Solar Energy Forecast

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Validation of short and medium term operational solarradiation forecasts in the US

Richard Perez a,⇑, Sergey Kivalov a, James Schlemmer a, Karl Hemker Jr. a,David Renne b, Thomas E. Hoff c

a ASRC, University at Albany, Albany, New York, United Statesb National Renewable Energy Laboratory, Golden, Colorado, United States

c Clean Power Research, Napa, California, United States

Received 4 May 2010; received in revised form 23 August 2010; accepted 25 August 2010Available online 8 October 2010

Communicated by: Associate Editor Frank Vignola

Abstract

This paper presents a validation of the short and medium term global irradiance forecasts that are produced as part of the US Solar-Anywhere (2010) data set. The short term forecasts that extend up to 6-h ahead are based upon cloud motion derived from consecutivegeostationary satellite images. The medium term forecasts extend up to 6-days-ahead and are modeled from gridded cloud cover fore-casts from the US National Digital Forecast Database.

The forecast algorithms are validated against ground measurements for seven climatically distinct locations in the United States for1 year. An initial analysis of regional performance using satellite-derived irradiances as a benchmark reference is also presented.Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Solar resource assessment; Irradiance; Forecast; Prediction; Validation

1. Introduction

There are two basic approaches to solar radiationforecasting.

The first approach consists of numerical weather predic-tion (NWP) models. NWP models can be global – e.g.,GFS Model (2003), ECMWF (2010) – regional or local – e.g., WRF (2010). For irradiance predictions, the NWP fore-casts are inherently probabilistic because they infer localcloud formation probability (and indirectly transmittedradiation) through dynamic modeling of the atmosphere.NWP models cannot, at this stage of their development, pre-dict the exact position and extent of individual clouds orcloud fields affecting a given location’s solar resource.

The second approach consists of projecting observedsolar radiation conditions based on immediate measuredhistory: The position and impact of future clouds isinferred from their motion determined from recentobservations; these observations can be either remote(from satellites) or from appropriate ground based sky-imaging instrumentation (e.g., Slater et al., 2001). Thisapproach is initially deterministic because the initialposition of clouds affecting a solar installation is pre-cisely known.

Lorenz et al. (2007) have shown that the cloud motion-based forecasts tend to provide better results than NWPforecasts up to forecast horizons of 3–4 h, beyond whichNWP models perform better.

In this article, we evaluate the NWP-based and satellite-derived cloud motion forecast models producing the Solar-Anywhere’s hourly global horizontal irradiance (GHI)forecasts.

0038-092X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.solener.2010.08.014

⇑ Corresponding author. Address: Atmospheric Sciences ResearchCenter, The University at Albany, 251 Fuller Rd., Albany, NY 12203,USA. Tel.: +1 5184378751; fax: +1 5184378711.

E-mail address: [email protected] (R. Perez).

www.elsevier.com/locate/solener

Available online at www.sciencedirect.com

Solar Energy 84 (2010) 2161–2172

http://dx.doi.org/10.1016/j.solener.2010.08.014

mailto:[email protected]



mailto:[email protected]






Table 1Yearly and seasonal MBE metric summary.

MBE Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State

All year

Mean observed GHI 498 357 369 364 349 397 323Clearness indexa (%) 90 75 71 76 69 76 66

Satellite model error 1 À4 7 14 À3 À1 4Forecast/persistence Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst

1-h ahead 1 11 À3 8 13 20 15 7 À2 6 À5 6 À4 52-h ahead 2 18 0 12 26 36 13 11 À3 11 À8 7 À7 63-h ahead 5 20 À3 13 33 47 9 10 À3 15 À13 4 À7 54-h ahead 5 16 À5 10 36 50 3 4 À2 17 À20 À3 À4 À35-h ahead 1 3 À7 2 38 44 0 À7 À3 12 À19 À14 À2 À166-h ahead À13 À23 À6 À13 38 28 À7 À19 0 3 À28 À31 À2 À321-Day (same day) À5 13 À22 À12 À14 À33 À282-Day (next day) À10 0 12 À2 À25 1 À17 À2 À19 À3 À35 À3 À30 À13-Day À16 0 14 À2 À23 0 À18 À3 À15 À1 À46 À4 À40 À24-Day À12 À1 17 À2 À14 2 À14 À2 À14 À1 À48 À4 À35 À25-Day À7 À1 22 À3 À7 0 À8 À1 À13 À1 À51 À4 À36 À36-Day 1 À1 24 À4 À6 À2 À13 À2 À14 À1 À48 À4 À40 À37-Day

