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Concentrated solar power (CSP) technology can not only match peak demand in power systems but also play an important role in the carbon neutrality pathway worldwide. Actions in China is decisive. Few previous studies have estimated CSP technology’s power generation and CO2 emission reduction potentials in China. To address this knowledge gap, the geographical, technical, and CO2 emission reduction potential of CSP in China was evaluated by province based on a high resolution geographical information system with up-to-date data. A comprehensive framework including geographic and technical constrains was proposed. Exclusion criteria including solar radiation, slope, land-use type, natural reserve, and water resources were adopted to determine the suitability of CSP plant construction. Then, based on the power conversion efficiency difference from various CSP technologies, the technical potential was calculated on suitable land. The results show that approximately 1.02 × 106 km2 of land is available to support CSP development in China. Based on the available solar resource on the suitable land, the geographical potential is 2.13 × 1015 kWh. The potential installed capacity is 2.45 × 107–5.40 × 107 MW, considering four CSP technologies. The corresponding annual energy generation potential is 6.46 × 1013–1.85 × 1014 kWh. Considering the scenario of using the potential of CSP to replace the current power supply to the maximum extent, CO2 emission would have been reduced by 5.19 × 108, 5.61 × 108, and 6.24 × 108 t in 2017, 2018, and 2019, respectively. At the provincial level, more than 99% of China’s technical potential is concentrated in five western provinces, including Xinjiang, Inner Mongolia, Qinghai, Gansu, and Tibet. These results provide policy guidance and serve as a reference for the future development of CSP and site selection for CSP plant construction both in China and worldwide.

Operating distribution grids is increasingly challenging due to the increasing penetration of photovoltaic systems. To address these challenges, modern photovoltaic inverters include features for local control, which sometimes lead to suboptimal results. Improved communication infrastructure and photovoltaic inverters favour global control strategies, which receive information from all the systems in the grid. An estimation of distribution algorithm is used to optimize a global control strategy that minimizes active power curtailment and use of reactive power of the photovoltaic inverters, while maintaining voltage stability. Optimized global control outperforms every other local control evaluated in terms of apparent energy used for control (9.9% less usage compared to the second best alternative in all scenarios studied) and ranks second in terms of voltage stability (with a 0.14% of total time outside the voltage limits). Two new indicators to compare control strategies are proposed, and optimized global control strategy ranks best for both efficiency index (0.98) and average apparent power use (0.48 kVA).

This paper presents an optimized logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. The model was optimized for the time of day. Trained on data from 57 EV charging stations around the University of California San Diego campus, the model achieved an 88.43% average accuracy and 92.23% maximum accuracy in predicting occupancy, outperforming a persistence model benchmark., 5 pages, 3 figures

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods. Refereed/Peer-reviewed

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.

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.

A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλτc, θ0, ϑs, ϑz, and RBRτc, θ0, ϑs, ϑz are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeasϑs, ϑz, in addition to RBRmeasϑs, ϑz, to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

Battery Energy Storage Systems (BESS) can mitigate effects of intermittent energy production from renewable energy sources and play a critical role in peak shaving and demand charge management. To optimally size the BESS from an economic perspective, the trade-off between BESS investment costs, lifetime, and revenue from utility bill savings along with microgrid ancillary services must be taken into account. The optimal size of a BESS is solved via a stochastic optimization problem considering wholesale market pricing. A stochastic model is used to schedule arbitrage services for energy storage based on the forecasted energy market pricing while accounting for BESS cost trends, the variability of renewable energy resources, and demand prediction. The uniqueness of the approach proposed in this paper lies in the convex optimization programming framework that computes a globally optimal solution to the financial trade-off solution. The approach is illustrated by application to various realistic case studies based on pricing and demand data from the California Independent System Operator (CAISO). The case study results give insight in optimal BESS sizing from a cost perspective, based on both yearly scheduling and daily BESS operation., Accepted at IFAC 2017 World Congress, Toulouse, France

In this paper a model is developed to solve the on/off scheduling of (non-linear) dynamic electric loads based on predictions of the power delivery of a (standalone) solar power source. Knowledge of variations in the solar power output is used to optimally select the timing and the combinations of a set of given electric loads, where each load has a desired dynamic power profile. The optimization exploits the desired power profiles of the electric loads in terms of dynamic power ramp up/down and minimum time on/off of each load to track a finite number of load switching combinations over a moving finite prediction horizon. Subsequently, a user-specified optimization function with possible power constraints is evaluated over the finite number of combinations to allow for real-time computation of the optimal timing and switching of loads. A case study for scheduling electric on/off loads with switching dynamics and solar forecast data at UC San Diego is carried out., Accepted at ACC 2016. arXiv admin note: substantial text overlap with arXiv:1512.09017

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities., Accepted by IEEE Energycon 2016

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.

