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  • View in gallery

    The FLASHFlux SSF dataflow diagram shows the subsystems that provide the MOA input data in beige, the clouds processing in blue, the spectral correction coefficients process in orange, and the inversion of the radiances into SSF fluxes in green. The rectangles indicate datasets: those with black outlines signify input parameters, while those without outlines signify intermediate parameters, and finally the rectangle with the boldface outlines and text signify the final SSF results. Ovals and circles indicate processing algorithms, with the boldface text signifying the principal algorithm in each subsystem.

  • View in gallery

    Comparison of the 2009–10 FLASHFlux minus CERES mean gridded 1° × 1° daytime LW TOA flux differences (W m−2) at the overpass times for (a) Terra and (c) Aqua; and (b),(d) the corresponding RMS differences, respectively.

  • View in gallery

    As in Fig. 2, but for the SW.

  • View in gallery

    As in Fig. 2, but for the LW surface flux.

  • View in gallery

    As in Fig. 4, but for the SW.

  • View in gallery

    Comparison of the FLASHFlux model-derived LW surface fluxes using Terra and Aqua TOA measurements at the overpass times with the (left) ground-measured and (right) the CERES model-derived LW surface fluxes for clear-sky conditions. The plots represent two-dimensional histograms that illustrate the number of coincident flux values found within each 20 W m−2 square bin. The legend defines the number of values within each bin.

  • View in gallery

    As in Fig. 6, but for cloudy-sky conditions.

  • View in gallery

    As in Fig. 6, but for the SW.

  • View in gallery

    Intercomparison of the FLASHFlux model-derived SW surface fluxes for clear-sky conditions using (left) the original LPSA Rayleigh scattering formulation and the WCP-55 aerosols, (middle) the FLASHFlux model-derived SW surface fluxes using the revised LPSA with the Bodhaine et al. (1999) Rayleigh scattering formulation and the MATCH aerosols, and (right) the ground-measured fluxes.

  • View in gallery

    As in Fig. 8, but for cloudy-sky conditions.

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The Fast Longwave and Shortwave Flux (FLASHFlux) Data Product: Single-Scanner Footprint Fluxes

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  • 1 Science Directorate, NASA Langley Research Center, Hampton, Virginia
  • 2 Science Systems and Applications Inc., Hampton, Virginia
  • 3 Colorado State University, Fort Collins, Colorado
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Abstract

The Clouds and the Earth’s Radiant Energy Systems (CERES) project utilizes radiometric measurements taken aboard the Terra and Aqua spacecrafts to derive the world-class data products needed for climate research. Achieving the exceptional fidelity of the CERES data products, however, requires a considerable amount of processing to assure quality and to verify accuracy and precision, which results in the CERES data being released more than 6 months after the satellite observations. For most climate studies such delays are of little consequence; however, there are a significant number of near–real time uses for CERES data products. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was therefore developed to provide a rapid release version of the CERES results, which could be made available to the research and applications communities within 1 week of the satellite observations by exchanging some accuracy for speed. FLASHFlux has both achieved this 1-week processing objective and demonstrated the ability to provide remarkably good agreement when compared with the CERES data products for both the instantaneous single-scanner footprint (SSF) fluxes and the time- and space-averaged (TISA) fluxes. This paper describes the methods used to expedite the production of the FLASHFlux SSF fluxes by utilizing data from the CERES and Moderate Resolution Imaging Spectroradiometer instruments, as well as other meteorological sources. This paper also reports on the validation of the FLASHFlux SSF results against ground-truth measurements and the intercomparison of FLASHFlux and CERES SSF results. A complementary paper will discuss the production and validation of the FLASHFlux TISA fluxes.

Corresponding author address: Dr. David P. Kratz, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681-2199. E-mail: david.p.kratz@nasa.gov

Abstract

The Clouds and the Earth’s Radiant Energy Systems (CERES) project utilizes radiometric measurements taken aboard the Terra and Aqua spacecrafts to derive the world-class data products needed for climate research. Achieving the exceptional fidelity of the CERES data products, however, requires a considerable amount of processing to assure quality and to verify accuracy and precision, which results in the CERES data being released more than 6 months after the satellite observations. For most climate studies such delays are of little consequence; however, there are a significant number of near–real time uses for CERES data products. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was therefore developed to provide a rapid release version of the CERES results, which could be made available to the research and applications communities within 1 week of the satellite observations by exchanging some accuracy for speed. FLASHFlux has both achieved this 1-week processing objective and demonstrated the ability to provide remarkably good agreement when compared with the CERES data products for both the instantaneous single-scanner footprint (SSF) fluxes and the time- and space-averaged (TISA) fluxes. This paper describes the methods used to expedite the production of the FLASHFlux SSF fluxes by utilizing data from the CERES and Moderate Resolution Imaging Spectroradiometer instruments, as well as other meteorological sources. This paper also reports on the validation of the FLASHFlux SSF results against ground-truth measurements and the intercomparison of FLASHFlux and CERES SSF results. A complementary paper will discuss the production and validation of the FLASHFlux TISA fluxes.

Corresponding author address: Dr. David P. Kratz, NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681-2199. E-mail: david.p.kratz@nasa.gov

1. Introduction

Defining the radiative energy exchange at the top of the earth–atmosphere system and at the earth’s surface has long been identified as critical to understanding climate processes (Suttles and Ohring 1986; GCOS 2003) and remains an active focus of research (Stephens et al. 2012; Wild et al. 2013). The reflected shortwave (SW) and outgoing longwave (LW) fluxes at the top of the atmosphere (TOA) constitute the radiative energy exchange of the entire earth–atmosphere system driven by the magnitude of the incoming total solar irradiance, while the surface energy budget constitutes the energy exchange between the atmosphere and the earth’s surface driven by radiative, sensible, and latent heating processes. Accurate derivations of the TOA and surface radiative quantities allow for closure in the atmospheric radiative budget and improvement in the calculations of the inferred heat transports within the earth–atmosphere system. The LW and SW net surface fluxes affect the heating–cooling of the surface and thus provide bounds for sensible and latent heat fluxes, as well as estimates for the horizontal oceanic and atmospheric heat transport. Changes to the input energy into the surface systems affect short- and long-term weather- and climate-related processes (Fasullo and Trenberth 2008a,b). Changes to cloudiness, aerosols, and gaseous profiles regulate the TOA and surface fluxes and, as such, ultimately impact the earth’s energy balance and climate. Even changes to the surface reflective–emissive properties, such as those caused by evolving land use or ice–snow coverage, can have profound effects on net surface fluxes, resulting in important regional feedbacks (Hall 2004). For these reasons, quantifying the surface radiative fluxes through changes to surfaces and the atmospheric state constitutes an important step in understanding the processes relating weather variability and climate variations.

