Improved estimation of diffuse photosynthetically active radiation using two spectral models

Agricultural and Forest Meteorology 111 (2002) 1–12

Improved estimation of diffuse photosynthetically active radiation using two spectral models I. Alados a , I. Foyo-Moreno b , F.J. Olmo b , L. Alados-Arboledas b,∗ , Grupo de F´ısica de la Atmósfera b

a Departamento de F´ısica Aplicada II, Universidad de Málaga, Málaga, Spain Departamento de F´ısica Aplicada, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain

Received 23 May 2001; received in revised form 22 January 2002; accepted 1 February 2002

Abstract The efficiency with which incoming photosynthetically active radiation (PAR) is intercepted by a canopy depends on the proportions of diffuse and direct radiation in the incoming PAR. In the present work, we developed a comparison between two spectral cloudless sky parameterization schemes in terms of their capability to provide accurate estimates of global, diffuse and direct incoming PAR. Both methods provide an estimation of the solar spectral irradiance that can be integrated spectrally within the limits of interest. For this purpose, data recorded at two radiometric stations one located at Granada, an inland location, and the other at Almer´ıa, a coastal location, were used. After our study, it appears that the information concerning the aerosol radiative effects is fundamental to obtain a good estimation, especially for the direct and diffuse components. In this sense SMARTS2 model offers increased flexibility concerning the selection of different aerosol models included in the code. The original version of SPCTRAL2 code does not offer this flexibility but in this work a modified version that allows the selection between several aerosol models was considered. Our results show that a rather accurate estimation of the global component can be obtained for any of the aerosol models tested in this work, thus indicating the lower influence of the scattering processes in this term. In our analyses, we considered different aerosol models compatible with the features of each one of the analyzed locations. The a priori choice of the aerosol models dictated by the features of each place provided estimation of the different components of PAR with mean bias deviation (MBD) smaller than the experimental error. This is a clear advantage over the simpler broadband parametric models that need local information of all the relevant aerosol properties and some ad hoc modifications to provide accurate estimates of the PAR components. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Photosynthetically active radiation; Solar irradiance; Direct irradiance; Diffuse irradiance; Spectral parametric models; Estimation model

1. Introduction Modelling of plant photosynthesis requires a knowledge of the incident photosynthetically active radiation (PAR) (400–700 nm), Qp , defined as the ∗ Corresponding author. Tel.: +34-958-244024; fax: +34-958-243214. E-mail address: [email protected] (L. Alados-Arboledas).

photon flux density, i.e. the number of photons in the 400–700 nm waveband incident per unit time on a unit surface (1 ␮mol photons m−2 s−1 = 6.022 × 1017 photons m−2 s−1 = 1 ␮E m−2 s−1 ). The efficiency with which incoming PAR is intercepted by a canopy, depends on its efficiency to intercept direct and diffuse incoming radiation and on the proportions of diffuse, Qpd and direct, Qpb photosynthetic photon flux densities. This is specially true for isolated plants

0168-1923/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 8 - 1 9 2 3 ( 0 2 ) 0 0 0 1 0 - 2

2

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

that can be treated as cylinders, spheres, etc. so that intercepted radiation must be estimated from measurements of direct and diffuse components (Van der Hage, 1993, 1995; Berninger, 1994). These radiative fluxes are seldom measured in extended networks and thus they are usually estimated from available information. Models of different complexity has been proposed including empirical models (Alados and Alados-Arboledas, 1999a,b), broadband parametric models (Gueymard, 1989a,b) and spectral parametric models (Bird and Riordan, 1986; Gueymard, 1995). In previous studies, we described the use of parametric models, based on a single spectral band parameterization, to estimate the global photosynthetic photon flux density, Qpt , and its direct, Qpb and diffuse, Qpd , components under cloudless conditions (AladosArboledas et al., 2000) and under cloudy skies (Alados et al., 2000). These models give good estimates of the global and direct fluxes while the diffuse component is estimated with a larger error. The estimation of the diffuse component presents some difficulties due to the complexities in the parameterization of the scattering processes. Thus, Alados-Arboledas et al. (2000) addressed the modification of some broadband models to improve the estimation of the diffuse component of PAR. The relative success obtained suggested the convenience of testing the performance of spectral models. Spectral parametric models provide an estimate of the different components of solar spectral irradiance and by spectral integration photosynthetic photon flux density under cloudless conditions can be obtained. Two examples are SPCTRAL2 (Bird and Riordan, 1986) and SMARTS2 (Gueymard, 1995). Both models use a parameterization of the radiative processes of the atmosphere and provide an estimation of the spectral solar irradiance in the complete solar spectrum. The required input parameters are similar to those included in the broadband models, but their parameterizations of the radiative processes, specially those associated to the scattering processes, directly related to the diffuse irradiance, are more sophisticated. Concerning the input parameters, it is very important that the models use readily accessible standard meteorological information. In our study, the tested models require information concerning the aerosol contribution to the extinction of the solar radiation in the atmosphere. This requires an evaluation of the aerosol load at a given time and knowledge on the optical

