Understanding the Earth s Energy Flows from a Constellation of Satellites

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1 Third Monitoring Committee Report Understanding the Earth s Energy Flows from a Constellation of Satellites Jake Gristey 8 th December Harry Pitt Conference Room, 11:00-12:00 Supervised by: Dr. C. Chiu, Prof. R. Gurney and Dr G. Stephens External partners: P. Evans (Met Office) and Prof. S. Briggs (ESA) Committee members: Prof. R. Allan and Prof. K. Shine 1. Overview and Objectives The incoming energy from the sun and its distribution within the atmosphere determines the weather and climate we experience on Earth. In an equilibrium climate system, this incoming solar radiation is balanced at the Top Of the Atmosphere (TOA) by radiation emitted or reflected back to space. However, if the total outgoing radiation is less than the incoming solar radiation, the Earth is imbalanced and its temperature will increase to adjust the system toward equilibrium. This TOA imbalance, arising from a complex set of inter-connected processes occurring between the atmosphere, ocean and land [Trenberth and Fasullo, 2011; Stephens et al., 2012], therefore provides a unique indicator of climate change, and our ability to measure it at a sufficient temporal resolution, spatial scale and accuracy is crucial for climate change studies. Recent estimates of the TOA radiation imbalance are inconsistent in magnitude, and even sign, particularly between observations and models. Observations of the incoming solar radiation are currently made at a high accuracy of 0.1 W m 2 or 0.04% [Kopp and Lean, 2011], but an accurate quantification of the TOA imbalance is restricted by insufficient measurements of the dynamic outgoing radiation, accurate to only 1.7 W m 2 or 2% in the shortwave (SW) and 2.5 W m 2 or 1% in the longwave (LW) [Loeb et al., 2009; Harries and Belotti, 2010]. The demand for highly accurate measurements of outgoing radiation in the future requires ever more expensive instruments [Wielicki et al., 2013] while available finances are limited [Ramanathan, 1987]. As a result, the long and continuous record of Earth Radiation Budget (ERB) measurements from space, stretching back to the beginning of the space era [Smith et al., 1977; Barkstrom, 1984; Kyle et al., 1993; Wielicki et al., 1996; Harries et al., 2005], is under threat. A technology revolution in small satellites and sensor miniaturisation [Sandau et al., 2010] has created a new and exciting opportunity for the next generation of ERB measurements to be made from a large constellation of low-cost CubeSats hosting Wide-Field-Of-View (WFOV) broadband radiometers. This fundamentally different approach would: 1) Permit global coverage multiple times per day providing unprecedented temporal resolution. 2) Provide new opportunities for inter-calibration of instruments. 3) Be much more robust to failures given the large number of instruments. 4) Offer a platform for international collaboration. Points 1) and 2) address two of the three dominant sources of uncertainty from current measurements [Wielicki et al., 1996], while the remaining source of uncertainty from angular influences is expected to be small when considering a WFOV. The advantages of a multi-platform observing system have long been documented [Salby, 1989; Hansen et al., 1996], but implementing these ideas has only become financially viable in recent years [Wiscombe and Chiu, 2013]. 1

2 WFOV broadband radiometers have been well used and tested in the past and are ideal for achieving global coverage in a short time period, which is a key element missing in current ERB missions. However, the spatial resolution of the collected data is sacrificed to the WFOV footprint size of around 6000 km in diameter at an altitude of 780 km. Through spherical harmonic analysis that uses discrete samples on a sphere to synthesise the complete distribution on the entire sphere, densely sampled WFOV data can be used to attain radiation fields at higher spatial resolution. Methods based on spherical harmonic analysis have been successfully used on the Gravity Recovery and Climate Experiment (GRACE) mission [Tapley et al., 2004] and applied to monthly and weekly-averaged ERB data from Nimbus 7 [Hucek et al. 1987, 1990]. Here, a new recovery method has been developed, based on a spherical harmonic analysis, which is applied to an advanced constellation configuration and at sub-diurnal temporal resolution for the first time. While broadband observations