OPTIMISING THE TEMPORAL AVERAGING PERIOD OF POINT SURFACE SOLAR RESOURCE MEASUREMENTS FOR CORRELATION WITH AREAL SATELLITE ESTIMATES Ian Grant Anja Schubert Australian Bureau of Meteorology GPO Box 1289 Melbourne, VIC 3001, Australia e-mail: i.grant@bom.gov.au e-mail: a.schubert@bom.gov.au ABSTRACT Satellite-based estimates of the solar resource are often compared or integrated with surface-based measurements, to develop and assess the satellite processing schemes, or to extend short term surface measurements. This paper studies the extent to which the correspondence between satellite and surface measurements of global horizontal irradiance is improved by temporally averaging the surface time series, for a range of averaging periods. The study uses time series of 1-minute radiation observations from Australian surface stations and satellite estimates on a 5-km grid. The reduction of the rms difference due to temporal averaging is negligible for clear-sky conditions, and is up to around 50% for cloudy conditions, and is greater for broken cloud conditions for which the direct component is fluctuating than for continuous cloud. While a 50-minute averaging period almost always captures essentially all of the improvement in rms difference, for particular cloud conditions, locations and months that improvement is captured by a critical averaging period that can be as short as 10-minutes. 1. INTRODUCTION Satellite-based estimates of the solar resource are valued for being spatially extensive and providing long time series. Satellite solar schemes are typically developed and evaluated by comparison with high quality surface-based observations at point locations. However, satellite- and surface-based measurements differ in their spatial and temporal characteristics. A single satellite measurement, and the solar radiation quantities derived from it, is an instantaneous measurement over the area of at least one satellite image pixel and often over several pixels, and is thus an areal average over a grid cell of side length 1 km to a few km. On the other hand, surface measurements are a time series at a single point. As a cloud field drifts past a station temporal averaging of the point station measurements will tend to correspond to a spatial average over a region of the cloud field, and so might be expected to correspond better to a satellite observation over the site with its inherent spatial averaging. Knowledge of the appropriate averaging interval can potentially yield better evaluation of satellite radiation estimate schemes by assessing them against temporally averaged surface data. Knowledge of the appropriate averaging interval would also indicate how well temporally sparse satellite observations represent the total solar energy over a given time period. The series of geostationary satellites operated by the Japan Meteorological Agency (JMA) over the western Pacific region has provided hourly images for most of the period since 1990 through to the present. More recently, the China Meteorological Administration (CMA) has acquired hourly geostationary imagery over eastern Asia. The combination of measurements from the JMA and CMA satellites potentially provides two measurements per hour for the last several years in the region of overlap. Prospective planning of solar power generation systems requires data on the time integrated energy available at locations, with resolution of the diurnal cycle. This information is provided, for instance, by time series or climatologies of the hourly solar exposure, which is the integration of the irradiance time series over each hour of the day. In the western Pacific region, such hourly totals must be estimated from one or two instantaneous satellite measurements per hour. An understanding of the temporal period that is represented by a single satellite measurement will inform an understanding of how well each instantaneous satellite value represents one hour, or conversely, the frequency of satellite measurements that is required to adequately sample an hour. This paper examines the choice of averaging time for surface observations required to optimise their correspondence to satellite estimates of global horizontal irradiance (GHI). It does 1
this using GHI estimates derived from geostationary satellite data supplied by JMA and surface GHI measurements from a network of stations across Australia. It examines the influence of sky conditions, geographical location and season on the dependence of the satellitesurface correspondence on averaging time. span a variety of climatic conditions, including tropical and temperate, coastal and inland. The data are subjected to ISO quality assurance processes and have traceable calibrations. This paper will primarily use the time series of GHI 1-minute means, for which the 95% uncertainty of the surface data is the greater of 1.5% and 15 W m -2. Diffuse irradiance surface data will also be used. 2. DATA The Australian Bureau of Meteorology (the Bureau) derives time series of hourly GHI over the Australian landmass on a 0.05 (~5 5 km 2 ) grid, extending from 1990 to the present, from geostationary satellite data. The primary data input to the satellite processing is visible-band imagery from the sequence of geostationary meteorological satellites that has been operated by JMA (GMS-4, GMS-5, MTSAT-1R, MTSAT-2) or by the US National Oceanic and Atmospheric Administration (GOES-9) over the western Pacific Ocean. The images have 1.25-km or 1.0-km resolution and are typically produced hourly. After spatial averaging to 0.05, the instantaneous global horizontal irradiance at the surface is calculated for each satellite image with a physical model that parameterises the important aspects of the atmospheric radiative transfer in two spectral bands, in the visible and near-infrared (1). The physical parameterisations are adapted to the spectral response characteristics of each satellite. The model uses ancillary data in the form of total column water vapour estimates from a numerical weather prediction model, column ozone amount as a fixed function of latitude, and surface albedo derived from a cloud-free composite of recent atmospherically