1. A 20-year 1-km Resolution GOES Satellite Solar Insolation – Evapotranspiration Dataset for State of Florida Water Resource Management
John Mecikalski1, Simon Paech1,
Barclay Shoemaker2, Michael Holmes2, Qinglong (Gary) Wu3
1The University of Alabama in Huntsville
2U.S. Geological Survey
3South Florida Water Management District
Florida GOES ET Science Meeting
19 October 2017
U.S. Geological Survey, Orlando, Florida

2. Considerable project support and guidance has been provided by the 5 Florida WMDs.
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3. Large spatial variability in (small-sized) cloud cover 
Large spatial variability in incoming solar radiation and ET

4. Overview
Solar insolation is an essential component in the estimation of potential and reference evapotranspiration (PET & RET)
Geostationary satellite data provides us with a seamless source for estimating solar insolation, versus ground-based instrumentation.
Our data source: GOES-8, -12, -13 and -14 satellite, 1 km & 30 min resolution visible sensor observations (~256,000 images)
2 x 2 km pixel resolution on USGS grid
Half-hourly solar insolation
Daily integrated solar insolation
Formation of PET/RET at 2 km resolution
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5. GOES Data
Considerable time and effort has gone into quality controlling the GOES data as used in this study (e.g., removing images with missing segments).
>99% of the entire 20-year dataset is readily usable.
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6. Example of Daily Integrated Product
Note scale: color magnitude differences…
Summer Winter
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7. Product Generation
Develop GOES satellite derived estimates of solar radiation from 1995 to 2016, inclusive.
Subtasks to develop the product are:
1. Retrieve and process archived GOES 1 km visible satellite imagery from to 2016, inclusive.
2. Develop half hour solar radiation estimates using the model of Diak and Gautier (1983) and Diak et al. (1996).
3. Develop calibrated, daily integrated solar radiation product with a minimum of a 2 km grid cell resolution. Use calibration methodology of Otkin et al. (2005).
5. Re-grid 2 km solar insolation to 2 km grid as provided by USGS.
6. Formation of RET and PET datasets, using weather data from objectively analyzed surface stations (Mecikalski et al. 2011).
7. Distribute PET and RET datasets via USGS data portal:
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8. Solar Insolation Accuracy
Two main influencing factors
Minimum surface albedo
Atmospheric precipitable water (PW) content
Accounting for these...
By the model during processing
Post-processing calibration
Additional factor
“Sun glint” effects. Not corrected for (complex, errors very small & only around midday during summer months).
Satellite degradation: unclear, but smaller in magnitude than PW-effects, and a bias.
Calibration
To bias-correct & adjust the data for parameters that the model does not account for
Validation
To ensure that the model performs well under varying atmospheric conditions: clear v. cloudy sky; dry versus moist atmosphere
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9. Choosing good data for comparison
Significant time & effort put into
screening WMD pyranometer data –
a “good” quality data flag was not
always a guarantee.
Data screening method
Supplied with Florida pyranometer data from the South Florida WMD
Compared pyranometer data with estimated clear-sky radiation (annual & daily average)
Applied to all WMD/Statewide data on cloud-free comparison days
Comparison with FL WMD pyranometer data, on:
Individual clear (cloud-free) days spanning the entire data period
A series of consecutive cloud-free days
Cloudy days
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10. Overall Product Accuracy
Summary of root mean square error (RMSE) (MJ m–2 day–1 and percent, %) for the daily-integrated GOES solar insolation fields over Florida, when compared to ground-based pyranometer observations.
Overall: RMSE 1.6 MJ m–2 day–1 (~9% error)
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11. Methods used for PET & RET
PET Equation
PET estimates are based on the Priestley-Taylor equation (Priestley and Taylor 1972) that estimates evaporation from an extensive wet surface under conditions of minimum advection.
λEPET is the latent heat flux equivalent to PET, λ is latent heat of vaporization (J kg–1) and E is AET (kg s–1m–2); αPT is 1.26 for potential or well-watered conditions; Δ is the slope of the saturated vapor pressure with respect to temperature; γ is the psychrometric constant; and Rn is net radiation.
RET Equation
ASCE (200) RET method, based on Penman-Monteith, following Allen et al. (1998).
T is air temperature; u2 is wind speed at 2 meters height above land surface; and es – e is the vapor pressure deficit.
Weather data derived from objectively analyzed surface stations across Florida
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12. Meteorological data
Inverse distance weighting interpolation of numerous NOAA, University of Florida (UF), and WMD weather station data to 2-km grid.
California used x y z interpolation
UF and WMD
NOAA
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13. Daily Solar Insolation (MJ m–2) 1996–2015
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14. Daily Solar Insolation (MJ m–2) 1996–2015
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15. RET (mm day–1) Results
RET data has been averaged over all Water Management Districts.
Annual maps of RET are available. The effects of various land covers and cities are apparent.
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16. Annual Mean RET & PET (mm day–1) for 2010
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17. Mean Daily PET (mm day–1): 1996–2015
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18. Annual Mean PET (mm day–1): 1996-2015
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19. P–PET (mm day–1): 1996–2011
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20. Applications
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21. Pilot Study Results
In 2015, a “Pilot Study” was conducted to perform the following:
Back-process 10 additional years of GOES insolation (~295,000 images) and ET (January 1985-June 1995) so to form a 30+ year dataset
Update the albedo information in the ET model with MODIS albedo data
Migrate to an improved weather (T, RH, wind) dataset
Prepare for 500 meter resolution GOES-16 and -17 imagery
A key aspect of the Pilot Study was to update the insolation model, moving to a “2017 Diak” insolation modeling framework (Diak 2017).
Finalize two additional science papers:
Mecikalski et al. (2017)
Shoemaker et al. (2017)
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22. Conclusions
Solar insolation is an essential component in the estimation of potential and reference evapotranspiration (PET & RET), and geostationary satellite data provides us with a seamless source for estimating solar insolation.
Data source for the 20-year Insolation dataset: GOES-8, -12, -13 and -14 satellite, 1 km & 30 min resolution visible sensor observations. ET was computed via the Priestley-Taylor algorithm.
The Pilot Study has defined:
A 31+ year insolation-ET dataset,
Updated both the insolation and ET models
See my forthcoming talk...
Florida GOES ET Science Meeting
19 October 2017
U.S. Geological Survey, Orlando, Florida