are well suited to monitor the total ERB, the processes that drive changes are actually wavelength dependent. Radiation reaching TOA has interacted with atmospheric gases, clouds, aerosols and the surface, each providing a unique spectral signature. These spectral signatures contain the fingerprints required to detect the processes of climate change and provide a strong argument for the next generation of ERB measurements to be spectrally resolved. Inspired by Wiscombe and Chiu [2013] that proposed to measure the TOA imbalance from a large constellation of WFOV broadband radiometers, over the course of this PhD we set out to: Conduct simulation experiments to optimise a constellation strategy for monitoring the Earth s total outgoing radiation. Generate simulated global maps of the outgoing radiation to identify large-scale fast-evolving phenomena. Explore how spectral radiation information, particularly in the SW region, can be used to detect and monitor the processes leading to TOA radiation imbalances, revealing the fingerprints of climate change. 2. Summary of previous report The previous report presented a method to recover global TOA outgoing SW and LW radiation from simulated broadband WFOV measurements made from an optimised constellation of 12 satellites. This recovery method, based on a spherical harmonic analysis, showed that the global TOA outgoing radiation fields could be presented at an enhanced resolution by optimally exploiting dense and overlapping global simulated measurements. The first aim set out in the previous report was to complete these broadband WFOV simulations and prepare a manuscript for this work. This included addressing assumptions made in the recovery algorithm, importantly the influence of time evolution of the radiation field within the measurement interval. Progress on this aspect is presented in section 3a. The second aim set out in the previous report was to begin working with hyperspectral earth observation data. Specifically, we planned to analyse spectral reflectance observations from the SCanning Imaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) instrument in conjunction with a visit to NASA JPL. Progress on this aspect is presented in section 3b. 3. Progress a. Completing broadband WFOV simulations Previously, the recovery method was tested using a static radiation field in a 3-hr duration. Once a more realistic time-evolving radiation field was applied the previous method did not work well, particularly in the SW, and thus two necessary changes were made for improvement. 2

3 First, the constellation configuration itself is revised, based on a recent NASA Cubesat opportunity and the feedback received from conferences and my visit to NASA JPL. The revised configuration aims to provide the required coverage in just 1-hr and use more common inclination angles with a minimal number of launches. As a result, a constellation is configured to have 36 satellites spread evenly across 6 orbital planes (i.e., 6 launches rather than 12 in the previous report) in 86.4 non-sun synchronous orbits, allowing every satellite to precess and provide sufficient samples for the recovery within an hour. The altitude of the satellites is ~780 km, which capitalises on the upper end of typical small satellite altitudes [Maessen, 2007; Lücking, 2011] and enables a WFOV as large as possible. For simplicity, we use circular orbits with all satellites at the same altitude, but the recovery method has been tested and worked for elliptical orbits and varied satellite altitudes. Second, the recovery method is adapted to account for SW time-evolving effects. Recovering a single map of outgoing radiation for measurements made over some duration implicitly assumes that the radiation field can be modelled as static during this time, but evolution of the SW radiation field actually occurs from two sources; one is the rapid change in the illuminated portion of the Earth as it rotates due to solar geometry and insolation, and the other is the albedo change due to changing radiative properties of the surface and atmosphere. For a 6000 km footprint size, the former has been found to be the dominant component that introduces instability in the recovery. To incorporate the sun geometry effects into the recovery method, the instantaneous scene albedo for each satellite measurement, A INS, is calculated as: A INS = F SAT,SW S INS (1) where S INS is the instantaneous incoming solar radiation over the WFOV and F SAT,SW is the simulated measurement