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Joseph J. Michalsky and Charles N. Long

1. Introduction Two papers published in the early 1990s comparing radiation transfer codes for the infrared ( Ellingson et al. 1991 ) and for the solar ( Fouquart et al. 1991 ) irradiance concluded that many of the radiation transfer codes (parameterized to reduce run time) used in climate models did not agree with state-of-the-art line-by-line radiative transfer codes; for the most part line-by-line codes agreed with one another. However, the measurements to confirm that the radiative fluxes

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J.-L. F. Li, D. E. Waliser, G. Stephens, and Seungwon Lee

1. Introduction Representing atmospheric convection, precipitating/nonprecipitating clouds, and their multiscale organization as well as their radiation interaction in GCMs remains a pressing challenge to reduce and quantify uncertainties associated with climate change projections ( Randall et al. 2007 ; Stephens 2005 ). Atmospheric radiative structures, such as fluxes and the vertical/horizontal distributions of heating, are one of the most important factors determining global weather and

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Eric A. Smith and Throy D. Hollis

Abstract

Currently, satellite algorithms are the methodology showing most promise for obtaining more accurate global precipitation estimates. However, a general problem with satellite methods is that they do not measure precipitation directly, but through inversion of radiation–rain relationships. Because of this, procedures are needed to verify algorithm-generated results. The most common method of verifying satellite rain estimates is by direct comparison with ground truth data derived from measurements obtained by rain gauge networks, ground-based weather radar, or a combination of the two. However, these types of comparisons generally shed no light on the physical causes of the differences. Moreover, ground validation measurements often have uncertainty magnitudes on the order of or greater than the satellite algorithms, motivating the search for alternate approaches. The purpose of this research is to explore a new type of approach for evaluating and validating the level-2 Tropical Rainfall Measuring Mission (TRMM) facility rain profile algorithms. This is done by an algorithm-to-algorithm intercomparison analysis in the context of physical hypothesis testing.

TRMM was launched with the main purpose of measuring precipitation and the release of latent heat in the deep Tropics. Its rain instrument package includes the TRMM Microwave Imager (TMI), the Precipitation Radar (PR), and the Visible and Infrared Scanner (VIRS). These three instruments allow for the use of combined-instrument algorithms, theoretically compensating for some of the weaknesses of the single-instrument algorithms and resulting in more accurate estimates of rainfall. The focus of this research is on the performance of four level-2 TRMM facility algorithms producing rain profiles using the TMI and PR measurements with both single-instrument and combined-instrument methods.

Beginning with the four algorithms' strengths and weaknesses garnered from the physics used to develop the algorithms, seven hypotheses were formed detailing expected performance characteristics of the algorithms. Procedures were developed to test these hypotheses and then applied to 48 storms from all ocean basins within the tropical and subtropical zones over which TRMM coverage is available (∼35°N–35°S). The testing resulted in five hypotheses verified, one partially verified, and one inconclusive. These findings suggest that the four level-2 TRMM facility profile algorithms are performing in a manner consistent with the underlying physical limitations in the measurements (or, alternatively, the strengths of the physical assumptions), providing an independent measure of the level-2 algorithms' validity.

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E. J. Mlawer and D. D. Turner

observations was the organization of the Spectral Radiation Experiment (SPECTRE; Ellingson and Wiscombe 1996 ; Ellingson et al. 2016 , chapter 1). This one-month field experiment deployed several infrared interferometers to Coffeyville, Kansas, to measure the downwelling infrared spectral radiance along with a range of sensors, both in situ (e.g., radiosonde, flask measurements of trace gases like carbon dioxide and methane, etc.) and remote (e.g., Raman lidar, Radio Acoustic Sounding System, cloud radar

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Robert G. Ellingson, Robert D. Cess, and Gerald L. Potter

−20 to −16 W m −2 , so that ΔCRF = G , then ΔCRF would amplify the direct radiative forcing by a factor of 2 (a twofold positive feedback). Zero cloud feedback corresponds to ΔCRF/ G = 0, while ΔCRF/ G < 0 denotes negative feedback. Thus ΔCRF/ G quantifies the net cloud feedback ( Cess et al. 1989 , 1990 ). Fig . 1-6. Earth’s TOA radiation budget together with a fictitious planet in which there are no clouds, but all else remains the same. LW denotes longwave (infrared) radiation and SW

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climatology and satellite cloud-radiation projects by contributing critical data and analyses from an intensive measurement and modeling program. Changes in cloud cover and cloud characteristics, because of their intimate relationship with infrared and solar radiation, are a major factor in determining the magnitude of potential warming resulting from increased concentrations of greenhouse gases. Also, the accuracy of radiative calculations, including the treatment of clouds, affects the accuracy of

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D. D. Turner, E. J. Mlawer, and H. E. Revercomb

, 2000 : Visible and near-infrared H 2 16 O line intensity corrections for HITRAN-96 . J. Quant. Spectrosc. Radiat. Transfer , 66 , 101 – 105 , doi: 10.1016/S0022-4073(99)00223-X . Gutman , S. I. , R. B. Chadwick , D. W. Wolf , A. Simon , T. Van Hove , and C. Rocken , 1994 : Toward an operational water vapor remote sensing system using the global positioning system. Proc. Fourth Atmospheric Radiation Measurement (ARM) Science Team Meeting , Charleston, SC, U.S. DOE, 173

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J. H. Mather, D. D. Turner, and T. P. Ackerman

calculations to radiation measurements at the surface. An important tool in these early clear-sky infrared closure studies was the AERI ( Knuteson et al. 2004a , b ; Turner et al. 2016b ). The AERI provided high-spectral-resolution radiance measurements at infrared wavelengths. Comparisons with simulations from the AER line-by-line radiative transfer model ( Clough et al. 1992 ) revealed that errors and uncertainties in water vapor measurements were a limiting factor in constraining surface infrared

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Ted S. Cress and Douglas L. Sisterson

the three-dimensional structure of the atmospheric column on the scale of a GCM grid cell Combined with the requirement to gather data to address these two questions neatly summarized by Ackerman et al. (2016 , chapter 3) above, plus the requirement for real-time data acquisition and quality control, a set of measurement requirements important to deployment planning could be specified: Continuous (24/7) measurements Measurements of solar and infrared radiation, both spectrally resolved and

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Eli J. Mlawer, Michael J. Iacono, Robert Pincus, Howard W. Barker, Lazaros Oreopoulos, and David L. Mitchell

drives the large-scale dynamics that moves energy from the tropics toward the poles. Radiation calculations are therefore essential for climate and weather simulations, but are themselves quite complex even without considering the effects of variable and inhomogeneous clouds. Clear-sky radiative transfer calculations have to account for thousands of absorption lines due to water vapor, carbon dioxide, and other gases, which are irregularly distributed across the spectrum and have shapes dependent on

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