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Author(s): Zapata, Monica Zamora; Norris, Joel R; Kleissl, Jan | Abstract: Abstract The impact of initial states and meteorological variables on stratocumulus cloud dissipation time over coastal land is investigated using a mixed-layer model. A large set of realistic initial conditions and forcing parameters are derived from radiosonde observations and numerical weather prediction model outputs, including total water mixing ratio and liquid water potential temperature profiles (within the boundary layer, across the capping inversion, and at 3 km), inversion-base height and cloud thickness, large-scale divergence, wind speed, Bowen ratio, sea surface fluxes, sky effective radiative temperature, shortwave irradiance above the cloud, and sea level pressure. We study the sensitivity of predicted dissipation time using two analyses. In the first, we simulate 195 cloudy days (all variables covary as observed in nature). We caution that simulated predictions correlate only weakly to observations of dissipation time, but the simulation approach is robust and facilitates covariability testing. In the second, a single variable is varied around an idealized reference case. While both analyses agree in that initial conditions influence dissipation time more than forcing parameters, some results with covariability differ greatly from the more traditional sensitivity analysis and with previous studies: opposing trends are observed for boundary layer total water mixing ratio and Bowen ratio, and covariability diminishes the sensitivity to cloud thickness and inversion height by a factor of 5. With covariability, the most important features extending predicted cloud lifetime are (i) initially thicker clouds, higher inversion height, and stronger temperature inversion jumps, and (ii) boundary forcings of lower sky effective radiative temperature.

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

The urban overheating, driven by the increasing expansion of our cities and the global climate change, is becoming one of the main environmental challenges of today. Consequently, cooling technologies are emerging as mitigation and adaptation strategies. Reflective roof and pavement surfaces have been widely studied for their potential benefits, but detailed evaluations of the effect of wall albedo on the urban microclimate are limited. This study addresses this gap by evaluating the effects of reflective walls on urban energy use and outdoor climate. The energy balance of an idealized neighborhood is represented using a 3D numerical model, Temperature of Urban Facets Indoor-Outdoor Building Energy Simulator (TUF-IOBES), which determines the cooling loads and outdoor air temperature. The study focuses on the tropical climate of Singapore, addressing the urban climate in highly-populated cities in low latitudes that are significantly affected by the UHI. Simulations are conducted for two neighborhoods representative of low-rise residential and high-rise commercial urban areas, spanning a range of urban density, canyon geometry, building construction, and occupant schedules. The building thermal load and outdoor temperature are then calculated for these two idealized neighborhoods, analyzing the effectiveness of cool walls while also considering the role of other design factors such as window-to-wall ratio (WWR) and glazing solar heat gain coefficient (SHGC) in modulating the impact. Unlike the analysis of cool roofs, we find that a universal conclusion regarding the impact of cool walls cannot be drawn. The role of wall albedo significantly depends on the collective design of urban areas as well as the use and occupancy of buildings. We find that urban density (in other words the local climate zone) followed by window properties are important factors in determining the impact of wall albedo on thermal loads and UHI, as they determine the radiative exchange between and into the buildings. Accordingly, contrary to the general expectation, for a high urban density (commercial neighborhood LCZ6) and high WWR and SHGC, we observe that cool (reflective) walls can increase the building energy use. Regarding UHI, increasing the reflectivity of walls decreases the canopy air temperature but the impact is marginal (∼ 0.1 °C) compared to other urban design parameters. Keywords: Cool walls; Reflective surfaces; Building energy use; Urban heat island; Urban design

Large ramps and ramp rates in photovoltaic (PV) power output are of concern and sometimes even explicitly restricted by grid operators. Battery energy storage systems can smooth the power output and maintain ramp rates within permissible limits. To enable PV plant and energy storage system design and planning, a method to estimate the largest expected ramps for a given location is proposed. Because clouds are the dominant source of PV power output variability, an analytical relationship between the worst expected ramp rate, cloud motion vector, and the geometrical layout of the PV plant is developed. The ability of the proposed method to bracket actual ramp rates is assessed over 10 months under different meteorological conditions, demonstrating an average compliance rate of 98.9% for a 2 min evaluation time window. The largest observed ramp of 29.7% s(-1)is contained with the worst case estimate of 34.3% s(-1). This method provides a convenient yet economical approach to worst-case PV ramp rate modeling and is compatible with solar irradiance measured at coarse temporal resolution. Juan Bosch was financed in part by Project No. PID2019-108953RB-C21, funded by the Ministerio de Ciencia e Innovación and co-financed by the European Regional Development Fund. In addition, Iñigo de la Parra was partially supported by the Spanish State Research Agency (AEI) and FEDER-UE under Grant Nos. DPI2016-80641-R and DPI2016-80642-R.

In many jurisdictions, policy-makers are seeking to decentralize the electric power system while also promoting deep reductions in the emission of greenhouse gases (GHG). We examine the potential roles for residential energy storage (RES), a technology thought to be at the epicenter of these twin revolutions. We model the impact of grid-connected RES operation on electricity costs and GHG emissions for households in 16 of the largest U.S. utility service territories under 3 plausible operational modes. Regardless of operation mode, RES mostly increases emissions when users seek to minimize their electricity cost. When operated with the goal of minimizing emissions, RES can reduce average household emissions by 2.2-6.4%, implying a cost equivalent of $180 to $5160 per metric ton of carbon dioxide avoided. While RES is costly compared with many other emission-control measures, tariffs that internalize the social cost of carbon would reduce emissions by 0.1-5.9% relative to cost-minimizing operation. Policy-makers should be careful about assuming that decentralization will clean the electric power system, especially if it proceeds without carbon-mindful tariff reforms.

International audience; Cloud height information is crucial for various applications. This includes solar nowcasting systems. Multiple methods to obtain the altitudes of clouds are available. In this paper, cloud base heights derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three low-cost and low-maintenance ground based systems are presented and compared against ceilometer measurements on 59 days with variable cloud conditions in southern Spain. All three ground based systems derive cloud speeds in absolute units of [m/s] from which cloud heights are determined using angular cloud speeds derived from an all-sky imager. The cloud speed in [m/s] is obtained from (1) a cloud shadow speed sensor (CSS), (2) a shadow camera (SC) or (3) derived from two all-sky imagers. Compared to 10-min median ceilometer measurements for cloud heights below 5000 m, the CSS-based system shows root-mean squared deviations (RMSD) of 996 m (45%), mean absolute deviations (MAD) of 626 m (29%) and a bias of −142 m (−6%). The SC-based system has an RMSD of 1193 m (54%), a MAD of 593 m (27%) and a bias of 238 m (11%). The two all-sky imagers based system show deviations of RMSD 826 m (38%), MAD of 432 m (20%) and a bias of 202 m (9%). The ECMWF derived cloud heights deviate from the ceilometer measurements with an RMSD 1206 m (55%), MAD of 814 m (37%) and a bias of −533 m (−24%). Due to the multi-layer nature of clouds and systematic differences between the considered approaches, benchmarking cloud heights is an extremely difficult task. The limitations of such comparisons are discussed. This study aims at determining the best approach to derive cloud heights for camera based solar nowcasting systems. The approach based on two all-sky imagers is found to be the most promising, having the overall best accuracy and the most obtained measurements.