À30

À2 11

À4

À11

À2

À17

À2

À17 2

À43

À5

À48

À4

Winter

Mean observed GHI 236 159 215 160 137 189 140Clearness indexa (%) 82 51 78 77 73 72 73Satellite model error 12 À49 18 23 32 17 47

1-h ahead 11 14 À63 10 14 11 9 8 17 5 7 10 27 62-h ahead 10 23 À66 15 7 20 0 14 9 9 6 11 16 103-h ahead 11 28 À70 17 À1 24 À9 17 0 11 3 5 12 134-h ahead 13 26 À72 13 À7 23 À15 15 2 13 2 0 12 125-h ahead 12 17 À69 5 À11 12 À17 8 4 11 3 À7 21 56-h ahead 7 0 À60 À5 À23 À2 À25 À1 25 2 11 À25 18 À91-Day (same day) À13 À47 À37 À39 À15 À9 À442-Day (next day) À15 6 À34 3 À31 5 À41 5 À21 3 À13 6 À37 33-Day À20 5 À26 1 À38 8 À33 8 À7 11 À23 7 À33 6

4-Day À28 3 À22 3 À34 8 À24 12 À7 11 À26 5 À27 95-Day À30 2 À19 4 À34 10 À8 11 6 14 À25 10 À30 66-Day À30 1 À22 7 À36 8 À1 9 9 6 À16 11 À30 37-Day À31 2 À24 9 À40 6 0 8 17 8 À14 15 À37 3

Spring

Mean observed GHI 548 377 416 391 373 416 361Clearness indexa (%) 90 72 68 73 65 72 64Satellite model error 7 À3 À1 11 À2 1 3

1-h ahead 5 14 2 13 12 21 18 7 8 7 À4 4 0 82-h ahead 7 22 7 21 20 35 18 11 5 12 À13 4 1 93-h ahead 12 26 4 24 20 42 15 11 9 16 À24 À6 1 54-h ahead 11 25 0 23 18 41 6 6 6 15 À37 À19 1 À75-h ahead 5 13 À6 17 17 31 3 À5 5 9 À37 À34 1 À256-h ahead

À6

À11 4 4 4 8 2

À16 16

À2

À37

À51 7

À46

1-Day (same day) À6 10 À35 À18 À17 À63 À202-Day (next day) À15 1 0 À2 À40 5 À26 À3 À23 À12 À46 À5 À35 À43-Day À28 4 À2 À4 À46 5 À38 À8 À23 À10 À50 À10 À42 À64-Day À21 8 1 À5 À33 6 À35 À8 À28 À10 À68 À13 À44 À35-Day À18 9 3 À7 À13 5 À30 À7 À25 À3 À56 À14 À42 06-Day À9 10 9 À8 À12 10 À35 À6 À34 2 À51 À12 À47 67-Day À41 8 4 À9 À20 12 À29 À1 À49 8 À57 À8 À62 12

Summer

Mean observed GHI 617 454 432 451 443 510 403Clearness indexa (%) 90 80 70 79 70 81 66Satellite model error À7 À3 4 12 À19 À8 À15

(continued on next page)

R. Perez et al. / Solar Energy 84 (2010) 2161–2172 2163



3. Validation

Forecasts are validated against single point ground-truth stations. In addition, the ability of forecast modelsto account for local microclimatology is investigated byobserving the distribution of mean predictions overextended areas.

3.1. Single point ground-truth validation

Hourly forecasts are tested against irradiance data fromeach station of the SURFRAD network (SURFRAD,

2010) including Desert Rock, Nevada; Fort Peck, Mon-tana; Boulder, Colorado; Sioux Falls, South Dakota;Bondville, Illinois; Goodwin Creek, Mississippi; and PennState, Pennsylvania.

These stations cover several distinct climatic environ-ments ranging from arid (Desert Rock) to humid continen-tal locations (Penn State) and from locations with somesubtropical influence (Goodwin Creek) to the northernGreat Plains (Fort Peck). Boulder is a challenging sitefor all types of solar radiation models, because of its highelevation ($2000 m) and of its position at the RockyMountains’ eastern edge, at the junction between two

weather regimes.

