How Good Are Ocean Buoy Observations of Radiative Fluxes

How Good are Ocean Buoy Observations of Radiative Fluxes?

R. T. Pinker, H. Wang, and Semyon A. Grodsky

Department of Atmospheric and Oceanic ScienceUniversity of Maryland, College Park,

MD 20742

February 2009

For Submission to:

Geophysical Research Letters

Abstract

Satellite-derived surface shortwave radiation (SWR) has been extensively evaluated against ground observations over land but to a lesserextent over oceansthat cover larger portion of the Earth surface. In this studynew surface radiative flux estimates from the Moderate Resolution Imaging Spectro-radiometer (MODIS)are evaluated against buoy measurements of downwelling SWR in the tropical oceans. As a benchmark for achievable accuracies from satellites, similar evaluation is performed over land against theBaseline Surface Radiation Network (BSRN) observations, believed to be of highest available quality. Comparable accuracy of the new satellite SWR data is found over land and over oceans.

1.Introduction

Many attempts have been made to estimate surface shortwave radiation (SWR) from satellite-observed radiances and atmospheric and surface variables, on both regional and global scales [Pinker and Ewing, 1985; Ramanathan, 1986; Raschke et al., 1991; Pinker and Lazlo, 1992; Zhang et al.,2004; Gupta et al., 1999; Mueller et al., 2004; Rigollier et al., 2004]. Most methods are implemented with observations from geostationary satellites that capture the diurnal variability of clouds but have coarser spectral and spatial resolution than low temporal resolution polar orbiting satellites.Observations from the International Satellite Cloud Climatology Project (ISCCP) [Rossow and Schiffer, 1999] (version D1) are widely used for estimating surface radiative fluxes at global scale. They are provided at a nominal resolution of 2.5◦at 3-hourly time intervals and use sampled pixels at 30 km spatial resolution. The inference schemes that utilize the ISCCP data require auxiliary information from independent sourcesthatarenot consistent in space and time with the satellites observations used to derive the SWR.

In this study we use three years (2003-2005) of global scale estimates of SWR and Photosynthetically Active Radiation (PAR)(0.4-0.7 µm) at 1ox1o grid cellsas derived from MODIS observations from Terra and Aqua satellites [Wang and Pinker, 2009]. They are evaluated against a comprehensive set of buoy observations over the oceans (SWR only) and ground observations over land (SWR and PAR).

2.Satellite estimates of surface SWR fluxes

2.1Satellite observations

The MODIS instrument is a state-of-the-art sensor with 36 spectral bands with an onboard calibration of both solar and infrared bands. The wide spectral range (0.41-14.24 µm), frequent global coverage (one to two days revisit), and high spatial resolution (250 m for two bands, 500m for five bands and 1000m for 29 bands), permit global monitoring of atmospheric profiles, column water vapor amount, aerosol properties, and clouds, at higher accuracy and consistency than previous Earth Observation Imagers [King et al., 1992]. The largest uncertainties in satellite estimates of surface SWR are due to inadequate information on cloud properties such as cloud fraction, cloud optical depth, and cloud thermodynamic phase. Hence, improvements in estimating SWR greatly depend on better detection of cloud properties.

The MODIS cloud mask is applied globally at single pixel resolution; it uses 17 spectral bands (visible at 250m and infrared at 1000m resolution) to improve cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or with fewer spectral bands [Ackerman et al., 1998]. Three bands in the infrared window regions of 8-11 µm and 11-12 µm are used to differentiate cloud phase. Spectral bands, including window regions in the visible and near-infrared, as well as 1.6, 2.1 µm shortwave infrared bands and 3.7 µm mid-wave infrared bands are used for the retrieval of cloud optical depth and cloud particle effective radius. Seven channels are designed to measure aerosol properties over oceans and three bands over land.

We use Level-3 MODIS Atmosphere Daily Global Product(MOD08_D3, MYD08_D3) model input parameters that include: Optical Depth for Land and Ocean, Cloud Top Pressure Day, Cloud Optical Thickness Liquid, Cloud Optical Thickness Ice, Cloud Effective Radius Liquid, Cloud Effective Radius Ice, Cloud Effective Radius Undetermined, Cloud Fraction Liquid, Cloud Fraction Ice, Cloud Fraction Undetermined, Cloud Optical Thickness Undetermined, Total Ozone, Atmospheric Water Vapor. Clouds with undetermined phase are treated as water clouds in the computation of radiative fluxes.