Improved coastal stratocumulus (Sc) cloud forecasts are needed because traditional satellite cloud motion vectors (CMV) do not accurately predict how Sc clouds move or dissipate in time, which often results in an underprediction of irradiance in the morning hours. CMV forecasts assume clouds move in the direction of the average regional wind field, which is not necessarily the case for Sc clouds. Sc clouds over the land form at night and typically reach their maximum coverage before sunrise. During the day, heating from solar radiation at the surface and entrainment of dry and warm air from above causes Sc clouds to dissipate. A Sc cloud edge forecast using Geostationary Operational Environmental Satellite is proposed to improve Sc cloud dissipation forecasts during the day. The inland edge of the Sc clouds is tracked in time and extrapolated into the future. For coastal regions where land elevation increases away from the coast, such as coastal California, the Sc cloud inland boundary is correlated to the land elevation. Dissipation after sunrise often follows land elevation as the mass of air required to be heated to become cloud-free decreases with increasing elevation as cloud top height is fairly constant along the cloud edge. The correlation between land elevation and the Sc cloud eastern boundary is exploited by extrapolating the evolution of cloud edge elevation in time. This method is tested in central and northern California on 25 days and in southern California on 19 days. When compared to the CMV (persistence forecasts), the proposed Sc cloud edge forecasts show a reduction of 30 W m−2 (104 W m−2) in hourly mean absolute error (MAE) of global horizontal irradiance (GHI). Additionally, out of 11 stations the Sc cloud edge forecast results show a higher forecast skill than CMV (persistence) at 7 (9) stations.

Urban morphology and inter-building shadowing result in a non-uniform distribution of surface heating in urban areas, which can significantly modify the urban flow and thermal field. In Part I, we found that in an idealized three-dimensional urban array, the spatial distribution of the thermal field is correlated with the orientation of surface heating with respect to the wind direction (i.e. leeward or windward heating), while the dispersion field changes more strongly with the vertical temperature gradient in the street canyon. Here, we evaluate these results more closely and translate them into metrics of “city breathability,” with large-eddy simulations coupled with an urban energy-balance model employed for this purpose. First, we quantify breathability by, (i) calculating the pollutant concentration at the pedestrian level (horizontal plane at $$z\approx 1.5$$ –2 m) and averaged over the canopy, and (ii) examining the air exchange rate at the horizontal and vertical ventilating faces of the canyon, such that the in-canopy pollutant advection is distinguished from the vertical removal of pollution. Next, we quantify the change in breathability metrics as a function of previously defined buoyancy parameters, horizontal and vertical Richardson numbers ( $$Ri_\text {h}$$ and $$Ri_\text {v}$$ , respectively), which characterize realistic surface heating. We find that, unlike the analysis of airflow and thermal fields, consideration of the realistic heating distribution is not crucial in the analysis of city breathability, as the pollutant concentration is mainly correlated with the vertical temperature gradient ( $$Ri_\text {v}$$ ) as opposed to the horizontal ( $$Ri_\text {h}$$ ) or bulk ( $$Ri_\text {b}$$ ) thermal forcing. Additionally, we observe that, due to the formation of the primary vortex, the air exchange rate at the roof level (the horizontal ventilating faces of the building canyon) is dominated by the mean flow. Lastly, since $$Ri_\text {h}$$ and $$Ri_\text {v}$$ depend on the meteorological factors (ambient air temperature, wind speed, and wind direction) as well as urban design parameters (such as surface albedo), we propose a methodology for mapping overall outdoor ventilation and city breathability using this characterization method. This methodology helps identify the effects of design on urban microclimate, and ultimately informs urban designers and architects of the impact of their design on air quality, human health, and comfort.

As urbanization progresses, more realistic methods are required to analyze the urban microclimate. However, given the complexity and computational cost of numerical models, the effects of realistic representations should be evaluated to identify the level of detail required for an accurate analysis. We consider the realistic representation of surface heating in an idealized three-dimensional urban configuration, and evaluate the spatial variability of flow statistics (mean flow and turbulent fluxes) in urban streets. Large-eddy simulations coupled with an urban energy balance model are employed, and the heating distribution of urban surfaces is parametrized using sets of horizontal and vertical Richardson numbers, characterizing thermal stratification and heating orientation with respect to the wind direction. For all studied conditions, the thermal field is strongly affected by the orientation of heating with respect to the airflow. The modification of airflow by the horizontal heating is also pronounced for strongly unstable conditions. The formation of the canyon vortices is affected by the three-dimensional heating distribution in both spanwise and streamwise street canyons, such that the secondary vortex is seen adjacent to the windward wall. For the dispersion field, however, the overall heating of urban surfaces, and more importantly, the vertical temperature gradient, dominate the distribution of concentration and the removal of pollutants from the building canyon. Accordingly, the spatial variability of concentration is not significantly affected by the detailed heating distribution. The analysis is extended to assess the effects of three-dimensional surface heating on turbulent transfer. Quadrant analysis reveals that the differential heating also affects the dominance of ejection and sweep events and the efficiency of turbulent transfer (exuberance) within the street canyon and at the roof level, while the vertical variation of these parameters is less dependent on the detailed heating of urban facets.

With the rapid rate of urbanization, outdoor thermal comfort is a growing health concern in densely-built areas. Accordingly, in order to achieve a comprehensive solution to urban environmental problems, more detailed and accurate prediction of outdoor thermal comfort is needed alongside building energy and urban wind flow analyses. To address this need, this study introduces an improved methodology of predicting outdoor thermal comfort and its spatial variability in urban streets using the comprehensive index standard effective temperature (SET). The improvement of the thermal comfort calculation is twofold. First, CFD simulations of the flow field dynamically coupled with the realistic urban surface heating are used to provide the input variables for the SET calculation. The CFD results provide detailed information on the heterogeneous urban flow field as a critical determinant of human comfort. Second, the SET calculations are improved by introducing a detailed model of mean radiant temperature that incorporates a) the visibility of urban surfaces to the pedestrians at any point, b) the spatial distribution of sky view factor, and c) inter-building shadowing and shortwave radiation effects on thermal comfort. These improvements allow for evaluating the spatial distribution of SET. Additionally, several sensitivity studies are carried out for an idealized configuration representing a compact low rise urban zone. The SET evaluated at the pedestrian level shows that urban density, wind patterns, and solar position concurrently influence thermal comfort. A sensitivity study on the effect of urban density reveals that higher urban concentration can favourably impact thermal comfort in warm climates due to the increased shading, despite the associated increase in air temperature and building energy consumption. The current study also demonstrates the critical importance of a comprehensive thermal comfort model that considers the flow field patterns as well as the realistic heating distribution of urban surfaces. We find that even in the shaded areas in the street canyon, SET changes up to 10 °C due to the wind sheltering in the urban roughness. Following this methodology, more complex scenarios can be evaluated in order to evaluate the effect of urban design on thermal comfort, and ultimately achieve a climate-conscious design.