The present validation period spans a little over 1 yearfrom August 23, 2008 to August 31, 2009.

3.1.1. Validation metrics

In addition to the standard Mean Bias and Root MeanSquare Errors (respectively RMSE and MBE) resultingfrom the direct comparison between hourly forecasts andhourly measurements, we also consider two metrics thatquantify the ability of a model to reproduce observed fre-quency distributions. The first of these is the Kolmogo-rov–Smirnov test Integral (KSI) goodness of fit test(Espinar et al., 2009) recommended by the International

Energy Agency Solar Heating and Cooling ProgrammeTask 36 for data benchmarking (Task 36, report, 2010).The KSI metric is obtained by integrating the absolute dif-ference between the measured and modeled cumulative fre-quency distributions of the considered variable asillustrated in Fig. 1. The second, termed OVER, is calcu-lated by integrating the absolute difference between themeasured distribution and the measured distribution plusor minus a buffer determined by the Kolmogorov–Smirnovcritical V

capproximated here by (NIST, 2010):

V c ¼ 1:63= ffiffiffi

np

where n is the number of consider data points.

Table 1 (continued )

MBE Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State

1-h ahead À6 9 2 3 11 21 13 7 À19 5 À8 3 À20 32-h ahead À3 15 7 4 34 40 12 7 À15 11 À9 4 À23 43-h ahead À1 13 4 4 55 58 9 3 À14 16 À12 4 À21 34-h ahead À2 5 4 À2 67 66 6 À5 À10 19 À18 1 À13 À65-h ahead

À9

À13 3

À12 76 64 3

À19

À13 14

À19

À10

À12

À20

6-h ahead À28 À49 À2 À28 68 52 À8 À30 À16 6 À41 À24 À10 À301-Day (same day) À15 18 À42 À13 À24 À21 À452-Day (next day) À22 0 23 À4 À53 2 À15 À1 À27 À4 À36 À8 À40 03-Day À24 0 29 À7 À41 4 À7 À2 À23 À5 À60 À10 À68 À14-Day À19 0 31 À7 À31 9 À3 1 À21 À5 À51 À14 À52 05-Day À8 0 39 À8 À30 8 0 3 À28 À4 À68 À14 À55 26-Day 7 0 38 À9 À35 3 À23 4 À29 0 À72 À12 À63 47-Day À46 1 10 À6 À40 2 À49 5 À35 5 À60 À12 À61 2

Fall

Mean observed GHI 406 246 291 250 265 327 253Clearness indexa (%) 90 76 72 72 70 76 69Satellite model error 0 24 15 18 6 1 11

1-h ahead 2 11 17 10 20 20 17 9 3 8

À7 11 1 4

2-h ahead 2 16 14 13 29 34 16 15 4 15 À9 13 À3 63-h ahead 3 19 10 13 32 41 11 19 6 22 À14 14 À5 54-h ahead 5 19 6 7 30 42 7 17 8 26 À15 10 À2 15-h ahead 4 7 2 À1 26 37 4 10 9 21 À13 À2 À1 À126-h ahead À14 À16 À7 À18 8 22 À7 À7 0 13 À24 À19 À7 À301-Day (same day) 20 33 41 13 10 À17 62-Day (next day) 21 0 30 À6 37 3 6 2 10 À3 À18 À6 7 33-Day 18 1 26 À7 38 4 2 2 14 À6 À21 À10 6 64-Day 22 1 31 À5 46 6 0 6 21 À5 À22 À13 13 95-Day 24 2 31 À5 51 9 15 9 27 À5 À26 À14 12 96-Day 21 4 35 À1 63 10 30 10 33 À3 À25 À13 17 127-Day 11 5 36 À3 59 5 62 13 45 0 À23 À13 3 15

a GHI/GHIclear.

2164 R. Perez et al. / Solar Energy 84 (2010) 2161–2172



Table 2Yearly and seasonal RMSE metric summary.