23. References
Diak, G. R., and C. Gautier, 1983: Improvements to a simple physical model for estimating insolation from GOES data. J. Climate. Appl. Meteor., 22, 505–508.
Diak, G. R., W. L. Bland, and J. Mecikalski, 1996: A note on first estimates of surface insolation from GOES-8 visible satellite data. Agric. For. Meteorol., 82, 219–226.
Otkin, J. A., M. C. Anderson, J. R. Mecikalski and G. R. Diak. 2005: Validation of GOES-based insolation estimates using data from the U.S. Climate Reference Network. J. Hydrometeorol., 6, 460–475.
Jacobs, J., J. Mecikalski, and S. Paech (2008). Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. A Technical Report prepared for the State of Florida Water Management Districts. Available online at: hdwp.er.usgs.gov/ET/ GOES_FinalReport.pdf
Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of Visible Geostationary Operational Meteorological Satellite Imagery in Mapping Reference and Potential Evapotranspiration over Florida. Evapotranspiration, Editor, Leszek Labedzki, Chapter 10, pp. 229–254.
Diak, G. D., 2017: Investigations of improvements to an operational GOES-satellite data-based insolation system using pyranometer data from the U.S. Climate Reference Network (USCRN). Rem. Sense. Environ., 195, 79– /j.rse
Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2017: A 20-year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. J. Irrigation Drainage Eng., In review.
Shoemaker, W. B., J. R. Mecikalski, Q. Wu, and M. A. Holmes, 2017: GOES potential and reference evapotranspiration in Florida – Enhancement and historical extension. J. Irrigation Drainage Eng., In preparation.
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