Improved method for estimating solar irradiance at the earth's surface

Abstract

An improved method for estimating solar irradiation at the Earth's surface. Existing atmospheric brightness measurements often rely on single-point data or satellite imagery, which introduces an undesirable amount of spatial and temporal variability. By contrast, the disclosed method dynamically provides a highly detailed estimate that accurately predicts and quantifies atmospheric brightness across diverse geographic locations and weather conditions. By accounting for both solar irradiance and cloud cover, for instance, the disclosed system captures the dynamic interplay of the various atmospheric phenomena that impact atmospheric brightness and provides a more accurate estimate of the solar irradiation impacting the Earth's surface. Accordingly, when compared to existing methods, the disclosed system provides an unprecedented level of detail and accuracy in atmospheric brightness quantification and prediction.

Description

IMPROVED METHOD FOR ESTIMATING SOLAR IRRADIANCE AT THE EARTH’S SURFACECROSS-REFERENCE TO RELATED APPLICATIONS[00011 This application claims priority to U.S. Prov. Pat. Appl. No. 63 / 558,446, filed February 27, 2024. which is hereby incorporated by reference.BACKGROUND10002] Determining and predicting atmospheric brightness is a crucial component of a number of practical applications. As described in Rajagukguk (2021),1for instance, determining and predicting atmospheric brightness is necessary to optimize photovoltaic energy production and storage (and to prepare backup energy sources as needed to cover any shortage in energy output). Meanwhile, in the field of aviation safety, quantifiable predictive information about atmospheric brightness can serve as a decision-making tool for pilots, alerting them to potential low-visibility conditions caused by fog, haze, or dust storms. Additionally, in the field of astronomy and astrophotography, atmospheric brightness determinations and predictions can inform astronomers about ideal observation windows with clear skies and minimal light interference. Furthermore, automated smart lighting systems can use atmospheric brightness determinations to automatically adjust indoor lighting based on changing outdoor brightness, improving energy efficiency and user comfort. Atmospheric brightness determinations can also improve public safety by enabling public health officials to alert the public to time periods of high ultraviolet exposure, encourage sun protection measures, and reduce the risk of skin cancer and eye damage. Finally, improved atmospheric brightness determinations and predictions can be used to improve safety and performance at sporting events.

[0003] While several methods currently exist for assessing atmospheric brightness, all of the existing methods have limitations. Ground-based pyranometers and satellite-borne sensors, for instance, can be used directly to measure incoming solar radiation. Those methods, however, require expensive equipment and may not provide high spatial or temporal resolution. Alternatively, satellite imagery and other techniques can be used to estimate cloud cover. While offering good spatial coverage, however, such methods struggle to quantify the impact of cloud type, thickness, and altitude on brightness. Finally, radiative transfer models can be used to simulate the interaction of solar radiation with the atmospherebased on detailed atmospheric parameters. However, those complex models often require significant computational resources and may not be suitable for real-time applications.1<)O041 Accordingly, there is a need for an improved method for estimating solar irradiation at the Earth’s surface. An ideal solar irradiation estimation method should provide a relatively precise estimate for a specific area (high spatial resolution) and a specific time period (high temporal resolution) without the need to deploy expensive equipment. Additionally, an ideal method would both quantify current and past solar irradiation and also forecast future solar irradiation.SUMMARY[00051 In order to overcome those and other drawbacks of the prior art, an improved method for estimating solar irradiation at the Earth’s surface is disclosed. While existing atmospheric brightness measurements often rely on single-point data or satellite imagery (introducing an undesirable amount of spatial and temporal variability), the disclosed method accounts for both solar irradiance and cloud cover, capturing the dynamic interplay of the various atmospheric phenomena that impact atmospheric brightness and providing a more accurate estimate of the solar irradiation impacting the Earth’s surface.[0006 By adapting to changing atmospheric conditions and providing location-specific predictions that consider local topography and weather patterns, the disclosed system enables real-time insights with higher spatial and temporal resolution than existing methods.Moreover, by utilizing existing data sources, the disclosed system eliminates the need to deploy expensive specialized equipment.[0007| Meanwhile, that improved accuracy when quantifying and predicting atmospheric brightness enables a number of practical applications, including optimization of photovoltaic energy production, real-time adjustment of indoor and outdoor lighting systems, enhanced air quality monitoring and pollution impact assessment, improved planning and scheduling of astronomical observations, the developing improved weather forecast models with detailed brightness information, improved public safety (e.g., in aviation and other transportation, personal sun protection, sporting events and other outdoor activities) and preparation for personal and commercial activities, etc.BRIEF DESCRIPTION OF THE DRAWINGS[0008| Aspects of exemplary embodiments may be better understood with reference to the accompanying drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of exemplar}’ embodiments.|0009] FIG. 1 is a block diagram illustrating a system for estimating solar irradiance at the Earth’s surface according to exemplary embodiments.10010] FIG. 2 is a drawing illustrating an overview of an architecture of the system of FIG. 1 according to an exemplary embodiment.