RMSE Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State

All year

Mean observed GHI 498 357 369 364 349 397 323Clearness indexa (%) 90 75 71 76 69 76 66

Satellite model error 77 103 112 72 87 83 89Forecast/persistence Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst

1-h ahead 80 85 94 88 120 130 68 80 85 91 80 93 86 1002-h ahead 88 109 106 118 139 167 84 106 98 122 101 123 99 1313-h ahead 96 118 123 135 154 183 102 127 112 135 114 139 113 1454-h ahead 104 123 132 145 166 193 115 142 122 150 127 154 124 1555-h ahead 116 133 138 154 175 199 126 159 132 164 134 166 129 1666-h ahead 142 160 147 168 200 207 155 178 156 177 166 181 150 1761-Day (same day) 125 148 188 140 151 149 1412-Day (next day) 139 122 145 154 189 187 155 205 161 199 164 191 152 2183-Day 142 141 142 174 188 227 165 220 167 226 176 219 174 2474-Day 147 145 140 181 191 242 170 229 178 238 177 237 179 2675-Day 147 152 151 194 203 249 176 240 184 239 178 243 179 2856-Day 141 150 162 196 206 242 186 235 196 246 185 254 188 2847-Day 169 147 172 196 212 231 198 238 200 243 193 243 196 278

Winter


1-h ahead 46 53 107 26 64 53 48 36 60 52 48 53 57 422-h ahead 48 65 105 37 71 71 58 54 66 61 59 67 57 593-h ahead 59 78 109 44 81 82 69 68 74 73 66 80 59 644-h ahead 70 84 112 50 85 87 78 80 81 81 70 87 65 675-h ahead 74 88 115 58 89 93 76 83 79 82 80 92 71 666-h ahead 85 94 117 67 108 98 96 83 102 83 110 101 90 681-Day (same day) 102 122 130 112 91 84 942-Day (next day) 114 137 98 117 117 144 100 133 105 132 99 133 94 1093-Day 107 153 93 99 125 141 111 125 88 136 121 147 111 143

4-Day 125 163 81 108 127 149 105 148 121 164 121 162 103 1485-Day 126 170 85 105 137 170 102 138 84 150 128 157 112 1456-Day 128 167 94 117 136 154 101 135 97 149 119 163 101 1417-Day 133 151 100 99 143 151 108 130 106 127 126 175 112 130

Spring



Summer


(continued on next page)




Both the KSI and OVER metrics are quantified here asfractions of the critical value.

3.1.2. Persistence benchmarking

The single site performance of the forecast models isevaluated by comparing it to measured persistence; sameday measured persistence is obtained by time extrapolationof measured irradiances using a constant KtÃindex. Nextday and multi-day persistences are obtained by extrapolat-ing the previous day’s mean daily measured KtÃ.

3.1.3. Results

Tables 1 and 2 report respectively the MBEs and RMSEsobserved at all sites for all time horizons for the forecast mod-els andpersistencebenchmarks. Results are reportedyearly aswell as seasonally. Forecasts include 1–6 h cloud motion fore-casts and same day to 6-days-ahead NDFD forecasts. Thesatellite model’s MBEs and RMSEs are also included inTables 1 and 2 as an additional performance reference.

All forecasts are validated against the same set of exper-imental values. Hence, because 6-h cloud motion forecastscannot be generated until the sun is up, the experimental“common validation denominator pool” is limited topoints 6 h after sunrise.

Fig. 2 summarizes the results of Table 2, plotting the

yearly RMSE trend for all sites and all models as a func-

tion of the forecast time horizon. The satellite model’sRMSE is included as a reference and appears as a horizon-tal line across all forecasts horizons.

Fig. 3 provides an illustrative sample of measured vs.model scatter plots at four of the seven sites, includingBondville, Boulder, Desert Rock and Goodwin Creek. Thissample includes the reference satellite model, the 1 and 3-hcloud-motion forecasts, the next day and 3-days-aheadNDFD forecasts, as well as the same time horizons forthe measured persistence benchmarks.

Tables 3 and 4 report the annual KSI and OVER Statis-tics for all sites, forecast time horizons, satellite reference,and persistence benchmarks.

3.1.4. Discussion

The NDFD–NWP forecasts analyzed here lead toresults which are largely consistent with initial evaluations(Perez et al., 2007; Remund et al., 2008). This is an impor-tant result, because these initial evaluations only covered alimited period spanning summer months only.