Before the present work, however, every major global TOA and surface radiation dataset was made available 6 months to several years after the actual satellite overpass. This includes the Clouds and the Earth Radiant Energy System (CERES) fluxes (Wielicki et al. 1996), International Satellite Cloud Climatology Project (ISCCP) fluxes (Zhang et al. 2004), and the National Aeronautics and Space Administration’s (NASA) Global Energy and Water Cycle Exchanges (GEWEX) surface radiation budget fluxes (Whitlock et al. 1995; Gupta et al. 1999; Stackhouse et al. 2011), where the emphasis is on the production of high-quality fluxes for climate research. Nevertheless, the importance of deriving TOA and surface radiative fluxes in near–real time has become increasingly evident as demonstrated by the studies such as Kay et al. (2008). In that study, the availability of near-real-time data allowed for a timely examination of the net surface radiative fluxes, revealing differences of 28 W m−2 in the net surface radiative fluxes for the western Arctic between the years 2007 and 2008. Kay et al. (2008) further attributed these flux difference to reductions in the clouds and sea ice amounts over that region during the summer of 2007. Thus, the objective of the Fast Longwave and Shortwave Radiative Flux (FLASHFlux) effort is to collect and process the near-real-time data and, then, make these results available within 1 week of the initial satellite measurements with the highest degree of accuracy possible within the limits of this time constraint.

The near-real-time fluxes provided by the FLASHFlux data product are satisfying the critical needs of organizations that require higher degrees of accuracy than those provided by synoptic weather forecasting datasets but whose processing schedules cannot wait for the climate quality datasets provided by systems such as CERES. For instance, FLASHFlux can contribute significantly to near-real-time data analyses such as CERES instrument calibration and subsystem quality checks, operational processing of CloudSat data (L’Ecuyer et al. 2008; Henderson et al. 2013), state-of-the-climate studies (Wong et al. 2012; Yu et al. 2012), experimental field programs (Mlynczak et al. 2006), operational ground testing of instruments such as the Scanner for Radiation Budget (ScaRaB) on Megha-Tropiques (O. Chomette 2013, personal communication), solar energy and agricultural applications, ocean and land assimilations, and education and public outreach programs (Chambers et al. 2003). FLASHFlux also has the potential to act as a prototype to help demonstrate the Interagency Working Group on Earth Observations/Global Earth Observation System of Systems (IWGEO/GEOSS).

Our discussion begins in section 2 with a synopsis of the method used to construct the FLASHFlux data product, emphasizing the process from data acquisition through the production of the single-scanner footprint (SSF) fluxes. A brief review is then provided in section 3 for the individual subsystems used to derive the FLASHFlux SSF fluxes from the TOA measurements and ancillary data. A thorough discussion is presented in section 4 wherein the FLASHFlux version 2G SSF results for the years 2009 and 2010 are intercompared with the CERES Terra and Aqua edition 3A SSF data and validated against surface measurements. A brief note is then provided as a guide to accessing the data, followed by the conclusions.

2. The FLASHFLux data product

The FLASHFlux effort was undertaken to develop an operational system that uses existing CERES processing techniques to derive global near-real-time radiative fluxes for the TOA and surface. Following the methods developed within the CERES project, FLASHFlux uses satellite data from a number of sources to obtain the LW and SW surface fluxes. Primary among these sources is the measurement of TOA radiances provided by the CERES instrument in three broadband channels: total (0.2–100 μm), SW (0.2–5 μm), and the infrared window (8–12 μm). The FLASHFlux effort uses modified versions of the algorithms developed for the CERES project to invert the TOA radiances into SW, LW (5–100 μm), and infrared window fluxes. Unlike CERES, however, FLASHFlux does not wait for the precisely calibrated radiances to be derived from the CERES measurements (Priestley et al. 2011), but instead uses previously derived best estimates of the CERES calibration available at the time of measurement. By not waiting for the latest calibration results, the FLASHFlux processing can be expedited to provide the user with near-real-time data. In addition to the CERES TOA data, FLASHFlux also incorporates the temperature and humidity profiles provided by the Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS). Once again, unlike the CERES climate data records, which emphasize algorithmic stability, the FLASHFlux data product does not depend upon a frozen assimilation for the temperature and humidity data and thus, is able to use the latest release of the GMAO-GEOS datasets (Bloom et al. 2005; Rienecker et al. 2008). Every upgrade, however, has the potential to produce a discontinuity in the data record and, thus, care has been taken to document any changes to the inputs, to quantify the effects of these changes on the derived fluxes, and to ensure that the upgrades to the input datasets are reasonable. FLASHFlux also obtains ozone amounts from the National Centers for Environmental Prediction’s (NCEP's) Stratosphere Monitoring Ozone Blended Analysis (SMOBA; Yang et al. 2012). Cloud properties are derived using the CERES processing strategy (Minnis et al. 2011a,b), which relies upon the imager data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (Salomonson et al. 1989).

FLASHFlux uses the all-sky surface flux algorithms from CERES in conjunction with the ancillary TOA quantities to ensure the retrieval of FLASHFlux SSF surface fluxes for every footprint where valid CERES data are available. Nominally, this process is completed within 4 days of the satellite measurements. The FLASHFlux data product is, therefore, able to provide reliable estimates of the TOA and surface radiative properties over the entire globe in near–real time and certainly well before the production of the climate-quality CERES flux results, which typically are made available some 6–12 months after the observations.

The FLASHFlux processing system can be schematically represented by a dataflow diagram (Fig. 1) that outlines both the process of satellite data acquisition and the application of algorithmic techniques used to derive surface and atmospheric properties for various research activities. Wielicki et al. (1998) presented a comprehensive dataflow diagram for the CERES processing, from which the FLASHFlux processing path has been derived. Since the focus of the present endeavor is directed toward the derivation of the FLASHFlux SSF results from the satellite measurements, only those components of the processing effort leading to the SSF fluxes are illustrated and discussed in the following sections. The spatial and temporal averaging of the data to produce global data products will be the focus of a future paper.

Fig. 1.
Fig. 1.

The FLASHFlux SSF dataflow diagram shows the subsystems that provide the MOA input data in beige, the clouds processing in blue, the spectral correction coefficients process in orange, and the inversion of the radiances into SSF fluxes in green. The rectangles indicate datasets: those with black outlines signify input parameters, while those without outlines signify intermediate parameters, and finally the rectangle with the boldface outlines and text signify the final SSF results. Ovals and circles indicate processing algorithms, with the boldface text signifying the principal algorithm in each subsystem.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

3. Derivation of single-scanner footprint results from measured quantities

The subsystems responsible for the derivation of the SSF fluxes within the FLASHFlux data processing formulation are presented in the dataflow diagram in Fig. 1. A brief synopsis is provided for each of these four subsystems (MOA, Clouds, Spectral Correction Coefficients, and Inversion), which were used to derive the TOA and surface fluxes from the satellite measurements.