A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W∙m−2 and mean absolute error (MAE) between 35 and 48 W∙m−2 for both calibrated and original functions, respectively. These results suggested that biases observed for instantaneous pixel-by-pixel values of Rni, Gi and other intermediate components of the algorithm were presumably absorbed into the computation of sensible heat flux as a result of the internal self-calibration of METRIC.

Changing cloud cover is a major source of solar radiation variability and poses challenges for the integration of solar energy. A compact and economical system is presented that measures cloud shadow motion vectors to estimate power plant ramp rates and provide short-term solar irradiance forecasts. The cloud shadow speed sensor (CSS) is constructed using an array of luminance sensors and a high-speed data acquisition system to resolve the progression of cloud passages across the sensor footprint. An embedded microcontroller acquires the sensor data and uses a cross-correlation algorithm to determine cloud shadow motion vectors. The CSS was validated against an artificial shading test apparatus, an alternative method of cloud motion detection from ground-measured irradiance (linear cloud edge, LCE), and a UC San Diego sky imager (USI). The CSS detected artificial shadow directions and speeds to within 15° and 6% accuracy, respectively. The CSS detected (real) cloud shadow directions and speeds with average weighted root-mean-square difference of 22° and 1.9 m s−1 when compared to USI and 33° and 1.5 m s−1 when compared to LCE results.

Soiling Losses for Solar Photovoltaic Systems in California Felipe A Mejia, Jan Kleissl Keywords: Soiling, PV Performance Center for Renewable Resources and Integration, Department of Mechanical and Aerospace Engineering, University of California, San Diego 9500 Gilman Dr., La Jolla, CA 92093, USA Abstract Soiling is the accumulation of dust on solar panels that causes a decrease in the solar photovoltaic (PV) system’s efficiency. The changes in conversion efficiency of 186 residential and commercial PV sites were quantified during dry periods over the course of 2010 with respect to rain events observed at nearby weather stations and using satellite solar resource data. Soiling losses averaged 0.051% per day overall and 26% of the sites had losses greater than 0.1% per day. Sites with small tilt angles (

Title: A high‐resolution, cloud‐assimilating numerical weather prediction model for solar irradiance forecasting. Authors: Patrick Mathiesen 1 , Craig Collier 2 , and Jan Kleissl 1 (corresponding author) Department of Mechanical and Aerospace Engineering, University of California, San Diego. 9500 Gilman Dr., La Jolla, CA, 92093‐0411, USA. Phone: +1 858 534 8087, email: jkleissl@ucsd.edu GL‐Garrad Hassan 9665 Chesapeake Drive #435, San Diego, CA, 92123, USA. Abstract: It is well established that most operational numerical weather prediction (NWP) models consistently over‐predict irradiance. While more accurate than imagery‐based or statistical techniques, their applicability for day‐ahead solar forecasting is limited. Overall, error is dependent on the expected meteorological conditions. For regions with dynamic cloud systems, forecast accuracy is low. Specifically, the North American Model (NAM) predicts insufficient cloud cover along the California coast, especially during summer months. Since this region represents significant potential for distributed photovoltaic generation, accurate solar forecasts are critical. To improve forecast accuracy, a high‐resolution, direct‐cloud‐assimilating NWP based on the Weather and Research Forecasting model (WRF‐CLDDA) was developed and implemented at the University of California, San Diego (UCSD). Using satellite imagery, clouds were directly assimilated in the initial conditions. Furthermore, model resolution and parameters were chosen specifically to facilitate the formation and persistence of the low‐altitude clouds common to the California coast. Compared to the UCSD pyranometer network, intra‐day WRF‐CLDDA forecasts were 17.4% less biased than the NAM and relative mean absolute error (rMAE) was 4.1% lower. For day‐ahead forecasts, WRF‐CLDDA accuracy did not diminish; relative mean bias error was only 1.6% and rMAE 18.2% (5.5% smaller than the NAM). Spatially, the largest improvements occurred for the morning hours along coastal regions when cloud cover is expected. Additionally, the ability of WRF‐CLDDA to resolve intra‐hour variability was assessed. Though the horizontal (1.3 km) and temporal (5 min.) resolutions were fine, ramp rates for time scales of less than 30 min. were not accurately characterized. Thus, it was concluded that the cloud sizes resolved by WRF‐CLDDA were approximately five times as large as its horizontal discretization. Keywords: Solar forecasting; High‐resolution numerical weather prediction (NWP); Direct cloud assimilation