Increasing distributed rooftop solar photovoltaic generation in the southern California coast necessitates accurate solar forecasts. In summertime mornings marine boundary layer stratocumulus commonly covers the southern California coast. The inland extent of cloud cover varies primarily depending on the temperature inversion base height (IBH, i.e. boundary layer height) and topography as confirmed using radiosonde sounding measurement and satellite irradiance data. Most operational numerical weather prediction models consistently overestimate irradiance and underpredict cloud cover extent and cloud thickness, presumably due to an underprediction of IBH. A thermodynamic method was developed to modify the boundary layer temperature and moisture profiles to better represent the boundary layer structure in the Weather and Research Forecasting model (WRF). Validation against satellite global horizontal irradiance (GHI) demonstrated that the best IBH ensemble improves GHI accuracy by 23% mean absolute error compared to the baseline WRF model and is similar to 24-h persistence forecasts for coastal marine layer region. The spatial error maps showed deeper inland cloud cover. Validation against ground observations showed that IBH ensembles were able to outperform persistence forecast at coastal stations.

Sky imaging systems are commonly used for aerosol characterization, cloud detection, and solar forecasting. We present an algorithm for measuring full-sky radiance with a range that exceeds the normal dynamic range of the camera system in question. Extended dynamic range over most of the sky is achieved with multiple exposures and High Dynamic Range (HDR) imaging, while solar beam intensity is estimated using CCD smear. Smear measurements are calibrated to match reference GHI based on pixel position on the sensor, and resulting irradiance measurements are validated. Global horizontal irradiance RMSE (root-mean-square error) for a year-long data set is in the 9–11% range for per-image data and 6–9% for hourly-averaged data when compared against a solid-state pyranometer. In addition, Direct Normal Irradiance (DNI) measurements for clear skies during a five-month period are compared to a non-co-located SPN1 DNI sensor, with RMSE of 8%.

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.

Historical observations from radiosondes, buoys, and satellite images are used to generate an analog ensemble (AnEn) solar forecast. In coastal California, Stratocumulus (Sc) clouds appear most frequently during late spring and summer months. Sc clouds form at night and begin to dissipate after sunrise, limiting solar energy generation in the morning hours. The AnEn method categorizes cloudy (as either well-mixed or decoupled) and clear events at the forecast initial time and uses several meteorological variables to find the closest analogs. The AnEn forecast is tested at the NKX weather station in San Diego, CA during May to September 2014-2017. The AnEn forecast has a lower root mean square error than a numerical weather prediction model and 24-hour persistence forecasts. The error is lowest for the clear cases and largest for the cloudy decoupled cases. The AnEn forecast is able to capture Sc dissipation for the well-mixed cases in the early morning, but decoupled cases display higher variability throughout the day and are much harder to predict as a result.

We characterize the irradiance enhancement events that occur when coastal stratocumulus clouds dissipate, caused by the breakup of the cloud layer into smaller cloud elements. The analysis includes 91 days in 2016 and 2017 that presented transitions from stratocumulus to clear skies in Southern California, using solar irradiance measurements and a clear sky model to describe the enhancement events. We find that a breakup process that starts later in the day lasts longer and is linked to stronger enhancement, as well as stronger down-ramps. Strong enhancement events are also linked to higher wind speeds during the breakup and lower solar zenith angle. The potential benefit of an irradiance enhancement forecast is assessed by evaluating a battery ramp-rate control system. Compared to a forecast that does not capture the solar variability, a good forecast would allow to use a smaller battery and to cycle it less, extending its lifetime.Presented at the World Solar Congress, Nov. 6, 2019, Santiago, Chile

The University of California, San Diego, has responded to the growth of Electric Vehicle (EV) ownership by continually adding charging stations all around campus over the past five years. In March 2020, the coronavirus pandemic caused a massive decline in EV charging as shelter-in-place orders were issued and the campus closed except for the medical centers. Overall, EV charging energy demand decreased by over 73%. But the decrease was uneven as direct current fast charger (DCFC) stations and the charging stations at the university's medical centers took on a larger percentage of the total campus load. Charging energy provided at the nonmedical stations decreased by 84%, while it decreased 50% at medical stations and 67% at DCFC stations. DCFC showed the strongest recovery in charging energy during the shutdown. The distribution of charging energy during the day and the charging duration did not change during the shutdown, while the plug-in duration increased. Implications for decarbonizing the transportation and electrical power systems are discussed.

The impact of atmospheric liquid water content at model initialization in the Weather Research and Forecasting (WRF) model is explored through the application of two preprocessing schemes. The first scheme, the Well-mixed Preprocessor (WEMPP), was designed and developed based on a well-mixed boundary layer to provide an initial guess at liquid water content when initializing with data from the North American Mesoscale (NAM) model, as liquid water content is not present in NAM output. The second scheme was adapted from a satellite Cloud Data Assimilation (CLDDA) package intended to make the initial model cloud field consistent with observations, using input data from the CIMSS GOES sounder cloud product. Preprocessed simulations were compared against baseline WRF simulations initialized with NAM and the Rapid Refresh (RAP) model (which contains liquid water output), as well as the raw parent model outputs. These intra-day forecasts were validated against both 5-min and 10-min averaged ground station and 30-min (hourly averaged) satellite irradiance observations over the course of a month. Due to their extensive spatial coverage, optical thickness, and reflectivity, stratocumulus (Sc) clouds are responsible for much of the variability in available solar resource at the surface in coastal California, where most rooftop photovoltaic systems are located. Currently, the trend of numerical weather prediction models is to underpredict both the presence and thickness of Sc. Therefore, the validation is conducted for a summer month in southern California, when Sc are most prevalent. Ground station validation showed average improvements by WEMPP in predicting surface irradiance over the baseline NAM (RAP) WRF initializations of 33% (−3%) MBE and 16% (9%) MAE, and by CLDDA of 47% (18%) MBE and 26% (20%) MAE. Additionally, simulations preprocessed by CLDDA were consistently able to outperform 24-h persistence forecast at 3 out of 4 ground stations. Validation against SolarAnywhere® satellite irradiance observations showed that the combination of both preprocessors provided the most improvement in the prediction of Sc spatial coverage, thickness, and lifetime in coastal regions where marine layer stratocumulus is most frequently observed, but cloud cover over the ocean was overestimated by all preprocessors.

The breakup of stratocumulus clouds over coastal land areas is studied using a combination of large-eddy simulations (LESs) and mixed-layer models (MLMs) with a focus on mechanisms regulating the timing of the breakup. In contrast with stratocumulus over ocean, strong sensible heat flux over land prevents the cloud layer from decoupling during day. As the cloud thins during day, turbulence generated by surface flux becomes larger than turbulence generated by longwave cooling across the cloud layer. To capture this shift in turbulence generation in the MLM, an existing entrainment parameterization is extended. The MLM is able to mimic cloud evolution for a variety of Bowen ratios, but only after this modification of the entrainment parameterization. Cloud lifetime depends on a combination of the cloud-top entrainment flux, the Bowen ratio of the surface, and the strength of advection of cool ocean air by the sea breeze. For dry land surface conditions, the authors’ MLM suggests a breakup time a few hours after sunrise. For relatively wet land surface conditions, the cloud layer briefly breaks into partly cloudy conditions during midday, and the stratocumulus cloud reforms in the evening.