  1. The meteorology, ozone, and aerosol (MOA) subsystem (beige in Fig. 1) processes information concerning the basic state of the atmosphere and makes that information available to the other subsystems. Meteorological data, such as the water vapor amount, the surface temperature, and the air temperatures, are obtained from the most recently available version of the NASA Goddard GEOS-GMAO four-dimensional data analysis product. No effort has been made to constrain this database, and therefore differences in the GEOS-GMAO data assimilations are reflected in the FLASHFlux output data. For the intercomparison between the FLASHFlux and CERES results presented in section 4, FLASHFlux version 2G uses the standard GEOS 5.2.0 (Rienecker et al. 2008), which may be updated to reflect minor changes to the processing algorithm, while CERES edition 3A uses a special GEOS 5.2.0 (G5-CERES) specifically created for climate studies and, thus, relies upon a static processing algorithm. As received, the resolutions of both GEOS-GMAO datasets are currently 0.5° × 0.667° latitude–longitude. The atmospheric synoptic products are 6-hourly and the surface synoptic and time-averaged products are 3-hourly. After acquisition, the data are reformatted to the standard CERES 1° nested processing grid. Ozone data are obtained from the SMOBA products (Yang et al. 2012) with a climatological map serving as a backup if the SMOBA data are unavailable at the time of processing. For the time frame of this study, the SMOBA data were always available on time and, thus, the climatological map was not accessed.
  2. The clouds subsystem (blue in Fig. 1) relies upon a set of algorithms developed by Minnis et al. (2011a,b) for the CERES project that brings together the MOA data, the CERES snow/ice maps derived from National Snow and Ice Data Center data (Key et al. 2001), the CERES clear-sky reflectance history (Baum et al. 1997), and the MODIS imager data (Platnick et al. 2003) for the purpose of determining the footprint-level values for cloud amount, cloud height, and other cloud optical and physical characteristics. Clouds convolves these cloud properties together with the CERES Instrument Earth Scan (IES) filtered radiances, that is, those which are uncorrected for the spectral response function and gains, by weighting the values over the point-spread function (Priestley et al. 2010) for each CERES footprint, which has an equivalent nadir diameter of 20 km at the altitude of the Terra and Aqua spacecrafts (~702 km). This results in the determination of the cloud properties on the footprint basis and provides for the scene identification (Minnis et al. 2011a,b). This process also results in the production of an intermediate SSF dataset that is available for input into the FLASHFlux inversion subsystem.
  3. The spectral correction coefficients (orange in Fig. 1) are used in the retrieval of the unfiltered radiances from the measured (filtered) radiances, which require a precise accounting of any changes to the spectral response functions and gains of the CERES instrument. For CERES production, a time-dependent analysis is used to derive the spectral response functions and gains (Priestley et al. 2011), which results in a substantial but necessary delay in the production of the climate-quality data. Since FLASHFlux does not produce a climate-quality data record, the strict data accuracy standards required for the CERES climate-quality data record can be relaxed to achieve a more rapid dissemination of the data. Thus, the FLASHFlux production process is able to use the most recently released spectral response functions and gains, hereafter combined together and referred to as the spectral correction coefficients or SCCs. Since the CERES data products are based on time-dependent SCCs, while the FLASHflux data products are based on SCCs that remain constant between updates, the offset between the two data products will not necessarily remain constant with time unless the SCCs remain constant. The result of the application of the SCCs upon the measured total, SW, and window TOA filtered radiances is the production of the unfiltered LW, SW, and window TOA radiances.
  4. The inversion (green in Fig. 1) of the unfiltered TOA radiances uses sophisticated angular distribution models (Loeb et al. 2005, 2007) to derive the TOA fluxes. Well-established LW and SW algorithms are then applied to the TOA data to derive the surface fluxes (Kratz et al. 2010). Unlike the CERES processing, however, the FLASHFlux processing relies upon the available data at the time of processing and, thus, allows some degree of accuracy to be exchanged for rapid delivery of the results. For the case of total solar irradiance this results in FLASHFlux using a constant value (1365 W m−2 in version 2G) rather than the time-dependent total solar irradiance values (Kopp and Lean 2011). The LW fluxes are produced using the Langley Parameterized Longwave Algorithm (LPLA), which was developed from an accurate narrowband radiative transfer model (Gupta 1989). The LPLA is based on the premise that the LW TOA and surface fluxes are decoupled, and is used to calculate the surface LW fluxes for both clear and cloudy conditions. The downward LW flux (DLF) is computed in terms of an effective emitting temperature of the atmosphere, the column water vapor, the fractional cloud amount, and the cloud-base height for each footprint (Gupta et al. 1992). The effective emitting temperature is a weighted average of the surface skin temperature and temperatures of the lower-tropospheric layers. The SW fluxes are produced using the Langley Parameterized Shortwave Algorithm (LPSA) (Gupta et al. 2001), which consists of a rapid method of accounting for the attenuation of the solar radiation separately for clear- and cloudy-sky conditions. LPSA calculates the surface insolation directly by using the incident TOA SW flux, the transmittance of the clear atmosphere, and the transmittance of the clouds (Darnell et al. 1988, 1992; Gupta et al. 2001). The clear-sky transmittance is calculated using a simple parameterized expression for the broadband extinction optical depth as well as a backscatter term. The broadband extinction optical depth is dependent upon H2O, O3, CO2, and O2 absorption; Rayleigh and aerosol scattering; and the solar zenith angle. The backscatter term represents an enhancement of the downward radiation caused by SW energy being scattered back to the surface by the atmosphere after having been reflected upward from the surface. The cloud transmittance is calculated using a standard threshold method, similar to that of Moser and Raschke (1984), which utilizes the overcast, clear, and measured reflectances associated with each CERES footprint.

As with CERES edition 3, the current version of the FLASHFlux SSF dataset contains 167 parameters associated with the corresponding footprint radiances. These parameters contain information on time and position, viewing angles, surface maps, scene type, filtered and unfiltered radiances, TOA and surface fluxes, footprint area (clear, cloudy, and all), footprint imager radiance statistics, and MODIS land and ocean aerosols. In practice, the FLASHFlux SSF dataset provides everything to estimate the surface radiation budget along with the flux derivations from the LW Model (LPLA) and SW Model (LPSA). The FLASHFlux SSF products have been produced, archived, and made publicly available since 1 January 2007 for both the Terra and Aqua satellites. The FLASHFlux data are intended to remain available until the CERES climate quality data for the corresponding times are produced and made publicly available.

4. Comparisons with CERES-derived and surface-measured fluxes

The FLASHFlux data product was created to provide CERES data on a near-real-time basis while simultaneously adhering to the CERES philosophy of collecting together only the highest quality data possible. Time constraints established for the FLASHFlux production, however, prevent the data processing from waiting for the most highly refined SCCs or accommodating significant delays in the assimilation of the ancillary data. Yet the extent to which the processing can be further accelerated is restricted by the requirement that FLASHFlux provide data having accuracies that satisfy the needs of studies such as those previously discussed in section 2. To quantify the consequences of the production acceleration and to test the accuracy of the FLASHFlux data, we compared the FLASHFlux version 2G surface fluxes to both the derived CERES edition 3A surface fluxes from the LPLA and LPSA algorithms and the measured surface fluxes at various validation sites for the years 2009 and 2010. This time frame was chosen since both FLASHFlux version 2G and CERES edition 3A were available for all of 2009 and 2010, thereby allowing for an intercomparison between unchanged versions of the processing codes and the inputs. Even though the CERES instruments aboard the Terra and Aqua spacecrafts are capable of various scanning modes (Wielicki et al. 1998), the present validation study has considered only the CERES cross-track data.