[0011] FIG. 3 is a flowchart illustrating a process for estimating solar irradiance at a given location according to an exemplary embodiment.

[0012] FIG. 4 illustrates an example of the disclosed method using the weather conditions in New York City on February' 11 and 12, 2023.

[0013] FIG. 5 illustrates an example of the disclosed method using the weather conditions in State College, Pennsylvania, on March 20-26, 2023.|0014] FIG. 6 illustrates an example of the disclosed method using the weather conditions for a station in Canada (CWPX, 69.036N, 107.823W) from September 15 to November 30, 2022.

[0015] FIG. 7 illustrates a spatial distribution of the disclosed metric for locations around the globe on October 15, 2022.

[0016] FIG. 8 illustrates a spatial distribution of the disclosed metric for locations around the globe on December 20, 2022.

[0017] FIG. 9 illustrates a spatial distribution of the disclosed metric for locations around the globe on June 20, 2022.DETAILED DESCRIPTION

[0018] Reference to the drawings illustrating various views of exemplary embodiments is now made. In the drawings and the description of the drawings herein, certain terminology is used for convenience only and is not to be taken as limiting the embodiments of the present invention. Furthermore, in the drawings and the description below, like numerals indicate like elements throughout.

[0001] FIG. 1 is a block diagram illustrating a system 100 for estimating solar irradiance at the Earth’s surface according to exemplary embodiments. As shown in FIG. 1, the system100 includes one or more databases 110, a graphical user interface (GUI) 190 and / or a mobile application 195, and a number of software modules, for example a maximum solar irradiance module 120, an actual solar irradiance module 160, and a solar irradiance estimation module 180. While the software modules are separately illustrated, any number of the separately illustrated software modules may be realized as a single software module.

[0020] The one or more databases 110 store locations 112 on the Earth’s surface and zenith angles ZA indicative of the angle between the vertical and a line of sight to the sun at those locations 112 on each day of the year at various times of day (e.g., the average zenith angle ZA during each hour from sunrise to sunset). The solar irradiance module 120 estimates the periodic (e.g., hourly) and daily maximum possible solar irradiances SAmaxat each location 112. The one or more databases 110 also store periodic (e.g., hourly) and daily cloud cover percentages Ccoverat each location 112. The actual solar irradiance module 160 estimates the periodic (e.g., hourly) and daily actual solar irradiances SA at each location 1 12. Finally, the solar irradiance estimation module 180 estimates the solar irradiance reaching the Earth’s surface at each location 112 and calculates a daily metric indicative of the estimated solar irradiance (referred to herein as the “AccuLumen Brightness Index” or ABI) at each location 112. Accordingly, users can specify one or more locations 112 and receive the solar irradiance metric ABI for each of those locations 112, for example via the graphical user interface 190 and / or mobile application 195.[00211 FIG. 2 is a drawing illustrating an overview of an architecture 200 of the system 100 according to an exemplary' embodiment of the present invention.|0022] As shown in FIG. 2, the architecture 200 may include one or more servers 210 and one or more storage devices 220 connected to a plurality of remote computer systems 240, such as one or more personal systems 250 and one or more mobile computer systems 260, via one or more networks 230. Additionally, in some embodiments, the architecture 200 includes ground-based weather stations 270 and / or weather satellites 280.[0023 [ The one or more servers 210 may include an internal storage device 212 and a processor 214. The one or more servers 210 may be any suitable computing device including, for example, an application server and a web server which hosts websites accessible by the remote computer systems 240. The one or more storage devices 220 may include external storage devices and / or the internal storage device 212 of the one or more servers 210. The one or more storage devices 220 may also include any non-transitorycomputer-readable storage medium, such as an external hard disk array or solid-state memory. The networks 230 may include any combination of the internet, cellular networks, wide area networks (WAN), local area networks (LAN), etc. Communication via the networks 230 may be realized by wired and / or wireless connections.

