CLOUD ADVECTION SCHEMES FOR SHORT-TERM SATELLITE-BASED INSOLATION FORECASTS

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1 CLOUD ADVECTION SCHEMES FOR SHORT-TERM SATELLITE-BASED INSOLATION FORECASTS Steve Miller Matt Rogers Cooperative Institute for Research in the Atmosphere 1375 Campus Delivery Colorado State University Fort Collins, CO Andy Heidinger Istvan Laszlo Cooperative Institute for Meteorological Satellite Studies University of Wisconsin 1225 West Dayton St. Madison, Wisconsin Manajit Sengupta National Renewable Energy Laboratory 1617 Blvd Golden, CO ABSTRACT Prediction of solar insolation is a problem increasingly well-suited to observationally-based techniques at shorter forecast times. Here, the cloud distribution is well characterized by satellite imagery and the evolution of this field can be approximated to first order as simply translational (i.e. neglecting the evolution of cloud morphology). In this research, geostationary satellite observations are used with the NOAA Pathfinder Atmospheres - Extended (PATMOS-x) retrieval package, a standalone radiative transfer code, and wind field data from a numerical weather prediction (NWP) model to derive short-term (0-3) hour forecasts of insolation for applications in solar power generation. Different cloud advection schemes are compared and contrasted. One simple advection scheme determines the cloud pixel location and cloud-top height, then advects the pixel forward in space/time using the cloud-top height model wind value. A more sophisticated scheme uses additional retrieval properties to classify cloud pixel groups into cohesive objects which are then advected using model wind fields appropriate to the characteristics of the cloud group. In both cases the predictive skill falls off over time unless cloud evolution properties are introduced. Both schemes provide a short-term forecast of cloud location, which, when combined with predicted solar geometry, terrain height information, and sensor geometry determine the location of cloud shadows. The advected cloud and geometry information is used initialize a radiative transfer model to forecast insolation at these shadow locations. Presented are results of satellite-derived insolation forecasts validated against the NOAA-ESRL Surface Radiation (SURFRAD) network both in terms of point verification and area-averaged statistics. 1. INTRODUCTION The variability in cloud cover for certain cloud scenarios (for example, fair-weather cumulus) presents a special challenge for generation of power using photovoltaic (PV) and concentrated solar power (CSP) systems, as the shadows generated by these cloud fields have a marked impact on the power generation capabilities of these systems. Integration of PV and CSP systems into the power grid therefore requires some knowledge of the likelihood of generation drop-offs due to cloud cover (referred to as ramps ) which in turn requires an ability to accurately forecast cloud locations, and critically, the location of cloud shadows, in the near-term (perhaps 1-6 hours.) Several possibilities for forecasting cloud shadow locations exist, and include (among others,) numerical weather prediction models and satellite-based techniques using real-time cloud location imagery. Numerical weather prediction (NWP) of surface insolation (1) has potential for longer-term forecasts (beyond three hours or so) of cloudiness due to the ability to correctly apply cloud evolution dynamics and microphysics; initialization of starting cloud fields remains a challenge for NWP techniques in the extreme near-term.