Since the MODIS atmospheric water vapor is retrieved only when at least 9 out of 25 Field of Views are cloud free, precipitable water from the National Centers for Environmental Prediction Reanalysis Data [Kistler et al., 2000] is used for conditions with large cloud fraction. Missing aerosol optical depths under cloudy conditions and over arid areas are filled with information from the Multi-angle Imaging Spectro-Radiometer Component Global Aerosol Product [Martonchik et al., 2002)] and monthly global aerosol climatology, derived from 'typical-year' aerosol transport model results [Kahn, 2001].Spectral surface albedo on 1ox1o grid cells is taken from the Filled Land Surface Albedo Product provided by the MODIS project [Moody el al., 2005]. Monthly meansea ice extent on 1ox1o gridsfrom the Special Sensor Microwave/Imager is provided bythe NOAANationalClimateDataCenter [Ferraro et al., 1997]. Daily snow cover data are from MODIS/Terra Snow Cover Daily Global 0.25 Degree Geographic Climate-Modeling Grid [Hall et al., 2002; Riggs et al., 2005]. Surface albedo of ice over oceans in visible and near-infrared is set to 0.77 and 0.33, respectively. The surface elevation on 1ox1o grid is calculated from the global Digital Elevation Model adopted in the Penn State University/National Center for Atmospheric Research meso-scale model (known as MM5).

2.2Ground observations

2.2.1Ground Observations over oceans

In-situ measurements from the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) moorings in the tropical Atlantic (Bourles et al., 2008), and the Tropical Atmosphere Ocean/ Triangle Trans-Ocean Buoy Network (TAO/TRITON) moorings in the tropical Pacific Ocean (McPhaden et al., 1998) are used for comparisons with the satellite estimates.Downwelling SWR is detected by the Eppley Laboratory pyranometers that have nominal resolution 0.4 W m-2 and relative accuracy of ±2% in the 0-1600 W m-2 interval in laboratory conditions [Cronin and McPhaden, 1997].

2.2.2Ground observations over land

Over land,we used are 18eighteenBSRN sites[Ohmura et al., 1998] from the United States, Europe and Africa.Since 1992 the BSRN stations provide data for the calibration of satellite-based measurements of radiative fluxes and other climate applications such as validationof radiative fluxesfrom numerical models. As of June 2008 there are 43 stations contributing to the BSRN. The BSRN stations over the United States (previously known as SURFRAD [Augustine et al., 2005]) also use independent instruments to measure PAR.These observations wereused to evaluate the MODIS based PAR estimates.

3.Methodology

A forward inference scheme was developed to derive surface, Top of the Atmosphere (TOA), and atmospheric spectral shortwave radiative fluxesat global scale using satellite based information [Wang and Pinker, 2009]. The model takes into account all atmospheric constituents and a multi-layered structure of the atmospherethat accounts allows to account for surface elevation effects and to describe the vertical distribution of radiative fluxes. Spectral fluxes such as PAR and near-infrared radiation can also be estimated. The model is implemented with products from the Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor both on Aqua and Terra at 1ox1o grid cellson global scale, for a three year period (2003-2005). The new model (UMD/SRB_MODIS) takes into account both water and ice clouds,the variation of cloud particle effective radius,and their corresponding optical properties by adopting a new cloud parameterization scheme. In the near-infrared band three spectral intervals are used so that characterization of spectral change of water absorption and cloud optical properties could be incorporated. The parameterization of water vapor absorption uses a scheme based on an extensive spectroscopic database and incorporates the a water vapor continuum model [Edwards and Slingo, 1996]. The aerosol models used include severalaerosol types following Hess et al. [1998]. In addition, the layered structure of the new inference scheme facilitates the treatment of surface elevation effects. The forward approach of the new inference scheme avoids intermediate steps such as narrow to broadband conversions and angular correctionsneeded if the original satellite radiance were to be used; instead, we utilizedare geophysical parameters derived independently from MODIS.

Information on Photosynthetically Active Radiation (PAR) (0.4-0.7 µm), is needed for applications dealing with biogeochemical processes such as net primary productivity and ecosystem modeling [Running et al., 1999; Platt, 1986; Prentice et al., 1992, Nemani et al., 2003]. When implemented with MODIS products the UMD/SRB_MODIS model can estimate SWR fluxes in several spectral intervals, one of which is the PAR region.

Ground measurements of PAR are very limited. There are far fewer ground stations measuring PAR than total SWR. Satellite observations are the only source of consistent information on PAR. MODIS observations have been previously applied to estimate PAR using simplified radiative transfer models over land and over oceans [Van Laake and Sanchez-Azofeifa, 2004]. Those simplified models are limited in their capability to deal with multiple scattering by clouds and aerosols and lack transferability to global scale.

