GRIDDED AEROSOL DATA FOR IMPROVED DIRECT NORMAL IRRADIANCE MODELING: THE CASE OF INDIA

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1 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 GRIDDED AEROSOL DATA FOR IMPROVED DIRECT NORMAL IRRADIANCE MODELING: THE CASE OF INDIA Christian A. Gueymard Solar Consulting Services P.O. Box 392 Colebrook, NH 3576 Chris@SolarConsultingServices.com Ray George National Renewable Energy Laboratory 67 Cole Blvd. Golden, CO 84 Ray.george@nrel.gov ABSTRACT Using point data from ground sites in and around equipped with sunphotometers, gridded data from either space measurements or models, and gridded data from aerosol climatologies, an improved database providing the aerosol optical depth (AOD) over has been produced. Data from 39 sunphotometer sites have been used here as ground truth to validate and optimally combine monthly gridded data over the period 2 2. Different optimal combinations of the satellite data were thus obtained for winter and summer. Most generally, the MODIS data were found to be closest from ground truth, albeit with some bias, which could be corrected separately for each season. Along with similar data for the Ångström exponent, the resulting AOD database was used to derive the broadband AOD dataset needed as input to the SUNY radiation model. Time series of hourly DNI and GHI were obtained with that model for 22 28, and showed reduced DNI in hazy areas, in contrast with high-dni results in the Himalayas region, where AOD is significantly lower.. INTRODUCTION Aerosols have a strong impact on direct irradiance (DNI), but only a modest impact on global irradiance (GHI). There is now a strong interest in development of concentrating solar power (CSP) in various parts of Asia, and particularly in, where the recent launch of the Jawaharlal Nehru National Solar Mission has sparked a lot of interest in various flavors of solar power technologies, most notably of the CSP kind. Hence there is a need to develop maps and databases of the DNI resource in the region. In recent years, the National Renewable Energy Laboratory (NREL) has been mandated to develop various solar resource maps for. Monthly datasets and maps of DNI and GHI were first developed for East Asia (including all of, Bangladesh, Sri Lanka, Bhutan, and Nepal as well as China) during the course of UNEP s SWERA project ([]; using the CSR radiation model [2, 3] and cloud data at 4-km resolution. A series of bilateral partnerships led to the developments of similar products, first for Afghanistan and Pakistan (using both the CSR 4- km and SUNY -km models), then for Bhutan and northwest, using the SUNY model [4] with satellite cloud data at -km resolution ( pdfs/ra_india_solar_methods_final.pdf). In 2, new products (datasets, maps and GIS data) of DNI and GHI were developed in cooperation with s Ministry of New and Renewable Energy, through funding from the U.S. Department of Energy. These products are again based on -km irradiance predictions obtained with the SUNY model, albeit with notable methodological differences compared to the previous series of products. Most importantly, the aerosol input database used by the SUNY model was completely revised. These developments, which are detailed in this report, led to the release of new products, now available at These products eventually replaced the older series of products, which were limited to the northwest part of. 2. AEROSOL CLIMATE OVER INDIA Asia is known for its large aerosol burden, which is caused by a combination of various causes: (i) large sources of local dust; (ii) long-range transport from deserts in Asia or Africa; (iii) smoke from biomass burning (e.g. for agricultural purposes); and (iv) anthropogenic pollution. This mixture of large quantities of aerosols results in haze and, consequently, intense scattering and noticeable absorption. The finest aerosol particles may be washed out by rain during the monsoon period, creating a scavenging effect. Winds

2 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 can import or export aerosols, depending on season and meteorological conditions. Finally, an important case is that of the Indo-Gangetic basin, in the north of. Considerable aerosol sources exist there due to the presence of agriculture, industries, and extremely high population density. Influx of aerosols from desert areas west of is frequent. An almost permanent thick haze is noticeable over the region, which can significantly impair the solar resource. Earlier studies have examined the long-term trends in aerosol emissions and/or aerosol optical depth (AOD), showing significant increases over large parts of [5-7]. The Delhi area appears most affected, with upward trends in AOD exceeding 2% per decade. These conditions make the development of AOD databases, and the subsequent modeling of DNI and GHI, more challenging than what was so far