Abstract

Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities.

To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

Abstract

The authors analyze the pretornadic phase (2100–2148 UTC; tornadogenesis began at 2152 UTC) of the Goshen County, Wyoming, supercell of 5 June 2009 intercepted by the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). The analysis relies on radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Cheyenne, Wyoming (KCYS), and a pair of Doppler-on-Wheels (DOW) radars, mobile mesonet observations, and mobile sounding observations.

The storm resembles supercells that have been observed in the past. For example, it develops a couplet of counter-rotating vortices that straddle the hook echo within the rear-flank outflow and are joined by arching vortex lines, with the cyclonic vortex becoming increasingly dominant in the time leading up to tornadogenesis. The outflow in the hook echo region, where sampled, has relatively small virtual potential temperature θ_υ deficits during this stage of evolution. A few kilometers upstream (north) of the location of maximum vertical vorticity, θ_υ is no more than 3 K colder than the warmest θ_υ readings in the inflow of the storm. Forward trajectories originating in the outflow within and around the low-level mesocyclone rise rapidly, implying that the upward-directed perturbation pressure gradient force exceeds the negative buoyancy.

Low-level rotation intensifies in the 2142–2148 UTC period. The intensification is preceded by the formation of a descending reflectivity core (DRC), similar to others that have been documented in some supercells recently. The DRC is associated with a rapid increase in the vertical vorticity and circulation of the low-level mesocyclone.

Abstract

The dynamical processes responsible for the intensification of low-level rotation prior to tornadogenesis are investigated in the Goshen County, Wyoming, supercell of 5 June 2009 intercepted by the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). The circulation of material circuits that converge upon the low-level mesocyclone is principally acquired along the southern periphery of the forward-flank precipitation region, which is a corridor characterized by a horizontal buoyancy gradient; thus, much of the circulation appears to have been baroclinically generated. The descending reflectivity core (DRC) documented in Part I of this paper has an important modulating influence on the circulation of the material circuits. A circuit that converges upon the low-level mesocyclone center prior to the DRC’s arrival at low levels (approximately the arrival of the 55-dBZ reflectivity isosurface in this case) loses some of its previously acquired circulation during the final few minutes of its approach. In contrast, a circuit that approaches the low-level mesocyclone center after the DRC arrives at low levels does not experience the same adversity.

An analysis of the evolution of angular momentum within a circular control disk centered on the low-level mesocyclone reveals that the area-averaged angular momentum in the nearby surroundings of the low-level mesocyclone increases while the mesocyclone is occluding and warm-sector air is being displaced from the near surroundings. The occlusion process reduces the overall negative vertical flux of angular momentum into the control disk and enables the area-averaged angular momentum to continue increasing even though the positive radial influx of angular momentum is decreasing in time.

Abstract

Observations obtained during the second Verification of the Origin of Rotation in Tornadoes Experiment (VORTEX2) are analyzed for three supercell intercepts. These intercepts used a fleet of deployable “StickNet” probes, complemented by mobile radars and a mobile mesonet, to map state quantities over the expanse of target storms.

Two of the deployments occurred for different stages of a supercell storm near and east of Dumas, Texas, on 18 May 2010. A comparison of the thermodynamic and kinematic characteristics of the storm provides a possible explanation for why one phase was weakly tornadic and the other nontornadic. The weakly tornadic phase features a stronger horizontal virtual temperature gradient antiparallel to the forward-flank reflectivity gradient and perpendicular to the near-surface flow direction, suggesting that air parcels could acquire more significant baroclinic vorticity as they approach the low-level mesocyclone.

The strongly tornadic 10 May 2010 case near Seminole, Oklahoma, features comparatively small virtual and equivalent potential temperature deficits, suggesting the strength of baroclinic zones may be less useful than the buoyancy near the mesocyclone for assessing tornado potential. The distribution of positive pressure perturbations and backed ground-relative winds within the forward flank are consistent with the notion of a “starburst” pattern of diverging winds associated with the forward-flank downdraft.

Narrow (~1 km wide) zones of intense baroclinic vorticity generation of O(~10⁻⁴) s⁻² are shown to exist within precipitation on the forward and left sides of the mesocyclone in the Dumas intercepts, not dissimilar from such zones identified in recent high-resolution numerical studies.

