Characterization of the solar irradiation field for the Trentino region in the Alps

Characterization of the solar irradiation field for the Trentino region in the Alps L. Laiti*, L. Giovannini and D. Zardi Atmospheric Physics Group University of Trento - Italy

outline of the talk Introduction The Solar Atlas of Trentino The radiometric network of Trentino Quality control of radiation data Homogeneity analysis of radiation data The gap-filling procedure The selected dataset Clear-sky radiation estimate All-sky radiation mapping Results clear-sky index maps Results radiation maps Comparison with available datasets Conclusions

introduction Characterization of the solar irradiation field for the Trentino region in the Alps Onset time, duration, speed and depth of thermally-driven wind systems and associated ABL structures and evolution depend on the (local) surface energy budget, i.e. on: solar irradiation terrain shape (orientation, slope, height, shadowing) ground cover land use soil moisture

introduction Characterization of the solar irradiation field for the Trentino region in the Alps conceptual model of Defant (1949) symmetric insolation symmetric slope winds + valley wind simultaneous morning/evening transitions

introduction Characterization of the solar irradiation field for the Trentino region in the Alps real valleys: asymmetric insolation, irregular valley shapes, asymmetric slope winds and cross-valley winds modified transition times Hennemuth and Schmidt (1985): cross-valley winds in the Dischma Valley

introduction Characterization of the solar irradiation field for the Trentino region in the Alps Whiteman et al. (1989): Brush Creek Valley (ASCOT program, 1984) strong spatial/temporal variability of solar irradiation on fair-weather days direct radiation dominates (orientation, shadows, slope) cross-valley winds are observed Matzinger et al. (2003): Riviera Valley (Mesoscale Alpine Programme, 1999) relevant site-to-site differences in global irradiation valley-floor vs. ridge-top contrasts and contrasts between opposite slopes are largest on valley wind days on overcast days solar irradiation variability is strongly reduced direct radiation dominates (orientation, shadows, slope)

introduction Characterization of the solar irradiation field for the Trentino region in the Alps Colette et al. (2003): modeling of the morning break-up of the nocturnal SBL in an idealized valley orographic shadowing may modify significantly the timing of the inversion break-up Rotach and Zardi (2007): in complex terrain sensible heat flux and global irradiation spatial variability are strictly associated

introduction Characterization of the solar irradiation field for the Trentino region in the Alps Leukauf et al. (2014): influence of the incoming solar radiation on the ABL of an idealized valley by means of LES with WRF model with increasing solar forcing the depth of the slope wind layer grows, the nocturnal inversion is dissolved earlier and only one double circulation cell for weak solar forcing additional cross circulation patterns appear and the vertical export of heat, moisture and momentum is reduced

introduction Characterization of the solar irradiation field for the Trentino region in the Alps the spatial and temporal distribution of solar irradiation is a key factor in the determination of the characteristics of thermally-driven winds and associated ABL structures this talk presents a characterization of the climatological spatial distribution of the solar irradiation field for the Trentino region in the Italian Alps, i.e. the Solar Atlas of Trentino

the Solar Atlas of Trentino Project: Climate Atlas of Trentino (Trentino Climate Observatory Autonomous Province of Trento) Applications: meteorology, climatology, hydrology, agriculture energy sector (PV systems, energy-efficient buildings etc.) Currently available datasets: uncertain in mountainous areas (e.g. PVGIS over the Alps, cf. Huld et al. 2012)

the Solar Atlas of Trentino Requirements: accuracy of database and results high-spatial resolution (modeling of orographic effects) Methodology: clear-sky radiation model digital terrain model (DTM) maximum irradiation estimate hourly data of horizontal global irradiation from local stations maps of monthly and annual mean horizontal global irradiation missing data estimate all-sky irradiation estimate satellite data DTM interpolation

the radiometric network of Trentino 104 active stations Meteotrentino (26) E. Mach Foundation (77) Trento University (1) 10 min 15 min 1 h global irradiation data starting from 1987 Courtesy of D. Andreis and F. Zottele

the radiometric network of Trentino spatial and temporal coverage of the database

quality control of radiation data after Journée and Bertrand (2011): physical thresholds (ESRA clear-sky radiation model; Rigollier et al. 2002) step test temporal persistence test spatial consistency test Each hourly record: error code and quality code night-time zero offset (-20 to 5 W m -2 )

homogeneity analysis of radiation data after Longman et al. (2013): based on observed-to-estimated irradiation ratio during peak hours of clear-sky days ESRA clear-sky radiation model (Rigollier et al. 2002) homogenization of inhomogeneous series

