain– Fritsch (KF) convective parameterizations. In addition, simulations were also run with a doubling of the convective time step, alternation of the two convective schemes within one run, and exclusion of a convective scheme in another run. For all 20 cases, 14 variants in the model initilization/moist physics were used, creating a high grid resolution (10-km grid spacing) ensemble. Although techniques (i) and (ii) both resulted in initial surface fields agreeing better with available observations, average skill scores for precipitation forecasts did not change appreciably when (i) was used, with (ii) resulting in a modest improvement in equitable threat score (ETS), with an increase in the bias that already exceeded 1.0 for most precipitation thresholds in the BMJ runs. Skill scores among the cases varied widely; no single adjustment consistently improved the scores. Interestingly, the simplest modification, the addition of water vapor in relatively dry atmospheric regions at points where radar echo was present, had the greatest positive impact on ETSs for most precipitation thresholds. Although the impacts were greatest in the first 6 h of the forecasts, some improvements occurred through the full 24-h integration period. Variations among the runs for a given case were far greater when different convective schemes were used than when initialization modifications were made, further supporting other recent research suggesting that high grid resolution short-range ensembles may benefit from the use of a variety of models or physical parameterizations

Pristine Nocturnal Convective Initiation: A Climatology and Preliminary Examination of Predictability

The prediction of convective initiation remains a challenge to forecasters in the Great Plains, especially for elevated events at night. This study examines a subset of 287 likely elevated nocturnal convective initiation events that occurred with little or no direct influence from surface boundaries or preexisting convection over a 4-month period of May–August during the summer of 2015. Events were first classified into one of four types based on apparent formation mechanisms and location relative to any low-level jet. A climatology of each of the four types was performed focusing on general spatial tendencies over a large Great Plains domain and initiation timing trends. Simulations from five convection-allowing models available during the Plains Elevated Convection At Night (PECAN) field campaign, along with four versions of a 4-km Weather Research and Forecasting (WRF) Model, were used to examine the predictability of these types of convective initiation. A dual-peak pattern for initiation timing was revealed, with one peak near 0400 UTC and another around 0700 UTC. The times and prominence of each peak shifted depending on the region analyzed. Positive thermal advection by the geostrophic wind was present in the majority of events for three types but not for the type occurring without a low-level jet. Models were more deficient with location than timing for the five PECAN models, with the four 4-km WRF Models showing similar location errors and problems with initiating convection at a lower altitude than observed