The New Goddard shortwave (SW) radiation scheme of the Weather Research and Forecasting (WRF) numerical weather prediction model leads to positive biases in the clear-sky downwelling SW radiation (also referred to as global horizontal irradiance, GHI). Clear-sky GHI is attenuated primarily by four atmospheric constituents: (i) ozone (ii) background gases (e.g., trace gases), (iii) precipitable water and, (iv) aerosols. The effect of each constituent in the New Goddard SW scheme is isolated here by subtracting from the GHI predicted for an atmosphere that lacks one constituent, the GHI predicted for an atmosphere with all the constituents. Compared with the WRF’s Rapid Radiative Transfer Model for Global Circulation Models (RRTMG), the main contributions to the clear-sky irradiance bias in the New Goddard SW scheme come from modeling issues with the absorptions by water vapor and ozone. Enhancing the absorption due to water vapor continuum and using the RRTMG’s ozone profiles in the New Goddard SW scheme improved the agreement with the WRF’s RRTMG predictions for both GHI and direct normal irradiance. These results are further confirmed with the REST2 radiative transfer model.

Some potential adverse impacts of high photovoltaics (PV) penetration on the power grid are an increasing number of tap operations, over-voltages, and large and frequent voltage fluctuations and PV power ramps. The ability to create realistic PV input profiles with high spatial and temporal resolution is crucial to assess these impacts. This paper proposes a unique method to improve the accuracy of feeder hosting capacity studies using (1) high resolution PV generation profiles from sky imagers, (2) quasi-steady state distribution system simulation, and (3) distribution models created from utility data. Solar penetration levels, defined as ratio of peak PV output to peak load demand, from 0% to 200% and various cloud conditions are considered. Three conclusions were drawn: (1) the impacts of high PV penetration depend on feeder topology and characteristics; (2) the use of a single PV generation profile overestimates the tap operation number up to 260% resulting from an overestimation of power ramp rates and magnitudes – therefore, multiple realistic profiles should be used; and (3) distributed PV resources increase the feeder hosting capacity significantly compared to a centralized setup.

The irradiance incident on photovoltaic (PV) generators can considerably exceed the expected clear sky irradiance. Due to this phenomenon, called cloud enhancement (CE), the maximum power of the PV generator can exceed the rated power of the inverter connecting the generator to the grid. CE event characteristics and the effects of CE on the electrical operation of PV generators were studied based on measured irradiances and cloud edge velocities. Over eleven months in San Diego, California, the highest measured peak irradiance was 1466 W/m2. In addition, the highest simulated average irradiances for up to 1 MW generators were over 1400 W/m2. The largest lengths for CE events exceeding 1000 W/m2 were multiple kilometers. These results indicate that even large utility-scale PV power plants can be affected significantly by CE events. Moreover, the operation of three PV plants was simulated during around 2400 measured CE events with a detailed spatiotemporal model. The effects of inverter sizing on the operation of the plants were also studied, and the negative impacts of CE on the operation of PV systems were shown to increase with the increasing DC/AC ratio. During the CE events, the energy losses due to power curtailment were from 5% to 50% of the available energy production. While CE affects the operation of the PV plants, these effects were small in terms of aggregate energy since CE events that most strongly impact PV system operation are very rare, meaning that CE does not cause major problems for the operation of PV systems. publishedVersion

Accurate information on the distribution of the surface energy balance components in arid riparian areas is needed for sustainable management of water resources as well as for a better understanding of water and heat exchange processes between the land surface and the atmosphere. Since the spatial and temporal distributions of these fluxes over large areas are difficult to determine from ground measurements alone, their prediction from remote sensing data is very attractive as it enables large area coverage and a high repetition rate. In this study the Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate all the energy balance components in the arid riparian areas of the Middle Rio Grande Basin (New Mexico), San Pedro Basin (Arizona), and Owens Valley (California). We compare instantaneous and daily SEBAL fluxes derived from Landsat TM images to surface-based measurements with eddy covariance flux towers. This study presents evidence that SEBAL yields reliable estimates for actual evapotranspiration rates in riparian areas of the southwestern United States. The great strength of the SEBAL method is its internal calibration procedure that eliminates most of the bias in latent heat flux at the expense of increased bias in sensible heat flux.

Integration of solar energy forecasts into the electric network is becoming essential because of the continually increasing penetration level of solar energy. Three-dimensional numerical weather prediction (NWP) models predict the weather based on the current weather conditions (called initialization) and simulate the ensuing atmospheric processes. The accuracy of forecasts therefore depends, in part, on the accuracy of the model initializations. Data assimilation is recognized as the most widely used technique to improve the initialization into NWP models. In this study, meteorological observations from the surface and upper-air in-situ networks over the southern California coast are assimilated into the advanced research version of the Weather Research and Forecasting (WRF) model using a three dimensional variational data assimilation technique (3DVAR). A single observation test was conducted to tune-up the length scale and variance scale along with the regional domain-dependent background error statistics. A customized version of 3DVAR data assimilation was deployed with two sets of cyclic data assimilation with 6-h and 1-h assimilation windows along with the cold-start mode. The cyclic data assimilation experiments consistently outperformed the cold-start data assimilation and WRF for intra-day Global Horizontal Irradiance (GHI) and Clear Sky Index (CSI) forecast. Hourly cyclic assimilation showed the highest forecast skill score against ground measurements and satellite measurements. Even at the coastal stations with more challenging meteorological conditions, the hourly cyclic assimilation consistently outperformed the 24-h persistence forecast. The average (mean of four case studies) hourly cyclic data assimilation showed the highest forecast skill score in GHI and CSI intra-day forecast with reference to 24-h persistence forecast up to 39.4% and 40.7% respectively at the coastal stations. The spatial distributions of GHI biases estimated against SolarAnywhere satellite measurements showed that the hourly cyclic assimilation consistently improved the stratocumulus cloud coverage, thickness, and life time over the coastal region, but biases are still present further inland.

In order to simulate historical Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) solar resources, a method using MODIS level 3 (L3) daily satellite data as input to the REST2 clear sky model is presented to derive clear sky solar irradiance for California. MODIS L3 precipitable water (PW) and especially aerosol optical depth (AOD) were found to be significantly biased and were therefore calibrated based on AOD and PW from Aerosol Robotic Network (AERONET) ground monitoring sites. For reference, MODIS input data was replaced by the following input data sources: 3 hourly PW and AOD from Monitoring Atmosphere Composition and Climate (MACC) and monthly climatological Linke Turbidity from Solar radiation Data (SoDa). Similarly, other clear sky models, specifically Ineichen and McClear, were also run for reference. Validation was conducted using irradiance anomalies defined as the difference between irradiance and its 15 day moving average against ground measurement from California Irrigation Management Information System (CIMIS), National Renewable Energy Laboratory (NREL), and Integrated Surface Irradiance Study (ISIS) stations. It was found that the calibration of MODIS data markedly improves the accuracy of modeled GHI and DNI anomalies and REST2 clear sky model with calibrated MODIS data achieved the highest accuracy among all model and input data combinations. The improvement in accuracy of PW and AOD input data through calibration is relatively more important than the choice of clear sky model.