of SW flux. Since the relative change in A INS in equation (1) is small, we assume that it remains constant throughout the measurement duration and use it to estimate the time-integrated simulated measurement of SW flux, F SAT,SW, by: F SAT,SW = A INS S (2) where S is the average incoming solar radiation over the WFOV within the measurement duration. a) b) c) d) Fig 1. Maps of the outgoing (a and c) SW and (b and d) LW radiation at a 1000 km spatial resolution from 00Z to 01Z on 29th August 2010, based on a spherical harmonic analysis. The truth (a and b) is an average of radiation fields during this period from the UK Met Office model output. The recovery (c and d) is from synthetic WFOV measurements taken during this period using the 36-satellite constellation. 3

4 Using the new 36-satellite constellation configuration and equation (2) for the set of simulated outgoing SW radiation measurements, TOA outgoing flux measurements are simulated and the radiation fields are recovered (Fig. 1c and d). Large scale features that are present in the true fields (Fig. 1a and b) are revealed that could not be observed otherwise. A power spectra analysis, as discussed in the previous report, revealed a recovery signal-to-noise ratio at 1000 km of greater than 1, showing quantitatively that a true signal is detected above the recovery noise. To test how well this 36-satellite configuration captures the diurnal cycle in global mean TOA outgoing radiation, the recovery is performed every 1-hr throughout 24-hrs and compared with the model truth diurnal cycle. The triple-peak diurnal structure of the TOA outgoing SW radiation (Fig. 2a) and the less variable diurnal structure in the outgoing LW radiation (Fig. 2b) are both captured well. These data have revealed some interesting structure in the global mean TOA outgoing radiation diurnal cycle; understanding the physical mechanisms at work and their relative importance for ERB warrants further exploration and will be one of the main research focuses for the next 6 months (section 4c). a) b) Fig 2. The diurnal cycle of global mean outgoing (a) SW and (b) LW radiation at TOA from 12 Z on 28 th to 12 Z on 29 th August The truth (blue) is the UK Met Office model output at a 5-min temporal resolution and the recovery (red dash) is performed every hour from the 36-satellite constellation. b. Analysing SCIAMACHY spectral reflectance SCIAMACHY was a passive remote sensing spectrometer observing outgoing radiation from the atmosphere and Earth's surface in the UV, visible and near-infrared wavelength ranges. The instrument flew on board the European Space Agency (ESA) ENVIronmental SATellite (ENVISAT), from March 2002 to April 2012, with a primary scientific objective of retrieving global vertical profiles of various trace gases. With a nadir viewing geometry, a typical footprint dimension is 30 km along track x 60 km across track, but can be much larger in some cases. For this work, SCIAMACHY raw spectral reflectance data is downloaded [ESA, 2015a], calibrated [ESA, 2015b] and extracted over the spectral range nm at a 1 nm spectral resolution for 2006 and An algorithm is applied to remove instrument noise. Stephens et al. [2015] concluded that the northern hemisphere (NH) and southern hemisphere (SH) show a remarkable symmetry in annual mean broadband albedo, but the SH is slightly more reflective at visible wavelengths and the NH is slightly more reflective at near-infrared wavelengths. I first attempted to reproduce this result with the SCIAMACHY spectral reflectance data from 2006 and For this period, the SCIAMACHY data shows the NH is actually more reflective than the SH throughout the spectrum (Fig. 3a). Stephens et al. [2015] state that inter-annual variability is small, and exists on a hemispherical scale, so the difference in averaging time shouldn t be the cause of the discrepancy. It is possible that some calibration/normalisation was carried out on their data and we will be checking if that was the case. Normalising our data such that the two hemispheres have the same broadband albedo that has been well observed from previous ERB instruments, the reversal in hemispherical reflectance is reproduced (Fig. 3b). This spectral structure can be attributed to a higher cloudiness in the SH, with clouds being most reflective in the visible, precisely offsetting the effects of enhanced reflection from the greater NH land masses, broadly consisting of vegetated surfaces most reflective in the near-infrared. 4