corrected geostationary satellite images. In further processing, the hourly satellite estimates of GHI are converted to hourly direct normal irradiance (DNI) by means of an empirical diffuse fraction model. These hourly time series are processed to daily and monthly time series and monthly and annual climatologies. Because the satellite GHI estimates form the basis for all of the Bureau s satellite solar radiation data products, this paper will focus on the hourly GHI time series. The time of the hourly satellite measurement depends on the start time and progression of the satellite imager scan and varies between stations and between satellites. For locations in Australia the measurement time varies from 28 to 53 minutes after the start of the UT hour. The surface-based irradiance measurements are taken from the Bureau s high quality radiation monitoring network. Each network station measures the direct, diffuse and global components independently, storing 1-minute statistics of 1- second samples. The network has collected data from the fourteen stations on the Australian mainland that are shown in Figure 1 over various intervals since 1993. These stations Fig. 1: Map of the surface radiation station locations. 3. ANALYSIS AND RESULTS 3.1 Data Stratification Intuitively, it is expected that the presence or absence of cloud, and the spatial continuity of cloud, will be an important control on the impact of temporal averaging. Therefore several broad cloud regimes were defined on the basis of characteristics of the surface 1-minute time series of GHI. Two indicators of the cloud regime are the diffuse fraction (1) d = DIF / GHI and the clearness index (2) k = GHI / GHI 0, where DIF is the diffuse irradiance, and GHI 0 is the GHI at the top of the atmosphere. Figure 2 plots the diffuse fraction against the clearness index for one sample per hour over one year at Wagga Wagga. Such plots, in which the data is spread about a characteristic curve with a logistic (reversed S ) shape, are commonly presented in the literature on the development of diffuse fraction models (2). We have defined three specific cloud regimes on the basis of ranges of diffuse fraction and clearness index, as shown in figure 2: overcast, where there is little or no direct beam; clear where there is a direct beam and there is little or no cloud in the sky; and broken, where there is a direct beam and sufficient diffuse radiation is reflected from clouds not obscuring the sun to raise the global radiation above its clear-sky 2
value. The names of the cloud regimes are chosen for convenience, and the regimes may include cases that are similar to but not exactly matching the above nominal descriptions. Fig. 2: Scatter plot of diffuse fraction versus clearness index for one year of hourly 1-minute mean data from Wagga Wagga, with the cloud regimes highlighted by different colours and symbols. The stations cover a range of climatic regimes, although none are in mountainous regions. The effect of location was examined by initially studying each station separately. Finally, for many stations the cloud conditions will vary seasonally, and so the behaviour at each station was examined for a month in each of four seasons. 3.2 Measure of Correspondence To each satellite GHI measurement over a station there corresponds a simultaneous sample in the time series of surface 1-minute mean GHI. We compare the satellite and surface measurements using temporal averages of the surface data over time windows that are centred on the simultaneous sample and with a range of window lengths dt from 1 minute (no averaging) to 80 minutes. We quantify the satellite-surface correspondence by the root mean square of the difference; if the surface measurement is considered truth, then this can be called the root mean squared error (rmse). The satellite GHI estimates, which come from the Bureau s physical satellite processing model, have an increasingly positive bias for lower irradiances. However, the spread of the satellite-surface differences is significantly greater than the bias, and the magnitude of the bias does not change with averaging period. Therefore, while the bias will increase all rmse values, it is not expected to qualitatively change comparisons of the effect of averaging between different averaging periods and between different conditions. 3.3 Stratification by Location and Month Figure 3 shows the dependence of rmse on dt for the separate cloud regimes as well as for all sky conditions combined, for four sites in four seasons. The cloud conditions are the strongest influence on both the magnitude of the rmse and on the variation of rmse with averaging period. Clear conditions invariably have small rmse values that have little or no dependence on the averaging period. For overcast conditions the rmse falls by up to 50%, and in a few cases by more, reaching a plateau for periods above a threshold that ranges from 10 to 50 minutes. The broken cloud results are the most erratic because this regime has many fewer samples, leading to poorer statistics. Indeed, because the cloud regime classification is made separately for each averaging period, broken cloud samples vanish for the longer averaging periods. Nevertheless, the overall pattern for the broken regime is similar to the overcast regime: for averaging periods longer than 10 to 50 minutes the rmse reaches a plateau a few tens of percent below the rmse value without averaging. For all-sky conditions (the curves labeled all in Figure 3), the curve of rmse variation with dt tends to have a smoother shape that is intermediate between the curves for the separate regimes: it falls smoothly by up to a few tens of percent, with most of the fall being achieved by an averaging period of 20 to 50 minutes. The shape of the all-sky curve will depend on the relative proportions of cloud conditions, and these will depend on location and season. The four stations shown in Figure 3 span a range of climatic and geographic conditions: Darwin (803) and Tennant Creek (851) are tropical while Kalgoorlie (924) and Wagga Wagga (201) are temperate; Darwin is coastal while the others are inland; of the temperate sites, Kalgoorlie is semi-arid while Wagga Wagga is in a crop growing region. There are no clear systematic variations of the rmse versus dt behaviour with respect to site or season. It appears that for some sites and months the overcast curve falls sharply to a plateau at a critical averaging period (for instance, Tennant Creek in April and July), while for other cases it falls more gradually with no distinct transition period (all four sites in January). 