Accurate forecasting of the spatio-temporal variability of solar power is a critical enabler of economical grid-integration of large amounts of solar power. A new physically-based endogenous method to forecast power output and ramps a few minutes ahead is presented. This cloud speed persistence method consists of advecting the current distribution of power output across the plant using endogenous measurements of cloud motion vectors. The method was validated at a 48 MW photovoltaic power plant in south-western Nevada, USA. Excluding clear days and in terms of the percentage root mean squared error the new method outperformed persistence by 16.2% at 20 s, 10.6% at 60 s, and 4.0% at 120 s forecast horizon. Given plant dimensions (1807 × 539 m) and cloud motion vectors at the site, the method can be applied out to forecast horizons of 65 s, on average.

Satellite remote sensing has enabled remarkable progress in the ocean, earth, atmospheric, and environmental sciences through its ability to provide global coverage with ever-increasing spatial resolution. While exceptions exist for geostationary ocean color satellites, the temporal coverage of low-Earth-orbiting satellites is not optimal for oceanographic processes that evolve over time scales of hours to days. In hydrology, time scales can range from hours for flash floods, to days for snowfall, to months for the snowmelt into river systems. On even smaller scales, remote sensing of the built environment requires a building-resolving resolution of a few meters or better. For this broad range of phenomena, satellite data need to be supplemented with higher-resolution airborne data that are not tied to the strict schedule of a satellite orbit. To address some of these needs, a novel, portable, high-resolution airborne topographic lidar with video, infrared, and hyperspectral imaging systems was integrated. The system is coupled to a highly accurate GPS-aided inertial measurement unit (GPS IMU), permitting airborne measurements of the sea surface displacement, temperature, and kinematics with swath widths of up to 800 m under the aircraft, and horizontal spatial resolution as low as 0.2 m. These data are used to measure ocean waves, currents, Stokes drift, sea surface height (SSH), ocean transport and dispersion, and biological activity. Hydrological and terrestrial applications include measurements of snow cover and the built environment. This paper describes the system, its performance, and present results from recent oceanographic, hydrological, and terrestrial measurements.

The impacts of high PV penetration on distribution feeders have been well documented within the last decade. To mitigate these impacts, interconnection standards have been amended to allow PV inverters to regulate voltage locally. However, there is a deficiency of literature discussing how these inverters will behave on real feeders under increasing PV penetration. In this paper, we simulate several deployment scenarios of these inverters on a real California distribution feeder. We show that minimum and maximum voltage, tap operations, and voltage variability are improved due to the inverters. Line losses were shown to increase at high PV penetrations as a side effect. Furthermore, we find inverter sizing was shown to be important as PV penetration increased. Finally we show that increasing the number of inverters and removing the deadband from the Volt/VAr control curve improves the effectiveness., Accepted to 2017 IEEE Power & Energy Society General Meeting

A battery storage dispatch strategy that optimizes demand charge reduction in real-time was developed and the discharge of battery storage devices in a grid-connected, combined photovoltaic-battery storage system (PV+ system) was simulated for a summer month, July 2012, and a winter month, November 2012, in an operational environment. The problem is formulated as a linear programming (LP; or linear optimization) routine and daily minimization of peak non-coincident demand is sought to evaluate the robustness, reliability, and consistency of the battery dispatch algorithm. The LP routine leverages solar power and load forecasts to establish a load demand target (i.e., a minimum threshold to which demand can be reduced using a photovoltaic (PV) array and battery array) that is adjusted throughout the day in response to forecast error. The LP routine perfectly minimizes demand charge but forecasts errors necessitate adjustments to the perfect dispatch schedule. The PV+ system consistently reduced non-coincident demand on a metered load that has an elevated diurnal (i.e., daytime) peak. The average reduction in peak demand on weekdays (days that contain the elevated load peak) was 25.6% in July and 20.5% in November. By itself, the PV array (excluding the battery array) reduced the peak demand on average 19.6% in July and 11.4% in November. PV alone cannot perfectly mitigate load spikes due to inherent variability; the inclusion of a storage device reduced the peak demand a further 6.0% in July and 9.3% in November. Circumstances affecting algorithm robustness and peak reduction reliability are discussed.

Solar irradiance forecast accuracy of a ground-based sky imaging system currently being developed at UC San Diego is analyzed by assessing its performance on thirty-one consecutive days of historical data collected during winter. Sky images were taken every 30 s, and then processed to determine cloud cover, optical depth (thick or thin), and mean cloud field velocity. Cloud locations were forecasted using a frozen cloud advection method at 30 s intervals up to a forecast horizon of 15 min. During the analysis period, cloud field matching errors, which monotonically increase as a function of forecast horizon, did not exceed 30% over the sky imager’s field-of-view. On average, frozen cloud advection forecasts were found to perform superiorly to image persistence forecasts for all forecast horizons during the analysis period. Six (later eleven) distributed pyranometer installations over the UCSD campus provided 1-s instantaneous GHI measurements with which to validate irradiance forecasts. Excluding clear days or days with small forecast sample size, sky imager irradiance forecasts were found to perform the same as or better than clear sky index (clear-sky normalized GHI) persistence forecasts on 4 out of 24 days for 5-min forecasts, 8 out of 23 days for 10-min forecasts, and 11 out of 23 days for 15-min forecasts. Furthermore, visual comparison of forecast irradiance with measured irradiance revealed the ability to accurately predict cloud-induced irradiance fluctuations, which persistence forecasts cannot offer. An additional month of data collected during summer was analyzed to evaluate performance consistency during a time period with different meteorological conditions. Due to sky conditions favoring persistence forecast and challenges with cloud detection, sky imager forecasts were unable to surpass persistence forecasts for all 32 days for 5-min forecasts and only succeeded on 1 day for 10-min forecasts. However, bulk errors indicated consistency with winter forecasts, with rRMSE of 24.3% (20.0% for winter) and 27.7% (22.9%) for 5- and 10-min forecasts, respectively. A discussion of the challenges and sources of error applicable to the sky imaging system used is also presented, as well as future research intended to address potential areas of improvement.