5 a) b) Fig 3. Average reflectance from 2006 and 2007 for the northern hemisphere (blue) and the southern hemisphere (green) at a 1 nm spectral resolution. The data is displayed as (a) raw extracted data and (b) normalised such that both hemispheres have the same broadband albedo. Grey shaded areas are where instrument channels overlap and sensitivity is low. The SCIAMACHY data was next used to analyse spectra over land/sea, latitudinal bands (tropics/midlatitudes/poles) and to study cloud phase. Here I show the results from the cloud phase experiment where a time-averaging approach has been used to study areas of the globe where we know particular cloud types to be more frequent. To highlight the spectral radiative signature of cloud phase, regions with frequent liquid clouds and ice clouds are selected, based on observations from the International Satellite Cloud Climatology Project. The selected liquid cloud region is in the stratocumulus deck off the west coast of Africa, bordered by [10 S, 20 S] and [5 W, 5 E], while the ice cloud region is to the south-east of India where cirrus clouds are most frequent, bordered by [5 S, 5 N] and [85 E, 95 E]. The average SCIAMACHY spectra over each region (Fig. 4b) show clear differences from nm, where absorption due to ice and liquid are both appreciable but differ substantially (Fig. 4a) [Pilewskie and Twomey, 1987]. At 1600 nm wavelength, absorption by ice cloud is around 5 times stronger than that by water cloud, leading to a lower TOA reflectance in the ice cloud regions. Conversely, at 1400 nm, absorption by ice cloud is substantially weaker than that by water cloud, and thus a higher TOA reflectance in the ice cloud region is shown. Although 1400 nm falls within a strong water vapour absorption band, an absorption coefficient signal can still be seen relative to other water vapour absorption bands (not shown). The dominance of the absorption coefficient signal suggests that, on average, thickness differences between the cloud types are less important at these particular wavelengths. A possible explanation is that the single scattering albedo at these wavelengths is significantly less than 1 [Petty, 2006], meaning multiple scattering events are limited and cloud thickness becomes less important in determining cloud reflectance. a) b) Fig 4. (a) Bulk absorption coefficient for water (solid curve) and ice (dashed curve). Blue shaded area indicates defined region of interest for SCIAMACHY data. (Adapted from Pilewskie and Twomey, [1987]). (b) Average reflectance from 2006 and 2007 for an ice cloud region (blue) and a water cloud region (green) at a 1 nm spectral resolution. 5

6 The hemispherical and cloud phase spectral reflectance differences are both clear examples of compensating spectral effects that would cancel each other out in broadband observations alone. The ability to observe and understand the relationship between these types of processes and changes in Earth s outgoing radiation is of fundamental importance in constraining future warming of our climate system. 4. Aims for the next 6 months a. Submit manuscript on broadband WFOV simulations for publication A draft manuscript has been prepared to present our method for recovering enhanced resolution global TOA radiation from a constellation of satellites. The majority of the content is now complete, with some sensitivity test results yet to be added. The manuscript will be submitted to the Journal of Geophysical Research (JGR) for publication in January. An abstract has been submitted to present this manuscript at the International Radiation Symposium to be held in April 2016 at the University of Auckland in New Zealand. However, a final decision on attendance has not yet been made. b. Perform further analysis on SCIAMACHY spectral reflectance The results presented in section 3b demonstrate the value of hyperspectral observations for identifying processes, but we plan to perform a more rigorous and systematic analysis to draw solid conclusions. An initial literature review revealed that a rigorous analysis has been performed on LW hyperspectral radiation [Huang et al., 2010] and some initial work has looked at understanding the spectral information content of SW radiation [Coddington et al., 2012]. A deeper literature review will be carried out to better understand what has been done and how our work can advance the field. Collaboration with the Met Office is in progress for the simulation of SCIAMACHY hyperspectral measurements. For computational purposes, they