usual in North America or Europe, for example, which were the focus of most solar resource assessment studies so far. 3. AEROSOL OPTICAL PROPERTIES Because of this adverse situation, the literature has been searched for published AOD data. However, there were instances where different authors reported conflicting data for the same site and same period. Attempts to resolve such inconsistencies through personal communications with the concerned authors were not successful, unfortunately. Dubious or conflicting data series had thus to be discarded. The total number of potential ground-truth sites finally reached 48 (33 from Aeronet and 5 from the literature). Out of this total, 9 sites were rejected because either they were at too high altitude in the Himalayas (where the spatial variability in AOD is high and may seriously affect the validation), or they were decommissioned before the first satellite data became available in 2. As a result, only 39 sites were kept to establish the final ground-truth database (Fig. ). Even though the target period for the end-product dataset was 22 28, all data available for the period 2 2 were used for development. For this extended period, the sources for the available gridded monthly-average AOD data to be validated appear in Table. Two commonly aerosol optical properties are used here: the spectral aerosol optical depth (AOD) at 55 nm (AOD55 or τ a55 ) and the Ångström exponent, α, which can be both determined from sunphotometer measurements [8]. The AOD at any other wavelength λ can be derived from the classic Ångström s equation. Its log-log transform can be used to derive AOD55 when it is not available, but the AOD at other wavelengths is measured. 4. SOURCES OF AEROSOL DATA Three broadly different kinds of data are used here, namely point data from ground sites equipped with sunphotometers (highest accuracy, frequent periods of missing data), gridded data from space measurements (lowest accuracy, many missing data), and gridded data from climatologies (low to intermediate accuracy, no missing data). The first kind of data is used as ground truth to validate and optimally combine data of the second and third kinds. Most validation studies of gridded aerosol products have used NASA s Aeronet data as ground truth. In the present case, this would have not provided enough sites or monthly data points to guarantee sufficient spatial coverage and statistical significance. To increase the number of ground-truth sites, the area of study has been increased to encompass not just but a much larger area, defined by latitudes from to 4 N, and longitudes from 65 to E. Furthermore, sunphotometer data were known to be available from n institutions. For instance, ISRO and IMD have gathered such kind of data for many years. Unfortunately, our repeated efforts to obtain such data did not succeed. Fig. : Location of 39 ground-truth sites used in this study. 5. AOD DATA PROCESSING Table reveals that the monthly MISR product has better resolution than the other data sources. All the x AOD datasets were thus regridded to.5x.5 so that they could be compared to the MISR data. Since AOD is a function of elevation, this regridding must take the local elevation into consideration. This can be done via the convenient scale height method, which assumes that the AOD at elevation h is lower than that at sea level (h=), in such a way that

3 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 AOD(h) = AOD() exp(-h/h a ) () where H a is the aerosol scale height. The method is convenient, but approximate because H a varies, depending on region, season, etc. Based on previous results [9], typical values of H a are either 29 m for inland sites or 2 m for coastal/island sites. Sites within 5 km of a coastline have been considered coastal. The same correction method has been applied to the ground-truth data, so that their corrected values are scaled to the average elevation over the.5x.5 cell where the site is located. The effect of elevation on α is much smaller than that on AOD, but is not known precisely. Therefore, no scale height correction was applied to α. Table. Sources of gridded aerosol data. overly aggregated annual analysis, the year was separated into two markedly different seasons: winter (October to April) and summer (May to September). This rough cut is such that winter mostly groups the dry months, whereas summer mostly groups the monsoon months. This segregation is only approximate since the monsoon s onset and duration has a large latitudinal and interannual variability. Considering the systematic (but seasonal) bias in each satellite dataset, the best way to reduce both the bias and scatter in the predicted AOD was searched, for each of the two seasons separately MISR MODIS-Terra MODIS-Aqua MODIS-DB MERIS Monthly AOD July Variable Data Source Period Resolution Observations AOD55 MERIS v x MISR v x.5 