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.

A retrieval method for deriving the hydrometeor mixing ratio within mesoscale convective system (MCS) is presented in this study. The hydrometeor retrieval method was designed to incorporate the flash extent densities (FED) data from the Feng-Yun-4 geostationary satellite into the S-band radar reflectivity (Z(h)) and ambient temperature (T) data-based hydrometeor retrieval method. Total lightning data are utilized to better discern regions containing graupel in clouds. In the quantitative estimation of rain mixing ratio, different intercept parameters are used for different ranges of Z(h) and different estimated precursors of raindrop in cold-cloud microphysical processes (i.e., graupel and snow aggregate). The hydrometeor retrieval method was evaluated through an observing system simulation experiment (OSSE) in which the pseudo-input-data for the hydrometeor retrieval (i.e., the FED, Z(h) and T data) were obtained from the cloud-scale (1-km) simulation of an MCS using explicit electrification implemented within the Weather Research and Forecasting model. By incorporating the FED data as an additional input data source into the Z(h) and T-based hydrometeor retrieval method, the hydrometeor retrieval accuracy was improved. The hydrometeor retrievals were then assimilated into the model using the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system. Assimilating more accurate hydrometeor fields slightly improved the analyses and forecasts of convective precipitation in the test MCS case. The improvement could be due to the more accurate hydrometeor analysis, which further affected the strength of the cold pool and gust front.

In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting‐Real‐Time Four‐Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio ( q g ) is retrieved from observed total lightning. To retrieve q g on model grid boxes, column‐integrated graupel mass is first calculated using an observation‐based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical q g vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved q g fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short‐term (0–6 h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short‐term lightning and precipitation forecasts by improving the model simulation of the q g fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2 h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0–2 h) applications.

In recent years, adopting renewable energy, such as wind power, has become a national energy policy for many countries due to concerns of pollution and climate change from fossil fuel consumption. However, accurate prediction of wind is crucial in managing the power load. Numerical weather prediction (NWP) models are essential tools for wind prediction, but they need accurate initial conditions in order to produce an accurate forecast. However, NWP models are not guaranteed to have accurate initial conditions over wind farms in isolated locations. This study hypothesizes that short-term, 0-3 h, wind forecast can be improved by assimilating anemometer wind speed observations from wind farm turbines into a numerical weather forecast system. A technique was developed to circumvent the requirement of simultaneously ingesting the wind speed and direction in a data assimilation/weather forecasting system. A six-day case study revealed that assimilating wind speed can improve the 0-3 h wind speed (power) forecast by reducing the mean absolute error up to 0.5-0.6 m s(-1) (30-40%). (C) 2017 Elsevier Ltd. All rights reserved.

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 degrees and 1.66 degrees 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.

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).

Fine scale numerical weather prediction (NWP) models are now widely applied to predict power production at wind farms. Given the fact that demand for specialized forecasts for wind farms is growing, it is important to understand the strengths and limitations of NWP models for producing wind forecasts. This paper seeks to partially fulfill this goal by exploring the sensitivity of NWP-based wind forecasts to the choice of model physics schemes. The authors used two distinct case studies to explore these sensitivities with a NWP model used in realtime wind power forecast, where the underlying meteorology in both cases had a profound impact on the wind ramp-up of a wind farm in Northern Colorado. The first case was a strong cold frontal system moving through the wind farm during winter, and the second case was for a line of strong thunderstorms passing through the wind farm during summer. The model results were compared with observed hub-height wind.In each case, sensitivity studies were conducted to explore the impact of the choice of physics schemes on the wind forecasts. For the winter case, the sensitivity to the representation of land surface and planetary boundary layer processes was quantified. For the summer case, the sensitivity to the representation of clouds and precipitation physics was explored. In the winter case, the wind forecast was less sensitive to the choice of physics schemes due to the hub-height winds. being generated by strongly forced large-scale weather systems. In contrast, the wind forecast for the summer case (driven by weaker meteorological forcing) was strongly affected by the choice of cloud and precipitation physics schemes. Published by Elsevier Ltd.

Several studies have examined the sensitivity of fronts and cyclones to model physics over the ocean or flat terrain in mesoscale models (e.g., Zhang et al. 1999). Nonetheless, fewer studies have examined the sensitivity of fronts and cyclones over complex terrain to model physics in the WRF model at very high resolution. Physics parameterizations in the WRF model, including multiple choices of land surface model (LSM), planetary boundary layer (PBL), radiation, and microphysics schemes were formulated and mainly tested for idealized scenarios and/or over flat terrain. Thus, it is important to evaluate the performance of these schemes and their combined performance for complex terrain.

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−3 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 domain‐averaged 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 (similar to 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.

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 Colorado State University Regional Atmospheric Modeling System (RAMS) has been used to emulate cloud seeding operations in the Colorado Rocky Mountains for the winter of 2003-2004 in a previous study (Cotton et al. 2006). This paper documents new developments in RAMS since that study using a winter storm simulation that occurred in Colorado from 3-4 November 2003 as an illustration. The authors reported the advantages and disadvantages in the precipitation prediction of the innermost grid (Δx=3 km) by using the Kain-Fritsch convective parameterization scheme (CPS) in the outer grids in order to reduce the excessive precipitation in the innermost grid. Also, we examined the impacts of the bin-emulation approach to riming on supercooled liquid water prediction and precipitation. The bin-emulation approach alleviated a negative bias in the prediction of cloud liquid water content that occurred in the older microphysics package without this feature. With this new feature, there should be improvements in emulating cloud seeding operations in RAMS.