properties of the aerosol prevailing in the study area. These two requirements are common to broadband and spectral parametric models. In order to address the aerosol load different turbidity coefficients can be used, but they are not measured in standard meteorological networks. For this purpose some procedures were used in order to derive this information from measurements of broadband solar irradiance, which is measured in extended networks around the world or can be derived from satellite data. On the other hand, the aerosol optical properties must be determined or an appropriate aerosol model must be selected considering the study area features. In spite of their greater complexity these spectral models can be run in a personal computer with short time consumption. Then they appear as an interesting alternative specially when a good estimate of the diffuse component is needed. The spectral distribution of solar irradiance under cloudless skies largely depends on atmospheric aerosol loading. Then the way the aerosol radiative effects are included in the estimation model, largely influences the quality of the estimates. In this sense, spectral parametric models include more complex parameterization of the scattering processes than the simpler broadband model. This paper compares estimates of direct, diffuse and global PAR calculated using the spectral models SPCTRAL2 and SMARTS2 with measurements made at two Spanish location with different climates: Granada, an inland location and Almer´ıa, a coastal Mediterranean location. The goal is to analyze the performance of the models in estimating the different PAR components under different assumptions for the aerosol properties. Then, we test different aerosol models included in the analyzed spectral codes, as in situ information on the aerosol properties is not usually available. Special interest were paid to the discussion of the accuracy of the estimates using the a priori choice of the aerosol models based on the location features.

2. Data and measurements The data set used in this study came from two radiometric stations. The first one is located in the outskirts of Granada (37.18◦ N, 3.58◦ W, 660 m a.s.l), an inland location. Granada is located in the south-eastern of the

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

Iberian Peninsula. Cool winters and hot summers characterise its inland location. Their diurnal temperature range is rather wide with the possibility of freezing on winter nights. Most rainfall occurs in spring and winter. The summer is very dry, with scarce rainfall in July and August. A second station is located at the University of Almer´ıa, a seashore location (36.83◦ N, 2.41◦ W, 10 m a.s.l.). The Almer´ıa radiometric station is located on the Mediterranean coast in south-eastern Spain and is characterized by a greater frequency of cloudless days and high humidity. Data collected at Granada at 1 min intervals during 1994 and 1995 has been used in the present study. At Almer´ıa the measurements cover the period 1993 and 1994, being registered as 5 min values. Solar global irradiance, Rst , was measured using a Kipp and Zonen model CM-11 radiometer (Delft, Netherlands), while a similar instrument with a polar axis shadowband was used to measure solar diffuse irradiance, Rd . Similarly, global and diffuse photosynthetic active photon flux density, Qpt and Qpd , were measured using LICOR model 190 SA quantum sensors (Lincoln, Nebraska, USA). Air temperature and relative humidity at 1.5 m were also recorded. Shadowband errors were corrected following the method proposed by Batlles et al. (1995). Hourly average values were calculated from the measured values at both locations. Normal incidence direct beam components, both for the solar broadband irradiance, Rb , and the photosynthetically active photon flux density, Qpb , were estimated from: Rb =

Rst − Rd cos θz

Qpb =

Qpt − Qpd cos θz

(1) (2)

where θ z is the solar zenith angle. Considering the period used, a complete range of seasonal conditions and solar angles is included among the samples. Analytical checks, for measurement consistency, were carried out to eliminate problems associated with shadowband misalignments and other instrumental errors. Due to cosine response problems, we limited our studies to cases with solar zenith angle less than 85◦ . The calibration constants of the radiometric instruments were checked periodically. The Quantum sensors were calibrated using a calibrated standard lamp and by field comparison

