Home » Historical Data » SolarAnywhere® High-resolution Data with True Dynamics™

SolarAnywhere® High-resolution Data with True Dynamics™


In addition to standard and enhanced resolution time-series data, SolarAnywhere® offers high resolution time-series data for more accurate sub-hourly PV modeling. High-resolution data has the following key features:

  1. Five-minute, 500-meter nominal native resolution using the maximum-available resolution of the latest generation of weather satellites for cloud detection to provide superior and reliable sub-hourly PV performance. The full resolution satellite data reveals the solar resource intermittency and is the best possible reference—i.e., source of truth—for sub-hourly solar models.
  2. True Dynamics (TD), a unique methodology for preparing satellite images for sub-hourly analysis and a feature of SolarAnywhere high-resolution data. True Dynamics describes solar data that’s been augmented with statistical techniques to behave more like ground-based measurements of real sunlight. TD is available as an option with high resolution data.

Applications of high-resolution data include estimation of ramp rates, firming requirements, clipping losses and more.

Historical, high-resolution data with and without True Dynamics can be downloaded from the SolarAnywhere data website or requested via the API with a High-resolution Sites license. Within the SolarAnywhere data website, existing Sites can be upgraded to high resolution on demand. High-resolution time-series data is currently available for locations in the continental United States (CONUS) region from January 1, 2020 to the historical data cutoff, updated monthly.


High-resolution data

SolarAnywhere high-resolution data takes maximum advantage of the latest generation of GOES satellites (GOES-East and GOES-West) operated by the National Oceanic and Atmospheric Administration (NOAA). The satellites scan the continental United States at a spatio-temporal resolution of 500-meters, 5-minute, which is 24 times the resolution (6X temporal, 4X spatial) of previous generations. The full resolution GOES data input reveals the solar resource intermittency and is the best possible reference—i.e., source of truth—for sub-hourly solar models.

High-resolution data with True Dynamics

To get the most accurate clipping loss and ramp rate simulations, solar data must have realistic sub-hourly variabilities and power distributions. True Dynamics was developed in partnership with Dr. Richard Perez and his team at the University at Albany’s Atmospheric Sciences Research Center and stands on the shoulders of decades of solar variability research.1

A key insight from their research is that the variability of solar data can be related to both the spatial and temporal averaging of the solar data. A point will have more variability than an area. In many cases, the relevant area for modeling is the footprint of the panels connected to a utility-scale inverter.2 (Solar plants with DC coupled storage may take advantage of increased spatial smoothing).3

Certain cloudy conditions can increase the amount of sunlight reaching solar PV systems owing to refraction from frozen water molecules in high-altitude clouds. Clear-sky exceedance events occur when irradiance exceeds clear-sky irradiance values due to nearby clouds, also termed as cloud enhancement. Empirical data shows that cloud enhancement is common in variable conditions and happens too fast to be represented in hourly models. To capture clear-sky exceedance events, True Dynamics synthetically enhances the variability observed in the high-resolution, 5-minute, 500-meter data.

Finally, decomposition models designed for hourly data must be adjusted for high-resolution input data.

As demonstrated in Figure 1, the 5-min TD data captures large, intra-hourly ramp rates and clear-sky exceedance events due to cloud reflection that are not evident in hourly-averaged data. Consequently, using the new 5-min TD data generally leads to a more accurate estimation of AC energy.

Figure 1: Time-series Simulation Using SolarAnywhere Hourly and 5-minute TD, and 1-minute Observed Data

Figure 1: SolarAnywhere Time-Series Simulation

The top graph is a comparison of GHI time series at Boulder on August 8, 2020, between SolarAnywhere hourly and 5-minute TD, and 1-minute Observed Data. The bottom graph shows the corresponding simulated AC power production using a prevailing configuration: horizontal single-axis tracking PV with power inverter nameplate capacity of 1kW. The total energy calculated for that day using the three data sources are also shown in the graph.

You can learn more about the TD methodology and results in the paper: “Enhancing temporal variability of 5-minute satellite-derived solar irradiance data.”4

Applications of SolarAnywhere High-Resolution and True Dynamics Data

Clipping Loss Assessment

Bright, sunny days are frequently broken up by intermittent clouds that move across a PV system at a sub-hourly timescale, causing short-term variability and possibly intermittent clipping.1 Using hourly data to predict power on bright, but cloudy, days can lead to over-predicted energy yield if the average irradiance during an hour results in generated power greater than the maximum inverter output rating. In 2012, researchers at Sandia found that hourly-averaged simulations overpredicted annual output by as much as 2%.5

More recently, the overprediction of AC energy due to clipping losses has been estimated at 1.5-4% annually depending on system design and location.6 Inverter saturation or clipping occurs when DC power from the PV array exceeds the maximum input rating of the inverter. In response, the inverter adjusts DC voltage to reduce the DC power. This results in lost DC power production, also known as ”inverter clipping.” Higher DC:AC ratios result in higher clipping losses. With the number of large utility-scale solar power plants increasing, higher DC:AC ratios are becoming more common, and some solar plants now even feature DC:AC ratios as high as 1.8.