MODIS-Terra v5 2 2 x MODIS-Aqua v x MODIS-Aqua Deep Blue v x Gridded AOD55.5 Alpha MISR v x.5 MODIS-Terra Land v5 2 2 x MODIS-Terra Ocean v x MODIS-Aqua Land v x MODIS-Aqua Ocean v x Climatologies AOD55 CM-SAF ( x MATCH [9] x MATMOD [9] x Meteonorm [Pers. comm. with Jan Remund, 2] x Alpha CM-SAF ( x MATCH [9] x 6. SATELLITE AOD DATA VALIDATION A preliminary comparison of the five satellite datasets described in Table 2 revealed that: (i) the magnitude of AOD has substantial seasonal and spatial variations over ; (ii) large differences in AOD magnitude exist between the observed datasets; and (ii) there is a lot of missing data pixels (particularly with the MERIS and MODIS-Aqua DB datasets). A detailed monthly comparison of gridded vs. ground-truth AOD shows more scatter in summer (Fig. 2) than in winter. Moreover, the satellite retrievals tend to underpredict AOD in winter and overpredict in summer. However, the scarcity of ground-truth data, particularly during the cloudier months of summer, has a negative impact on the number of valid data points and on the overall statistical significance of these monthly results. As a reasonable compromise between a full-fledged monthly analysis and an Fig. 2: Gridded AOD55 from satellites (at.5x.5 resolution) vs. ground truth for July. To complement satellite observations in case of missing data, an AOD climatology has been specially built for. It is obtained by optimally combining the CM-SAF, MATCH, MATMOD and Meteonorm datasets indicated in Table. One difficulty with the latter dataset is that it contains erroneous zero AOD values for some pixels in the study area. For each season, an optimal combination, AOD C, is obtained by multilinear fitting: i=4 AOD C = a +! a i AOD i (2) i= AOD55 Ground Truth where AOD i represents each of the four sources of data just mentioned. The resulting climatology, dubbed CLIMIN- DI, is compared to the ground truth in Fig. 3. Note that the latter is constituted by series of actual monthly AODs from different years, whereas the climatology is for a hypothetical long-term average year, which is therefore used here for any year between 22 and 28. During winter, the three MODIS datasets provide the best correlation between the gridded and ground-truth AOD.

4 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 Apart from having many missing data, MERIS is found to disagree the most from ground truth, compared to all other datasets. Although MISR is less accurate than MODIS in general, it appears better for AODs below.5. For AODs larger than.5, raw data from MODIS-Aqua DB is the preferred source (based on the lowest RMS of residuals), after linear correction for its bias. For pixels with no raw data from MODIS-Aqua DB, the next best source is MODIS-Aqua, after a similar bias correction. For pixels with no raw data from MODIS-Aqua, the next best source is MODIS-Terra, again after bias correction. Finally, for all remaining pixels, the CLIMINDI climatology described above is used as default. During summer, the satellite data tends to be more scattered compared to ground truth, with many high-aod outliers (Fig. 2). This might result from insufficient cloud masking. Otherwise, the same basic findings as above still apply about the performance of MERIS, and the relatively good performance of MISR for AODs below.5. For larger AODs, the best source becomes MODIS-Terra. In the absence of data from the latter, default values from the CLIMINDI climatology are used, since they are found more accurate than the MODIS-Aqua values, which are outnumbered by the MODIS-Terra data anyway. Due to the paucity of ground-truth sites, the performance of this new dataset cannot be assessed against independent data. However, the dataset covers only the period 22 28, which is shorter than the development dataset (2 2), so that the validation dataset is actually different in size (smaller) compared to the development dataset. A comparison between the predicted and measured AOD appears in Fig. 4. Although the scatter in the raw data has been reduced significantly, things are still not perfect, particularly in summer. It is emphasized that access to more groundtruth data should seriously help this situation. Gridded AOD Monthly AOD Winter Gridded AOD Monthly AOD Summer.5.5 Climatology (CLIMINDI).5.5 AOD55 Ground Truth Gridded AOD Monthly AOD Summer AOD55 Ground Truth Fig. 3: Predicted AOD climatology vs. measured mean monthly AOD over the study area in summer. Using the data sources described above, a monthly AOD55 database, dubbed AERINDI, has been constructed to cover the period over the whole study area at.5x.5 resolution. Its advantages over any satellite dataset are that there is no missing data, and that most outliers have been removed, particularly in summer..5.5 AOD55 Ground Truth Fig. 4: Predicted vs. measured mean monthly AOD over for (a) Winter (top); (b) Summer (bottom).