[0024] A remote computer system 240 may be any suitable electronic device configured to send and / or receive data via the networks 230. A remote computer system 240 may be, for example, a network-connected computing device such as a personal computer system 250, a mobile computer system 260, a notebook computer, a smartphone, a personal digital assistant (PDA), a tablet, a notebook computer, a portable weather detector, a global positioning satellite (GPS) receiver, network-connected vehicle, a wearable device, etc. A personal computer system 250 may include an internal storage device 252, a processor 254, output devices 256 and input devices 258. The one or more mobile computer systems 260 may include an internal storage device 262, a processor 264, output devices 266 and input devices 268. An internal storage device 212, 252, and / or 262 may include one or more non-transitory computer-readable storage mediums, such as hard disks or solid-state memory, for storing software instructions that, when executed by a processor 214, 254, or 264, carry out relevant portions of the features described herein. A processor 214, 254, and / or 264 may include a central processing unit (CPU), a graphics processing unit (GPU), etc. A processor 214, 254, and 264 may be realized as a single semiconductor chip or more than one chip. An output device 256 and / or 266 may include a display, speakers, external ports, etc. A display may be any suitable device configured to output visible light, such as a liquid crystal display (LCD), a light emitting polymer display (LPD), a light emitting diode display (LED), an organic light emitting diode display (OLED), etc. The input devices 258 and / or 268 may include keyboards, mice, trackballs, still or video cameras, touchpads, etc. A touchpad may be overlaid or integrated with a display to form a touch-sensitive display or touchscreen.|0025] Referring back to FIG. 1, the one or more databases 110 may be any organized collection of information, whether stored on a single tangible device or multiple tangible devices, and may be stored, for example, in the one or more storage devices 220. The software modules (e.g., the maximum solar irradiance module 120, the cloud cover analysis module 140, the actual solar irradiance module 160, the solar irradiance estimation module 180, etc.) may be realized by software instructions stored on one or more of the internal storage devices 212, 252, and / or 262 and executed by one or more of the processors 214. 254, or 264.[0026| The graphical user interface 190 and / or the mobile application 195 may be realized by software instructions stored on one or more of the internal storage devices 212. 252, and / or 262 and executed by one or more of the processors 214, 254, or 264. The graphical user interface 190 may be any interface that allows a user to input information for transmittal to the system 100 and / or outputs information received from the system 100 to a user. The graphical user interface 190 may be, for example, desktop computer program (e.g., a weather widget), a web application, and / or website (e.g., AccuWeather.com). The mobile application 195 may be, for example, a smartphone application, a smartphone widget, etc. The graphical user interface 190 and / or the mobile application 195 may provide functionality for users to specify a location 112 and display the solar irradiance metric ABI for the specified location 112. Additionally, the mobile application 195 and / or the graphical user interface 190 may determine the location of the remote computer system 240 (e.g., using GPS, network identification, etc.) and display the solar irradiance metric ABI for the specified location 112.Estimating Solar Irradiation at the Earth’s Surface[0027[ The average amount of solar radiation reaching the Earth’s upper atmosphere S (generally referred to as the “solar constant”) is approximately 1366 watts per square meter (W / m2). At any given time and given location 112 on the Earth’s surface, the maximum possible solar irradiance SImax(i.e., assuming a clear sky) is a function of the angle of the sun (relative to that location 112 at that time) and an atmospheric factor (e.g., 0.8) indicative of the absorption and reflectivity of the atmosphere. Accordingly, the maximum possible solar irradiance SImaxmay be approximated as shown in Equation 1:SImax=0'8 X S X COS (ZA) where Z is the zenith angle (i.e., the angle between the vertical and a line of sight to the sun) at that location 112 during that time and S is the solar radiation at the top of the atmosphere (i.e., 1366 W / m2).