While the FLASHFlux version 2G processing uses essentially the same algorithms and inputs as the CERES edition 3A processing, there are some notable differences. For instance, since the CERES project requires climate quality data, the CERES processing waits for updated SCCs that correspond directly with the time of observation (Priestley et al. 2011). In contrast, FLASHFlux processing uses the most recently available SCCs, even though these coefficients can induce nonnegligible differences in the retrievals. Both CERES and FLASHFlux processing use the MODIS Collection 5 and GEOS 5.2.0 meteorological input data, which were available for the 2009 and 2010 time frame considered in this study. Unlike the MODIS data, however, the GEOS data undergo periodic upgrades to their analysis system. Since such upgrades could produce changes to the data product that could be misinterpreted as climate signals, the CERES project has arranged for the production and use of a special frozen version of the GEOS data, known as G5-CERES. Having no such requirement, FLASHFlux uses the most recently available version of the GEOS meteorology. To improve accuracy, FLASHFlux version 2G also uses modified versions of the Terra and Aqua cloud detection and identification algorithms that were developed for the CERES edition 3 processing, and which deal with the transition between polar and nonpolar algorithms and ozone absorption. The FLASHFlux 2G LW algorithm, however, does not include an interim improvement that was incorporated into the CERES edition 3A processing. This improvement excludes cases that would otherwise tend to produce severe temperature inversions caused by very cold, very dry conditions, such as those encountered over the Antarctic Plateau (Hudson and Brandt 2005). By preventing these severe near-surface temperature inversions in the calculation of the downward LW surface fluxes, the CERES 3A algorithm avoids the retrieval of unrealistically low thermal emission at the surface. A further refinement of the inversion correction technique, which limits all inversions to 10 K km−1, has since been developed and will be incorporated into the processing algorithms for CERES 4A and the next version of FLASHFlux.

Since both the Terra and Aqua spacecrafts orbit at an altitude of approximately 702 km, the nominal size of the CERES nadir-viewing footprint is approximately 20 km. To facilitate comparisons between the surface fluxes derived from TOA retrievals and those obtained from surface measurements, we averaged together the CERES footprint values located within 1 min of time and 10 km of distance of the surface sites. We have also applied the Gupta et al. (2004) recommendation to average the surface site measurements over 1 min for the clear- and cloudy-sky LW fluxes and the clear-sky SW fluxes, and over 60 min for the cloudy-sky SW fluxes. For our present purposes, we have defined cloudy sky to represent those cases that have cloud amounts greater than 0.1%, and clear sky to represent those cases that have cloud amounts less than or equal to 0.1%. The locations of the surface validation sites and a description of our handling of the surface data have been discussed in section 3 of Kratz et al. (2010). While several of the surface sites used in that study did not provide data during the time frame of our present study, and several additional surface sites have subsequently begun to provide data, overall, the majority of the surface sites have continued to provide data as before. To avoid ambiguity in the sign of the fluxes, the CERES project has defined all fluxes directed into the surface–atmosphere system as being positive (warming), and all fluxes directed out of the surface–atmosphere system as being negative (cooling).

To provide a global perspective on the differences between the FLASHFlux and CERES data retrievals, we have examined both the mean differences (FLASHFLux − CERES) and the root-mean-square (RMS) differences for both the TOA and the surface fluxes. To facilitate this comparison, the instantaneous footprint data falling within each 1° × 1° element of the grid system overlaying the earth have been linearly averaged together for both LW and SW fluxes at the daytime overpass times, which are 1030 LT for Terra and 1330 LT for Aqua for each of the 730 days within the years 2009 and 2010. The field of the mean differences provides an overall visualization of the impact of implementing the requirement for rapid processing by FLASHFlux, while the field of RMS values for those differences provides a visualization of the variability of the mean differences. This section also considers the intercomparison of the SSF surface fluxes retrieved by FLASHFlux with both the SSF surface fluxes retrieved by CERES and the measurements obtained from instrumentation at the surface validation sites.

a. Geographical distributions

1) LW TOA

Figure 2 illustrates the distribution of the FLASHFlux and CERES daytime LW TOA flux differences and RMS flux differences for Terra and Aqua at the overpass times for 2009 and 2010. Immediately evident in both the Terra (Fig. 2a) and Aqua (Fig. 2c) daytime LW TOA flux comparisons are the global average biases of −3.8 (−1.6%) and −3.2 W m−2 (−1.3%), respectively, along with spatially fluctuating patterns ranging from 0 to −9 W m−2. The corresponding RMS differences for Terra (Fig. 2b) and Aqua (Fig. 2d) represent the magnitude of the temporal variability, which averages around 4 W m−2 globally while having spatial differences within 10 W m−2. The spatial patterns in the RMS difference fields generally follow the spatial patterns of the mean difference fields. The fluctuating pattern is associated with modifications in the cloud retrieval algorithms used by FLASHFlux beyond those used by CERES. Such cloud retrieval modifications indirectly affect the TOA fluxes through changes in the scene-type selection by the angular distribution models (ADMs) used in those retrievals (Loeb et al. 2005, 2007). Meanwhile, the systematic differences between FLASHFlux and CERES can be attributed to differences between the earlier SCCs used by FLASHFlux and the more recent SCCs used by CERES. Since the daytime TOA LW flux is computed from the combined retrievals of the LW portion of the total channel, plus the SW portion of the total channel, minus the SW channel, that is,
e1
and since each of these terms have their own SCCs (Priestley et al. 2011), there are several potential sources for the observed systematic differences. Since the LW portion of the total channel only affects the nighttime LW flux, the effect of the LW component can be easily separated from the effects of the SW components. Comparisons between the FLASHFlux and CERES nighttime LW fluxes taken at the overpass times of 1030 local time (LT) for Terra and 0130 LT for Aqua show a modest (less than −0.5 W m−2) globally averaged flux difference for both Terra and Aqua that is slightly greater near the equator than at the poles. The magnitude of this difference is sufficiently small that no difference would be visible using the scale presented in Fig. 2. Thus, no figure is presented for the LW nighttime flux comparisons.
Fig. 2.
Fig. 2.

