Search Results

You are looking at 1 - 6 of 6 items for

  • Author or Editor: E. J. Mlawer x
  • Refine by Access: All Content x
Clear All Modify Search
E. J. Mlawer and D. D. Turner
Full access
D. D. Turner and E. J. Mlawer

Accurately accounting for radiative energy balance between the incoming solar and the outgoing infrared radiative fluxes is very important in modeling the Earth's climate. Water vapor absorption plays a critical role in the radiative heating rate profile in the midtroposphere by strongly absorbing both infrared and solar radiation in several absorption bands throughout the electromagnetic spectrum. One of the most important of these absorption bands is in the far-infrared portion of the spectrum, where the far-infrared is defined here to be wavelengths longer than 15 microns. A large fraction (~40%) of the outgoing infrared flux is emitted by water vapor in the far-infrared. Errors in the radiative transfer models associated with the far-infrared and other strong water vapor absorption bands will therefore affect the calculation of the planet's total outgoing radiative flux and its vertical distribution of the radiant energy; these errors may result in inaccurate modeling of the general circulation of the planet.

A set of field experiments, called the Radiative Heating in Underexplored Bands Campaigns (RHUBC), has been conducted as part of the Atmospheric Radiation Measurement (ARM) program. The RHUBC campaigns deployed spectrally resolved far-infrared spectrometers alongside other ARM observations in extremely dry environments to provide a robust and complete dataset that allows radiative transfer models to be evaluated in the far-infrared and other spectral regions where water vapor absorbs strongly. RHUBC I was conducted in February–March 2007 in Barrow, Alaska, and RHUBC II was conducted in August–October 2009 in the Atacama Desert region of Chile at an altitude of 5.3 km. The motivation for and initial results from these experiments are described, as well as the implications for global climate models.

Full access
D. D. Turner, E. J. Mlawer, and H. E. Revercomb
Full access
K. E. Cady-Pereira, M. W. Shephard, D. D. Turner, E. J. Mlawer, S. A. Clough, and T. J. Wagner

Abstract

Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

Full access
D. D. Turner, D. C. Tobin, S. A. Clough, P. D. Brown, R. G. Ellingson, E. J. Mlawer, R. O. Knuteson, H. E. Revercomb, T. R. Shippert, W. L. Smith, and M. W. Shephard

Abstract

Research funded by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has led to significant improvements in longwave radiative transfer modeling over the last decade. These improvements, which have generally come in small incremental changes, were made primarily in the water vapor self- and foreign-broadened continuum and the water vapor absorption line parameters. These changes, when taken as a whole, result in up to a 6 W m−2 improvement in the modeled clear-sky downwelling longwave radiative flux at the surface and significantly better agreement with spectral observations. This paper provides an overview of the history of ARM with regard to clear-sky longwave radiative transfer, and analyzes remaining related uncertainties in the ARM state-of-the-art Line-by-Line Radiative Transfer Model (LBLRTM).

A quality measurement experiment (QME) for the downwelling infrared radiance at the ARM Southern Great Plains site has been ongoing since 1994. This experiment has three objectives: 1) to validate and improve the absorption models and spectral line parameters used in line-by-line radiative transfer models, 2) to assess the ability to define the atmospheric state, and 3) to assess the quality of the radiance observations that serve as ground truth for the model. Analysis of data from 1994 to 1997 made significant contributions to optimizing the QME, but is limited by small but significant uncertainties and deficiencies in the atmospheric state and radiance observations. This paper concentrates on the analysis of QME data from 1998 to 2001, wherein the data have been carefully selected to address the uncertainties in the 1994–97 dataset. Analysis of this newer dataset suggests that the representation of self-broadened water vapor continuum absorption is 3%–8% too strong in the 750–1000 cm−1 region. The dataset also provides information on the accuracy of the self- and foreign-broadened continuum absorption in the 1100–1300 cm−1 region. After accounting for these changes, remaining differences in modeled and observed downwelling clear-sky fluxes are less than 1.5 W m−2 over a wide range of atmospheric states.

Full access
H. W. Barker, G. L. Stephens, P. T. Partain, J. W. Bergman, B. Bonnel, K. Campana, E. E. Clothiaux, S. Clough, S. Cusack, J. Delamere, J. Edwards, K. F. Evans, Y. Fouquart, S. Freidenreich, V. Galin, Y. Hou, S. Kato, J. Li, E. Mlawer, J.-J. Morcrette, W. O'Hirok, P. Räisänen, V. Ramaswamy, B. Ritter, E. Rozanov, M. Schlesinger, K. Shibata, P. Sporyshev, Z. Sun, M. Wendisch, N. Wood, and F. Yang

Abstract

The primary purpose of this study is to assess the performance of 1D solar radiative transfer codes that are used currently both for research and in weather and climate models. Emphasis is on interpretation and handling of unresolved clouds. Answers are sought to the following questions: (i) How well do 1D solar codes interpret and handle columns of information pertaining to partly cloudy atmospheres? (ii) Regardless of the adequacy of their assumptions about unresolved clouds, do 1D solar codes perform as intended?

One clear-sky and two plane-parallel, homogeneous (PPH) overcast cloud cases serve to elucidate 1D model differences due to varying treatments of gaseous transmittances, cloud optical properties, and basic radiative transfer. The remaining four cases involve 3D distributions of cloud water and water vapor as simulated by cloud-resolving models. Results for 25 1D codes, which included two line-by-line (LBL) models (clear and overcast only) and four 3D Monte Carlo (MC) photon transport algorithms, were submitted by 22 groups. Benchmark, domain-averaged irradiance profiles were computed by the MC codes. For the clear and overcast cases, all MC estimates of top-of-atmosphere albedo, atmospheric absorptance, and surface absorptance agree with one of the LBL codes to within ±2%. Most 1D codes underestimate atmospheric absorptance by typically 15–25 W m–2 at overhead sun for the standard tropical atmosphere regardless of clouds.

Depending on assumptions about unresolved clouds, the 1D codes were partitioned into four genres: (i) horizontal variability, (ii) exact overlap of PPH clouds, (iii) maximum/random overlap of PPH clouds, and (iv) random overlap of PPH clouds. A single MC code was used to establish conditional benchmarks applicable to each genre, and all MC codes were used to establish the full 3D benchmarks. There is a tendency for 1D codes to cluster near their respective conditional benchmarks, though intragenre variances typically exceed those for the clear and overcast cases. The majority of 1D codes fall into the extreme category of maximum/random overlap of PPH clouds and thus generally disagree with full 3D benchmark values. Given the fairly limited scope of these tests and the inability of any one code to perform extremely well for all cases begs the question that a paradigm shift is due for modeling 1D solar fluxes for cloudy atmospheres.

Full access