1. Use of GOES solar radiation data to improve long-term retrospective land surface simulations Nathalie Voisin 1, Dennis P. Lettenmaier 1 and Rachel Pinker 2 1 Department of Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195 2 Department of Meteorology, University of Maryland, College Park, MA 20742 84 th AMS annual meeting, Seattle, 2004-P1.21 ABSTRACT Solar radiation is one of the primary determinants of land-atmosphere interactions. Long- term retrospective simulations using macroscale hydrologic models like the Variable Infiltration Capacity (VIC) model have proved useful for various purposes including examination of the role of land surface variables in long-term climate predictability, and initialization of regional climate models. We have performed such simulations for a 50-year period (1950-2000), which is now being extended to the early 1900s. Over most of this period, there were few or no direct observations of downward solar radiation, so we have instead estimated daily total insolation using algorithms developed by others based on the daily temperature range, and rescaled the diurnal cycle (3-hourly time resolution in our case) to theoretical clear sky insolation for the given time of year. The availability of a multi-year (1996-1999) GOES-based hourly solar radiation data set for the continental U.S. now offers the opportunity to evaluate and adjust the temperature index results for the overlap period, which we extend to the entire retrospective period of record. We show the geographic distribution of adjustments both to the total daily insolation, and its diurnal distribution, over the continental U.S. In addition, we compare both the GOES and adjusted temperature range-based values to six Surface Radiation Budget Network (SURFRAD) sites distributed across the continental U.S. Introduction 1 Methodology For the Adjustment 3 Phase 1: Adjustment to GOES incoming solar radiation 2 Derivation of Incoming Solar Radiation for input into VIC References Kimball, J.S., S.W. Running and R. Nemani (1997). An improved method for estimating surface humidity from daily minimum temperature, Agricultural and Forest Meteorology 85, 87-98. Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002, A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States, J. Climate 15, 3237-3251 National Renewable Energy laboratory (NREL) 1993: Solar and meteorological surface observation network, 1961-1990. U.S. Department of Energy, national Renewable Energy laboratory, Golden, CO. Roads, J., E. Bainto, M. Kanamitsu, T. Reichler, R. Lawford, D. Lettenmaier, E. Maurer, D. Miller, K. Gallo, A. Robock, G. Srinivasan, K. Vinnikov, D. Robinson, V. Lakshmi, H. Berbery, R. Pinker, Q. Li, J. Smith, T. von der Haar, W. Higgins, E. Yarosh, J. Janowiak, K. Mitchell, B. Fekete, C. Vorosmarty, T. Meyers, D. Salstein S. Williams, 2003, GCIP Water and Energy Budget Synthesis, J. Geophys. Res. (in press) Thornton, P.E. and S.W. Running, 1999: An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric. For. Meteorol., 93(1999), 211-228. A published paper by Roads et al (2003) describing the GCIP Water and energy Balance Synthesis (WEBS) suggested that the Variable Infiltration Capacity model (VIC) model under- simulated “observations” of incoming solar radiation by an average of 20 W m-2 in the summer months of 1996-1999 over the Mississippi basin. The incoming solar radiation “observations” used by Roads et al (2003) are a GOES-based satellite data set produced by the University of Maryland. The “VIC” values were in fact produced by Maurer et al (2002) as a model forcing (hence are unrelated to the VIC model) using the algorithm of Thornton and Running (1999), hereafter called T&R, which is based on the daily temperature range and is tuned to surface observations from the Solar and Meteorological Surface Observation Network (SAMSON) database (NREL; 1993). Figure 1: Locations of SURFRAD stations. (http://www.srrb.noaa.gov/surfrad/sitepage.html) Figure 2: JJA 1996-1999 3 hourly Incoming Solar radiation SURFRAD Stations, as observed at the station (red), GOES 8 satellite observation derived (green) and as simulated with Thornton and Running Algorithm (blue). Penn State and Desert Rock stations are average over 1998-1999 only. The National Climatic Data Center (NCDC) digital archives of daily climatological data (primarily precipitation and daily temperature maxima and minima) for the continental U.S. are now available in electronic from the beginning of the instrumental records. These long records, and hydrologic simulations (e.g., of soil moisture, snow water storage, and runoff) that can be derived from them make possible a better understanding of hydrologic variability in the 20th century (Maurer et al 2002). However, long records do not include the typical atmospheric forcing required for hydrologic simulations of