Comparison of the 2009–10 FLASHFlux minus CERES mean gridded 1° × 1° daytime LW TOA flux differences (W m−2) at the overpass times for (a) Terra and (c) Aqua; and (b),(d) the corresponding RMS differences, respectively.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Table 1 presents the systematic and RMS differences between the FLASHFlux and CERES retrievals for the LW TOA fluxes derived from the CERES radiance measurements taken aboard both the Terra and Aqua spacecrafts. The results, which consider the combined day and night overpass times, are reported for seven scene types derived from a set of 20 International Geosphere–Biosphere Programme (IGBP) surface types (Loveland et al. 2000) that are representative of large areas of the earth’s surface. The results in Table 1 for cloudy-sky conditions show only modest variations in bias and RMS across the various scene types and are consistent with the combined daytime and nighttime results discussed in the previous paragraph, which indicate that persistent differences in cloud cover rather than differences in the scene types dominate the observed LW TOA flux differences that have not already been explained by the SCC differences. Since the systematic differences in the daytime LW fluxes cannot be entirely attributed to differences in the LW portion of the total channel SCCs, some combination of the SW channel and/or SW portion of the total channel measurements must be causing these systematic differences. Thus, to understand fully the observed differences in the daytime LW fluxes, we will next examine the differences in the SW fluxes.

Table 1.

Difference between the FLASHFlux and CERES LW TOA fluxes (FLASHFlux − CERES) for clear-sky and cloudy-sky conditions based on the Terra and Aqua measurements at the overpass times. The seven scene types are representative of the earth’s surface and consist of subgroups derived from the 20 IGBP surface types. The first column represents the scene type. The second and fourth columns represent the systematic differences (bias) and the third and fifth columns represent the RMSs between the FLASHFlux and CERES model-derived fluxes for the clear- and cloud-sky cases, respectively.

Table 1.

2) SW TOA

Figure 3 illustrates the distribution of the FLASHFlux and CERES SW TOA flux differences and RMS flux differences for Terra and Aqua at the overpass times for 2009 and 2010 using the same format as in Fig. 2. Since the SW TOA fluxes are retrieved using only the SW channel measurements, any differences between the FLASHFlux and CERES retrievals that are not attributed to differences in the cloud algorithm and ADMs must be directly attributed to the SW channel SCCs. Thus, the SW TOA flux comparisons allow for an understanding of the effect of changing the SW SCC as well as providing information for an understanding of the differences in the daytime LW TOA flux retrievals.

Fig. 3.
Fig. 3.

As in Fig. 2, but for the SW.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Unlike the daytime LW TOA flux comparisons, which yield similar difference patterns for both Terra and Aqua, the SW TOA flux comparisons yield dissimilar patterns for Terra and Aqua. Specifically, the Terra SW TOA flux differences reveal a very small systematic difference, approximately 0.2 W m−2 (0.05%), along with a spatially fluctuating pattern ranging from −7 to 5 W m−2 that can be attributed to modifications in the cloud algorithm. This effect is especially noticeable for the modifications that treat the transition from polar to nonpolar clouds. Since the systematic difference in the daytime Terra LW TOA flux is −3.8 W m−2, the nighttime Terra LW TOA flux is −0.2 W m−2, and the Terra SW TOA flux is 0.2 W m−2, then according to Eq. (1), the systematic difference in the SW portion of the Total channel is approximately −3.4 W m−2. Each of these values is in good agreement with the observed differences in the SCCs between the CERES editions 2 and 3 (K. Priestley 2013, personal communication).

In contrast with the Terra SW TOA results, the Aqua SW TOA flux comparisons reveal a significant systematic difference, approximately −3.2 W m−2 (−1.4%). As before, superimposed upon this difference is a spatially fluctuating pattern ranging from −9 to 16 W m−2 that can be attributed to modifications in the cloud algorithm, though in this case, the differences occurring at lower latitudes are comparable to the differences produced by the polar to nonpolar cloud transition. Since the systematic difference in the daytime Aqua LW TOA flux is −3.3 W m−2, the nighttime Aqua LW TOA flux is −0.5 W m−2, and the Aqua SW TOA flux is −3.2 W m−2, then according to Eq. (1), the systematic difference in the SW portion of the Total channel should be approximately −6.0 W m−2. As before, each of these values agrees with the observed differences in the SCCs between the CERES editions 2 and 3 (K. Priestley 2013, personal communication). The corresponding RMS differences for Terra and Aqua represent the magnitude of the temporal variability, which globally averages around 2 W m−2 for Terra and 4 W m−2 for Aqua, while having spatial differences within 10 W m−2. The spatial patterns in the RMS difference fields generally follow the spatial patterns of the mean difference fields.

Table 2 presents the systematic and RMS differences between the FLASHFlux and CERES retrievals for the SW TOA fluxes derived from the CERES radiance measurements taken aboard the Terra and Aqua spacecrafts. As with the LW TOA fluxes, the results for the SW TOA fluxes indicate that persistent differences in cloud cover rather than differences in the scene types dominate the observed SW TOA flux differences observed in Fig. 3 that have not already been explained by the SCC differences. There is, however, a tendency for larger RMS differences between CERES and FLASHFlux for the snow/ice surface types in both the Terra and Aqua data.

Table 2.

As in Table 1, but for the SW.

Table 2.

3) LW surface

Figure 4 illustrates the distribution of the FLASHFlux and CERES daytime LW surface flux differences and RMS flux differences for Terra and Aqua at the overpass times for 2009 and 2010 using the same format as in Fig. 2. Comparing Fig. 4 to Fig. 2 reveals no discernable similarity in the LW TOA and surface flux differences for the intercomparison of the FLASHFlux and CERES results. This is not surprising since the LPLA, which is used to retrieve the LW surface fluxes, was developed on the premise that the LW TOA and surface fluxes are essentially decoupled for all-sky conditions (Gupta 1989). Indeed, the LW surface fluxes are found to be much more dependent upon atmospheric state variables such as the effective emitting temperature of the lower atmosphere, column water vapor amount, fractional cloud amount, and cloud-base height, rather than upon the LW TOA fluxes. Previous studies (e.g., Kratz et al. 2010; Gupta et al. 2010) confirmed that the LPLA provides accurate retrievals of the surface fluxes, and thus no concern is raised by the lack of correlation between the daytime LW TOA flux differences presented in Fig. 2 and the daytime LW surface flux differences presented in Fig. 4. Since comparisons between the FLASHFlux and CERES nighttime LW surface flux results yield similar results to the daytime LW surface flux results presented in Fig. 4, the nighttime LW surface flux comparisons are not presented separately. While many of the LW surface flux differences presented in Fig. 4 are a tangle of effects from several sources, some of the observed LW surface flux differences can be specifically attributed to a particular source. For instance, the CERES edition 3A processing avoids calculating unrealistically low thermal emission to the surface for cases involving high altitude (pressure < 700 hPa) and high latitude (>70°) by neglecting the effects of strong inversions on the calculations of downward fluxes. Thus, the FLASHFlux 2G processing, which has not implemented this correction, tends to produce lower downward LW flux estimates under conditions similar to those encountered over Greenland and much of coastal Antarctica. Other differences attributed to modifications implemented in the CERES edition 3A code include the increased cloud fractions attributed to modifications in the cloud identification method along with increased cloud temperatures attributed to lower cloud altitudes resulted in increased LW surface fluxes over the Tibetan Plateau. Meanwhile decreased cloud fractions due to modifications in the cloud identification method in the CERES 3A code have resulted in decreased LW surface fluxes over northwestern North America. In addition, lower cloud altitudes in the Terra retrievals produced higher cloud-base temperature over the Sea of Okhotsk, which resulted in higher LW surface fluxes for that location. The spatial patterns in the RMS difference fields generally follow similar patterns for both Terra and Aqua and globally average about 4 W m−2. The global difference (FLASHFlux minus CERES) for the LW surface fluxes, however, is only −1.1 W m−2 (−0.4%) for Terra and −0.6 W m−2 (−0.2%) for Aqua.