3

a well-calibrated field spectroradiometer (LI-1800). Measurements of solar global and diffuse irradiance have an estimated experimental error of about 2–3%, while the quantum sensor has a relative error less than 5%, relative to the measured values. 3. Description of models 3.1. SPCTRAL2 model The SPCTRAL2 model used in this study is the spectral model described by Bird and Riordan (1986). The model uses the extraterrestrial spectral irradiance compiled by Fröhlich and Wehrly (1981) of the World Radiation Center, with a 10 nm resolution for 122 wavelengths in the range 300–4000 nm. The required input parameters are local geographic coordinates, atmospheric pressure, precipitable atmospheric water vapor, surface albedo, ozone columnar content and aerosol information. The aerosol information required by the model is the aerosol optical depth at 500 nm; the model uses fixed values for the rest of optical features of the aerosol as the Angstrom’s exponent α or the single scattering albedo. In this sense, we can say that the model includes its own aerosol model. The SPCTRAL2 code includes some modifications proposed by Bird and Riordan (1986) in order to reduce the overestimation in the visible and ultraviolet spectral ranges. These changes also affect to the diffuse component. Recently Boscá et al. (1997) modified SPCTRAL2 code by allowing the use of a set of aerosol models. The models available are maritime–rural– clear (MRC); mean rural (MRURAL); rural–urban (RURBAN); mean urban (MURBAN) and polluted urban (PURBAN). Utrillas et al. (1998) checked the complete modified version of this model with spectral data. In our study, we considered the convenience of introducing additional flexibility to the SPCTRAL2 code, allowing the selection among different aerosol models. Thus, the aerosol models described by Boscá et al. (1997) were included as possible choices in the code. As the name of this aerosol models suggest each one is considered representative of the type of aerosols prevailing in urban areas with different degrees of pollution, PURBAN and MURBAN, in rural areas, MRURAL, or in areas located in the interface

4

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

between rural and urban locations, RURBAN, finally MRC correspond to areas away from important sources of aerosols, both of urban or rural origin. It is interesting to note that the greater the urban contribution the lower the single scattering albedo, i.e. there is an increase of the aerosol absorption. For the locations considered in this study the a priori choice dictated by their corresponding features were MURBAN for Granada and RURBAN for Almer´ıa. Our analyses will show the success of these choices. 3.2. SMARTS2 model The model Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS2) was first proposed by Gueymard (1993) and the version used here was that described by Gueymard (1995). The model calculates the direct beam and diffuse radiation components using separate parameterization of the various extinction processes (gaseous absorption, aerosol and molecule scattering) affecting the transfer of short-wave radiation in a cloudless atmosphere. The solar extraterrestrial spectrum used in the model covers the wavelength range between 280 and 4020 nm with a resolution of 1 nm. In this sense, this model uses more accurate spectral information than SPCTRAL2. The model allows the use of nine different built-in aerosol models or models defined by the user. There are two models proposed by Braslau and Dave (1973), B&DC and B&DCL. Additionally, there are four models proposed by Shettle and Fenn (1979) that depend on the relative humidity: maritime (MAR), rural (RURAL), urban (URBAN) and tropospheric (TROPO). Finally, the last three models correspond to standard atmospheres (WMO, 1986), SCONT (continental), SMAR (maritime) and SURBAN (urban). This feature makes this model more flexible than the original version of SPCTRAL2. Nevertheless, as we decided to use the modified version of SPCTRAL2 suggested by Boscá et al. (1997), both spectral models provide the capability to select an aerosol model for the computations. Anyway, SMARTS2 includes a greater number of aerosol models, some of them with a dependence on relative humidity conditions. As in the case of the SPCTRAL2 model the required input parameters are local geographic coordinates, atmospheric pressure, precipitable atmospheric

water vapor, surface albedo, ozone columnar content and aerosol information in terms of the Angstrom turbidity coefficient, β, defined as δA (λ) = βλ−α

(3)

where α is the Angstrom exponent that defines the spectral dependence of the aerosol optical depth, δ A . The aerosol models included in SMARTS2 have two average values of Angstrom’s wavelength exponent: α 1 and α 2 , for wavebands separated by 500 nm, respectively, thus the Angstrom’s exponent α is the average value. This is another differential feature of SMARTS2 when compared with the aerosol models included in SPCTRAL2.

4. Performance of models SPCTRAL2 model requires as input the aerosol optical depth at 500 nm, while SMARTS2 uses the Angstrom coefficient, β. Both turbidity parameters were estimated from global and diffuse broadband measurements of solar irradiance and meteorological variables with the procedure described by Gueymard (1998). These aerosols parameters present associated errors up to 10%. This maximum error implies a relative error in the models output of a 1.5% in SPCTRAL2 and a 2.4% in SMARTS2. The procedure followed in the determination of the aerosol input information of both models guarantees the accessibility of the variables required for the estimation of the aerosol load. The precipitable water can be determined by different methods, being possible to use climatological averages or empirical equations from surface data of temperature and humidity. In this paper the precipitable water was obtained from the meteorological data acquired at surface level following the model proposed by Won (1977). The ozone optical depth was computed using the determinations of total ozone columnar concentration performed at El Arenosillo (37.1◦ N, 6.7◦ E, 17 m a.s.l.) by the Instituto Nacional de Técnica Aeroespacial, INTA, with the Dobson instrument 120 (Global Ozone Observing System Station 213). Considering the features of the surface in the surroundings of both radiometric stations, we selected a surface albedo of 0.15 (Alados and Alados-Arboledas, 1999b).