The performance of the Total Sky Imager (TSI) and University of California, San Diego Sky Imager (USI) instruments has been evaluated using correlation and dispersion metrics applied to clear sky index. The TSI was located at a 48MW PV plant in Nevada, USA, and the USI was located at the UCSD campus in San Diego, USA. Distributed pyranometer measurements over several square kilometers were available at both locations to provide spatial averages of irradiance. Clear sky index was forecast for a 15 minute horizon at 30 sec. intervals, and the correlation and Euclidean dispersion were evaluated. A persistence forecast was generated for each location to provide a reference for comparison. For the aggregate dataset, the forecast performance of the USI exceeded that of persistence beyond the 10 minute forecast horizon, whereas the TSI did not show skill over persistence. For the period of study, the USI gave correlations of clear sky index between 0.70 and 0.82, whereas the TSI provided correlations between 0.55 and 0.69. Overall, the mean value and the trend of the correlation results were more consistent for the USI than the TSI on the days evaluated. It is concluded that the USI is a more reliable solar resource assessment instrument for the wide range of atmospheric conditions evaluated.

Soiling is the accumulation of dust on solar panels that causes a decrease in optical efficiencies of CSP systems. However, geographically widespread data is only available for solar photovoltaic (PV) systems. The changes in efficiency of a large commercial site (86.4 kWdc) was quantified during the summer dry period over the course of 2010 with respect to rain events observed at a nearby weather station (3.4 km away) and using satellite solar resource data. Soiling losses were found to be 0.21% per day. The site was observed to have a decrease in efficiency from 7.2% to 5.6% during a 108 day dry period in the summer at which point a rain event occurred that recovered most of the lost efficiency going back to 7.1%.

Cloud Speed Impact on Solar Variability Scaling - Application to the Wavelet Variability Model Matthew Lave Jan Kleissl University of California, San Diego 9500 Gilman Dr. #0411 La Jolla, CA 92093 mlave@ucsd.edu jkleissl@ucsd.edu Abstract The wavelet variability model (WVM) for simulating solar photovoltaic (PV) powerplant output given a single irradiance sensor as input has been developed and validated previously. Central to the WVM method is a correlation scaling coefficient ( ) that calibrates the decay of correlation of the clear sky index as a function of distance and timescale, and which varies by day and geographic location. Previously, a local irradiance sensor network was required to derive . In this work, we determine from cloud speeds. Cloud simulator results indicated that the value is linearly proportional to the cloud speed ( ): . Cloud speeds from a numerical weather model (NWM) were then used to create a database of daily values for North America. For validation, the WVM was run to simulate a 48MW PV plant with both NWM values and with ground values found from a sensor network. Both WVM methods closely matched the distribution of ramp rates (RRs) of measured power, and were a strong improvement over linearly scaling up a point sensor. The incremental error in using NWM values over ground values was small. The ability to use NWM-derived values means that the WVM can be used to simulate a PV plant anywhere a single high- frequency irradiance sensor exits. This can greatly assist in module siting, plant sizing, and storage decisions for prospective PV plants. 1. Introduction The variable nature of power produced by PV power plants can be of concern to electric operators. For example, the Puerto Rico Electric Power Authority (PREPA) requires that utility-scale PV plants in Puerto Rico limit ramps (both up and down) to 10% of capacity per minute (PREPA). At short timescales such as 1-minute, the variability of solar PV power production is mostly caused by the movement of clouds across the PV plant. While a single PV module can produce highly variable output due to the instantaneous crossing of cloud edges, geographic diversity of modules within a PV plant will lead to smoothing of the total power output. Geographic diversity can be quantified through the correlation coefficients between the timeseries of power output of different PV modules within the plant. This correlation generally decreases with distance and increases with fluctuation timescale. Irradiance and power measurements have been used to quantify the relative reduction in aggregate variability for a combination of sites. Sites a few to hundreds of kilometers apart were shown to lead to a smoothed aggregate output and the amount of smoothing varied based on the distances between sites and local meteorological conditions (Curtright and Apt, 2008, Lave and Kleissl, 2010, Otani, et al., 1997, Wiemken, et al., 2001). Other investigators (Mills and Wiser, 2010, Perez, et al., 2011, Perez, et al., 2012) calculated the correlation of irradiance fluctuations between sites and found decorrelation distances – the distances over which sites become independent of one another – to vary based on fluctuation timescale and distance between sites. Accounting for cloud speed further enhanced the accuracy of these correlation models (Hoff and Perez, 2012). Correlation was also shown to depend on orientation relative to the direction of cloud motion (Hinkelman, et al., 2011).

Coastal California has enormous potential for rooftop solar photovoltaic (PV) energy production. To reduce grid-integration costs, accurate and certain solar energy forecasts are required. However, frequent marine layer fog and stratus conditions limit the accuracy of numerical weather prediction models, especially during the summer. Thus, irradiance uncertainty is large and probabilistic forecast intervals are generally wide. To produce narrow and meaningful forecast intervals, the correlation of uncertainty to local meteorological conditions describing synoptic-scale atmospheric flow was considered. Specifically, the direction and magnitude of geostrophic flow were used as an indicator of coastal cloud cover probability to produce regime-dependent forecast intervals. The method was tested in summer 2011 for coastal California. The forecast interval was smaller than that provided by previous methods for all except clear conditions and contained 80% of irradiance measurements.

Aggregate ramp rates of 86 distributed photovoltaic (PV) systems installed in Southern California were analyzed and compared to irradiation measured at five ground stations and estimated from satellite. Irradiation data was converted to power output using a PV performance model to evaluate whether widespread on-line metering and telemetry of PV systems is necessary to track output of distributed generation for resource-adequacy applications. The satellite data were able to closely follow the aggregate power output and detect the timing of the ramps while the five weather stations were not as accurate due to smaller number and non-representative geographical distribution with respect to the PV sites. Over one year, the largest hourly aggregate ramp was a 50% increase based on the Performance Test Conditions (PTC) rating but ramps over 30% of PTC occurred only about once per day. The effects of specific meteorological conditions, such as coastal marine layer clouds and frontal system effects, on occurrence of large ramps were investigated over the area using satellite imagery. Evaporation of morning marine layer clouds caused a disproportionally large amount of up-ramps.

Spatio-temporal variability of solar radiation is the main cause of fluctuating photovoltaic power feed-in to the grid. Clouds are the dominant source of such variability and their velocity is a principal input to most short-term forecast and variability models. Two methods are presented to estimate cloud speed using radiometric measurements from 8 global horizontal irradiance sensors at the UC San Diego Solar Energy test bed. The first method assigns the wind direction to the direction of the pair of sensors that exhibits the largest cross-correlation in the irradiance timeseries. This method is considered the ground truth. The second method requires only a sensor triplet; cloud speed and the angle of the cloud front are determined from the time delays in two cloud front arrivals at the sensors. Our analysis showed good agreement between both methods and nearby METAR and radiosonde observations. Both methods require high variability in the input radiation as provided only in partly cloudy skies.

A control strategy to reduce OLTC operations resulting from high PV penetration on distribution feeders was developed and applied in this paper. The strategy uses 5-minute-ahead solar forecast to derive future voltage states on the distribution feeder. Unnecessary tap operations (TO) are identified as those that are reversed within 5 minutes, likely because of temporary cloudy or clear conditions over adjacent PV systems. Unnecessary TO are eliminated. On a feeder where TO were abundant at over 750 per day on average, the strategy resulted in the avoidance of 56% of the TO, which would result in significant savings in OLTC maintenance costs. Average extreme voltages were not affected by applying the TO reduction techniques. While daily maximum and minimum voltage excursions over the entire feeder slightly increased on several days, statistical analysis demonstrates that these deviations happened very rarely. The strategy is most effective on partly cloudy days and on voltage regulators with a large number of TO, while the fraction of avoided TO decreased substantially for less than 10 to 40 TO per day. The control algorithm was shown to be robust against forecast errors inherent in state-of-the art sky imager forecasts.