Fig. 4.
Fig. 4.

As in Fig. 2, but for the LW surface flux.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Table 3 presents the systematic and RMS differences between the FLASHFlux and CERES retrievals for the LW surface fluxes calculated from measurements taken aboard the Terra and Aqua spacecrafts. Even though the LW surface fluxes are largely decoupled from the LW TOA fluxes, the results for the LW surface fluxes still indicate that persistent differences in cloud cover rather than differences in the scene types dominate the observed LW surface flux differences, with the notable exception of the snow/ice scene type for clear-sky conditions. The cause of this exception is an interim improvement incorporated into the CERES edition 3A LW algorithm but not into the FLASHFlux 2G LW algorithm that excluded cases that would otherwise tend to produce severe temperature inversions caused by very cold, very dry conditions, such as those encountered over the Antarctic Plateau (Hudson and Brandt 2005).

Table 3.

As in Table 1, but for LW surface fluxes.

Table 3.

4) SW surface

Figure 5 illustrates the distribution of the FLASHFlux and CERES SW surface flux differences and RMS flux differences for Terra and Aqua at the overpass times for 2009 and 2010 using the same format as in Fig. 2. Unlike Fig. 4 for the daytime LW surface flux differences, which showed virtually no correlation with the corresponding daytime LW TOA flux difference figure (Fig. 2), Fig. 5 for the SW surface flux differences shows a high, albeit negative, correlation with the corresponding SW TOA flux difference figure (Fig. 3). This strong negative correlation is anticipated since a strong coupling exists between the SW TOA and surface fluxes. Indeed, whatever energy is not reflected away to space is transferred into the surface–atmosphere system, and much of that energy reaches the surface (Lacis and Hansen 1974; Kratz and Cess 1985). The relatively small systematic differences (FLASHFlux minus CERES) in the Terra SW TOA fluxes of −0.1 W m−2 (−0.04%) allow for a more detailed appraisal of the effects of algorithm changes, while the relatively large systematic differences in the Aqua SW TOA fluxes of 3.2 W m−2 (0.7%) emphasize not only the critical role of correctly determining the SCCs, but also the negative correlation of the SW-reflected TOA and absorbed surface fluxes. The spatially nonuniform pattern of differences between the FLASHFlux and CERES-retrieved SW surface fluxes is principally attributable to revisions in the FLASHFlux algorithms. Specifically, the FLASHFlux algorithms have been updated by utilizing finer-resolution (th° mesh) National Snow-Ice Data Center (NSIDC) data maps (Ramsay 1998), by including cloud algorithm code modifications with refined transitions zones between polar and nonpolar clouds, and by implementing a correction to the SW TOA flux inversion technique. Since changes to the cloud field have a dominating effect upon any comparisons of the SW surface fluxes, any cloud algorithm changes will be reflected in the surface flux comparisons. The spatial patterns in the RMS difference fields indicate a somewhat larger temporal variability in the SW surface fluxes than in the SW TOA fluxes, globally averaging slightly over 4 W m−2 for Terra and nearly 6 W m−2 for Aqua.

Fig. 5.
Fig. 5.

As in Fig. 4, but for the SW.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Table 4 presents the systematic and RMS differences between the FLASHFlux and CERES retrievals for the SW Surface fluxes calculated from measurements taken aboard the Terra and Aqua spacecrafts. Since the SW surface fluxes are highly anticorrelated with the SW TOA fluxes, the results for the SW surface fluxes are expected to follow the same relationship with opposite sign as the SW TOA fluxes. Indeed, as with the SW TOA fluxes, the SW surface fluxes indicate that persistent differences in cloud cover rather than differences in the scene types dominate the observed SW surface flux differences. Moreover, as with the SW TOA fluxes, the largest RMS differences in the SW surface fluxes are again observed over the snow/ice regions.

Table 4.

As in Table 3, but for the SW.

Table 4.

b. Surface flux validation

1) LW clear sky

Table 5 and Fig. 6 provide comparisons of the FLASHFlux model-derived downward LW clear-sky surface fluxes retrieved during the years 2009 and 2010 using the Terra and Aqua measurements taken at the satellite overpass times, which are 1030 LT for Terra and 1330 LT for Aqua, with the corresponding LW fluxes both measured directly by the radiometers located at 34 distinct surface validation sites (Rutan et al. 2001; Colbo and Weller 2009) and retrieved by the CERES project. The systematic and RMS flux differences reported in Table 5 are presented along with their corresponding percentage differences to facilitate an improved understanding of the relative importance of the LW flux values. For brevity, the comparisons in Table 5 are arranged by surface type—island, coastal, polar, continental, desert, buoy, global, and global weighted—rather than by individual surface site. The global surface type represents the average value of all the points from all of the surface types. As an alternative, the global-weighted (hereafter global-w) surface type is computed by first averaging the contribution from all of the surface sites within each individual 10° latitude bin, multiplying that value by the fractional surface area within each latitude bin, and then summing together all of the area-weighted contributions from each latitude bin. As a consequence, the global-w values can provide a more representative global value by accounting for the nonuniform surface coverage observed by the Terra and Aqua polar-orbiting satellites and the nonuniform distribution of the surface validation sites and, thus, can mitigate the effects caused by the disproportionate number of matched footprints that occur over higher-latitude regions. The comparisons presented in Table 5 reveal small systematic differences between the FLASHFlux and surface validation results, ranging from −9.4 to 0.9 W m−2 for the various surface types, along with a global-w precision of ±12.9 W m−2. Table 5 further reveals even smaller systematic differences between the FLASHFlux and CERES results, ranging from −1.4 to −0.4 W m−2 for the various surface types, along with a global-w precision of ±4.7 W m−2. The overall results presented in Table 5 are similar to the results presented by Kratz et al. (2010) in their Table 2, although a noticeable improvement is seen in the precision of the desert case because of the implementation of the Gupta et al. (2010) temperature constraint technique in both the FLASHFlux version 2G and CERES edition 3A processing algorithms. A close examination of the accuracy and precision results in Table 5 reveals that while the flux difference values for the polar case are typical of the other cases, the percentage values are somewhat higher since the magnitude of the polar fluxes are significantly lower than in the other cases.

Table 5.