2 Satellite-derived techniques, such as the one described by this work, bridge the gap in capabilities between shorterand longer-term forecasts of solar insolation; the ability to recognize cloud structures beyond the horizon and track the motion of cloud systems using advection schemes may prove of use for the 1-3 hour forecast range. In this work, we will describe preliminary results from a cloud advection scheme utilizing geostationary satellite observations. The advection scheme leverages cloud properties from a satellite retrieval code, guidance winds from a numerical weather prediction model, and a standalone radiative transfer code to compute surface radiation values of advected cloud systems. 2. RESOURCES This section will provide details of the satellite retrieval code, ancillary input, and radiative transfer models that are used by the cloud advection scheme described later. 2.1 PATMOS-x Satellite Retrieval Code The AVHRR Pathfinder Atmospheres Extended (PATMOS-x) (2) retrieval code is a comprehensive retrieval code originally developed for the AVHRR instrument, and has been extended to utilize several spaceborne observations platforms, to include geostationary observations from the GOES system. Utilizing the available channel data from GOES, PATMOS-x provides information on cloud-top height and cloud depth, which are used in the advection scheme to be described presently. Accuracy of the retrieval algorithms utilized by PATMOS-x is facilitated by a comprehensive representation of the radiative transfer in the atmosphere, a sophisticated surface reflectance characterization utilizing a GOES-derived dark-sky background, and enhanced surface parameters such as snow cover. Output from the PATMOS-x retrieval code includes cloud properties that form the basis of the cloud advection scheme; latitude/longitude-marked cloud properties from PATMOS-x are stored in an array for later interaction with advection schemes. Properties from PATMOS-x that are utilized include a simple cloud mask, cloud type, and retrievals of cloud top pressure and cloud optical depth. 2.2 Ancillary model data Once the initial cloud location and relevant properties are identified from the retrieval code, a means to predict the future location of the cloud field is required. The method described in this research uses model-derived wind fields to advect cloud fields (either at the pixel level or in cloud groups) forward in time at a time-step resolution determined by the user. To accomplish this step, model winds corresponding to the cloud field are taken from the Global Forecast System (GFS) model. Model winds at all levels from the nearest-in-time initialization of the model are collected, and a simple linear interpolation between forecasted wind values is used for sub-timestep resolution of model winds between GFS forecasts, allowing for continuity in the wind field. As will be described, the advection scheme computes the appropriate vertical level using the cloud retrieval information and advects the cloud field using the GFS wind for that vertical level. Additionally, information on column ozone and precipitable water, required by the standalone radiative transfer code described presently, are taken from the GFS model and used as appropriate. 2.3 Standalone Radiative Transfer Code There are different advection schemes that can be applied to cloud properties; once the cloud field has been advected, however, what remains is to determine the surface insolation based on advected cloud positions, regardless of advection scheme. To accomplish this, a standalone version of the Satellite Algorithm for Shortwave Radiation Budget (SASRAB) (3) radiative transfer code used in PATMOS-x was developed to be used in a forecast mode. Inputs to the standalone SASRAB radiative transfer code include cloud properties, GOES reflectances, information about precipitable water and ozone, solar and satellite geometry, and surface reflectance properties. The bulk of the information required for the radiative transfer code is provided from the advected cloud properties taken from PATMOS-x; solar and satellite geometries are updated manually, while precipitable water and ozone information are taken from the GFS model also used to provide steering winds. The dark-sky background input needed for forecasting solar radiation represents an extension of the dark-sky processing code used for PATMOS-x, and uses CONUS-centered images of GOESderived dark sky images for all scan times in the two-week period, valid at forecast time, preceding the forecast. For example, a two-hour satellite forecast valid at 18Z on the 15 th of February, 2012 would utilize the composite darksky GOES image at 18Z from the period including 1-14 February The radiative transfer code is intended to be run on a pixelby-pixel basis; to address concerns about cloud masking issues, it is also possible to run the radiative transfer code on 3x3 pixel groups of advected cloud properties.

3 3. METHODS AND INITIAL RESULTS Here we will describe two advection schemes being developed; a pixel-by-pixel advection scheme, described as the version 1 code, and a modular cloud grouping advection scheme, described as the version 2 code. 3.1 Advection of Cloud Field The basis for both cloud advection schemes is to interpolate a wind vector fields to the location of GOES cloud pixels, for the purpose of advecting the cloud forward in space/time under the assumption that the cloud does not evolve significantly over the period of advection. Colocation of cloud pixels or groups with model winds is first accomplished, then a simple linear time-step is applied to compute a new latitude and longitude pair to assign to each cloud pixel or group. The cloud property arrays retrieved from PATMOS-x maintain their values in this manner; only the location of the cloud properties is changed in the advection scheme. Typically, cloud advection occurs on a 2- or 5-minute timestep to create continuity of motion of the cloud field; as mentioned previously, interpolation between GFS forecasted time steps is used to accomplish this task. In the version 1 code, which we will primarily focus on here, determination of the cloud advection steering height is accomplished by utilizing GFS wind at a vertical level that matches the cloud-top height as retrieved from PATMOSx. (It is also possible to utilize different steering heights for different cloud type retrievals in the version 2 code, which is currently under development. For example, a cumulonimbus cloud with a vertical extend from 900 hpa- 350 hpa might be guided more reliably using the hpa flow, while stratus and other clouds of primarily horizontal extend are suitably steered using cloud-top height winds. Determination of steering height for the version 2 code is discussed further in section 4.) The ultimate output of the advection code is a stored array of cloud properties retrieved from PATMOS-x whose location latitude and longitude evolve in time. Essentially, cloud properties in the advection scheme follow streamlines of model flow between GFS forecasts of wind, which allows for quasi-realistic advection behavior of the cloud field. An example of an initial cloud field is shown as Figure 1; an example of the same cloud field advected forward in time by 60 minutes using the version 1 code is shown as Figure 2. Fig. 1: Cloud optical depth retrieval from 18 UTC on January 19 th, 2012, computed from GOES-15 observations using PATMOS-x. Fig. 2: Cloud optical depth properties from 18 UTC on January 19 th 2012 advected forward in time 60 minutes utilizing GFS steering winds. In the example provided in Figures 2 and 3, a cold front stretches west-northwest through east-southeast from Nebraska through southern Iowa, with the low pressure system situated along the South Dakota/Iowa border. Cloud advection (represented here by cloud optical depth) flows largely along the frontal boundary in the eastern half of the image, with low-level cloud in southeast South Dakota advecting south into central Nebraska under the influence of the low pressure system. Again, no cloud evolution is performed in the advection code; there is no additional generation of cloud field due to lifting along the front, which represents a limitation of cloud advectionbased forecast techniques. Forecasts based on advection