will provide us with the Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) to simulate SCIAMACHY measurements. The decision to use HT- FRTC over other fast radiative transfer codes is based on the fact that it has been well tested in the SW [Thelen et al., 2012] as well as LW spectral regions. HT-FRTC uses a principle component approach to achieve speed and provides monochromatic radiances. Using this tool, the hyperspectral radiative signature of the atmosphere, clouds, aerosols and surface can be investigated using Met Office model data, and compared with the observations. c. Investigate components of global TOA diurnal cycle An interesting structure was revealed when looking at the global mean TOA outgoing radiation diurnal cycle from the model data (Fig. 2). The processes behind this structure are not obvious and warrant further investigation. As a first step, the Empirical Orthogonal Functions (EOFs) of this broadband Met Office data will be investigated to identify what are the dominant components of the TOA outgoing radiation diurnal cycle, and how much of the variation these components can explain. Eventually, this analysis will be extended to look at the SCIAMACHY hyperspectral data, although implementing this with the sun-synchronous orbit of ENVISAT may prove challenging. 6

7 APPENDIX A: Additional training Next 6 months Older present Autumn term 2015/2016 *Attendance TBD MT11D Weather and Climate Fundamentals Demonstrating (problem classes) University of Reading, UK Time period Date Event Type Location Previous 6 9/06/15 SCENARIO conference Conference University of Surrey, UK months 13/06/15- Field Spectroscopy Training Course NERC Advanced Albacete, Spain 20/06/15 Training Short Course 26/07/15- Gordon Conference: Radiation and Conference Bates College, MA, USA 31/07/15 Climate 31/08/15 - NASA JPL Center for Climate Summer school Caltech, CA, USA 04/09/15 Sciences Summer School: Using Satellite Observations to Advance Climate Models 07/09/15 - NASA JPL placement Work placement NASA JPL, CA, USA 16/10/15 17/09/2015- NASA CubeSat proposal writing Mission proposal N/A 04/11/2015 Introduction to teaching and learning RRDP/ teaching University of Reading, UK course 05/11/2015 Laboratory demonstrating and leading small groups RRDP/ teaching course University of Reading, UK 16/04/16- International Radiation Symposium* Conference Auckland, New Zealand 27/04/16 27/01/16 How to write a paper* RRDP University of Reading, UK Spring term MT12C Experimental skills for Demonstrating University of Reading, UK 2015/16 environmental scientists (computing) Autumn SEMIP12 Image Processing (not Masters module University of Reading, UK term assessed) 2014/15 Spring term MTMG06 Statistics for Weather and Masters module University of Reading, UK 2014/15 Climate Science (not assessed) Spring term MTMD01 Environmental Data Masters module University of Reading, UK 2014/15 Exploration and Visualisation (89%) 15/10/2014 Classic Papers - The Green House Effect RMetS meeting Imperial College London, London, UK 25/11/2014 Preparing posters RRDP University of Reading, UK 26/11/2014 You and your supervisor RRDP University of Reading, UK 02/12/2014 How to summarise your research in 3 RRDP University of Reading, UK minutes 12/02/2015 How to write a literature review RRDP University of Reading, UK 18/03/2015 Future measurements for climate monitoring meeting RMetS meeting Imperial College London, London, UK 19/03/2015 NERC Responding to environmental change event SCENARIO event with poster Euston Square, London, UK presentation 26/03/2015 Presentation skills RRDP University of Reading, UK 13/05/2015 Light and the environment Institute of Physics Cedars Conference Centre, event University of Reading, UK 19/05/2015 LaTex SCENARIO training University of Reading, UK course 7

8 References Barkstrom B. R., 1984: The Earth Radiation Budget Experiment (ERBE). Bull. Amer. Meteor. Soc., 65, Coddington O., P. Pilewskie, and T. Vukicevic, 2012: The Shannon information content of hyperspectral shortwave cloud albedo measurements: Quantification and practical applications, J. Geophys. Res., 117, D ESA, cited 2015: ENVISAT SCIAMACHY. [Available online at]. ESA, cited 2015: SciaL1C Command-line Tool. [Available online at Hansen J., W. Rossow, B. Carlson, A. Lacis, L. Travis, A. Del Genio, I. Fung, B. Cairns, M. Mishchenko, M. Sato, 1996: Low-Cost Long-Term Monitoring of Global Climate Forcings and Feedbacks. Long- Term Climate Monitoring by the Global Climate Observing