[0028] The actual solar irradiance SI reaching the Earth’s surface differs from the maximum possible solar irradiance SImaxdepending on the cloud cover at that location 112. Cloud cover can be quantified by calculating a cloud cover percentage Ccoverindicative of the percentage of the sky covered by clouds at that time in that location 112. Cloud cover percentage Ccoveris generally expressed as a decimal between 0 (indicative of a completely clear sky) and 1 (indicative of a completely overcast sky). The cloud cover percentage Ccoverat specific time period at a specific location 112 may be estimated, for example, using data from ground stations 270, satellite imagery from weather satellites 280. and / or weather forecasting models. Additionally, those weather forecasting models may be used to forecast global cloud cover and those global cloud cover forecasts may be used to determine a forecasted cloud cover percentage Ccoverat a specific location 112 over a future time period (e.g., hourly, daily, etc.).[0029| Alternatively, the cloud cover percentage Ccovermay be periodically calculated using sky images. For example, a sky image may be captured using a sky camera. To improve the contrast and reduce noise, the sky image captured by the sky camera may be converted to a one channel image. For example, the sky image may be converted to a one channel image by applying the RBR method, where each pixel value ranging from 0 to 255 is set equal to the ratio of the red channel and the blue channel. (To avoid dividing by 0 in those embodiments, the blue channel is increased by 1 if equal to 0.) To distinguish between pixels depicting clouds and pixels depicting sky in those embodiments, each pixel value is compared to a threshold value k. Because the brightness of the sky image may differ on account of the ever-changing position of the sun, the threshold value k (used to classify each pixel as depicting either clouds or sky) may be dynamically for each sky image. An optimal threshold value k* may be selected, for example, to maximize the separability of the resultant classes in gray levels. For a sky image having pixel levels from 1 to L, for instance, an optimal threshold value k* may be identified by selecting the threshold value k that produces a maximum between-class variance crj as shown in Equation 2:100301 The actual solar irradiance SI at a given time in a given location 112 may be approximated using the cloud cover percentage Ccoveras shown in Equation 3:SI = 0.8 x S x cos (ZA) x (1 - 0.95 x (0.125 x Ccoverx 8)A3.4 )[00311 In some instances, additional factors (e.g., the type of clouds, their altitude, their optical thickness, etc.) may be used to adjust the cloud cover percentage Ccoverand form a more comprehensive cloud cover factor Cfactor. Accordingly, in some embodiments, the actual solar irradiance SI at a given time in a given location 112 may be approximated as shown in Equation 4:SI = 0.8 x S x cos (ZA) x Cfactor

[0032] For a given location 112, the actual solar irradiance SI may be calculated periodically (e.g., hourly) and the daily actual solar irradiance SI and maximum possible solar irradiance SImaxmay be calculated by averaging the respective periodically calculated metrics (e.g., hourly metrics) for that day. Similarly, cloud cover percentage Ccovermay be calculated periodically (e.g., hourly) and a daily cloud cover percentage Ccovermay be calculated by averaging the penod cloud cover percentages Ccoverfor each time period between sunrise and sunset hours.[00331 The disclosed system calculates a metric indicative of the estimated solar irradiance on a given day at a given location 112 (the AccuLumen Brightness Index or ABI) as shown in Equation 5 :ABI = AVG(SI) / AVG(SImax) X 10 X (1 - AVG(Ccover) X 0.3) where AVG(SI) is the average actual solar irradiance SI (calculated, e.g., using Equation 3 or Equation 4 above) on the given day at the given location 112, AVG(SImax) is the average maximum possible solar irradiance SI,nax(calculated, for example, using Equation 1 above) on the given day at the given location 112, and AVG(Ccover) is the average cloud cover percentage Ccoverbetw een sunrise and sunset on the given day at the given location 112.