Integration of forecasting of solar energy feed-in to the electric network is becoming essential because of its continually increasing penetration level. Three-dimensional numerical weather prediction (NWP) models predict the weather based on the current weather conditions (called initialization) and simulate of the ensuing atmospheric processes. The accuracy of forecasts therefore depend, in part, on the accuracy of the model initializations. Data assimilation is recognized as the most widely used technique to improve the initialization into NWP models. In specific, meteorological observations from the surface and upper-air in-situ networks over the southern California coast are assimilated into the Weather Research and Forecasting (WRF) model. It is found that the global horizontal irradiance (GHI) produced by WRF data assimilation experiments are significantly closer to the observations as compared to the standard WRF forecast. Especially, the GHI biases are reduced significantly during the morning hours.

© 2016 Elsevier Ltd. Cloud base height (CBH) is a critical input to short-term solar forecasting algorithms, yet CBH measurements are difficult to obtain. Existing methods to detect CBH include radiosondes, ceilometers, and the stereographic method. However, these methods are deficient for intra-hour forecasting due to high costs or low temporal resolution. While satellite images could overcome these limitations, only the cloud top height can be determined from the thermal IR channel. We describe the integration of a cloud shadow speed sensor (CSS) with angular cloud speed from a sky imager to determine CBH. Furthermore, an improved methodology to determine cloud motion vectors from the CSS is presented, which offers lower noise and greater accuracy and stability than existing methods. Two months at the UC San Diego campus were used for validation against measurements from meteorological aerodrome reports (METAR) and an on-site ceilometer. Typical daily root mean square differences (RMSD) are 126 m which corresponds to 16.9% of the observed CBH. Normalized RMSD remains below 30% for all days. The daily bias is usually less than 80 m which suggests that the method is robust and that most of the RMSD is driven by short-term random fluctuations in CBH. Unlike sky image stereography the present method can be applied to measurements at a single site making it widely applicable.

Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed Chi Wai Chow 1 , Bryan Urquhart 1 , Matthew Lave 1 , Anthony Dominguez 1 , Jan Kleissl 1 , Janet Shields 2 , Byron Washom 3 Department of Mechanical and Aerospace Engineering Marine Physical Laboratory, Scripps Institution of Oceanography Director, Strategic Energy Initiatives University of California, San Diego Phone: +1 858 534 8087 email: jkleissl@ucsd.edu Abstract A method for intra-hour, sub-kilometer cloud forecasting and irradiance nowcasting using a ground- based sky imager at the University of California, San Diego is presented. Sky images taken every 30 seconds were processed to determine sky cover using a clear sky library and sunshine parameter. From a two-dimensional cloud map generated from coordinate-transformed sky cover, cloud shadows at the surface were estimated. Limited validation on four partly cloudy days showed that (binary) cloud conditions were correctly nowcast 70% of the time for a network of six pyranometer ground stations spread out over an area of 2 km 2 . Cloud motion vectors were generated by cross-correlating two consecutive sky images. Cloud locations up to five minutes ahead were forecasted by advection of the two-dimensional cloud map. Cloud forecast error increased with increasing forecast horizon due to high cloud cover variability over the coastal site.

Effects of Solar Photovoltaic Panels on Roof Heat Transfer Anthony Dominguez a , Jan Kleissl a , and Jeffrey C. Luvall b a University of California, San Diego, Department of Mechanical and Aerospace Engineering b NASA, Marshall Space Flight Center, AL 35812, USA Corresponding author Jan Kleissl, jkleissl@ucsd.edu Office: (858) 534‐8087; Fax: (858) 534‐7599; Address: 9500 Gilman Dr, EBUII – 580, University of California, San Diego, La Jolla, CA, 92093‐ Abstract Indirect benefits of rooftop photovoltaic (PV) systems for building insulation are quantified through measurements and modeling. Measurements of the thermal conditions throughout a roof profile on a building partially covered by solar photovoltaic (PV) panels were conducted in San Diego, California. Thermal infrared imagery on a clear April day demonstrated that daytime ceiling temperatures under the PV arrays were up to 2.5 K cooler than under the exposed roof. Heat flux modeling showed a significant reduction in daytime roof heat flux under the PV array. At night the conditions reversed and the ceiling under the PV arrays was warmer than for the exposed roof indicating insulating properties of PV. Simulations showed no benefit (but also no disadvantage) of the PV covered roof for the annual heating load, but a 5.9 kWh m ‐2 (or 38%) reduction in annual cooling load. The reduced daily variability in rooftop surface temperature under the PV array reduces thermal stresses on the roof and leads to energy savings and/or human comfort benefits especially for rooftop PV on older warehouse buildings. Keywords: Building energy use; cooling load; photovoltaic; roof heat flux; thermal infrared camera

Title: Evaluation of numerical weather prediction for intra‐day solar forecasting in the continental United States Authors: Patrick Mathiesen 1 and Jan Kleissl 1 (corresponding author) Department of Mechanical and Aerospace Engineering, University of California, San Diego. 9500 Gilman Dr., La Jolla, CA, 92093, USA. phone: +1 858 534 8087, email: jkleissl@ucsd.edu Abstract: Numerical weather prediction (NWP) models are generally the most accurate tools for forecasting solar irradiation several hours in advance. This study validates the North American Model (NAM), Global Forecast System (GFS), and European Centre for Medium‐Range Weather Forecasts (ECMWF) global horizontal irradiance (GHI) forecasts for the continental United States (CONUS) using SURFRAD ground measurement data. Persistence and clear sky forecasts are also evaluated. For measured clear conditions all NWP models are biased by less than 50 W m ‐2 . For cloudy conditions near solar noon these biases can exceed 200 W m ‐2 . In general, the NWP models (especially GFS and NAM) are biased towards forecasting clear conditions resulting in large, positive biases. Mean bias errors (MBE) are obtained for each NWP model as a function of solar zenith angle and forecast clear sky index, kt*, to derive a bias correction function through model output statistics (MOS). For forecast clear sky conditions, the NAM and GFS are found to be positively biased by up to 150 W m ‐2 , while ECMWF MBE is small. Outside of the relatively few clear forecasts that were actually cloudy, the reason for this bias is that the GFS and especially the NAM forecasts can exceed clear sky irradiances by up to 40%, indicating an inaccurate clear sky model. For forecast cloudy conditions (kt* < 0.4) the NAM and GFS models have a negative bias of up to ‐150 W m ‐2 . ECMWF forecasts are most biased for moderate cloudy conditions (0.4 < kt* < 0.9) with an average over‐prediction of 100 W m ‐2 . MOS‐corrected NWP forecasts based on solar zenith angle and kt* provide an important baseline accuracy to evaluate other forecasting techniques. MOS minimizes MBE for all NWP models. Root mean square errors are also reduced by 50 W m ‐2 , especially for intermediate clear sky indices. The MOS‐ corrected GFS provides the best solar forecasts for the CONUS with an RMSE of about 85 W m ‐2 . ECMWF is the most accurate forecast in cloudy conditions, while GFS has the best clear sky accuracy. Keywords: Model output statistics (MOS), Numerical Weather Prediction (NWP), Solar Forecasting

