Abstract

This study examines the sensitivity of varying the horizontal heterogeneities of the soil moisture initialization (SMI) in the cloud-resolving grid of a real-data simulation of a midlatitude mesoscale convective system (MCS) during its genesis phase. The quasi-stationary MCS of this study formed in the Texas/Oklahoma panhandle with a lifetime of 9 h (2200 UTC 26 July to 0700 UTC 27 July 1998). Soil moisture for the finest nested grid (the cloud-resolving grid) was derived from the antecedent precipitation index (API) using 4-km-grid-spacing precipitation data for a 3-month period. In order to vary the heterogeneities of the SMI in the cloud-resolving grid, (i) Barnes objective analysis was used to alter the resolution of the soil moisture initialization, (ii) the amplitudes of the soil moisture anomalies were reduced, (iii) the position of a soil moisture anomaly was altered, and (iv) two experiments with homogeneous SMI (31% and 50% saturation) were performed. Because of the severe drought in the Texas/Oklahoma panhandle area, the saturation API value was lowered in order to introduce heterogeneities in the soil moisture for the sensitivity experiments.

All of the experiments with heterogeneous SMI (in addition to an experiment with a homogeneous SMI at 31% saturation) produced an MCS with a quasi-circular cloud shield, similar to the observed timing, size, and location. The authors' findings suggest that a soil moisture dataset with approximately 40-km grid spacing may be adequate to initialize a cloud-resolving model for simulating MCSs. For the simulations in this study, the soil moisture distribution determined where convection was likely to occur. Wetter soil tended to suppress convection for this case, and convection preferentially occurred around the peripheries of wet soil moisture anomalies.

Abstract

Despite improvements in numerical weather prediction, model errors, particularly near the surface, are unavoidable due to imperfect model physics, initial conditions, and boundary conditions. Here, three techniques for improving the accuracy of 2-m temperature, 2-m dewpoint, and 10-m wind forecasts by the Eta/North American Meso (NAM) Model are evaluated: (i) traditional model output statistics (ETAMOS), requiring a relatively long training period; (ii) the Kalman filter (ETAKF), requiring a relatively short initial training period (∼4–5 days); and (iii) 7-day running mean bias removal (ETA7DBR), requiring a 7-day training period. Forecasts based on the ETAKF and ETA7DBR methods were produced for more than 2000 MesoWest observing sites in the western United States. However, the evaluation presented in this study was based on subjective forecaster assessments and objective verification at 145 ETAMOS stations during summer 2004 and winter 2004/05. For the 145-site sample, ETAMOS produces the most accurate cumulative temperature, dewpoint, and wind speed and direction forecasts, followed by ETAKF and ETA7DBR, which have similar accuracy. Selected case studies illustrate that ETAMOS produces superior forecasts when model biases change dramatically, such as during large-scale pattern changes, but that ETAKF and ETA7DBR produce superior forecasts during quiescent cool season patterns when persistent valley and basin cold pools exist. During quiescent warm season patterns, the accuracy of all three methods is similar. Although the improved ETAKF cold pool forecasts are noteworthy, particularly since the Kalman filter can help better define cold pool structure by producing forecasts for locations without long-term records, alternative approaches are needed to improve forecasts during periods when model biases change dramatically.

Abstract

An evaluation of the surface sensible weather forecasts using high-density observations provided by the MesoWest cooperative networks illustrates the performance characteristics of the Cooperative Institute for Regional Prediction (CIRP) Weather Research and Forecast (WRF) and the Eta Models over the western United States during the 2003 warm season (June–August). In general, CIRP WRF produced larger 2-m temperature and dewpoint mean absolute and bias errors (MAEs and BEs, respectively) than the Eta. CIRP WRF overpredicted the 10-m wind speed, whereas the Eta exhibited an underprediction with a comparable error magnitude to CIRP WRF. Tests using the Oregon State University (OSU) Land Surface Model (LSM) in CIRP WRF, instead of a simpler slab-soil model, suggest that using a more sophisticated LSM offers no overall advantage in reducing WRF BEs and MAEs for the aforementioned surface variables. Improvements in the initialization of soil temperature in the slab-soil model, however, did reduce the temperature bias in CIRP WRF. These results suggest that improvements in LSM initialization may be as or more important than improvements in LSM physics. A concerted effort must be undertaken to improve both the LSM initialization and parameterization of coupled land surface–boundary layer processes to produce more accurate surface sensible weather forecasts.