[0034] In some specific conditions, the disclosed system may adjust the ABI metric to form an adjusted metric ABIadj, for example as follows: when then (SImax) > 50 and AVG(Ccover) < 0.05 ABIadj = 10 ) > 50 and AVG(Ccover) > 0.05 and ABI > 9 ABIadj = 9 AVG(SImax) < 50 ABIadj= AVG(SImax) X 0.02 X ABI AVG(SImax) < 20 and ABI < 1 ABIadj= 1AVG(SImax) = 0 ABIadj= 0

[0035] FIG. 3 is a flowchart illustrating a process 300 for estimating solar irradiance at a given location according to an exemplary embodiment. The process 300 may be performed, for example, by the software modules described above with reference to FIG. 1. For example, the process 300 may be performed to calculate the AccuLumen Brightness Index ABI for each of a number of locations 112, which may be stored in the one or more databases 110 and provided to users, for example via the graphical user interface 190, the mobile application 195, etc.[0036| The maximum possible solar irradiance SImaxis calculated periodically (e.g., hourly) for the given location 112 in step 310. For instance, the maximum possible solar irradiance SImaxmay be calculated using Equation 1 above.

[0037] The periodic cloud cover percentage Ccovermeasurements (e.g., for each hour between sunrise and sunset) for the given location 112 are received in step 320. Those cloud cover percentage Ccovermeasurements may be received from a weather services provider (e.g., AccuWeather, Inc.). As described above, those cloud cover percentages Ccovermay be estimated using data from ground stations 270, satellite imagery from weather satellites 280, and / or weather forecasting models. Additionally, weather forecasting models may be used to forecast future cloud cover percentages Ccoverat the given location 112 over a future time period.

[0038] The actual solar irradiance SI is calculated periodically (e.g., hourly) for the given location 112 in step 330. For instance, the actual solar irradiance SI may be calculated using Equation 3 or Equation 4 above.

[0039] The daily maximum possible solar irradiance SImaxfor the given location 1 12 is calculated in step 360, for example by averaging the periodic (e.g., hourly) maximum possible solar irradiances SImaxcalculated in step 310.

[0040] The daily solar irradiance SI for the given location 112 is calculated in step 370, for example by averaging the periodic (e.g., hourly) solar irradiances SI calculated in step 330.[0041 [ The daily cloud cover percentage Ccoverfor the given location 112 is calculated in step 380, for example by averaging the periodic (e.g., hourly) cover percentages Ccoveridentified between sunrise and sunset in step 320.

[0042] The solar irradiance that reached the Earth surface at the given location 112 on the given day is estimated in step 390, for example by calculating the AccuLumen Brightness Index ABI using Equation 5 above. If any of a number of conditions exist, the ABI calculated in step 390 may be adjusted to form an adjusted metric ABIadjin step 395 as described above.Advantages of the Improved Method

[0043] Existing atmospheric brightness measurements often rely on single-point data or satellite imagery, which introduces an undesirable amount of spatial and temporal variability. By contrast, the disclosed method dynamically provides a highly detailed estimate thataccurately predicts and quantifies atmospheric brightness across diverse geographic locations and weather conditions. By accounting for both solar irradiance and cloud cover, for instance, the disclosed system captures the dynamic interplay of the various atmospheric phenomena that impact atmospheric brightness and provides a more accurate estimate of the solar irradiation impacting the Earth’s surface. Accordingly, when compared to existing methods, the disclosed system provides an unprecedented level of detail and accuracy in atmospheric brightness quantification and prediction.[00441 By adapting to changing atmospheric conditions and providing location-specific predictions that consider local topography and weather patterns, the disclosed system enables real-time insights with higher spatial and temporal resolution than existing methods. Moreover, by utilizing existing data sources, the disclosed system eliminates the need to deploy expensive specialized equipment.