Modeling PV production with high-resolution time-series data instead of hourly resolution data can result in more site-specific and accurate clipping loss and PV production estimates.7 In internal testing, 5-minute simulations with SolarAnywhere True Dynamics data reduced clipping loss errors by more than 90% versus hourly data for high DC:AC scenarios (see Figure 2).

In practice, users may also compare simulations with hourly and 5-min TD data to estimate the clipping loss error adjustment that is appropriate for the hourly-averaged simulation.

Figure 2: Comparison of Estimated AC Clipping Losses Error

Fig 2: Comparison of Estimated AC Clipping Losses Error

Estimated Losses Relative to 1-minute Observations for Hourly Averaged Observations, 5-minute Observations, SolarAnywhere Hourly, SolarAnywhere 5-minute, and SolarAnywhere 5-minute TD Data

Designing Hybrid Solar Energy Resources

The high variability in solar resource due to moving clouds can cause fluctuations in PV power over sub-hourly timescales. As PV penetrations increase, this variability can lead to a negative impact on grid stability. To mitigate this, many grid codes incorporate ramp-rate (RR) limitations on the injected PV power. Generally, these limitations are defined by a second or minute time frame. For instance, Germany and Puerto Rico require a maximum ramp rate of 10% per minute of the rated PV power. Since the transient fluctuations in PV power can occur over minutes, hourly simulations are inadequate for modeling the sub-hourly ramps of solar power.

With a maturing solar market, off-takers are increasingly asking for clean, guaranteed power production regardless of the weather. Solar Power Purchase Agreements (PPAs) are shifting from unit contingent to firm solar power, wherein the solar plant owner may be required to incur the weather (or shape) risk. As a result, solar developers increasingly need to design systems that cost-effectively deliver firm power—regardless of the weather.

Hybrid solar-plus-storage power plants can help PV plants provide essential grid balancing services and meet ramping and smoothing requirements by reducing solar intermittency. Their growing popularity is evidenced by a report from the Berkeley Lab, which states that hybrid solar-plus-storage power plants now dominate interconnection queues in some regions in the U.S.8 Better sub-hourly PV modeling using SolarAnywhere True Dynamics can help solar developers optimize PV project designs and attain tighter design margins, reducing the capital expenditure (CAPEX) needed to meet project requirements.

Together with hindcast data to evaluate battery dispatch and firming strategies, SolarAnywhere users can get the next-generation solar data needed to confidently model today’s hybrid solar energy resources.


1 Perez R, David M, Hoff T, Jamaly M, Kivalov S, Kleissl J, Lauret P, Perez M, 2016. Spatial and Temporal Variability of Solar Energy. Foundations and Trends® in Renewable Energy, Volume 1, Number 1: 1-44. doi.org/10.1561/2700000006. Link

2 Hobbs W, 2020. Overestimating output in hourly models due to high DC:AC ratios and solar variability: an introduction. ESIG System Planning Working Group, Summer Session. Link

3 Ahlstrom M, Mays J, Gimon E, Gelston A, Murphy C, Denholm P, Nemet G, 2021. Hybrid Resources: Challenges, Implications, Opportunities, and Innovation. IEEE Power and Energy Magazine, Volume 19, Issue 6: 37-44. DOI: 10.1109/MPE.2021.3104077. Link

4Huang J, Perez R, Schlemmer J, Kubiniec A, Perez M, Bhat A, Keelin P, 2022. Enhancing temporal variability of 5-minute satellite-derived solar irradiance data. IEEE Photovoltaic Specialists Conference (PVSC). Link

5 Hansen C W, Stein J S, Riley D, 2012. Effect of Time Scale on Analysis of PV System Performance. Sandia National Laboratories, Report number: SAND2012-1099. DOI:10.13140/2.1.1150.3368. Link

6 Bradford K, Walker R, Moon D, Ibanez M, 2020. A Regression Model to Correct for Intra-Hourly Irradiance Variability Bias in Solar Energy Models. 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), June 15-August 21, 2021. DOI: 10.1109/PVSC45281.2020.9300613. Link

7 Cormode D, Croft N, Hamilton R, Kottmer S, 2019. “A method for error compensation of modeled annual energy production estimates introduced by intra-hour irradiance variability at PV power plants with a high DC to AC ratio. 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC): 2293–2298. DOI: 10.1109/PVSC40753.2019.8981206. Link

8 Seel J, Warner C, Mills A D, October 2021. Influence of Business Models on PV-Battery Dispatch Decisions and Market Value. Advances in Applied Energy. DOI: 10.1016/j.adapen.2021.100076. Link