4 on a longer timescale than one hour begin to increasingly suffer from the lack of cloud evolution and cohesion. 3.2 Application of Radiative Transfer Code and Initial Results At each timestep in the advection process, we have a new location for cloud properties retrieved by PATMOS-x, as well as GFS forecast information (model winds, precipitable water, and ozone). The advection scheme also computes updated solar parameters, and accesses the appropriate composite dark sky reflectance valid at the current advection time; with this information it is possible to run the standalone SASRAB code to provide a surface insolation value for a specific site in the domain. An example insolation forecast using initial advection parameters only, from the 19 th of January case shown above, and forecasting the surface insolation at the Sioux Falls SURFRAD site, is shown as Figure 3. The preliminary forecast product shown in Figure 3 utilizes the version 1 advection scheme, with the limitation that radiative transfer is not currently applied for individual time steps beyond the initial computation of insolation based on retrieved cloud properties. The cloud advection schemes described at the time of this writing are currently under development; full time-step retrievals at 5-minute resolution are anticipated using the version 1 code by mid- April With that important limitation in mind, the results of Figure 3 show promise in that independent computations of surface insolation taken from disparate sources roughly agree with the observed surface insolation dataset. 4. DISCUSSION AND FUTURE WORK As mentioned previously, a limitation of cloud advection schemes is in the lack of cloud evolution throughout the advection period. For advection periods longer than the evolutionary timescale of cloud systems (perhaps an hour or two) attempts to compute a surface radiation field will be confounded by an increasing unrealistic cloud field. Furthermore, long-term advection (beyond one hour) of the single-pixel approach followed by the version 1 cloud advection code leads to unrealistic shearing and destruction of cloud layers. To address this concern, an attempt to group cloud pixels into cohesive cloud structures is being developed in the version 2 advection code. Several cloud grouping algorithms exist and are being investigated for this version of the advection scheme. Additionally, an appropriate steering flow based on a combination of cloud properties representative of the cloud group once determined would be used to provide enhanced steering characteristics to the cloud group. It is thought that this will reduce or eliminate the shearing issue experienced in the version 1 code; while the issue of cloud evolution will ultimately limit the timescale of utility for any cloud advection approach, it is hoped that more realistic cloud grouping can extend the usefulness of the advection scheme beyond one hour. Fig. 3: SURFRAD observation of GHI (black line) versus satellite-advection derived forecast (red line) of GHI using cloud advection scheme. Satellite scheme utilizes onehour forecast time step, initialized at the top of each hour. Continued development of the version 2 advection scheme will utilize a modular approach to grouping algorithms, allowing a best practices approach to cloud advection. Application of the radiative transfer code to the advected cloud field will proceed in the same manner as in the version 1 code. Finally, a database of satellite-derived insolation forecasts compared against surface observations will be used to further refine and improve the advection scheme, and to find the handoffs in skill between satellite and NWP-derived forecasts of the solar resource.

5 5. ACKNOWLEDGEMENTS This work is sponsored by the National Renewable Energy Laboratory under Subcontract AGJ , and the National Oceanic and Atmospheric Administration under Subcontract NA09OAR # REFERENCES (1) M.A. Rogers, S.D. Miller, C. Combs, M. Sengupta, S. Benjamin, C. Alexander, P. Mathiesen, and J. Kleissl, (2012). Validation and Analysis of HRRR Insolation Forecasts using SURFRAD. In preparation for WREF ASES 2012 Conference (2) Thomas, S., Heidinger, A., and Pavlonis, M. (2004). Comparison of NOAA s operational AVHRR-derived cloud amount to other satellite-derived cloud climatologies. J. Climate, 17, (3) Pinker, R.T., and Laszlo, I. (1992). Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteorol. 11,