A wavelet variability model (WVM) for simulating solar photovoltaic (PV) power plant output given a single irradiance point sensor timeseries using spatio-temporal correlations is presented. The variability reduction (VR) that occurs in upscaling from the single point sensor to the entire PV plant at each timescale is simulated, then combined with the wavelet transform of the point sensor timeseries to produce a simulated power plant output. The WVM is validated against measurements at a 2-MW residential rooftop distributed PV power plant in Ota City, Japan and at a 48-MW utility-scale power plant in Copper Mountain, NV. The WVM simulation matches the actual power output well for all variability timescales, and the WVM compares well against other simulation methods.

Previous laboratory and atmospheric experiments have shown that turbulence influences the surface temperature in a convective boundary layer. The main objective of this study is to examine land-atmosphere coupled heat transport mechanism for different stability conditions. High frequency infrared imagery and sonic anemometer measurements were obtained during the boundary layer late afternoon and sunset turbulence (BLLAST) experimental campaign. Temporal turbulence data in the surface-layer are then analyzed jointly with spatial surface-temperature imagery. The surface-temperature structures (identified using surface-temperature fluctuations) are strongly linked to atmospheric turbulence as manifested in several findings. The surface-temperature coherent structures move at an advection speed similar to the upper surface-layer or mixed-layer wind speed, with a decreasing trend with increase in stability. Also, with increasing instability the streamwise surface-temperature structure size decreases and the structures become more circular. The sequencing of surface- and air-temperature patterns is further examined through conditional averaging. Surface heating causes the initiation of warm ejection events followed by cold sweep events that result in surface cooling. The ejection events occur about 25 % of the time, but account for 60–70 % of the total sensible heat flux and cause fluctuations of up to 30 % in the ground heat flux. Cross-correlation analysis between air and surface temperature confirms the validity of a scalar footprint model.

A field experiment was carried out to evaluate the METRIC (mapping evapotranspiration at high resolution with internalized calibration) model to estimate the actual evapotranspiration (ETa) and crop coefficient (Kc) of a drip-irrigated Merlot vineyard during the 2007/2008 and 2008/2009 growing seasons. The Merlot vineyard located in the Talca Valley (Chile) was trained on a vertical shoot positioned system. The performance of METRIC was evaluated using measurements of ETa and Kc from an eddy covariance (EC) system. METRIC overestimated ETa by about 9 % with a root mean square error (RMSE) and mean absolute error (MAE) of 0.62 and 0.50 mm d−1, respectively. For the main phenological stages of the Merlot vineyard, METRIC overestimated the Kc by about 10 % with RMSE = 0.10 and MAE = 0.08. Furthermore, the indexes of agreement were 0.70 for Kc and 0.85 for ETa. Mean values of Kc measured from EC were 0.41, 0.53, 0.56, and 0.46, while those estimated by METRIC were 0.46, 0.54, 0.59, and 0.62 for the bud break to flowering, flowering to fruit set, fruit set to veraison, and veraison to harvest stages, respectively.

In a convective boundary layer, coherent structures were detected through their thermal signature on an artificial turf surface using high-frequency thermal infrared (TIR) imagery and surface layer turbulence measurements. The coherent structures cause surface temperature variations over tens of seconds and spatial scales of tens to a few hundred meters. Evidence of processes similar to those in a renewal event was observed. Spatial and temporal correlation analysis revealed the geometric and velocity information of the structures at the ground footprint of air temperature measurements. The velocity of the coherent structures was consistent with the wind speed at 6.5 m AGL. Practical implications of turbulence-driven surface temperature variability for thermal remote sensing are also discussed.

The connection between perception of individual exposure to different environmental stimuli; microclimate, noise and especially particulate matter (PM) was examined. Microclimate, noise and PM were monitored during field surveys with 260 questionnaire-guided interviews at a road construction site and a traffic site on the UC San Diego campus. The overall comfort was determined primarily by the thermal environment. The air quality was considered to be poor by 42% of the interviewees at the construction site, which was burdened with higher PM counts and sound levels. Overall, higher PM concentrations were correlated with perception of poor air quality. Similarity between the overall air quality and how dusty it feels suggests that visual clues of PM, such as dust, affect the perception of air quality and pollution. The effect of medical or smoking history on the perceived air quality was also examined. People with a medical history of hay fever voted more frequently for poor air quality conditions than those without, whereas current smokers were the least sensitive to ambient air quality conditions. Through the exposure-response relationships between the various perception votes and PM, it was possible to predict perceived air cleanness using the PM count. Understanding the human assessment of environmental stimuli could inform the design and development of urban spaces, in relation to the allocation of uses and activities, along with air quality management schemes.