[0045] Meanwhile, that improved accuracy when quantifying and predicting atmospheric brightness enables a number of practical applications, including optimization of photovoltaic energy production, real-time adjustment of indoor and outdoor lighting systems, enhanced air qualify monitoring and pollution impact assessment, improved planning and scheduling of astronomical observations, the developing improved weather forecast models with detailed brightness information, improved public safety (e g., in aviation and other transportation, personal sun protection, sporting events and other outdoor activities) and preparation for personal and commercial activities, etc.Examples of the Improved Method

[0046] FIG. 4 illustrates an example of the disclosed method using the weather conditions in New York City on February 11 and 12, 2023. Specifically, FIG. 4 includes graphs illustrating the hourly solar irradiance SI, the hourly maximum possible maximum SImax, and the hourly cloud cover percentage Ccoverbetween sunrise and sunset (shown in dashed lines). As the solar irradiance SI on February 11 was nearly equal to the maximum possible maximum SImaxand cloud cover percentage Ccoverwas minimal, the ABI calculated by the disclosed system was 10. In contrast, due to the overcast conditions (though not a dark overcast) on February 12 (i.e., a relatively high cloud cover percentage Ccoverduring the day and a lower ratio of solar irradiance SI relative to maximum possible maximum SImax), the ABI calculated by the disclosed system for New York City on February' 12 was 4.[0047| FIG. 5 illustrates an example of the disclosed method using the weather conditions in State College, Pennsylvania, on March 20-26, 2023. Specifically, FIG. 5 includes graphs illustrating the hourly solar irradiance SI and the hourly maximum possible maximum SImax. (The hourly cloud cover percentage Ccoveris omitted for clarity). Again, the disclosed system generates a higher solar irradiance metric ABI (i.e., 10) for March 20, 21, and 26, when the solar irradiance SI is nearly equal to the maximum possible maximum SImax. Meanwhile, the disclosed system generates lower solar irradiance metrics ABI when the solar irradiance SI is lower relative to the maximum possible maximum SImax.[0048} FIG. 6 illustrates an example of the disclosed method using the weather conditions for a station in Canada (CWPX, 69.036N, 107.823W) from September 15th, 2022, to November 30th, 2022. Some locations 112 located near the poles (such as CWPX) will experience extended periods of darkness between late fall and early spring. Accordingly, the disclosed method does not generate a high ABI during those conditions, even if the ratio between the actual solar radiation (SI) relative to the maximum solar radiation (SImax) multiplied by 10 approaches 10. Accordingly, to achieve more accurate ABI values for locations 112 near the poles, an adjustment is applied as described above. Specifically, when the daily maximum solar irradiance SImaxis less than 50 W / m2(as indicated by the arrows around October 15th), the disclosed system generates an adjusted metric ABIadj = AVG(SImax) x 0.02 x ABI as described above. Those values are then rounded to the nearest integer as shown in FIG. 6.[0049| FIGS. 7 and 8 are heatmaps illustrating a spatial distribution of the brightness index ABI and adjusted brightness ABIadj for locations around the globe on October 15, 2022 (FIG. 7) and December 20th, 2022 (FIG. 8). As shown in FIGS. 7 and 8, the disclosed system demonstrates a significant improvement in estimating brightness, particularly for stations located north of 60°N (identified using the dashed line) where daylight is limited and the sky remains dark for most of the day. FIG. 9 illustrates that the same adjustment also works for stations located south of 60°S for June 20th, 2022, which corresponds to the austral winter.[0050| While preferred embodiments have been described above, those skilled in the art who have reviewed the present disclosure will readily appreciate that other embodiments can be realized within the scope of the invention. Accordingly, the present invention should be construed as limited only by any appended claims.1Rajagukguk, R.A. Kamil, R. and Lee, II. J., 2021. A deep learning model to forecast solar irradiance using a sky camera. Applied Sciences, / / ( I l ), p. 5049, https: / / doi.org / 10.3390 / appl l 115049

Claims

CLAIMSWhat is claimed is:

1. A method of estimating the solar irradiance reaching the Earth’s surface at a given location on a given day, the method comprising: estimating periodic measurements indicative of the maximum possible solar irradiance SIniax during a plurality of time periods on the given day in the given location; receiving periodic information indicative of cloud cover during the plurality of time periods on the given day in the given location; calculating, based on the cloud cover during the plurality of time periods, periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods on the given day in the given location; estimating the maximum possible solar irradiance Slmaxon the given day in the given location; quantifying the cloud cover on the given day in the given location; estimating the actual solar irradiance SI on the given day in the given location; and generating a metric indicative of the solar irradiance reaching the Earth’s surface at the given location on the given day based on the cloud cover on the given day in the given location and a ratio of the actual solar irradiance SI on the given day in the given location relative to the maximum possible solar irradiance SImaxon the given day in the given location.