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

5

Table 1 Statistical results for SPCTRAL2 using the SPCTRAL2 default and mean urban aerosol models for Granada Aerosol model

Components of PAR

b

R2

MBD (%)

RMSD (%)

SPCTRAL2 default SPCTRAL2 default SPCTRAL2 default Mean urban Mean urban Mean urban

Global Direct Diffuse Global Direct Diffuse

0.906 0.966 0.761 0.968 0.973 0.988

0.872 0.904 0.697 0.846 0.904 0.796

−9.8 −3.7 −19.5 −3.5 −3.0 2.4

13.0 7.7 30.4 7.4 7.2 17.4

The coefficient b represents the slope of the linear fit forcing the intercept through zero. R2 is the determination coefficient of the linear fit of estimated vs. measured. Mean Bias Deviation, MBD and root mean square deviation, RMSD, expressed as percentage of mean experimental value. Mean experimental values for global PAR 1504 ␮E m−2 s−1 , for direct PAR 1381 ␮E m−2 s−1 , for diffuse PAR 410 ␮E m−2 s−1 . Total number of hourly data, N = 1167.

The performance of the models was evaluated using the root mean square deviation (RMSD) and the mean bias deviation (MBD). These statistics allow for the detection of both the differences between experimental data and model estimates and the existence of systematic under- or overestimation tendencies, respectively. Linear regression between estimated and measured values was also computed. The linear fitting was forced through zero, thus the slope, b, provides information about the relative under- or overestimation associated with the model. The determination coefficient, R2 , gives an evaluation of the experimental data variance explained by the model. For the selection of cloudless sky conditions, we used the meteorological observations that the cloud amount is zero octas. Nevertheless, the cloud observations performed by the Spanish Meteorological Service, at the radiometric station site in the case of Granada and at a near-by meteorological station at Almer´ıa, are not registered on an hourly basis. Thus, in order to enlarge the data base of cloudless sky data we applied a cloudless skies criterion developed in a previous work (Alados-Arboledas et al., 2000). The procedure is based on the analysis of the cloudless sky values of the hemispherical broadband transmittance, kt , defined as the ratio of measured broadband global radiation to the theoretical radiation on a horizontal surface at the top of the atmosphere. This hemispherical broadband radiation is influenced both by aerosol and clouds, the latter being the greater influence. The criterion developed (Alados-Arboledas et al., 2000) states that cloudless conditions correspond to those cases satisfying that kt > 0.53 + 0.31 cos θz − 0.15 cos 2θz

(4)

where θ z is the sun zenith angle. In this way, we increased the number of cases that can be used in the evaluation of the performance of both models. 4.1. SPCTRAL2 Tables 1 and 2 show the RMSD, MBD, b and R2 for global, direct and diffuse components of PAR, for Granada and Almer´ıa, respectively. The SPCTRAL2 model using the built-in aerosol model tends to underestimate the global component of PAR. This is a result of the underestimation associated with the estimates of both the direct and diffuse components of PAR, being especially high for the diffuse component, which is obtained with a MBD close to −20% at Granada and greater than this value at Almer´ıa. Only the underestimate for the direct component is smaller than the associated experimental error. The magnitudes of the RMSD associated to the estimation of each component are rather high, especially for the diffuse component. Tables 1 and 2 also show the results obtained for SPCTRAL2 code using other aerosol models. At each location, we present the results for those models that can be considered representative of the prevailing aerosols on the base of the local features. Thus, at Granada we present the results for the aerosol model MURBAN considering that the radiometric station is located in the outskirts of a medium size non-industrialized city. On the other hand, at Almer´ıa we present the results for the same model MURBAN but considering that the radiometric station at the University Campus is located in a rural area some kilometers away from the city, the results for the model RURBAN are also presented. It is evident that at both places MURBAN model gave better results than the

6

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

Table 2 Statistical results for SPCTRAL2 using the default SPCTRAL2, rural–urban and mean urban aerosol models for Almer´ıa Aerosol model

Components of PAR

b

R2

MBD (%)

RMSD (%)

SPCTRAL2 default SPCTRAL2 default SPCTRAL2 default Rural–urban Rural–urban Rural–urban Mean urban Mean urban Mean urban

Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse

0.906 0.995 0.692 0.965 0.967 1.023 0.957 1.001 0.885

0.878 0.899 0.845 0.953 0.899 0.887 0.943 0.897 0.885

−9.6 −0.8 −26.5 −3.6 −3.8 5.0 −4.4 −0.1 −8.2

12.8 7.2 36.3 6.4 8.5 15.4 7.2 7.1 18.6

The coefficient b represents the slope of the linear fit forcing the intercept through zero. R2 is the determination coefficient of the linear fit of estimated vs. measured. Mean Bias Deviation, MBD and root mean square deviation, RMSD, expressed as percentage of mean experimental value. Mean experimental values for global PAR 1546 ␮E m−2 s−1 , for direct PAR 1447 ␮E m−2 s−1 , for diffuse PAR 414 ␮E m−2 s−1 . Total number of hourly data, N = 2268.

built-in aerosol model, with MBD values smaller than the experimental error, except for the diffuse component at Almer´ıa. The use of RURBAN model at Almer´ıa provided a better global performance, being all the fluxes estimated with deviation from the experimental value smaller than the experimental error. It is interesting to note that for all the aerosol models added to SPCTRAL2 code, excluding PURBAN, the MBD for the global photosynthetic photon density flux were smaller than the experimental error, thus indicating the slight dependence of global PAR on the aerosol features. Fig. 1 shows the scatter plot for the three components of PAR estimated with the model SPCTRAL2MURBAN at Granada. On the other hand, Fig. 2 presents the scatter plot of estimated versus measured values for the model SPCTRAL2-RURBAN at Almer´ıa. These scatter plots show the difficulties in estimating the diffuse component, that present in the corresponding figures a greater spreading than the other components of PAR. Nevertheless, the experimental points are located along the line 1:1 of perfect fit, thus suggesting that there is not an special trend to over- or underestimate in a given range of values. The analysis of the residuals shows that error trends were not dependent on solar elevation or aerosol load. This result can be considered as an improvement of the spectral models over the broadband parametric models that shown for the diffuse component a negligible MBD, but marked overestimation–underestimation for the lower–higher range of values (Alados-Arboledas et al., 2000).

4.2. SMARTS2 As mentioned above, this model permits the choice among nine aerosol models. As in the case of SPCTRAL2 we evaluated SMARTS2 at Granada and Almer´ıa using those aerosols models representing the aerosol features prevailing at each location. In this case for Granada, we considered the models URBAN and SURBAN, additionally we considered SCONT and TROPO. On the other hand, for Almer´ıa we considered the model RURBAN and additionally TROPO, MAR and SMAR. Tables 3 and 4 present the results obtained in estimating the different components of PAR at each analyzed place. At Granada, we can see that the model URBAN with aerosol optical properties dependent on relative humidity, gave better results than SURBAN. The results for the model SCONT suggest that this model is not appropriate for the area. The model TROPO provided the best global results, including the smaller MBD and RMSD for the diffuse component. A common feature of these results is that the global component is estimated with MBD slightly greater than the experimental error, also in the case of the model with the smallest MBD for the diffuse component. This is a result of the coincidence in the sign of MBD both for the diffuse and direct component for all the models. In this sense we found the opposite situation to that encountered in the analyses of SPCTRAL2 model, where the reduced MBD for the global component was due to the compensation of the opposite signs of MBD for the direct and diffuse components. At Almer´ıa, we

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

Fig. 1. Scatter plot of estimated vs. measured values using model SPCTRAL2 with the mean urban aerosol model for Granada: (a) global photosynthetic photon flux density, Qpt ; (b) direct photosynthetic photon flux density, Qpb ; (c) diffuse photosynthetic photon flux density, Qpd .

7

Fig. 2. Scatter plot of estimated vs. measured values using model SPCTRAL2 with rural–urban aerosol model for Almer´ıa: (a) global photosynthetic photon flux density, Qpt ; (b) direct photosynthetic photon flux density, Qpb ; (c) diffuse photosynthetic photon flux density, Qpd .

8

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

Table 3 Statistical results for SMARTS2 using tropospheric, urban and standard continental and urban, local A and local B aerosol models for Granada Aerosol model

Components of PAR

b

R2

MBD (%)

RMSD (%)

Tropospheric Tropospheric Tropospheric Urban Urban Urban Standard continental Standard continental Standard continental Standard urban Standard urban Standard urban Local model A Local model A Local model A Local model B Local model B Local model B

Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse

0.940 1.027 1.119 0.933 0.947 0.855 1.051 0.912 1.390 0.932 0.958 0.820 0.971 0.954 0.981 0.967 0.938 1.002

0.949 0.901 0.823 0.874 0.891 0.724 0.927 0.889 0.745 0.874 0.893 0.692 0.906 0.893 0.741 0.899 0.889 0.728

6.0 2.9 4.5 −7.0 −5.7 −10.2 5.1 −9.6 44.4 −7.2 −4.6 −13.7 −2.9 −4.3 1.5 −3.6 −6.7 4.6

8.3 6.9 22.0 11.0 9.3 23.4 8.4 12.5 49.5 11.2 8.5 26.1 7.9 8.4 19.2 8.5 10.1 20.2

Same notation as in Table 1.