Comparison of the FLASHFlux model-derived LW surface fluxes based on the Terra and Aqua measurements at the overpass times with the surface-measured and CERES model-derived LW surface fluxes for clear-sky conditions. The columns represent the surface type, the number of measurements (n), and the mean values of the FLASHFlux model-derived fluxes, along with the systematic differences (bias) and the RMS differences for two cases: FLASHFlux minus ground-measured fluxes and FLASHFlux minus CERES model-derived fluxes. For the systematic and RMS differences, percentage differences are provided in parentheses in addition to the flux values. The global surface type represents a summation of the fluxes measured at all the surface sites. The global-w type represents a latitude area-weighted sum of the fluxes measured at the surface sites to account for the nonuniform coverage provided by both the surface sites and the polar-orbiting Terra and Aqua satellites.

Table 5.
Fig. 6.
Fig. 6.

Comparison of the FLASHFlux model-derived LW surface fluxes using Terra and Aqua TOA measurements at the overpass times with the (left) ground-measured and (right) the CERES model-derived LW surface fluxes for clear-sky conditions. The plots represent two-dimensional histograms that illustrate the number of coincident flux values found within each 20 W m−2 square bin. The legend defines the number of values within each bin.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Figure 6 illustrates an intercomparison of the global LW clear-sky surface flux results shown in Table 5. The left-hand plot in Fig. 6 compares the FLASHFlux and the surface measurements while the right-hand plot compares the FLASHFlux and the CERES results. Overall, both comparisons show a high degree of correlation, especially the intercomparison of the FLASHFlux and CERES results in the right-hand plot. The left-hand plot in Fig. 6, however, does reveal a modest number of cases where the FLASHFlux results underestimate the measured surface fluxes. As can be anticipated from a simultaneous examination of the left-hand and right-hand plots in Fig. 6, an intercomparison of the CERES-retrieved and surface-measured fluxes should produce similar results to those observed in the left-hand plot in Fig. 6. Indeed, Kratz et al. (2010) reported similar results in their Fig. 5c, where they attributed the cause for the underestimation to be inadequacies in the day–night temperature retrieval and in the cloud detection algorithms (see Minnis et al. 2008) that were used in both the FLASHFlux version 2G and CERES edition 3A algorithms.

2) LW cloudy sky

Table 6 and Fig. 7 provide comparisons of the FLASHFlux model-derived downward LW cloudy-sky surface fluxes retrieved during the years 2009 and 2010 using the Terra and Aqua measurements taken at the satellite overpass times with the corresponding LW fluxes both measured at the surface and retrieved by the CERES project. The comparisons presented in Table 6 reveal small systematic differences between the FLASHFlux and surface validation results, ranging from −4.4 to 3.8 W m−2 for the various surface types, along with a global-w precision of ±17.8 W m−2. The accuracy of the results for the LW cloudy-sky fluxes is comparable to that for the LW clear-sky fluxes shown in Table 5; however, the slightly degraded precision indicates a nonnegligible increase in the dispersion of the LW cloud-sky flux values, which is due to uncertainties introduced by the presence of clouds, the retrieval of the cloud properties, and the differences in the cloud fields as viewed from the surface and from space (Parding et al. 2011). Table 6 further reveals values for the comparison between the FLASHFlux and CERES LW cloudy-sky results that are very similar to the LW clear-sky cases, with systematic differences ranging from −2.0 to −0.3 W m−2 for the various surface types, along with a global-w precision of ±4.1 W m−2. Thus, as anticipated by the similarity of the routines, the presence of clouds affects both the FLASHFlux and CERES algorithms to a comparable extent.

Table 6.

As in Table 5, but for cloudy-sky conditions.

Table 6.
Fig. 7.
Fig. 7.

As in Fig. 6, but for cloudy-sky conditions.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Figure 7 illustrates an intercomparison of the global LW cloudy-sky surface flux results shown in Table 6. The left-hand plot in Fig. 7 compares the FLASHFlux and the surface measurements while the right-hand plot compares the FLASHFlux and the CERES results. As expected from previous studies (e.g., Kratz et al. 2010), and as confirmed by comparing the cloudy-sky results in Table 6 to the clear-sky results in Table 5, a lower degree of precision is seen in Fig. 7 for the cloudy-sky cases than is seen in Fig. 6 for the clear-sky cases. An examination of scatterplots for the individual surface types (not shown) further reveals that the modest tendency of the FLASHFlux cloudy-sky results to overestimate the ground-truth measurements within the flux range from 150 to 250 W m−2 is associated almost exclusively with the polar cases, while the modest tendency of the FLASHFlux cloudy-sky results to underestimate the ground-truth measurements within the flux range from 250 to 350 W m−2 is associated with the continental cases. Since the FLASHFlux results compare favorably with the CERES results in the right-hand plot of Fig. 7, we conclude that the observed differences in the left-hand plot are representative of both the FLASHFlux and CERES results, a conclusion that is confirmed by separately comparing the CERES results to the ground-truth measurements (not shown).

3) SW clear sky

Table 7 and Fig. 8 provide comparisons of the FLASHFlux model-derived downward SW clear-sky surface fluxes retrieved from the Terra and Aqua measurements taken at the satellite overpass times during the years 2009 and 2010 with the corresponding SW fluxes both measured at the surface and retrieved by the CERES project. The comparisons presented in Table 7 reveal systematic differences between the FLASHFlux and surface validation sites for the SW clear-sky fluxes that are noticeably larger than those reported for the LW clear-sky fluxes in Table 5. Indeed, the systematic difference for the global-w case is −11.1 W m−2 while the global-w precision is ±24.0 W m−2. Figure 8 graphically illustrates the severity of the systematic difference, with both the FLASHFLux and CERES retrievals obviously underestimating the measured ground-truth fluxes. A recent investigation attributed the cause of this clear-sky discrepancy to the molecular and aerosol scattering routines that were incorporated into the original LPSA, and are still being used in the FLASHFlux version 2G processing. The solution to this problem is the simultaneous replacement of these routines in this SW model. Figure 9 illustrates an intercomparison of the FLASHFlux model-derived SW surface fluxes for clear-sky conditions calculated using the original LPSA Rayleigh scattering formulation along with the World Meteorological Organization Report WCP-55 aerosols (Deepak and Gerber 1983), the SW surface fluxes calculated with the revised LPSA using the Bodhaine et al. (1999) Rayleigh scattering formulation along with the Model of Atmospheric Transport and Chemistry (MATCH) aerosols (Rasch et al. 1997; Collins et al. 2001), and the SW surface fluxes measured by ground-based stations. As can be gleaned from Fig. 9, the combined replacement of the molecular and aerosol-scattering routines in the SW model leads to a dramatic improvement, virtually eliminating the systematic differences in the SW surface fluxes between the satellite retrievals and the ground-truth measurements for clear-sky conditions. This improvement will be incorporated into the next version of FLASHFlux and the next edition of the CERES processing. In contrast to the comparison of the FLASHFlux results with the ground-truth measurements, however, the intercomparisons in Table 7 between the FLASHFlux and CERES SW clear-sky results show very small systematic differences, ranging from −0.4 to 0.6 W m−2 for the various surface types, along with a global-w precision of ±2.8 W m−2. Thus, in analogy with the comparisons shown in Table 5 for the LW clear-sky surface fluxes, the comparison shown in Table 7 for the SW clear-sky surface fluxes demonstrates that the FLASHFlux and CERES processing streams produce differences in the retrieved surface fluxes that are considerably smaller than those observed to occur between the satellite-retrieved and the ground-measured surface fluxes.