the land surface energy balance. These forcings typically include downward solar radiation, downward longwave radiation, precipitation, humidity, wind speed and atmospheric pressure. Therefore, the missing inputs have to be derived from commonly available inputs like daily precipitation, and minimum and maximum temperatures. The Thornton and Running algorithm derives downward solar radiation from observations of daily minimum and maximum temperatures, daily average dew point temperature and daily total precipitation. Because the dew point temperature is not observed at nearly as many locations as are precipitation and temperature, the algorithm uses the dew point temperature estimation derived by Kimball et al (1996) within an iterative process described below. Basic inputs to T&R algorithm are daily precipitation, and minimum and maximum temperatures. T&R daily incoming solar radiation derivation is based on the following equation: Daily Incoming Solar Radiation = Transmittance * Potential Incoming Solar Radiation (1) based on solar constant, latitude, longitude, local time Transmittance = ( dry transmittance +  * vapor pressure * [ 1 - 0.9. exp(-B.  T C ) ] ) * P (2) First Estimation: Tdew = Tmin  Vapor pressure (Tmin) Second loop: Vapor pressure (Tdew) Incoming solar radiation using (1) and (2) and then corrected for diffuse and direct beams Kimball et al Method: 1.Derivation of net solar radiation assuming a surface albedo of 0.2 2.Derivation of potential evaporation (PET) using net solar radiation, mean daily temperature 3.Derivation of main daily dew point temperature using PET, Tmin and Tmax and annual precipitation with B = b 0 + b 1. exp (-b 2.  T avg ) and P = 0.75 if raining else =1 where , b 0, b 1, b 2, C and P are constant parameters optimized to match NREL observations. Transmittance for dry air and clear sky counts for elevation and optical air mass The calibration keeps the transmittance between 0 and 1. Final Daily Incoming solar radiation using (1) and (2) and then corrected for diffuse and direct beams The adjustment consists of calibrating , b 0, b 1, b 2, and and C in order to match daily averaged GOES observed incoming solar radiation ( bias and mean absolute error (MAE). The suggested methodology is the following; Phase 1; Comparisons and first attempt of calibration over a few thousands of grid cell over the continuous U.S., including the SURFRAD. Phase 2; Extension to the continuous US with possible calibration regression depending on the annual precipitation accumulation and on the annual daily temperature range. Phase 3; Validation over the entire domain. Phase 4; Sensitivity study on the energy and water budgets. 5 Ongoing and Future work We expect some changes in the energy and water budgets because the calibration impacts not only solar radiation, but also indirectly the incoming longwave radiation and the air humidity limiting the evaporation. A second calibration might be necessary so at to adjust the 3 hourly Incoming Solar radiation to those of GOES. We might get limited by the availability of GOES data throughout the entire daylight time. An additional sensitivity study on the water balance to the sub daily distribution of incoming solar radiation will be performed as well. As preliminary analysis we chose randomly 24 grid cells throughout the U.S. that complement the 6 SURFRAD stations, for a total of 30 grid cells (see figure 3). In a later analysis, we consider a few thousands of grid cells as the calibration domain for the 104,000 grid cells that represent the continuous U.S. domain. More especially, this later calibration grid cells are selected based on their proximity to Coop stations. Indeed, the meteorological forcing data we use are derived from the Coop stations. This way, we might expect less variability in the comparison between GOES and T&R. Besides, we will also differentiate comparisons for rainy and non-rainy days. NW NE SE SW 4 This preliminary analysis shows a lot of variability throughout the domain in the bias that may exist between T&R driven by our meteorological data (LDAS retrospective analysis, Maurer et al 2002) and GOES (see figure 4). In their papers, both Thornton and Running and Kimball et al mentioned that the performance of the algorithms vary with different climate as the transmittance is largely influenced by the water vapor pressure and aerosols. A preliminary crude climate differentiation shown on the map above showed about the same results over the whole domain: a general T&R underestimation at all seasons a large variability in the comparisons The underestimations vary slightly with seasons and regions but more grid cells are needed to quantify it. Figure 3: Locations of the 30 grid cells for preliminary study Figure4: Comparisons of daily averaged Incoming Solar Radiation over 30 grid cells for the period 1996-1999.