the gap-filling procedure HelioMont satellite dataset (years: 2004-2012, Δx = 2 km, Δt = 15 min; Stöckli 2013) monthly linear regressions of daily irradiation data error: 3-15% (cross-validation)

the selected dataset 25 stations 2004-2012 period monthly mean daily irradiation

clear-sky radiation estimate Modeling of clear-sky daily irradiation (= maximum irradiation) GRASS GIS r.sun module (astronomical + orographic effects; Hofierka and Suri, 2002) Input data: DTM (Δx = 200 m) 12 monthly maps of Linke turbidity factor (from observations, after Polo et al. 2009) 12 monthly maps of albedo (from WRF simulations, Δx = 2 km) Output maps: 365 daily maps of global, direct, diffuse clear-sky irradiation (Δx = 200 m) 12 monthly maps of mean global, direct, diffuse clear-sky irradiation (Δx = 200 m) 1 annual map of mean global, direct, diffuse clear-sky irradiation (Δx = 200 m)

clear-sky radiation estimate Monthly mean daily clear-sky irradiation JAN FEB MAR APR MAY JUN

all-sky radiation mapping Calculation of monthly mean clear-sky index (K c ) values @ selected stations K c = G G c

all-sky radiation mapping Mapping of monthly mean K c over the Trentino region: interpolation method? Inverse Squared Distance (ISD) Ordinary Kriging (OK; Goovaerts 1997) Residual Kriging with linear drift with height (RK; Odeh et al. 1994, 1995) Cross-validation results ass(mbe) MAE RMSE ISD OK RK ISD OK RK ISD OK RK JAN 1.42E-03 2.47E-04 1.72E-04 4.77E-02 4.60E-02 4.58E-02 5.52E-02 5.57E-02 5.52E-02 FEB 2.92E-04 4.35E-05 2.16E-05 4.50E-02 4.50E-02 4.43E-02 5.43E-02 5.54E-02 5.49E-02 MAR 9.91E-04 5.55E-11 1.37E-11 3.96E-02 3.63E-02 3.50E-02 4.97E-02 4.62E-02 4.46E-02 APR 3.72E-03 2.94E-04 3.16E-04 3.66E-02 3.71E-02 2.99E-02 5.12E-02 5.07E-02 4.02E-02 MAY 8.28E-03 1.23E-03 4.74E-08 3.77E-02 3.80E-02 2.56E-02 5.24E-02 5.15E-02 3.30E-02 JUN 9.94E-03 2.36E-03 1.13E-06 3.84E-02 3.78E-02 2.73E-02 5.58E-02 5.48E-02 3.71E-02 JUL 1.08E-02 2.22E-03 1.87E-05 3.87E-02 3.81E-02 2.72E-02 5.53E-02 5.40E-02 3.67E-02 AUG 7.18E-03 5.23E-04 8.16E-07 4.07E-02 3.89E-02 2.80E-02 5.42E-02 5.22E-02 3.55E-02 SEP 5.55E-03 1.02E-03 5.10E-04 3.50E-02 3.41E-02 2.97E-02 4.48E-02 4.37E-02 3.62E-02 OCT 2.36E-03 6.83E-11 1.82E-04 3.77E-02 3.48E-02 3.43E-02 4.41E-02 4.23E-02 4.18E-02 NOV 1.55E-03 2.11E-04 1.65E-04 4.24E-02 4.05E-02 4.01E-02 4.94E-02 4.91E-02 4.89E-02 DEC 1.35E-03 7.12E-11 4.00E-11 5.04E-02 4.68E-02 4.55E-02 5.64E-02 5.52E-02 5.44E-02