Abstract

To better understand the physical processes of the stable boundary layer and to quantify “submeso motions” in moderately complex terrain, exploratory case-study analyses were performed using observational field data supplemented by gridded North American Regional Reanalysis data and Pennsylvania State University real-time Weather Research and Forecasting Model output. Submeso motions are nominally defined as all motions between the largest turbulent scales and the smallest mesoscales. Seven nighttime cases from August and September of 2011 are chosen from a central Pennsylvania [“Rock Springs” (RS)] network of eight ground-based towers and two sound detection and ranging (sodar) systems . The observation network is located near Tussey Ridge, ~15 km southeast of the Allegheny Mountains. The seven cases are classified by the dominant synoptic-flow direction and proximity to terrain to assess the influence of synoptic conditions on the local submeso and mesogamma motions. It is found that synoptic winds with a large crossing angle over nearby Tussey Ridge can generate mesogamma wave motions and larger-magnitude submeso temperature and wind fluctuations in the RS network than do winds from the direction of the more distant Allegheny Mountains. Cases with synoptic winds that are nearly parallel to the topographic contours or are generally weak exhibit the smallest fluctuations. Changes in the magnitude of near-surface submeso temperature and wind fluctuations in response to local indicator variables are also analyzed. The observed submeso wind and temperature fluctuations are generally larger when the low-level wind speed and thermal stratification, respectively, are greater, but the synoptic flow and its relation to the terrain also play an important role.

Abstract

The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000–2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.

Abstract

Studies have shown that echo returns from clear-air Bragg scatter (CABS) can be used to detect the height of the convective boundary layer and to assess the systematic differential reflectivity (Z _DR) bias for a radar site. However, these studies did not use data from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) or data from a large variety of sites. A new algorithm to automatically detect CABS from any operational WSR-88D with dual-polarization capability while excluding contamination from precipitation, biota, and ground clutter is presented here. Visual confirmation and tests related to the sounding parameters’ relative humidity slope, refractivity gradient, and gradient Richardson number are used to assess the algorithm. Results show that automated detection of CABS in operational WSR-88D data gives useful Z _DR bias information while omitting the majority of contaminated cases. Such an algorithm holds potential for radar calibration efforts and Bragg scatter studies in general.

Abstract

Clear-air Bragg scatter (CABS) is a refractivity gradient return generated by turbulent eddies that operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems can detect. The randomly oriented nature of the eddies results in a differential reflectivity (Z _DR) value near 0 dB, and thus CABS can be used as an assessment of Z _DR calibration in the absence of excessive contamination from precipitation or biota. An automated algorithm to estimate Z _DR bias from CABS was developed by the Radar Operations Center and can be used to assess the calibration quality of the dual-polarized WSR-88D fleet. This technique supplements existing Z _DR bias assessment tools, especially the use of other external targets, such as light rain and dry snow.

The estimates of Z _DR bias from CABS using a 1700–1900 UTC time window were compared to estimates from the light rain and dry snow methods. Output from the automated CABS algorithm had approximately the same amount of bias reported as the light rain and dry snow estimates (within ±0.1 dB). As the 1700–1900 UTC time window appeared too restrictive, a modified version of the algorithm was tested to detect CABS diurnally on a volume-by-volume basis (continuous monitoring). Continuous monitoring resulted in a two- to fourfold increase in the number of days with CABS detections. Results suggest estimates from CABS are viable for many sites throughout the year and provide an important addition to existing bias estimation techniques.

The second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2), which had its field phases in May and June of 2009 and 2010, was designed to explore i) the physical processes of tornadogenesis, maintenance, and demise; ii) the relationships among tornadoes, tornadic storms, and the larger-scale environment; iii) numerical weather prediction and forecasting of supercell thunderstorms and tornadoes; and iv) the wind field near the ground in tornadoes. VORTEX2 is by far the largest and most ambitious observational and modeling study of tornadoes and tornadic storms ever undertaken. It employed 13 mobile mesonet–instrumented vehicles, 11 ground-based mobile radars (several of which had dual-polarization capability and two of which were phased-array rapid scan), a mobile Doppler lidar, four mobile balloon sounding systems, 42 deployable in situ observational weather stations, an unmanned aerial system, video and photogrammetric teams, damage survey teams, deployable disdrometers, and other experimental instrumentation as well as extensive modeling studies of tornadic storms. Participants were drawn from more than 15 universities and laboratories and at least five nations, with over 80 students participating in field activities. The VORTEX2 field phases spanned 2 yr in order to increase the probability of intercepting significant tornadoes, which are rare events. The field phase of VORTEX2 collected data in over three dozen tornadic and nontornadic supercell thunderstorms with unprecedented detail and diversity of measurements. Some preliminary data and analyses from the ongoing analysis phase of VORTEX2 are shown.

Abstract

In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.