Nevertheless, the model SMAR provided the best results with negligible MBD. Summing up this results, we can say that the model TROPO can provide estimates for all the components with MBD smaller than the experimental error, excluding the global component at Granada. In any case, this could be considered the model with the best global results for Granada. At Almer´ıa, the use of the model SMAR provides

tested the aerosol models RURAL, MAR, SMAR and TROPO. The model RURAL provided estimates with MBD lower than the experimental error for the direct and global components, while the diffuse component presents a MBD slightly greater than the experimental error. As at Granada the model TROPO provides a good global performance with MBD smaller than the experimental error for all the components of PAR.

Table 4 Statistical results for SMARTS2 using using tropospheric, rural, maritime and standard maritime aerosol models for Almer´ıa Model

Components of PAR

b

R2

Maritime Maritime Maritime Rural Rural Rural Tropospheric Tropospheric Tropospheric Standard maritime Standard maritime Standard maritime

Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse Global Direct Diffuse

1.036 0.992 1.170 1.028 1.018 1.056 1.037 1.041 1.019 1.009 1.015 0.987

0.958 0.867 0.887 0.958 0.865 0.893 0.966 0.869 0.897 0.947 0.865 0.885

Same notation as in Table 2.

MBD (%) 3.9 −1.3 17.4 3.0 1.5 6.3 4.0 4.2 2.4 1.2 1.5 −0.3

RMSD (%) 6.3 9.2 25.6 5.7 8.4 16.5 5.9 8.7 14.7 5.3 7.1 14.1

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

slightly better results than TROPO. This could be a result of the maritime influence in this coastal area. Nevertheless, the results obtained with the best choice of aerosol model for SMARTS2 are slightly worse than that obtained with the SPCTRAL2 and the corresponding choice of aerosol model. Thus, it is evident that an appropriate choice of the aerosol model is needed if the interest is the estimation of the direct and diffuse photosynthetic photon density flux. For this reason, the SMARTS2 model using a user defined aerosol model was also tested at Granada. The aerosol properties were defined according to the results of a previous study at Granada (Alados-Arboledas et al., 2000, Sánchez-Oliveros, 2000). Thus, we define model A as SMARTS2 code using a single scattering albedo (ω0 ) of 0.75 (Alados-Arboledas et al., 2000; Sánchez-Oliveros, 2000), a α value of 1.3 and an aerosol asymmetry factor, g, of 0.7, all appropriate for Granada (Sánchez-Oliveros, 2000). Model B was similar to model A except for the selection of an Angstrom exponent α dependent on relative humidity (Alados-Arboledas et al., 2000). Table 3 shows the results for the user defined models A and B. The MBD for the three components of photosynthetic photon flux density for both models was smaller than the experimental error. It is interesting to note that the rather small magnitude of MBD encountered for the global component was a result of the compensation of the underestimation of the direct components and the overestimation of the diffuse component. This confirms the convenience of the SMARTS2 code, but evidences the necessity of accurate information on the prevailing atmospheric aerosols, if the goal is to obtain estimates of direct and diffuse photosynthetic photon density flux with a high confidence level. Large values of RMSD indicate the difficulty in estimating the diffuse component of photosynthetically active photon flux density. These difficulties also apply to other spectral ranges (Batlles et al., 2000; Olmo et al., 2001). In spite of its additional complexity, model B does not give better estimates than model A. Thus suggesting that the use of α variable with the relative humidity does not represent an improvement over the use of a fixed value of the Angstrom parameter, α. Fig. 3 shows the scatter plot for the three components of the photosynthetically active photon flux density estimated according to the model SMARTS2-A.

9

Fig. 3. Scatter plot of estimated vs. measured values using model SMARTS2 with the aerosol model A (single scattering albedo, ω0 of 0.75, Ansgström exponent, α of 1.3 and aerosol asymmetry factor, g of 0.7) for Granada: (a) global photosynthetic photon flux density, Qpt ; (b) direct photosynthetic photon flux density, Qpb ; (c) diffuse photosynthetic photon flux density, Qpd .

10

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

For the direct and global components, we can see the closeness to the line 1:1 of perfect fit, while the diffuse component presents a higher spreading along this line and a clear underestimation tendency for the higher range of values. For Almer´ıa, Fig. 4 shows the results for the model SMAR, which is the one providing the best results at this location. Similar comments can be drawn from these figures, specially for the diffuse component.

