ALBERT

Description: The wind stress formulation in an atmospheric model over shallow waters is investigated using year-long observations of the wind profile within the first 100 m of the atmosphere and mesoscale simulations. The model experiments use a range of planetary boundary layer parameterizations to quantify the uncertainty related to the turbulent closure assumptions and thus to isolate the dominant influence of the surface roughness formulation. Results indicate that a positive wind speed bias exists when common open-ocean formulations for roughness are adopted for a region with a water depth of 30 m. Imposition of a wind stress formulation that is consistent with previous shallow-water estimates is necessary to reconcile model wind speeds with observations, providing modeling evidence that supports the increase of surface drag over shallow waters. The possibility of including water depth in the parameterization of roughness length is examined.

Description: The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

Description: WRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) Model designed for solar energy applications. Recent upgrades to the WRF Model contribute to making the model appropriate for solar power forecasting and comprise 1) developments to diagnose internally relevant atmospheric parameters required by the solar industry, 2) improved representation of aerosol–radiation feedback, 3) incorporation of cloud–aerosol interactions, and 4) improved cloud–radiation feedback. The WRF-Solar developments are presented together with a comprehensive characterization of the model performance for forecasting during clear skies. Performance is evaluated with numerical experiments using a range of different external and internal treatment of the atmospheric aerosols, including both a model-derived climatology of aerosol optical depth and temporally evolving aerosol optical properties from reanalysis products. The necessity of incorporating the influence of atmospheric aerosols to obtain accurate estimations of the surface shortwave irradiance components in clear-sky conditions is evident. Improvements of up to 58%, 76%, and 83% are found in global horizontal irradiance, direct normal irradiance, and diffuse irradiance, respectively, compared to a standard version of the WRF Model. Results demonstrate that the WRF-Solar model is an improved numerical tool for research and applications in support of solar energy.

Description: The shortwave radiative impacts of unresolved cumulus clouds are investigated using 6-h ensemble simulations performed with the WRF-Solar Model and high-quality observations over the contiguous United States for a 1-yr period. The ensembles use the stochastic kinetic energy backscatter scheme (SKEBS) to account for implicit model uncertainty. Results indicate that parameterizing the radiative effects of both deep and shallow cumulus clouds is necessary to largely reduce (55%) a systematic overprediction of the global horizontal irradiance. Accounting for the model’s effective resolution is necessary to mitigate the underdispersive nature of the ensemble and provide meaningful quantification of the short-range prediction uncertainties.

Description: This study assesses the impact of the atmospheric stability on the turbulent orographic form drag (TOFD) generated by unresolved small-scale orography (SSO) focusing on surface winds. With this aim, several experiments are conducted with the Weather Research and Forecasting (WRF) Model and they are evaluated over a large number of stations (318 at 2-m height) in the Iberian Peninsula with a year of data. In WRF, Jiménez and Dudhia resolved the SSO by including a factor in the momentum equation, which is a function of the orographic variability inside a grid cell. It is found that this scheme can improve the simulated surface winds, especially at night, but it can underestimate the winds during daytime. This suggests that TOFD can be dependent on the PBL’s stability. To inspect and overcome this limitation, the stability conditions are included in the SSO parameterization to maintain the intensity of the drag during stable conditions while attenuating it during unstable conditions. The numerical experiments demonstrate that the inclusion of stability effects on the SSO drag parameterization improves the simulated surface winds at diurnal, monthly, and annual scales by reducing the systematic daytime underestimation of the original scheme. The correction is especially beneficial when both the convective velocity and the boundary layer height are used to characterize the unstable conditions.

Description: As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

Description: As wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF model with the Fitch parameterization. The first issue is a simple bug in the WRF code and the second issue is the excessive value of a coefficient that relates TKE to the turbine electro-mechanical losses, called CTKE. These two issues directly affect the way a wind farm wake evolves and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependency on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF model for a single turbine with and without the bug fix and the corrected CTKE and compare them against high-fidelity Large-Eddy Simulations (LES). These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected and their conclusions may need to be revisited.

Description: Recently, a new set of parameters produced by the Beckman-Coulter’s GEN’S, LH500, LH750 and the latest LH780 automated blood counters, derived from leukocyte and red cell’s morphological data by means of the VCS technology are available and can be printed as research data together with the leukocyte differential and reticulocyte analysis. With the use of these Research Population Data (RPD) parameters several recent publications have shown their usefulness for the diagnosis of malaria (Fourcade et al), sepsis (Chaves ), apoptosis detection (Lab hem 2005) and the diagnosis and differentiation of lymphoptoliferative disorders (Silva M et al) In order to evaluate the analytical behaviour of these newly described red blood cell and leukocyte morphological parameters in routine bood samples and in the Beckman-Coulter’s calibration and control 5C, we conducted a study to evaluate the paramters’ precision and performance during short and long term sample’s storage: Precision: 10 samples from normal individuals were analyzed 10 times and the CV for each parameter was calculated Short term stability stability at 4C and 22C: 5 normal samples were evaluated from the time of blood drawing until 5 hours later, both at room temperature (22C) and at 4 C Long term stability at 4C: 15 normal samples were evaluated at various times after blood drawing, from 4 hours (which was found to be the best time for sample analysis in the short term stability study, above) up to 77 hours after blood withdrawal. Control and Calibration with 5C: We evaluated the performance of the research parameters in the 5C by running the same batch of 5C control in 5 different counters from 4 different centers, comparing the results with those from 5 normal individuals Stability in a period of time: We have evaluated every week during two months 15 normal samples, recording the temperature and the avergae of all RPD values. RESULTS Precission: The CV% for the Mean Volume, Conductivity and Scatter of the different leukocyte types was always less than 1%, with an average of 0,6%. The CV% for the Standard Deviation of Volume, Conductivity and Scatter varied between 2.45% and 7.7%, wit an average of 4.9%. The CV% of any SD always being higher than the primary parameter. Short term stability: We observed no significant differences between samples kept at 4C and 22C during the first 5 hours after blood drawing. During the first two hours, however, minor fluctuations in results occurred, always

Description: This study presents a systematic analysis of convective parameterizations performance with interactive radiation, microphysics, and surface on an idealized day with shallow convection. To this end, we analyze a suite of mesoscale numerical experiments (i.e., with parameterized turbulence). In the first set, two different convection schemes represent shallow convection at a 9-km resolution. These experiments are then compared with model results omitting convective parameterizations at 9- and 3-km horizontal resolution (gray zone). Relevant in our approach is to compare the results against two simulations by different large-eddy simulation (LES) models. Results show that the mesoscale experiments, including the 3-km resolution, are unable to adequately represent the timing, intensity, height, and extension of the shallow cumulus field. The main differences with LES experiments are the following: a too late onset, too high cloud base, and a too early transport of moisture too high, overestimating the second cloud layer. Related to this, both convective parameterizations produce warm and dry biases of up to 2 K and 2 g kg−1, respectively, in the cloud layer. This misrepresentation of the cloud dynamics leads to overestimated shortwave radiation variability, both spacewise and timewise. Domain-averaged shortwave radiation at the surface, however, compares satisfactorily with LES. The shortwave direct and diffuse partition is misrepresented by the convective parameterizations with an underestimation (overestimation) of diffuse (direct) radiation both locally and, by a relative 40% (10%), of the domain average.