Table 7.

As in Table 5, but for the SW.

Table 7.
Fig. 8.
Fig. 8.

As in Fig. 6, but for the SW.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

Fig. 9.
Fig. 9.

Intercomparison of the FLASHFlux model-derived SW surface fluxes for clear-sky conditions using (left) the original LPSA Rayleigh scattering formulation and the WCP-55 aerosols, (middle) the FLASHFlux model-derived SW surface fluxes using the revised LPSA with the Bodhaine et al. (1999) Rayleigh scattering formulation and the MATCH aerosols, and (right) the ground-measured fluxes.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

4) SW cloudy sky

Table 8 and Fig. 10 provide comparisons of the FLASHFlux model-derived downward SW cloudy-sky surface fluxes retrieved from the Terra and Aqua measurements taken at the satellite overpass times during the years 2009 and 2010 with the corresponding SW fluxes both measured at the surface and retrieved by the CERES project. Unlike the cases previously discussed, we have used 60-min averaging for the surface measurements of the cloudy-sky fluxes since the spatial variability within the cloud fields frequently produces conditions where 1-min averaging of the surface measurements is not representative of the conditions over the 20-km CERES footprint. Even though the 60-min-averaged surface fluxes are considered optimal (Gupta et al. 2004), the systematic difference for the global-w case is rather large, 28.1 W m−2, and there remains a significant amount of scatter in the comparisons, nearly 90 W m−2 for the global-w case. These RMS flux difference results are consistent with previous studies (e.g., Rossow and Zhang 1995; Gautier and Landsfeld 1997), where similar precision levels were observed for instantaneous comparisons for cloudy-sky conditions. Since downward-viewing satellite and upward-viewing surface instruments measure significantly different conical sections of the sky, these two perspectives sense very different spatial distributions of clouds. Thus, despite optimizing the averaging times, the precision of the cloudy-sky retrievals tends to be significantly degraded versus the clear-sky retrievals. The large RMS flux differences observed for the SW fluxes, however, are not entirely attributable to the FLASHFlux or CERES surface flux retrievals. Significant differences can also arise from the surface measurement process as well as the spatial and temporal variability of the insolation fields in the real world (Zelenka et al. 1999). Despite the differences between the retrieved SW surface fluxes and the surface measurements, the comparisons between the FLASHFlux and CERES SW cloudy-sky results in Table 8 show only small systematic differences, ranging from 0.3 to 5.8 W m−2 for the various surface types, along with a global-w precision of ±8.3 W m−2. Hence, these results demonstrate that FLASHFlux retrieves surface fluxes that are very similar to those retrieved by CERES.

Table 8.

As in Table 7, but for cloudy-sky conditions.

Table 8.
Fig. 10.
Fig. 10.

As in Fig. 8, but for cloudy-sky conditions.

Citation: Journal of Applied Meteorology and Climatology 53, 4; 10.1175/JAMC-D-13-061.1

5. Data accessibility

The ASDC processes, archives, and distributes earth science data for NASA’s Langley Research Center. In this capacity, ASDC has gathered together the FLASHFlux SSF data, which include the CERES-measured TOA radiances, the ancillary input data that is incorporated into the FLASHFlux processing, and the derived TOA and surface fluxes. ASDC has further expedited the availability of the FLASHFlux SSF data archive by making the data accessible using a web-based tool, which is available online (http://ceres.larc.nasa.gov/products.php?product=FLASHFlux-Level2). Instructions have been provided to guide the user through the acquisition and use of the data, as well as links to render assistance and to provide access to associated websites such as the CERES data products webpage. The data used in the production of this report have been made available online (https://eosweb.larc.nasa.gov/project/ceres/flash_ssf_terra-fm3-modis_version2g_table for Terra and https://eosweb.larc.nasa.gov/project/ceres/flash_ssf_aqua-fm3-modis_version2g_table for Aqua).

6. Conclusions

Even though the FLASHFlux effort was not designed to provide either the algorithm or data input stability necessary to derive climate quality data, FLASHFlux produces SSF surface fluxes that have small global biases when compared with the climate-quality CERES SSF fluxes for the years 2009 and 2010. Specifically, the flux biases are −1.1 W m−2 (Terra) and −0.6 W m−2 (Aqua) for LW, and −0.1 W m−2 (Terra) and 3.2 W m−2 (Aqua) for SW. Furthermore, the biases and RMS values for the available validation sites have been found to be −1.1 ±5.4 W m−2 for LW clear sky, −1.4 ±7.1 W m−2 for LW cloudy sky, −0.1 ±3.1 W m−2 for SW clear sky, and 3.6 ±15.2 W m−2 for SW cloudy sky. Similar values for the bias and RMS values are obtained when area weighting is taken into consideration. The differences between the FLASHFlux and CERES flux retrievals have been principally attributed to the refinements in the spectral response function and gains used in the CERES processing, which cause a significant delay in the processing of the CERES data, and to differences between the clouds algorithms used by the two processing streams.

The small differences between the CERES and FLASHFlux results demonstrate that the FLASHFlux data are capable of fulfilling the critical needs of those organizations requiring higher accuracy than that provided by synoptic weather forecasting datasets but whose operations cannot wait for climate-quality datasets provided by systems such as CERES. By providing data through the auspices of the Atmospheric Science Data Center (ASDC) at NASA’s Langley Research Center, the FLASHFlux SSF data products are contributing significantly to experimental and operational field programs, such as the Far-Infrared Spectroscopy of the Troposphere project (FIRST; Mlynczak et al. 2006), and are currently being used by satellite missions such as CloudSat and Megha-Tropiques to provide data quality control information within 4–5 days of data acquisition.

The present paper has focused upon the production and validation of the instantaneous FLASHFlux SSF data products. Many applications, however, require spatially gridded and temporally averaged input data. Thus, the FLASHFlux SSF data product has been spatially averaged and temporally combined with ancillary meteorological data to produce hourly and daily 1° × 1° time- and space-averaged (TISA) gridded fluxes. The production and application of the FLASHFlux TISA product will be the focus of a complementary paper.

Acknowledgments

ARM data have been made available through the U.S. Department of Energy as part of the Atmospheric Radiation Measurement Program. GMD data have been made available through NOAA’s Earth System Research Laboratory/Global Monitoring Division–Radiation (G-RAD). SURFRAD data have been made available through NOAA’s Air Resources Laboratory/Surface Radiation Research Branch. Snow and ice data were provided through the National Snow and Ice Data Center. The authors thank D. A. Rutan for providing access to the CERES/ARM Validation Experiment (CAVE) database, and A. C. Edwards and J. C. Mikovitz for providing programming assistance. The authors would also like to thank G. G. Gibson and N. G. Loeb for providing valuable advice and suggestions regarding the manuscript. This research was supported through the NASA Science Mission Directorate as part of the CERES project.

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