Optimum fixed orientations and benefits of tracking for capturing solar radiation in the continental United States Matthew Lave and Jan Kleissl Department of Mechanical and Aerospace Engineering University of California, San Diego Abstract Optimum tilt and azimuth angles for solar panels were calculated for a grid of 0.1⁰ by 0.1⁰ National Solar Radiation Data Base (NSRDB-SUNY) cells covering the continental United States. The average global irradiation incident on a panel at this optimum orientation over one year was also calculated, and was compared to the solar radiation received by a flat horizontal panel and a 2-axis tracking panel. Optimum tilt and azimuth angles varied by up to 10⁰ from the rule of thumb of latitude tilt and due south azimuth, especially in coastal areas, Florida, Texas, New Mexico, and Colorado. Compared to global horizontal irradiation, irradiation at optimum fixed tilt increased with increasing latitude and by 10% to 25% per year. Irradiation incident on a 2-axis tracking panel in one year was 25% to 45% higher than irradiation received by a panel at optimum fixed orientation. The highest increases in tracking irradiation were seen in the southwestern states, where irradiation was already large, leading to annual irradiation of over 3.4 MWh m -2 . 1. Introduction Solar photovoltaic (PV) systems are quickly gaining popularity in the United States (U.S.), thanks to incentive programs and enhanced interest in environmental sustainability and energy independence. As more PV systems are installed across the U.S., it becomes increasingly important to maximize their power output. Aside from increasing a panel’s solar conversion efficiency, power output can be increased by considering the solar geometry as well as the seasonal and daily variation of atmospheric transmissivity at a particular site. Specifically, it is important to know what the optimum tilt and azimuth angles are at which to mount a fixed tilt panel on a flat roof or on the ground such that it receives maximum irradiation. In addition, knowing the increase in solar radiation incident on a two-axis tracking panel will allow analysis of the economics of tracking PV systems, which are more expensive to install and maintain. Since the power production of a PV panel is close to linearly proportional to the amount of solar radiation (photons) reaching the panel surface, incident irradiation is an excellent proxy for power output. To maximize absorption of solar radiation in clear skies, the normal to the plane of the PV panel should be pointing towards the sun such that the solar direct beam is perpendicular to the panel surface. While a fixed tilt panel can only be normal to the incident sunlight once a day, a two-axis tracking panel improves over a fixed tilt panel by following the sun through the sky such that the plane of array normal is always parallel to the incident sunlight. However, when the majority of global irradiance is diffuse, horizontal alignment often provides the maximum global irradiance [1]. Some previous studies used modeled extraterrestrial radiation incident on the top of the atmosphere to find equations for optimum tilt over a large area [2,3]. This method accounts for the deterministic (celestial) variables which affect solar radiation, but it does not consider the stochastic (clouds and other weather) variables which also affect the optimum angles. Using an extraterrestrial

Convection over the building envelope is a critical determinant of building cooling load, but parameterization of convection in building energy models and urban computational fluid dynamics models is challenging. An experimental investigation intended to clarify the heat transfer mechanism of a convective wall boundary layer (WBL) on a leeward, vertical building wall was conducted at the Comprehensive Outdoor Scale Model (COSMO) facility for urban atmospheric research. Comparison of mean and turbulent temperature fluctuation intensity profiles showed that the dominant regime of the WBL flow was turbulent natural convection. Implications for parameterization of convective heat fluxes in urban areas are discussed.

Satellite derived global horizontal solar irradiance (GHI) from the SUNY modeled dataset in the National Solar Radiation Database (NSRDB) was compared to measurements from 27 weather stations in California during the years 1998–2005. The statistics of spatial and temporal differences between the two datasets were analyzed and related to meteorological phenomena. Overall mean bias errors (MBE) of the NSRDB–SUNY indicated a GHI overprediction of 5%, which is smaller than the sensor accuracy of ground stations. However, at coastal sites, year-round systematic positive MBEs in the NSRDB–SUNY data up to 18% were observed and monthly MBEs increased up to 54% in the summer months during the morning. These differences were explained by a tendency for the NSRDB–SUNY model to overestimate GHI under cloudy conditions at the coast during summer mornings. A persistent positive evening MBE which was independent of site location and cloudiness occurred at all stations and was explained by an error in the time-shifting method applied in the NSRDB–SUNY. A correction method was derived for these two errors to improve the accuracy of the NSRDB–SUNY data in California.

A new method to estimate tree biomass heat storage from thermal infrared (TIR) imaging of biomass surface temperature is presented. TIR images of the canopy are classified into trunk, branches, and leaves. The one-dimensional heat equation in cylindrical coordinates is forced with trunk and branch surface temperatures to simulate the temperature distribution and heat storage in tree trunks and branches. Assuming uniform leaf temperatures, heat storage in leaves is computed from the surface temperature of the leaves separately for the sunlit upper and shaded lower canopy. The sum of trunk, branches, leaf, and air heat storage gives the canopy heat storage. Measurements in a walnut orchard near Davis, California, in early June 2007 showed that biomass heat storage was of the same order as air heat storage and about 1% of daytime and 9% of nighttime net radiation.

The effects of artificial turf (AT) on the urban canopy layer energy balance, air and surface temperatures, and building cooling loads are compared to those of other common ground surface materials (asphalt, concrete, and grass) through heat transfer modeling of radiation, convection, and conduction. The authors apply the Temperatures of Urban Facets in 3D (TUF3D) model—modified to account for latent heat fluxes—to a clear summer day at a latitude of 33° over a typical coastal suburban area in Southern California. The low albedo of artificial turf relative to the other materials under investigation results in a reduction in shortwave radiation incident on nearby building walls and an approximately equal increase in longwave radiation. Consequently, building walls remain at a relatively cool temperature that is similar to those that are adjacent to irrigated grass surfaces. Using a simple offline convection model, replacing grass ground cover with artificial turf was found to add 2.3 kW h m−2 day−1 of heat to the atmosphere, which could result in urban air temperature increases of up to 4°C. Local effects of AT on building design cooling loads were estimated. The increased canopy air temperatures with AT increase heat conduction through the building envelope and ventilation in comparison with a building near irrigated grass. However, in this temperate climate these loads are small relative to the reduction in radiative cooling load through windows. Consequently, overall building design cooling loads near AT decrease by 15%–20%. In addition, the irrigation water conservation with AT causes an embodied energy savings of 10 W h m−2 day−1. Locally, this study points to a win–win situation for AT use for urban landscaping as it results in water and energy conservation.