3.4 Stratification by Cloud Conditions Only Inspection of the rmse versus dt curves for all sites in the network has not revealed variations in morphology that are systematic with location or season. Thus in Figure 4 we show the rmse versus dt curves for all stations and months taken together. In this case the number of samples is much greater than for the panels of Figure 3, and the curve for the broken regime shows a steadier downward trend and extends to the longest averaging period. All four curves are smoother and confirm the relative impact of averaging for the cloud regimes described above: clear conditions showing minimal impact, broken conditions showing the biggest improvement from averaging, followed by overcast, and all-sky conditions showing a significant variation with dt that is less than the broken or overcast regimes on their own. 3
Fig. 3: Plots of rmse versus dt for four stations and four months. Within each panel there is a trace for each cloud regime separately and one trace for all-sky conditions. The first number in each panel s title is the station number (201 Wagga Wagga, 851 Tennant Creek, 803 Darwin, 924 Kalgoorlie) and the second number is the month number. Figure 4 shows no distinctive averaging period by which most of the impact of averaging has been achieved. However, this is not inconsistent with critical periods applying to individual stations in individual months. To demonstrate the effect of temporal averaging on the satellite-surface correspondence we choose a single averaging period of 50 minutes. This period was sufficient to capture most of the rmse reduction in individual stationmonth analyses such as those in Figure 3, and captures most of the rmse fall of the aggregated results in Figure 4. Figure 5 shows scatter plots of satellite versus surface measurements of GHI, without averaging and with averaging over a 50-minute window. The averaging reduces the spread, with the rmse values falling from 140 W m -2 to 105 W m -2. In both case the positive bias of the satellite estimates at low GHI is small compared to the spread. Fig. 4: Plots of rmse versus dt for all stations and months taken together, for separate cloud regimes and for all-sky conditions. 4
time, because the speed will determine the time taken for the cloud field to move a distance comparable to the size of the satellite grid cell. This preliminary study has examined only the radiation measurements themselves in order to build an initial broad description of the behaviour of time averaging. A more detailed study could examine data on the wind at cloud height at the time of the radiation measurements, or at least climatologies of the wind at cloud height. Future work could also investigate the relationship between averaging time and the spatial scale of the cloud texture with case studies that examine the spatial structure by means of geostationary satellite images, finer scale polar orbiter images or surface-based sky images. The results presented give an indication of the likely magnitude and variation of the most effective averaging interval for other regions. The work could be extended to DNI, which is a more relevant parameter for solar focusing applications. 5. CONCLUSIONS Fig. 5: Scatter plots of the satellite-based versus surfacebased measurements of GHI, without (top) and with (bottom) temporal averaging. 4. DISCUSSION The impact of temporal averaging on the satellite-surface GHI correspondence varies with cloud conditions in a way that would be intuitively expected. Averaging has negligible impact for clear conditions, a significant impact for more continuous cloud that blocks the direct beam, and the greatest impact for broken cloud for which the direct beam component would be fluctuating significantly. Increasing the averaging period in some cases results in a gradual improvement in correspondence out to the longest periods examined, while in other cases there is a threshold period at which the correspondence essentially stops improving. This threshold period varies from 10 minutes to 50 minutes, with no clear relation to the cloud regime, location or month. The averaging time for which all or most of the improvement would be captured is likely to depend on the size of the satellite grid cell, the spatial scale of the texture of the cloud field, and the cloud speed. In cloudy conditions the speed of the clouds past the station would be expected to influence the optimum averaging We have examined how the correspondence between surfacebased GHI time series and satellite-based measurements of GHI on a 5-km grid, as quantified by the rmse, is improved by temporally averaging the surface time series over a range of averaging periods. The improvement with averaging is negligible for clear conditions, significant for continuously cloudy conditions, and greatest for broken cloud for which the direct beam is fluctuating. In the continuous and broken cloud conditions the improvement is typically a few tens of percent and up to 50%. In some cases the improvement is achieved for a characteristic averaging period which can vary from 10 minutes to 50 minutes, with no clear prediction of this value from the cloud conditions, location or season. In other cases there is a more gradual improvement with lengthening averaging period, with most of the improvement captured by periods of approximately 50 minutes. 6. REFERENCES (1) Weymouth, G.T., and J. F. Le Marshall, Estimate of daily surface solar exposure using GMS-5 stretched-vissr observations: The system and basic results, Australian Meteorological Magazine, 50, 263 278, 2001 (2) Ridley, B., J. Boland, and P. Lauret, Modelling of diffuse solar fraction with multiple predictors, Renewable Energy, 35, 478 483, 2010 5