The Canopy Horizontal Array Turbulence Study (CHATS) took place in spring 2007 and is the third in the series of Horizontal Array Turbulence Study (HATS) experiments. The HATS experiments have been instrumental in testing and developing subfilterscale (SFS) models for large-eddy simulation (LES) of planetary boundary layer (PBL) turbulence. The CHATS campaign took place in a deciduous walnut orchard near Dixon, California, and was designed to examine the impacts of vegetation on SFS turbulence. Measurements were collected both prior to and following leafout to capture the impact of leaves on the turbulence, stratification, and scalar source/sink distribution. CHATS utilized crosswind arrays of fast-response instrumentation to investigate the impact of the canopy-imposed distribution of momentum extraction and scalar sources on SFS transport of momentum, energy, and three scalars. To directly test and link with PBL parameterizations of canopy-modified turbulent exchange, CHATS also included a 30-m profile tower instrumented with turbulence instrumentation, fast and slow chemical sensors, aerosol samplers, and radiation instrumentation. A highresolution scanning backscatter lidar characterized the turbulence structure above and within the canopy; a scanning Doppler lidar, mini sodar/radio acoustic sounding system (RASS), and a new helicopter-observing platform provided details of the PBL-scale flow. Ultimately, the CHATS dataset will lead to improved parameterizations of energy and scalar transport to and from vegetation, which are a critical component of global and regional land, atmosphere, and chemical models. This manuscript presents an overview of the experiment, documents the regime sampled, and highlights some preliminary key findings.

Solar Global Horizontal Irradiance (GHI) fluctuates on both short (seconds to hours) and long (days to months) timescales leading to variability of power produced by solar photovoltaic (PV) systems. Under a high PV penetration scenario, fluctuations on short time scales may require a supplementary spinning power source that can be ramped quickly, adding significant external cost to PV operation. In order to examine the smoothing effect of geographically distributed PV sites, GHI timeseries at 5 min resolution at four sites across the state of Colorado were analyzed. GHI at the four sites was found to be correlated due to synchronous changes in the solar zenith angle. However, coherence analysis showed that the sites became uncorrelated on time scales shorter than 3 h, resulting in smoother average output at short time scales. Likewise, extreme ramp rates were eliminated and the spread in ramp rate magnitude was significantly reduced when all four sites were averaged. Nevertheless, even for the averaged output, high frequency fluctuations in PV power output are relatively larger in magnitude than fluctuations expected from wind turbines. Our results allow estimation of the ancillary services required to operate distributed PV sites.

Optimization of building energy use in an urban area requires understanding of the complex interaction between urban morphology, materials, and climate, which can have unanticipated effects on urban microclimates and building energy use. Reflective pavements reduce urban air temperatures and have been proposed as a mitigation measure for urban heat islands. However, the increased solar reflectivity also transports more solar radiation into (through windows) and onto adjacent buildings possibly increasing building energy use. The effect of albedo changes in the urban canopy floor surface on building thermal loads is investigated using the Temperature of Urban Facets Indoor-Outdoor Building Energy Simulator (TUF-IOBES). A case study for a four storey office building with 1820 m 2 floor area and 47% window to wall ratio in Phoenix, Arizona was conducted. Increasing pavement solar reflectivity from 0.1 to 0.5 increased annual cooling loads up to 11% (33.1 kWh m −2 ). The impacts on annual heating loads and canopy air temperatures were small. The confounding impacts of canopy aspect ratio, building insulation conditions reflective of building age, and window type and size were also quantified. Policymakers should carefully weigh the benefits and local energy use implications of reflective pavements for each site to ensure their optimal application.

An earlier study by one of the authors reported significant differences of up to 21% in linear regression slopes between six Kipp & Zonen large-aperture scintillometers. In this note, the consistency of this increasingly popular instrument for measuring sensible heat fluxes at the km scale was quantified by comparing measurements from four Scintec boundary-layer scintillometers and one large-aperture scintillometer over nearly identical transects. The Kipp & Zonen instrument’s sensible heat fluxes were more than 20% larger than those from the Scintec instruments, while the difference in regression slopes amongst the Scintec instruments was 3% or less.