When considering the RMSE metric, it is remarkable toobserve that the performance of the 1 and 2-h cloudmotion forecasts is comparable to that of the satellitemodel from which they are extracted. The 1-h forecastsactually have a lower RMSE than the satellite model at

all sites but Boulder: despite the loss of deterministic infor-

Table 2 (continued )

RMSE Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State


Fall


1-h ahead 55 56 59 62 85 84 49 52 58 63 55 58 60 70

2-h ahead 62 67 67 76 97 112 54 69 68 89 66 81 71 923-h ahead 69 74 83 93 110 131 64 86 84 107 81 93 76 964-h ahead 72 78 88 103 120 142 80 100 89 116 94 109 83 1095-h ahead 83 91 92 115 129 148 90 114 97 115 102 119 91 1186-h ahead 113 111 114 130 159 150 110 134 128 116 144 132 102 1321-Day (same day) 81 100 141 86 96 116 992-Day (next day) 77 69 102 127 135 146 91 133 108 158 106 144 109 1783-Day 83 84 95 142 136 181 121 160 112 209 117 179 119 1824-Day 88 87 93 152 149 179 115 171 124 209 121 195 125 1745-Day 85 87 102 151 158 183 126 175 133 194 126 195 125 1946-Day 92 82 111 147 155 195 129 151 143 197 147 202 123 2057-Day 103 79 116 156 156 164 134 159 141 203 152 205 138 196

a GHI/GHIclear.




mation due to cloud motion, the image smoothing inherentto the forecasts – via convergence and divergence of motionvectors, and additional post-processing pixel averaging –

results in lowering the RMSE that quantify short termaccuracy. Hence, in effect, lowering the resolution of thesatellite model increases its short term accuracy. A corol-lary of this is that attempting to achieve better short termaccuracy for satellite models by increasing spatial resolu-tion might be illusory given the satellite navigation errorsand parallax uncertainties (cloud shadowing, sun/satelliteangles) if these uncertainties are not specifically addressedvia more complex models. Note that this pixel-averagingperformance improvement technique is known and hasbeen previously discussed, e.g., by Stuhlmann et al.(1990) when developing his physical satellite-to-irradiance

model.

Cloud motion forecasts are always better than persis-tence forecasts derived from actual measurements, evenafter a little as 1 h.

The break-even point between cloud motion and NDFDforecasts is between 5 and 6 h ahead. We note, however,that satellite-aided multiple output statistics (MOS) real-time feedback (e.g., see Dennstaedt, 2006), whereby inthe present case the numerical weather forecasts would becorrected from the most recent satellite-derived irradiancehistory, could improve the NDFD forecasts. Such a feed-back process has not been implemented here.

The cloud motion forecasts’ MBE is consistently small,to the exception of sites experiencing important wintersnow cover where the accuracy of the current satellitemodel, relying solely on the visible channel, is limited – a

new model proposed by the authors and utilizing the infra-

Measured persistence

Cloud-Motion forecast

NDFD

Satellite Reference

DESERT ROCK

50

100

150

200

250

300

BOULDER SIOUX FALLS

50

100

150

200

250

300

BONDVILLE GOODWIN CREEK

1-hr 2-hr 3-hr 4-hr 5-hr 6-hr 2-day 3-day 4-day 5-day 6-day 7-day

50

100

150

200

250

300

PENN STATE

FORT PECK

7-SITE COMPOSITE

1-hr 2-hr 3-hr 4-hr 5-hr 6-hr 2-day 3-day 4-day 5-day 6-day 7-day

50

100

150

200

250

300

FORECAST TIME HORIZON

R M S E ( W m - 2 )

Fig. 2. Annual RMSEs as a function of forecast time horizon.




red as well as the visible channels of eliminates such bias(Perez et al., 2010); however the model is not yet opera-

tional as of this writing.

The NDFD bias exhibits a seasonal pattern as well assite dependence: the bias is smallest for the sites that expe-

rience either little cloud cover or fast passing frontal type

Fig. 3. Hourly forecasts and persistence vs. measured GHI for 1 and 3 h ahead, next day and 3-days-ahead.




cloud cover (Western US and Great Plains). The eastern-most sites, Penn State and Goodwin Creek, where localizedcloud formation is more frequent, exhibit a tendency to neg-ative bias. The seasonal pattern shows a tendency of NDFDforecasts towards positive irradiance bias in the fall (cloudi-ness underprediction) and negative bias in the other seasons,particularly in the spring (cloudiness overprediction).Despite these shortcomings, the NDFD forecast performconsiderably better than persistence up to 6 days-ahead.