Abstract

Large-eddy simulations (LESs) were performed to study the dynamical, microphysical, and radiative processes in the 26 November 1991 FIRE II cirrus event. The LES model inherits the framework of the RAMS version 3b, developed at Colorado State University. It includes a new two-stream radiation model developed by and a new subgrid-scale model developed by .

The LES model successfully simulated a single thin cloud layer for LES-1 and a deep cloud structure for LES-2. The simulations demonstrated that latent heat release can play a significant role in the evolution of thick cirrus clouds. For the thin cirrus in LES-1, the latent heat release was insufficient for the cirrus clouds to become positively buoyant. However, in some special cases such as LES-2, positively buoyant cells can be embedded within the cirrus layers. The updrafts from these cells induced its own pressure perturbations that affected the cloud evolution.

Vertical profiles of the total radiative and latent heating rates indicated that for well-developed, deep, and active cirrus clouds, radiative cooling and latent heating could be comparable in magnitude in the cloudy layer. This implies that latent heating cannot be neglected in the construction of a cirrus cloud model.

The probability density function (PDF) of the vertical velocity (w) was analyzed to assist in the parameterization of cloud-scale velocities in large-scale models. For the more radiatively driven, thin cirrus case, the PDFs are approximately Gaussian. However, in the interior of the deep, convectively unstable case, the PDFs of w are multimodal and very broad, indicating that parameterizing cloud-scale motions for such clouds can be very challenging.

Abstract

At Colorado State University the Regional Atmospheric Modeling System (RAMS) has been used to study the radiative effect on the diffusional growth of ice particles in cirrus clouds. Using soundings extracted from a mesoscale simulation of the 26 November 1991 cirrus event, the radiative effect was studied using a two-dimensional cloud-resolving model (CRM) version of RAMS, coupled to an explicit bin-resolving microphysics.

The CRM simulations of the 26 November 1991 cirrus event demonstrate that the radiative impact on the diffusional growth (or sublimation) of ice crystals is significant. Even in a radiatively cooled atmospheric environment, ice particles may experience radiative warming because the net radiation received by an ice particle depends upon the emission from the particle, and the local upwelling and downwelling radiative fluxes.

Model results show that radiative feedbacks on the diffusional growth of ice particles can be very complex. Radiative warming of an ice particle will restrict the particle’s diffusional growth. In the case of radiative warming, ice particles larger than a certain size will experience so much radiative warming that surface ice saturation vapor pressures become large enough to cause sublimation of the larger crystals, while smaller crystals are growing by vapor deposition. However, ice mass production can be enhanced in the case of radiative cooling of an ice particle. For the 26 November 1991 cirrus event, radiative feedback results in significant reduction in the total ice mass, especially in the production of large ice crystals, and consequently, both radiative and dynamic properties of the cirrus cloud are significantly affected.

Abstract

Spurious grid-scale precipitation (SGSP) occurs in many mesoscale numerical weather prediction models when the simulated atmosphere becomes convectively unstable and the convective parameterization fails to relieve the instability. Case studies presented in this paper illustrate that SGSP events are also found in the North American Regional Reanalysis (NARR) and are accompanied by excessive maxima in grid-scale precipitation, vertical velocity, moisture variables (e.g., relative humidity and precipitable water), mid- and upper-level equivalent potential temperature, and mid- and upper-level absolute vorticity. SGSP events in environments favorable for high-based convection can also feature low-level cold pools and sea level pressure maxima. Prior to 2003, retrospectively generated NARR analyses feature an average of approximately 370 SGSP events annually. Beginning in 2003, however, NARR analyses are generated in near–real time by the Regional Climate Data Assimilation System (R-CDAS), which is identical to the retrospective NARR analysis system except for the input precipitation and ice cover datasets. Analyses produced by the R-CDAS feature a substantially larger number of SGSP events with more than 4000 occurring in the original 2003 analyses. An oceanic precipitation data processing error, which resulted in a reprocessing of NARR analyses from 2003 to 2005, only partially explains this increase since the reprocessed analyses still produce approximately 2000 SGSP events annually. These results suggest that many NARR SGSP events are not produced by shortcomings in the underlying Eta Model, but by the specification of anomalous latent heating when there is a strong mismatch between modeled and assimilated precipitation. NARR users should ensure that they are using the reprocessed NARR analyses from 2003 to 2005 and consider the possible influence of SGSP on their findings, particularly after the transition to the R-CDAS.

