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.

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[1] To investigate the effects of both cloud condensational nuclei (CCN) and giant CCN (GCCN), the Regional Atmospheric Modeling System was used to investigate the effects of various CCN and GCCN concentrations on the development of precipitating trade wind cumuli in a large eddy simulation (LES) framework. The sounding to initialize the LES was taken from the Rain in Cumulus over the Ocean Experiment archive for 11 January 2005. Several sensitivity experiments were performed in which two levels of CCN (GCCN) concentrations were used: 100 (0.01) and 1000 (0.1) cm À3 corresponding to low and high values, respectively. Both CCN and GCCN can affect the precipitation processes. With low GCCN concentration, raising the CCN concentration from low to high reduced the precipitation rate as well as the accumulated precipitation due to the presence of a large number of small cloud droplets that are inefficient in forming drizzle. However, GCCN can have a greater response in increasing the precipitation rate and accumulation when the cloud system has a high CCN concentration. The total cloud coverage (TCC) was reduced for the higher CCN concentration experiments because of the susceptibility of evaporation of cloud droplets in the upper parts of the cloud as a result of entrainment. On the other hand, the TCC was increased for the higher GCCN concentration experiments. For this trade wind cumuli case, the time-and domainaveraged albedo changed very slightly with increased [CCN] and/or [GCCN] because of a compensating increase/decrease among the optical depth, liquid water path, cloud coverage, and cloud droplet concentration.

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 (ϳ4-5 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.

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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.

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.

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.