5 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 Another validation approach is to compare the proposed data to measured ground truth at a specific site over the whole period Currently, this can be done at the Aeronet site of Kanpur only, since all other available sites have only much shorter periods of record or many missing months. (Still, Kanpur had some missing data months in 27.) This comparison appears in Fig. 5. In general, the predicted AOD follows the actual (large) seasonal and interannual variations that have been measured locally. Exceptions do occur, however, particularly in During the decade of measurement at Kanpur (2 2), the increase in AOD has been significant (.4% per decade). This is not captured correctly by the proposed dataset, which predicts an increase of only.2% per decade there (based on 7 years only, however). Therefore, the dataset should not be used as a sole source of data to evaluate trends or future projections in AOD. What the dataset does provide, however, is a sense of interannual variations in AOD. AOD 55 nm Kanpur, Gridded Dataset vs. Ground Truth.2 Aeronet (measured) Dataset (predicted) Month Fig. 5: Measured vs. predicted time series of mean monthly AOD at Kanpur, The usual performance statistics (MBD and RMSD) for the AOD database (AERINDI) and the climatology (CLIMINDI) are compared in Table 2. These results show that, whenever possible, it is better to use the former rather than the latter, although AERINDI s gain in performance is not as large in summer as it is in winter. Interestingly, the climatology has actually less random errors than AERINDI at Kanpur in summer. 7. ALPHA DATABASE AND VALIDATION As described in Table, the availability of satellite retrievals for α is different than for AOD: There are no α datasets from MERIS or MODIS-Aqua Deep Blue. Furthermore, the MODIS-Terra and MODIS-Aqua data separate the pixels over Land from those over Ocean. Both are used here since there are islands and coastal locations. As was the case with AOD, the retrievals of α from the two MODIS instruments are similar, although Aqua tends to predict higher values than Terra in summer (when α is low), and vice versa in winter. Table 2. Performance statistics of the database (AERINDI) and the climatology (CLIMINDI) using all ground-truth AOD data points available. Statistic Meas. AERINDI CLIMINDI Kanpur Mean MBD RMSD All Sites Mean MBD RMSD Like for AOD, a CLIMINDI climatology has been specially built for α. The best results have been found by correcting the CM-SAF data with a linear function, α new = a + a α raw, whose coefficients depend on season (summer vs. winter) and geographic location (land vs. island). When comparing the satellite retrievals of α to the ground truth, it has been found that, again, the MODIS results were better than those from MISR. A more critical finding, however, was that these satellite retrievals had considerable scatter during both winter and summer, but most particularly in winter. (The same is true of the climatology before correction.) This problem may reflect inadequacies in the retrieval algorithms elaborated for the various satellite instruments, and may also be exacerbated over due to the intricacies of its aerosol climate. As with the α climatology, a distinction is made here between island sites and land sites. For island sites, the priority (again based on the lowest possible RMS of residuals) is given to data from MODIS-Terra Ocean, with the usual type of bias correction. If missing, default values are obtained from CLIMINDI. For land sites, the analysis is seasonal, like with AOD. The priority is here given to MODIS-Aqua Land. For summer, the priority is given to a linear combination of MODIS-Terra Land and CM-SAF. Again, CLIM- INDI is used to fill data gaps. The α monthly series from AERINDI are compared to the ground truth in Fig. 6. Although the corrections applied to the original data have reduced its bias and a large part of its scatter, this is accompanied by substantial compression of the monthly dynamic range, which is large over as noted above. Performance statistics for Kanpur and