5. Discussion

Fig. 4. Scatter plot of estimated vs. measured values using model SMARTS2 with standard maritime aerosol model for Almer´ıa: (a) global photosynthetic photon flux density, Qpt ; (b) direct photosynthetic photon flux density, Qpb ; (c) diffuse photosynthetic photon flux density, Qpd .

Our results show the necessity of an appropriate choice of the aerosol model in order to get accurate estimates for the direct and specially the diffuse component of PAR. This choice is not so important for the appropriate estimation of the global component. The use of aerosol models allows to obtain accurate estimates of the different components of PAR without any previous fit of the model to local conditions. Thus in the case of SPCTRAL2, the a priori selection of the aerosol model considering the location features gave the best results at both places. In any case the results are better than the experimental error. These results are similar to those found with broadband models specially fitted to the local aerosol properties (Alados-Arboledas et al., 2000). The main advantage for the spectral model tested is that it uses a prescribed aerosol model instead of the measured aerosol properties, which are not always available. The results obtained with SMARTS2 shows some problems with the parameterization of the diffuse irradiance in the higher range of values. These problems are reduced in comparison with the different overestimation–underestimation trends encountered for the broadband model over the complete range of values (Alados-Arboledas et al., 2000). The selection of a given aerosol model influences the estimation of the direct and diffuse components and through these affects also the estimation of the global component. In a previous study, Foyo-Moreno et al. (2000) showed that for the global component of UV (290–385 nm) this influence is greater than that encountered for the global component of photosynthetic photon density flux. This is a result of the greater percentage of the diffuse component in the global UV irradiance.

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

6. Concluding remarks We compared the use of two spectral models for calculating the photosynthetically active density flux against carefully measured data for an inland midlatitude radiometric station. Both models are developed to parameterize the different radiative processes that affect to the solar radiation in its pass through the atmosphere. In its original version, the SPCTRAL2 code did not allow for a choice of different aerosol models. The use of different aerosol models improved the estimates of the original version. The model MURBAN, with a set of aerosol parameters similar to those derived for our area in an independent study, gave acceptable estimates for all the photosynthetic photon density flux components at Granada. While the model RURBAN provided the best results at Almer´ıa. SMARTS2 code has a higher flexibility than SPCTRAL2 and allows the selection of different aerosol models and the use of standard models of the atmosphere if in situ meteorological information is not available. We tested the model using available meteorological information and using the different aerosol models. Although several models provide good estimates of the global component, at Granada the best results were given by the user supplied models describing local aerosol conditions, models A and B. At Almer´ıa the model SMAR provided the best results. At both places the model TROPO provided rather appropriate global results. Our results show that both spectral codes SPCTRAL2 and SMARTS2 could provide appropriate estimates of the different components of the photosynthetic photon density flux using an appropriate aerosol model. Although we have tested their performance at two different places, the usefulness of the models in other circumstances/locations should be discussed. An important question is the effect of uncertainty in assessing the correct aerosol model to use on the accuracy of the estimation of the components of PAR when there is no a priori local information on the aerosol properties. Our results indicate that the a priori choice among the aerosol models included in SPCTRAL2 has leaded to estimates with MBD smaller than the experimental error. The estimate of the diffuse component showed a better agreement to the experimental values than that obtained using a broadband

11

model that uses local information on the aerosol properties.

Acknowledgements This work was supported by La Dirección General de Ciencia y Tecnolog´ıa from the Education and Research Spanish Ministry through the project no: CLI1999-0835-C02-01. We are very grateful to the Armilla Air Base Meteorological Office Staff and especially to Guillermo Ballester Valor, Meteorologist Chief of the Meteorological Office for the maintenance of the radiometric devices. The Instituto Nacional de Meteorolog´ıa kindly provided the cloud observation information for the two-radiometric stations. The ozone values required for both models were kindly provided by INTA (Instituto Nacional de Técnica Aeroespacial). We are very grateful to Dr. Christian Gueymard that has kindly provided the SMARTS2 code. The authors are indebted to the Regional Editor Dr. J.B. Stewart and the anonymous referees who read the manuscript and made valuable suggestions. References Alados, I., Alados-Arboledas, L., 1999a. Direct and diffuse photosynthetically active radiation: measurements and modelling. Agric. Forest Meteorol. 93, 27–38. Alados, I., Alados-Arboledas, L., 1999b. Validation of an empirical model for photosynthetically active radiation. Part I. J. Climatol. 19, 1145–1152. Alados-Arboledas, L., Olmo, F.J., Alados, I., Pérez, M., 2000. Parametric modelling of photosynthetically active radiation in Spain. Agric. Forest Meteorol. 101, 187–201. Alados, I., Olmo, F.J., Foyo-Moreno, I., Alados-Arboledas, L., 2000. Estimation of photosynthetically active radiation under cloudy conditions. Agric. Forest Meteorol. 102, 39–50. Batlles, F.J., Olmo, F.J., Alados-Arboledas, L., 1995. On shadowband correction methods for diffuse irradiance measurements. Solar Energy 54, 105–114. Batlles, F.J., Tovar, J., Olmo, F.J., Alados-Arboledas, L., 2000. Comparison of cloudless sky parameterizations of solar irradiance at various Spanish mid-latitude locations. Theor. Appl. Climatol. 66, 81–93. Berninger, F., 1994. Simulated irradiance and temperature estimates as a possible source of bias in the simulation of photosynthesis. Agric. Forest Meteorol. 71, 19–32. Bird, R.E., Riordan, C., 1986. Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at