1. Results

To illustrate products derived from from MODIS observations In Figure 1 we shownis the global distribution of the annual time-mean surface SWR derived from MODIS observations aton a1ox1o grid cells as averaged over a three year period (2003-2005 (Fig.1a)) (1a), while.the illustration for the PAR (Fig. 1b) In Figure 1 we presentfocuses on tThe July mean surface PAR as derived from the MODIS observations, (averaged over the three Julys) that is the peak season for the northern Hemisphere (where te ground truth comparison is done). is presented in Figure 1b. These three year data setshave been evaluated against the best available observations both over tropical oceans and over land.

In Figure 2 we shown are results for daily mean surface SWR fluxes against PIRATA and TAO/TRITON buoy observations during January 2003 to December 2005. Small fraction of data are eliminated from the comparisons (Eliminated were 1.1% for (PIRATA) and 1.3% for (TAO/TRITION) cases are eliminated. The removal of extreme outliers was done by eliminating cases where the difference between estimated and observed flux was larger than three times the standard deviation of the differences. After averaging for each buoy network and averaging in time over the three years of records tThe bias of the time-space mean of daily DSWRfor both oceanic basins at daily time scale it is 3 and 1 W/m2for the tropical Atlantic and the tropical Pacific,respectively. The difference between instantaneous daily satellite and buoy data is better characterized by corresponding RMSE that is 25 and 36 W/m2, respectively. IfOnmMonthly average data is used instead of daily average datatime scale, the bias remains low at 2 and -1 W/m2for the Atlantic and Pacific, correspondingly, while the RMSE is reduced to 13 and 11 W/m2which is about 5 % from the observed mean values.

In Figure 3we presentedis evaluation of daily mean surface SWR fluxes estimated with the UMD/SRB_MODIS model against measurements at 18 BSRN stations over land for the same time period. For At daily average data (IS THIS DAILY AVERAGE OR INSTANTENEOUS???)time scale, the bias is -3 W/m2 while the RMSEis21 W/m2. Both biases and RMSE are similar for the ocean and land comparisons for downwelling SWR.The differences between oceans and land are not significant.Similar good comparison is found for In Figure 4 we presented are evaluation results of daily mean surface PAR fluxes against the SURFRAD (BSRN) measurements over the United States for January, 2003 to December, 2005 (Fig. 4). No data were excluded from the analysis.The PAR estimates agree well with ground measurements, with a correlation coefficient of 0.96, a bias of 0 Wm2 and a RMSE of 11 W/m2 (14%) for the daily time scale and coefficient of 0.99, a bias of 0 Wm2 and a RMSE of 5 W/m2 (6%) on a monthly time scale.

4.Summary

The need for improving air-sea fluxes in atmospheric re-analyses products, which are widely used to study climatic changes in the ocean and the atmosphere, has been recognized (e.g., US-CLIVAR Program). A major component of these fluxes are the radiative ones.. The advantages of using reanalysis fields in climate research are that they are readily available and that they provide basin-wide coverage, but their quality must always be tested by independent means. Radiative fluxes are particularly difficult to obtain from global atmospheric analysis models, since their cloud schemes often are unrealistic, especially when relatively high temporal and spatial resolution is desired (days and 50 to 100 km scales). Quality assessment of the reanalysis fluxes involves comparisons with in situ and satellite observations. In situ observations are sparse, however, leaving satellite observations as the only quality-control tool of the fluxes on a basin scale. Satellite observations provide nearly global coverage and routine sampling.The objective of this study was to evaluate comprehensively satellite based estimates of radiative fluxes over the oceans using the most advanced buoy observational systems. It was demonstrated the there are no significant differences between the validation results over oceans when compared to land. It was also shown that PAR results over land are even in better agreement with ground observations than the SWR fluxes. Therefore, it is reasonable to assume that similar results can be obtained over oceans where PAR measurements are as yet not available.

Acknowledgement

We thank NFS for providing support for this work under grant ATM0631685 to the University of Maryland. Support for SG is provided by the NASA OWVST. The work also benefited from support under NASA grant NNG05GB35G to the University of Maryland. We are greatful to the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) Giovanni web system

for providing excellent support with the MODIS data and to the various teams that produced information used in this study. We also thank the anonymous Reviewers for the sincere effort to improve this contribution.

References

Ackerman, S. A., Strabala, K., Menzel, P., Frey, R., Moeller, C., Gumley, L., Baum, B., Seeman, S. W., and Zhang, H. (1998,) Discriminating clear-sky from cloud with MODIS: algorithm theoretical basis document (MOD35),J. Geo. Res. Atmos. V.103, Dec. 27, 1998.