2. The method of claim 1, wherein: estimating the maximum possible solar irradiance Slmaxon the given day in the given location comprises averaging the periodic measurements indicative of the maximum possible solar irradiance Slmaxduring each of the plurality of time periods; and estimating the actual solar irradiance SI on the given day in the given location comprises averaging the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods.

3. The method of claim 1, wherein: the periodic information indicative of cloud cover comprises cloud cover percentages CCover during each of the plurality of time periods; and quantifying the cloud cover on the given day in the given location comprises averaging the cloud cover percentages Ccoverbetween sunrise and sunset.

4. The method of claim 3, wherein the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods are calculated according toSI = 0.8 x S x cos (ZA) x (1 - 0.95 x (0.125 x Ccoverx 8)A3.4 ) where S is the solar constant indicative of the average amount of solar radiation reaching the Earth’s upper atmosphere, ZA is the zenith angle indicative of the angle between the vertical and a line of sight to the sun at the given location during the time period, and Ccoveris the cloud cover percentage at the given location during the time period.

5. The method of claim 3, wherein the metric indicative of the solar irradiance reaching the Earth’s surface is calculated according toABI = AVG(SI) / AVG(SImax) X 10 X (1 - AVG(Ccover) X 0.3) where AVG(SI) is the average of the periodic measurements indicative of the actual solar irradiance SI during each of the plurality of time periods, AVG(SImax) is the average of the periodic estimates indicative of the maximum possible solar irradiance SImaxduring each of the plurality of time periods, and AVG(Ccover) is the average of the cloud cover percentages during each of the plurality of time periods.

6. The method of claim 1, further comprising: adjusting the metric indicative of the solar irradiance reaching the Earth’s surface in response to a determination that the average of the periodic measurements indicative of theactual solar irradiance SI during each of the plurality of time periods is less than a predetermined threshold.

7. The method of claim 1 wherein generating the metric indicative of the solar irradiance reaching the Earth’s surface at the given location on the given day comprises forecasting the solar irradiance reaching the Earth’s surface on a future date.

8. A system for estimating the solar irradiance reaching the Earth’s surface at a given location on a given day, comprising: non-transitory computer readable storage media that stores periodic information indicative of cloud cover during a plurality of time periods on the given day in the given location; a hardware computer processor adapted to: estimate periodic measurements indicative of the maximum possible solar irradiance SImaxduring a plurality of time periods on the given day in the given location; calculate, based on the cloud cover during the plurality of time periods, periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods on the given day in the given location; estimate the maximum possible solar irradiance SImaxon the given day in the given location; quantify the cloud cover on the given day in the given location; estimate the actual solar irradiance SI on the given day in the given location; and generate a metric indicative of the solar irradiance reaching the Earth’s surface at the given location on the given day based on the cloud cover on the given day in the given location and a ratio of the actual solar irradiance SI on the given day in the given location relative to the maximum possible solar irradiance SImaxon the given day in the given location.

9. The system of claim 8, wherein: estimating the maximum possible solar irradiance SImaxon the given day in the given location comprises averaging the periodic measurements indicative of the maximum possible solar irradiance SImaxduring each of the plurality of time periods; andestimating the actual solar irradiance SI on the given day in the given location comprises averaging the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods.

10. The system of claim 8, wherein: the periodic information indicative of cloud cover comprises cloud cover percentages CCOver during each of the plurality of time periods; and quantifying the cloud cover on the given day in the given location comprises averaging the cloud cover percentages Ccoverbetween sunrise and sunset.