12

I. Alados et al. / Agricultural and Forest Meteorology 111 (2002) 1–12

the earth’s surface for cloudless atmospheres. J. Climate Appl. Meteorol. 25 (1), 87–97. Boscá, J.V., Pinazo, J.M., Cañada, J., 1997. Modificación del programa SPCTRAL2 para la estimación de irradiancias solares espectrales directa, difusa y global a partir de unas m´ınimas medidas experimentales. In: de Oliveira Fernandes, E., Maldonado, E., Gueldes, M. (Eds), Proccedings of VIII Congreso Ibérico de Energ´ıa Solar, SPESISES. Porto, Portugal, pp. 111–115 (in Spanish). Braslau, N., Dave, J.V., 1973. Effects of aerosols on the transfer of solar energy through realistic model atmospheres. J. Appl. Meteorol. 30, 601–619. Foyo-Moreno, I., Vida, J., Olmo, F.J., Alados-Arboledas, L., 2000. Estimating solar ultraviolet irradiance (290–385 nm) by means of the spectral parametric models: SPCTRAL2 and SMARTS2. Ann. Geophys. 18, 1382–1389. Fröhlich and Wehrly, C. 1981. Spectral distribution of solar irradiance from 250–25,000 nm. World Radiation Center, Davos, Switzerland, private comminication. Gueymard, C., 1989a. An atmospheric transmittance model for the clear sky beam, diffuse and global photosynthetically active radiation. Agric. Forest Meteorol. 45, 215–229. Gueymard, C., 1989b. A two-band model for the calculation of clear sky solar irradiance, illuminance and photosynthetically active radiation at the earth’s surface. Solar Energy 43, 253– 265. Gueymard, C., 1993. Critical analysis and performance assessment of clear sky solar irradiance models using theoretical and measured data. Solar Energy 51, 121–138. Gueymard, C., 1995. SMARTS2: Simple Model of the Atmospheric Radiative Transfer of Sunshine: Algorithms Description and Performance Assessment. Florida Solar Energy Center, Report FSEC-PF-271-94.

Gueymard, C., 1998. Turbidity determination from broadband irradiance measurements: a detailed multicoefficient approach. J. Appl. Meteorol. 37, 414–435. Olmo, F.J., Vida, J., Foyo-Moreno, I., Tovar, J., Alados-Arboledas, L., 2001. Performance reduction of the solar irradiance parametric models due to the limitations in required aerosol data: case of the CPCR2 model. Theor. Appl. Meteorol. 69, 253–263. Sánchez-Oliveros, C., 2000. Estimation of aerosol optical properties from sky radiance measurements, Doctoral Thesis, University of Granada. Shettle, E.P., Fenn, R.W., 1979. Models for the Aerosol of the Lower Atmosphere and the Effects of Humidity Variations on their Optical Properties. AFGL-TR-79-0214 Environmental Research Paper no. 676, Hanscom AFL, MA. Utrillas, M.P., Boscá, J.V., Mart´ınez-Lozano, J.A., Cañada, J., Tena, F., Pinazo, J.M., 1998. A comparative study of SPECTRAL2 and SMARTS2 parametrized model based on spectral irradiance measurements at Valencia (Spain). Solar Energy 63, 161– 171. Van der Hage, J.C.H., 1993. The horizontal component of solar radiation. Agric. Forest Meteorol. 67, 79–93. Van der Hage, J.C.H., 1995. Intercepted flux and the horizontal component of solar radiation. Theor. Appl. Climatol. 52, 143– 149. Won, T., 1977. The simulation of hourly global radiation from hourly reported meteorological parameter. In: Proceedings of the 3rd Conference on Canadian prairie. Canadian Solar Energy Society, Edmonton, Alberta, 1977. WMO, 1986. A Preliminary Cloudless Standard Atmosphere for Radiation Computation. World Climate Programme, WCP-112. WMO/TD-no 24. World Meteorological Organization, Geneva, Switzerland, 1986.