all-sky radiation mapping RK with linear drift with height: estimate of interpolation accuracy through cross-validation analysis Kc error G error (MJ m -2 ) G error (%) month MBE MAE RMSE MBE MAE RMSE MBE MAE RMSE JAN -1.72E-04 4.58E-02 5.52E-02-0.02 0.32 0.39-0.32 6.39 7.78 FEB 2.16E-05 4.43E-02 5.49E-02-0.02 0.48 0.60-0.20 6.00 7.49 MAR -1.37E-11 3.50E-02 4.46E-02-0.01 0.61 0.78-0.07 4.91 6.30 APR 3.16E-04 2.99E-02 4.02E-02 0.00 0.70 0.95-0.01 4.53 6.11 MAY -4.74E-08 2.56E-02 3.30E-02 0.00 0.70 0.91-0.01 3.58 4.67 JUN -1.13E-06 2.73E-02 3.71E-02 0.00 0.80 1.10 0.00 3.86 5.34 JUL -1.87E-05 2.72E-02 3.67E-02 0.00 0.77 1.06 0.00 3.56 4.88 AUG -8.16E-07 2.80E-02 3.55E-02 0.00 0.70 0.89-0.02 3.78 4.84 SEP -5.10E-04 2.97E-02 3.62E-02-0.02 0.56 0.69-0.11 4.04 4.92 OCT -1.82E-04 3.43E-02 4.18E-02-0.02 0.44 0.53-0.18 4.99 6.02 NOV -1.65E-04 4.01E-02 4.89E-02-0.01 0.32 0.39-0.25 5.94 7.30 DEC -4.00E-11 4.55E-02 5.44E-02-0.01 0.27 0.32-0.33 6.85 8.28 Annual mean -5.93E-05 3.44E-02 4.32E-02-0.01 0.56 0.72-0.13 4.87 6.16

results - clear-sky index maps Monthly mean daily clear-sky index JAN FEB MAR APR MAY JUN

results - clear-sky index maps Monthly mean daily clear-sky index JUL AUG SEP OCT NOV DEC

results - radiation maps Monthly mean daily irradiation (MJ m -2 ) JAN FEB MAR APR MAY JUN

results - radiation maps Monthly mean daily irradiation (MJ m -2 ) JUL AUG SEP OCT NOV DEC

results - radiation maps Mean annual irradiation (MJ m -2 )

comparison with available datasets Mean annual irradiation 1) PVGIS classic 1981-1990 Δx = 1 km (Suri et al. 2007) 2) PVGIS CMSAF 1998-2011 Δx 2 km (Huld et al. 2012) 3) HelioMont 2004-2012 Δx 2 km (Stöckli 2012) PVGIS classic PVGIS CMSAF HelioMont differences in absolute values: -30% to +30% very different spatial distributions ground-data weaknesses vs. satellite-data weaknesses HelioMont: specifically developed for complex terrain and bright surfaces MJ m -2

comparison with available datasets Mean annual irradiation 1) PVGIS classic 1981-1990 Δx = 1 km (Suri et al. 2007) 2) PVGIS CMSAF 1998-2011 Δx 2 km (Huld et al. 2012) 3) HelioMont 2004-2012 Δx 2 km (Stöckli 2012) TSA PVGIS classic (%) TSA PVGIS CMSAF (%) TSA HelioMont (%) TSA PVGIS classic TSA PVGIS CMSAF TSA HelioMont MBE (MJ m -2 ) -266.44-317.04-460.07 rmbe (%) -5.43-6.62-9.46 MAE (MJ m -2 ) 411.65 366.13 467.14 rmae (%) 8.77 7.76 9.62

comparison with available datasets Mean annual irradiation 1) PVGIS classic 1981-1990 Δx = 1 km (Suri et al. 2007) 2) PVGIS CMSAF 1998-2011 Δx 2 km (Huld et al. 2012) 3) HelioMont 2004-2012 Δx 2 km (Stöckli 2012) TSA PVGIS classic (%) TSA PVGIS CMSAF (%) TSA vs. HelioMont (%) TSA PVGIS classic TSA PVGIS CMSAF TSA HelioMont MBE (MJ m -2 ) -266.44-317.04-460.07 rmbe (%) -5.43-6.62-9.46 MAE (MJ m -2 ) 411.65 366.13 467.14 rmae (%) 8.77 7.76 9.62

comparison with available datasets Monthly mean daily irradiation HelioMont 2004-2012 Δx = 2 km Trentino Solar Atlas 2004-2012 Δx = 200 m January: TSA HelioMont (%) July: TSA HelioMont (%) spatial resolution G c under orographic shadowing conditions JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC rmbe (%) -25.94-19.53-11.58-8.83-8.93-7.42-7.08-3.09-5.41-9.91-10.78-18.66

conclusions Characterization of the solar irradiation field for the Trentino region in the Alps Results and considerations: 12 maps of monthly mean daily global irradiation on horizontal surface (Δx = 200 m) 1 map of mean annual global irradiation on horizontal surface (Δx = 200 m) interpolation accuracy 5% (better in summer months) better accuracies at valley floors and over «flat» areas comparison with other datasets highlights slight systematic underestimate Future developments different mapping methods (e.g. co-kriging with satellite data) application of decomposition models and separate mapping of D and B maps of monthly mean hourly global irradiation on horizontal surface maps for inclined surfaces (terrain slope and orientation, ) Test Reference Years comparison with WRF model high-resolution simulations

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