When considering KSI and OVER statistics it is not sur-prising to observe that the persistence-based forecasts tendto score better than the forecast models. For the shortterm, same day forecasts, the statistical distribution of per-sistence forecasts should be almost identical to measure-ments’ (since they are measurements themselves, albeittime-extrapolated) hence exceed the performance of thesatellite model and of its derived forecasts. One exceptionis Boulder, CO, where the very marked diurnal patternsat that site produce different statistical distributions for dif-ferent times of day and where the cloud motion provides

better results. The 1–6-days-ahead persistence forecast also

exhibit a better performance than the NDFD whenassessed via the KSI and OVER metrics. As for cloudmotion, the NDFD performance statistics deteriorate sen-sibly with the time horizon, reflecting a loss of dynamicrange for both. This is due to pixel convergence/averagingin the case of cloud motion, and likely due to the tendencyavoid extreme forecasts (clear or cloudy) as the time hori-zon increases for the NDFD models.

Fortunately, the KSI and OVER metrics are largely rel-evant to microclimatological site-characterization and arenot critical for forecast operations where the short termaccuracy (RMSE) is the key performance factor.

3.2. Extended-area validations

This validation is largely qualitative and focuses on theability of the forecast models to account for the solarresource’s microclimatic features over a given period. Thevalidation criterion is a visual evaluation of mapped solarresource computed from ongoing forecast data. Because

we do not dispose of gridded instrumentation spanning

Table 3Annual KSI metric summary.

KSI Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State All sites

Satellite model error (%) 23 19 21 30 55 43 40 33Forecast/persistence Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst

1-h ahead (%) 39 19 13 18 62 45 32 15 56 13 56 16 56 10 45 20

2-h ahead (%) 37 33 20 26 84 86 32 23 58 16 66 21 60 11 51 313-h ahead (%) 39 43 15 29 102 112 45 23 58 30 82 28 62 13 58 404-h ahead (%) 47 51 18 20 109 125 63 32 57 36 97 33 66 29 65 475-h ahead (%) 55 60 22 22 121 118 81 48 71 24 103 58 77 55 76 556-h ahead (%) 71 94 28 47 117 102 90 65 81 22 138 97 83 89 87 74% 64 61 89 43 59 108 702-Day (next day) (%) 65 64 63 55 117 104 66 49 74 67 113 117 75 72 82 763-Day (%) 69 66 65 55 123 105 70 52 75 65 139 121 99 77 92 774-Day (%) 62 65 76 58 140 106 70 51 82 64 144 122 89 82 95 785-Day (%) 61 63 87 55 145 106 86 49 93 64 156 122 97 83 104 776-Day (%) 63 63 99 54 159 107 121 53 106 58 151 121 115 82 116 777-Day (%) 67 62 104 53 161 108 137 51 121 56 146 122 134 82 124 76

Table 4Annual OVER metric summary.

OVER Desert Rock Fort Peck Boulder Sioux Falls Bondville Gdwn Creek Penn State All sites

Satellite model error (%) 0 0 0 0 6 0 3 1Forecast/persistence Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst Fcst Prst

1-h ahead (%) 5 0 0 0 30 0 0 0 5 0 5 0 1 0 8 02-h ahead (%) 10 0 0 0 60 58 10 0 10 0 10 0 0 0 14 83-h ahead (%) 6 0 0 0 84 102 15 0 5 0 50 0 0 0 23 154-h ahead (%) 15 6 0 0 95 118 17 0 10 0 67 0 2 0 29 185-h ahead (%) 19 26 0 0 102 107 58 0 36 0 84 21 8 0 44 226-h ahead (%) 48 78 0 0 97 86 65 45 51 0 122 64 10 16 56 41% 19 25 67 0 0 81 82-Day (next day) (%) 25 25 40 22 103 88 34 10 31 15 85 95 3 5 46 373-Day (%) 37 26 42 21 113 84 35 10 41 15 114 97 26 8 58 37

4-Day (%) 24 26 49 21 129 83 39 10 43 10 128 102 20 10 62 385-Day (%) 18 26 59 21 135 83 64 5 67 10 139 102 25 10 72 376-Day (%) 29 25 75 21 151 90 109 15 85 5 128 10 43 11 89 387-Day (%) 38 25 82 21 153 95 124 14 106 0 124 103 60 10 98 38




the considered areas, we rely on satellite-derived irradi-ances data as a performance benchmark.