6 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 all ground truth sites appear in Table 3, which is similar to Table 2. Compared to Table 2, much smaller differences are found between AERINDI and CLIMINDI. The results in Fig. 6 and Table 3 clearly indicate that better retrievals of satellite-observed AOD at various wavelengths would be highly desirable. 2.5 Monthly Alpha Frequency (%) Kanpur, Daily Aeronet Data 2 2 Winter Summer Year Gridded Alpha Fig. 6: Predicted vs. measured mean monthly α over. Table 3. Performance statistics of the database (AERINDI) and the climatology (CLIMINDI) using all ground-truth α data points available. Statistic Meas. AERINDI CLIMINDI Kanpur Mean MBD RMSD All Sites Mean MBD.7.5 RMSD MODE VS. MEAN AOD Alpha Ground Truth Summer Winter The development of AERINDI s AOD dataset is based exclusively on mean monthly values. This is because the raw satellite data used here are only available as monthly means. However, recent unpublished results tend to confirm previous validation studies [9, ], which pointed out that using the mean monthly AOD often resulted in noticeable underestimations of the mean monthly direct irradiance. Since the daily AOD is log-normally rather than normally distributed, it is usually observed that the mode is significantly lower than the mean. An example of such behavior, using years of data from Kanpur, is provided in Fig. 7. AOD55 (binned) Fig. 7: Frequency distributions of the daily AOD at Kanpur. The mean and mode of each distribution are indicated by a down-pointing arrow and a straight line, respectively. Based on these findings, it is now assumed that using the monthly mode rather than the monthly mean AOD should bring better agreement between the modeled and measured direct irradiance. Using daily AOD data from 24 Aeronet sites around the world, the relationship between Mode and Mean has been studied for 28 monthly AOD distributions based on at least 9 days of data each. This relationship is illustrated in Fig. 8. Some scatter is obvious, and can be explained in great part by (i) the calculated value of the mode being dependent on the bin size selected to aggregate the daily data and derive frequency distributions; and (ii) frequency distributions being not stable when based on too few data points. A dataset providing the AOD monthly mode over has been constructed from these results. 9. BROADBAND AOD AND REGRIDDING The broadband AOD (BAOD) is still used in a few broadband radiative models (such as SUNY), but is not an intrinsic optical property of aerosols. Consequently, there is no exact relationship between AOD and BAOD. It has been shown [] that, at any given moment, BAOD depends on AOD, α, zenith angle, and precipitable water (PW). No known physical model can currently take all these variables into consideration, hence some simplification is needed. Here, the effect of PW has been ignored and a simple formula has been used [2]. Converting AOD into BAOD is even more difficult when starting from monthly mean values, since the mean monthly zenith angle must be defined a priori. This is done here using the PSI model [3].