Abstract

Operational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in situ soil moisture observations. Bias correction of these soil moisture analyses using data from 42 North American Soil Moisture Database stations throughout the Intermountain West reduces both the afternoon and early morning bias errors and improves forecasts of upper-level temperature and stability. These results illustrate that the assimilation of in situ and remotely sensed soil moisture observations, including those from the recently launched NASA Soil Moisture Active Passive (SMAP) mission, have the potential to greatly improve land surface analyses and near-surface temperature forecasts over arid regions.

Abstract

The National Center for Atmospheric Research and the National Renewable Energy Laboratory (NREL) collaborated to develop a method to assess the interannual variability of wind and solar power over the contiguous United States under current and projected future climate conditions, for use with NREL’s Regional Energy Deployment System (ReEDS) model. The team leveraged a reanalysis-derived database to estimate the wind and solar power resources and their interannual variability under current climate conditions (1985–2005). Then, a projected future climate database for the time range of 2040–69 was derived on the basis of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) simulations driven by free-running atmosphere–ocean general circulation models. To compare current and future climate variability, the team developed a baseline by decomposing the current climate reanalysis database into self-organizing maps (SOMs) to determine the predominant modes of variability. The current climate patterns found were compared with those of an NARCCAP-based future climate scenario, and the CRCM–CCSM combination was chosen to describe the future climate scenario. The future climate scenarios’ data were projected onto the Climate Four Dimensional Data Assimilation reanalysis SOMs. The projected future climate database was then created by resampling the reanalysis on the basis of the frequency of occurrence of the future SOM patterns, adjusting for the differences in magnitude of the wind speed or solar irradiance between the current and future climate conditions. Comparison of the changes in the frequency of occurrence of the SOM modes between current and future climate conditions indicates that the annual mean wind speed and solar irradiance could be expected to change by up to 10% (increasing or decreasing regionally).

Abstract

The number of tornado observations in Canada is believed to be significantly lower than the actual occurrences. To account for this bias, the authors propose a Bayesian modeling approach founded upon the explicit consideration of the population sampling bias in tornado observations and the predictive relationship between cloud-to-ground (CG) lightning flash climatology and tornado occurrence. The latter variable was used as an indicator for quantifying convective storm activity, which is generally a precursor to tornado occurrence. The CG lightning data were generated from an 11-yr lightning climatology survey (1999–2009) from the Canadian Lightning Detection Network. The results suggest that the predictions of tornado occurrence in populated areas are fairly reliable with no profound underestimation bias. In sparsely populated areas, the analysis shows that the probability of tornado occurrence is significantly higher than what is represented in the 30-yr data record. Areas with low population density but high lightning flash density demonstrate the greatest discrepancy between predicted and observed tornado occurrence. A sensitivity analysis with various grid sizes was also conducted. It was found that the predictive statements supported by the model are fairly robust to the grid configuration, but the population density per grid cell is more representative to the actual population density at smaller resolution and therefore more accurately depicts the probability of tornado occurrence. Finally, a tornado probability map is calculated for Canada based on the frequency of tornado occurrence derived from the model and the estimated damage area of individual tornado events.

Abstract

Destruction and fatalities from recent tornado outbreaks in North America have raised considerable concerns regarding their climatic and geographic variability. However, regional characterization of tornado activity in relation to large-scale climatic processes remains highly uncertain. Here, a novel Bayesian hierarchical framework is developed for elucidating the spatiotemporal variability of the factors underlying tornado occurrence in North America. It is demonstrated that regional variability of tornado activity can be characterized using a hierarchical parameterization of convective available potential energy, storm relative helicity, and vertical wind shear quantities. It is shown that the spatial variability of tornado occurrence during the warm summer season can be explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is clearly better at capturing the spatial variability of the cool season tornado activity. The results suggest that the Bayesian hierarchical modeling approach is effective for understanding the regional tornadic environment and in forming the basis for establishing tornado prognostic tools in North America.