Augustine, J. A., G. B. Hodges, C. R. Cornwall, J. J. Michalsky, C. I. Medina (2005), An update on SURFRAD - The GCOS Surface Radiation budget network for the continental United States, J. Atmos. Oceanic Technol.,22 (10), 1460-1472.

Cronin, M.F. and M.J. McPhaden (1997), The upper ocean heat balance in the western equatorial Pacific warm pool during September-December 1992,J. Geophys. Res., 102, 8533-8553.

Edwards, J. M., and A. Slingo (1996), Studies with a flexible new radiation code. I: choosing a configuration for a large-scale model, Quart. J. Roy. Meteor. Soc, 122, 689–719.

Gupta, S. K., N. A. Ritchey, A. C. Wilber, C. H. Whitlock, G. G. Gibson, and P. W. Stackhouse Jr. (1999),a climatology of surface radiation budget derived from satellite data,J. Climate, 12, 2691-2710.

Hess, M., P. Koepke, and I. Schultz (1998), Optical Properties of Aerosols and Clouds: The software package OPAC, Bull. Am. Met. Soc., 79, 831-844.

King, M. D., Kaufman, Y. J., Menzel, W. P., and Tanré, D. (1992), Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS),IEEE Transactions on Geoscience and Remote Sensing, 30, 2-27.

Martonchik, J. V., D. J. Diner, R. Kahn, T. P. Ackerman, M. M. Verstraete,

B. Pinty, and H. R. Gordon (1998), Techniques for the retrieval of aerosol

properties over land and ocean using multi-angle imaging, Transactions on Geoscience and Remote Sensing, 36(4), 1212– 1227. McPhaden, M. J., and Coauthors (1998), The Tropical Ocean-Global Atmosphere observing system: A decade of progress, J. Geophys. Res.,(C7), 14,169-14,240.

Moody, E. G., M. D. King, S. Platnick, C. B. Schaaf, and F. Gao, 2005: Spatially complete global spectral surface albedos: Value-Added datasets derived from Terra MODIS land products. IEEE Transactions on Geoscience and Remote Sensing, 43, 144-158.

Mueller, R.W., Dagestad, K. F., Ineichen, P., Schroedter, M., Cros, S., Dumortier, D., Kuhlemann, R., Olseth, J. A., Piernavieja, C., Reise, C., Wald, L. and Heinemann, D. (2004), Rethinking satellite based solar irradiance modeling - The SOLIS clear sky module,Remote Sensing of the Environment, Vol. 91, pp. 160 – 174

Nemani, R. R., Charles D. Keeling, Hirofumi Hashimoto, William M. Jolly, Stephen C. Piper, Compton J. Tucker, Ranga B. Myneni, Steven W. Running (2003), Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999,Science, 300.

Ohmura A., E.G. Dutton, B. Forgan, C. Froelich, H. Gilgen, H. Hegner, A. Heimo, G. Koenig-Langlo, B. McArthur, G. Mueller, R. Philipona, R. Pinker, C.H. Whitlock, K. Dehne, M. Wild, 1998: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull.Am. Met.Soc., 79, 2115-2136.
Galen, H., M. Wild and A. Ohmura, 1998: Means and trends of shortwave incoming radiation at the surface estimated from Global Energy Balance Archive data,J. of Climate, 11, 2042-2061.

Pinker, R. and J. Ewing (1985), Modeling surface solar radiation: Model formulation and validation,J. Climate Appl. Meteor., 24, 389-401.

Pinker, R. T. and I. Laszlo (1992), Modeling surface solar irradiance for satellite application on a global scale, J. Appl. Meteor., 31, 194-211.

Platt, T. (1986), Primary production of the ocean water column as a function of surface light intensity: Algorithms for remote sensing, Deep Sea Res., 33, 149-163.

Ramanathan, V. (1986), Scientific use of surface radiation budget data for climate studies. Surface Radiation Budget for Climate Application, J. T. Suttles and G. Ohring, Eds., NASA Reference Publication 1169, 58-86

Raschke, E., Stuhlmann R., Palz W., Steemers T. C. (1991),Solar Radiation Atlas of Africa, Publishedfor the Commission of the European Communities by A. Balkema, Rotterdam, 155 p.

Rigollier C., Lef`evre M., and Wald L. (2004), The method Heliosat-2 for deriving shortwave solar radiation data from satellite images, Solar Energy, 77(2), 159–169, 2004.