System., P. T. R. Karl, Ed., Springer Netherlands, Harries J. E., J. E. Russell, J. A. Hanafin, H. Brindley, J. Futyan, J. Rufus, S. Kellock, G. Matthews, R. Wrigley, A. Last, J. Mueller, R. Mossavati, J. Ashmall, E. Sawyer, D. Parker, M. Caldwell, P. M. Allan, A. Smith, M. J. Bates, B. Coan, B. C. Stewart, D. R. Lepine, L. A. Cornwall, D. R. Corney, M. J. Ricketts, D. Drummond, D. Smart, R. Cutler, S. Dewitte, N. Clerbaux, L. Gonzalez, A. Ipe, C. Bertrand, A. Joukoff, D. Crommelynck, N. Nelms, D. T. Llewellyn-Jones, G. Butcher, G. L. Smith, Z. P. Szewczyk, P. E. Mlynczak, A. Slingo, R. P. Allan, and M. A. Ringer, 2005: The Geostationary Earth Radiation Budget Project. Bull. Amer. Meteor. Soc., 86, Harries J. E. and C. Belotti, 2010: On the variability of the global net radiative energy balance of the nonequilibrium Earth. J. Clim., 23, Huang Y., S. Leroy, P. J. Gero, J. Dykema, and J. Anderson, 2010: Separation of longwave climate feedbacks from spectral observations, J. Geophys. Res., 115, D07104 Hucek, R. R., H. L. Kyle, and P. E. Ardanuy, 1987: Nimbus 7 earth radiation budget wide field of view climate data set improvement: 1. The Earth albedo from deconvolution of shortwave measurements. J. Geophys. Res., 92, Hucek R. R., P. Ardanuy, and H. L. Kyle, 1990: The Constrained Inversion of Nimbus-7 Wide Field-of- View Radiometer Measurements for the Earth Radiation Budget. J. Appl. Meteor., 29, Kopp G. and J. L. Lean, 2011: A new, lower value of total solar irradiance: Evidence and climate significance. Geophys. Res. Lett., 38, L Kyle H. L., A. Arking, J. R. Hickey, P. E. Ardanuy, H. Jacobowitz, L. L. Stowe, G. G. Campbell, T. Vonder Haar, F. B. House, R. Maschhoff, and G. L. Smith, 1993: The Nimbus Earth Radiation Budget (ERB) Experiment: 1975 to Bull. Amer. Meteor. Soc., 74, Loeb N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget. J. Clim., 22, Lücking, C., 2011: A passive high altitude deorbiting strategy. AIAA/USU Conf. on Small Satellites, UT, USA. Technical Session VIII. Maessen D. C., 2007: Development of a generic inflatable de-orbit device for CubeSats. Dissertation, TU Delft, Delft University of Technology, 11. Petty, 2006: Radiative Properties of Natural Surfaces. A First Course in Atmospheric Radiation. Sundog Publishing,

9 Pilewskie and Twomey, 1987: Discrimination of Ice from Water in Clouds by Optical Remote Sensing. Atmospheric Research, 21, Ramanathan V., 1987: The role of Earth radiation budget studies in climate and general circulation research. J. Geophys. Res., 92(D4), Salby M. L., 1989: Climate Monitoring from Space: Asynoptic Sampling Considerations. J. Clim., 2, Sandau R., K. Brieß and M. D Errico, 2010: Small satellites for global coverage: Potential and limits. ISPRS J. Photogramm., 65, Stephens G. L., J. Li, M. Wild, C. A. Clayson, N. Loeb, S. Kato, T. L'Ecuyer, P. W. Stackhouse Jr, M. Lebsock, and T. Andrews, 2012: An update on Earth's energy balance in light of the latest global observations. Nature Geoscience, 5, Stephens G. L., D. O'Brien, P. J. Webster, P. Pilewski, S. Kato, and J. Li, 2015: The albedo of Earth. Rev. Geophys., 53, Tapley B. D., S. Bettadpur, M. Watkins, and C. Reigber, 2004: The gravity recovery and climate experiment: Mission overview and early results. Geophys. Res. Lett., 31, L Thelen J. C., S. Havemann, and J. P. Taylor, 2012: Atmospheric correction of short-wave hyperspectral imagery using a fast, fullscattering 1Dvar retrieval scheme. Proc. SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 8390, 10 Trenberth K. E. and J. T. Fasullo, 2011: Tracking Earth s Energy: From El Nino to Global Warming. Surv. Geophys, 33, Wielicki B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc., 77, Wielicki B. A., D. F. Young, M. G. Mlynczak, K. J. Thome, S. Leroy, J. Corliss, J. G. Anderson, C. O. Ao, R. Bantges, F. Best, K. Bowman, H. Brindley, J. J. Butler, W. Collins, J. A. Dykema, D. R. Doelling, D. R. Feldman, N. Fox, X. Huang, R. Holz, Y. Huang, Z. Jin, D. Jennings, D. G. Johnson, K. Jucks, S. Kato, D. B. Kirk-Davidoff, R. Knuteson, G. Kopp, D. P. Kratz, X. Liu, C. Lukashin, A. J. Mannucci, N. Phojanamongkolkij, P. Pilewskie, V. Ramaswamy, H. Revercomb, J. Rice, Y. Roberts, C. M. Roithmayr, F. Rose, S. Sandford, E. L. Shirley, W. L. Smith Sr., B. Soden, P. W. Speth, W. Sun, P. C. Taylor, D. Tobin, and X. Xiong, 2013: Achieving Climate Change Absolute Accuracy in Orbit. Bull. Amer. Meteor. Soc., 94, Wiscombe and Chiu, 2013: The next step in Earth radiation budget measurements. AIP Conference Proceedings, 1531,