7 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 Aeronet World Data AOD55: Mode vs. Mean.9.8 Monthly Mode Monthly Mean Fig. 8: Relationship between the Mean and Mode AOD at 24 Aeronet sites. Regridding of the monthly-mode BAOD dataset at.5x.5 spatial resolution is necessary to accommodate the finer resolution (.x. ) of the cloud data used by the SUNY model. This regridding is done by calculating the sea-level BAOD for each.5x.5 grid cell, using the average elevation of the cells along with the exponential correction of AOD explained in Section 5. The sea-level BAOD is then interpolated to the.x. SUNY model grid using a nearest-neighbor algorithm. Finally, the BAOD for each SUNY grid cell is adjusted back to the average elevation of those.x. cells. A map showing the annual corrected and regridded BAOD mode over the period appears in Fig. 9.. IRRADIANCE CALCULATIONS Based on the BAOD data just described, and other atmospheric input data, time series of hourly DNI and GHI were obtained for the period with the SUNY model. These products can be found at international/ra_india.html, where GIS toolkits, as well as monthly and annual resource maps, are also available. For instance, the annual average DNI over is shown in Fig.. This map shows reduced DNI in the hazy areas apparent in Fig. 9, in contrast with high-dni areas in the Himalayas region, where AOD is significantly lower. The correspondence between the two maps is only approximate because cloudiness, and to some extent, water vapor and other factors, also strongly affect DNI. For the northwest part of, these new DNI results can be compared to older maps, which were previously developed using the same model and Fig. 9: Annual BAOD s mode over, cloud data, but with cruder AOD data. Higher DNI values are found now, as a result of using the mode rather the mean AOD, even though the mean AOD is now larger than before. More generally, it is emphasized that a correct determination of AOD is of crucial importance over all regions where CSP projects are envisioned, as was demonstrated recently [4]. A detailed validation of the DNI resource maps described above would be highly desirable. Such an analysis will become possible when access to high-quality measured data is eventually obtained.. CONCLUSION Various existing datasets have been tested and optimally combined to develop AERINDI, a dataset of monthly values of AOD (both mean and mode) and α (mean only) for the n subcontinent and the period These datasets are largely based on satellite information (particularly from MODIS Terra and Aqua), after proper scaling to match ground truth as closely as possible, and data filling with climatological values to compensate for missing data. It is suggested that future studies be devoted to verify which of the Mode and Mean datasets leads to the best predictions of direct irradiance.

8 Solar 2 Conf., Raleigh, NC, American Solar Energy Soc., May 2 Congress. Orlando, FL, International Solar Energy Society, Maxwell E.L., et al. A climatological solar radiation model. Proc. Solar 998 Conf. Albuquerque, NM, American Solar Energy Soc., George R. and Maxwell E.L. High-resolution maps of solar collector performance using a climatological solar radiation model. Proc. Solar 999 Conf. Portland, ME, American Solar Energy Soc., Perez R., et al., A new operational model for satellitederived irradiances: Description and validation. Solar Energy. 73: 37-37, Datar S.V., et al., Trends in background air pollution parameters over. Atmos. Envir. 3: , Porch W., et al., Trends in aerosol optical depth for cities in. Atmos. Envir. 4: , Ramanathan V., et al., Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc. Nat. Acad. Sci. 2: , Gueymard C.A., REST2: High performance solar radiation model for cloudless-sky irradiance, illuminance and photosynthetically active radiation Validation with a benchmark dataset. Solar Energy. 82: , Gueymard C.A. and Thevenard D., Monthly average clear-sky broadband irradiance database for worldwide solar heat gain and building cooling load calculations. Solar Energy. 83: , 29. Fig. : Annual DNI resource map for. Current limitations in satellite retrieval techniques and in availability of ground-truth data have caused large scatter in the resulting aerosol datasets, particularly in summer for AOD and winter for α. Considering these current deficiencies, it is hoped that future versions of the MISR and MODIS algorithms will improve the accuracy of their retrievals. Additionally, a large increase in ground-truth aerosol data of high quality would be necessary to develop better corrections to the satellite data. Similarly, high-quality direct irradiance measurements are necessary to assess the performance of the proposed solar resource maps and underlying radiation datasets. 2. ACKNOWLEDGMENTS This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-8-GO2838 with the National Renewable Energy Laboratory. 3. REFERENCES. Renné D., et al. Results of solar resource assessments in the UNEP/SWERA project. Proc. Solar World. Gueymard C.A., Advanced solar irradiance model and procedure for spectral solar heat gain calculation (RP43). ASHRAE Trans. 3 (): 49-64, 27.. Gueymard C.A., Turbidity determination from broadband irradiance measurements: A detailed multicoefficient approach. J. Appl. Meteorol. 37: 44435, Molineaux B., et al., Equivalence of pyrheliometric and monochromatic aerosol optical depths at a single key wavelength. Appl. Opt. 37: 78-78, Gueymard C.A., Mathematically integrable parameterization of clear-sky beam and global irradiances and its use in daily irradiation applications. Solar Energy. 5: , Gueymard C.A., Variability in direct irradiance around the Sahara: Are the modeled datasets of bankable quality? J. Sol. Energ-T. ASME, 2 (in press).