We considered 2° Â 2° regions ($30,000 sq. km) sur-rounding each ground-truth station. The results are illus-trated by presenting the case of Boulder and Desert Rock,which have the strongest (orography-driven) microclimatic

features. Fig. 4 compares the mapped irradiances for DesertRock in summer and for Boulder in the fall, spring and year-around. The maps consistof the satellite model, the 1 and 3-hcloud motion forecasts and the next day, 3 days, and 6-days-ahead forecasts. The orographic features that influence solarresource microclimates may be seen in Fig. 5.

The NDFD model does account for orography-drivenmicroclimates, but, apparently, only when cloudinessincreases with elevation. This underlying assumption holdsin Desert Rock in summer and in the spring time in Boul-der. However in the fall of 2008 clouds preferentiallyformed immediately east of the Rocky Mountains likely

linked to the presence of easterly winds“

upslope”

cloudformation. This preferential cloud formation trend wasnot taken in account by the NDFD models.

The smoothing effect of the cloud motion tends to erasesome of the terrain features (pixel convergence andaveraging).

Desert Rock

Summer

Boulder

Fall

Boulder

Spring

Boulder

Year

S a t e l l i t e

1 - H o u r

F o r e c a s t

3 - H

o u r

F o r

e c a s t

N e x t - D a y

F o r e c a s t

3 D

a y s A h e a d

F

o r e c a s t

6 D a y s A h e a d

F o r e c a s t

520 W/m2 680 360 W/m2 500 300 W/m2 600 300 W/m2 550

Fig. 4. Long-term average GHI from in a 2° Â 2° region surrounding the sites of Boulder and Desert Rock, for the satellite model, cloud motion forecasts(1 and 3 h ahead) and NDFD forecasts (1, 3 and 6-days-ahead). Note that maps contain the same number of points for all models (i.e., slightly biased

towards afternoon conditions to accommodate the fact that 3-h cloud motion forecasts cannot be made for the first 3 h of each day).




Finally, Fig. 4 also shows the discontinuities inherent tothe NDFD process, whereby global forecasts are modifiedindependently by regional offices before being reassembledon the NDFD grid. The discontinuity at the top of the Boul-der maps (appearing as a horizontal discontinuity) marksthe boundary between two US National Weather Serviceoffices which produce a different assessment of local cloud-iness that becomes apparent over integrated time scales.

4. Conclusions

The numerical weather prediction-based irradiance fore-cast analyzed here lead to results which are consistent withour previous limited evaluations. The present validationsinclude a more diverse set of climatic environments andinclude winter months when models performance tends tobe poorer then in summer.

All the considered short-term and long-term forecastsperform significantly better than persistence from actualhigh accuracy measurements. Satellite-derived cloud

motion-based forecasting leads to a significant improve-ment over NDFD forecasts up to 5 h ahead. One-hourforecasts are on par or slightly better than the satellitemodel from which they are derived; the probable reasonis that the cloud motion methodology results in a smooth-ing of the predicted images which tends to mitigate satel-lite’s navigation and parallax uncertainties. A corollary of this maybe that the short term accuracy of satellite modelsmay not be improved significantly by increased image res-olution – of course this comment applies only to short termdata and does not pertain to long-term means and thedelineation of solar microclimates, where high resolution

would be beneficial.

Acknowledgements

The forecast modeling capability was developed as partof the construction of SolarAnywhereÒ under fundingfrom Clean Power Research. The present validation analy-sis performed under funding from NREL (ContractAEK98833801). The first author of this paper and his teamat the University at Albany receives funding from Clean

Power Research to develop and produce the Solar Any-where solar resource satellite and forecast data evaluatedin this paper.

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Fig. 5. Illustration of the orographic features in the regions analyzed in Fig. 4.


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Glossary

ECMWF: European Center for Medium-Range Weather Forecasts

GFS: NOAA’S Global Forecasting SystemGHI: global horizontal irradianceGHI clear: Clear Sky Global Horizontal Irradiance

KSI: Kolmogorov–Smirnov test integralKtÃ: GHI index equal to GHI/GHIclearMBE: Mean Bias ErrorMOS: Model Output Statistics

NDFD: National Digital Forecast Data BaseNOAA: National Oceanic and Atmospheric Administration

NWP: numerical weather predictionOVER: that part of the KSI which integrates above (over) the critical

value V c

RMSE: Root Mean Square Error

V c : Kolmogorov–Smirnov critical valueWRF: Weather Research Forecasting model


http://www.solaranywhere.com/


http://www.wrf-model.org/






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Solar Energy Forecast