11. The system of claim 10, wherein the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods are calculated according toSI = 0.8 x S x cos (ZA) x (1 - 0.95 x (0.125 x Ccoverx 8)A3.4 ) where S is the solar constant indicative of the average amount of solar radiation reaching the Earth’s upper atmosphere, ZA is the zenith angle indicative of the angle between the vertical and a line of sight to the sun at the given location during the time period, and Ccoveris the cloud cover percentage at the given location during the time period.

12. The system of claim 10, wherein the metric indicative of the solar irradiance reaching the Earth’s surface is calculated according toAB1 = AVG(Sl) / AVG(SImax) x 10 x (1 - AVG(Ccover) X 0.3) where AVG(Sl) is the average of the periodic measurements indicative of the actual solar irradiance SI during each of the plurality of time periods, AVG(SImax) is the average of the periodic estimates indicative of the maximum possible solar irradiance Slmaxduring each of the plurality of time periods, and AVG(Ccover) is the average of the cloud cover percentages during each of the plurality of time periods.

13. The system of claim 8, further comprising: adjusting the metric indicative of the solar irradiance reaching the Earth’s surface in response to a determination that the average of the periodic measurements indicative of theactual solar irradiance SI during each of the plurality of time periods is less than a predetermined threshold.

14. The system of claim 8, wherein the processor is further adapted to forecast the solar irradiance reaching the Earth’s surface on a future date.

15. Non-transitory computer readable storage media (CRSM) storing instructions that, when executed by a hardware computer processor, cause a computing system to estimate the solar irradiance reaching the Earth’s surface at a given location on a given day by: estimating periodic measurements indicative of the maximum possible solar irradiance Slmaxduring a plurality of time periods on the given day in the given location; receiving periodic information indicative of cloud cover during the plurality of time periods on the given day in the given location; calculating, based on the cloud cover during the plurality of time periods, periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods on the given day in the given location; estimating the maximum possible solar irradiance SImaxon the given day in the given location; quantifying the cloud cover on the given day in the given location; estimating the actual solar irradiance SI on the given day in the given location; and generating a metric indicative of the solar irradiance reaching the Earth’s surface at the given location on the given day based on the cloud cover on the given day in the given location and a ratio of the actual solar irradiance SI on the given day in the given location relative to the maximum possible solar irradiance SImaxon the given day in the given location.

16. The CRSM of claim 15, wherein: estimating the maximum possible solar irradiance Slmaxon the given day in the given location comprises averaging the periodic measurements indicative of the maximum possible solar irradiance SImaxduring each of the plurality of time periods; andestimating the actual solar irradiance SI on the given day in the given location comprises averaging the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods.

17. The CRSM of claim 15, wherein: the periodic information indicative of cloud cover comprises cloud cover percentages CCOver during each of the plurality of time periods; and quantifying the cloud cover on the given day in the given location comprises averaging the cloud cover percentages Ccoverbetween sunrise and sunset.

18. The CRSM of claim 17, wherein the periodic measurements indicative of the actual solar irradiance SI during each of plurality of time periods are calculated according toSI = 0.8 x S x cos (ZA) x (1 - 0.95 x (0.125 x Ccoverx 8)A3.4 ) where S is the solar constant indicative of the average amount of solar radiation reaching the Earth’s upper atmosphere, ZA is the zenith angle indicative of the angle between the vertical and a line of sight to the sun at the given location during the time period, and Ccoveris the cloud cover percentage at the given location during the time period.

19. The CRSM of claim 17, wherein the metric indicative of the solar irradiance reaching the Earth’s surface is calculated according toAB1 = AVG(Sl) / AVG(SImax) x 10 x (1 - AVG(Ccover) X 0.3) where AVG(Sl) is the average of the periodic measurements indicative of the actual solar irradiance SI during each of the plurality of time periods, AVG(SImax) is the average of the periodic estimates indicative of the maximum possible solar irradiance Slmaxduring each of the plurality of time periods, and AVG(Ccover) is the average of the cloud cover percentages during each of the plurality of time periods.

20. The method of claim 1, wherein the instructions further cause the computing system to adjust the metric indicative of the solar irradiance reaching the Earth’s surface in response to a determination that the average of the periodic measurements indicative of theactual solar irradiance SI during each of the plurality of time periods is less than a predetermined threshold.