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The Sun is a distant source of energy that reaches the Earth as solar radiation. Solar radiation has a rich spectral structure. It consists of ultra-violet radiation largely absorbed by stratospheric ozone, visible radiation to which the atmosphere is mostly transparent, and near-infrared and solar infrared radiation where some absorption by atmospheric water vapor occurs. In addition to solar radiation, the spectral region of longer-wavelength thermal-infrared radiation dominated by terrestrial sources is equally important for weather and climate. Scientists in the Climate and Radiation Lab (CRL) study the individual spectral regions of solar and thermal radiation as well as the propagation of total (also known as “broadband”) shortwave and longwave fluxes to better understand the Earth’s radiation budget. The total solar flux across all wavelengths reaching the Earth is ≈ 1361 W/m2, a number that matters tremendously for the Earth’s climate. Its slight variations are monitored as continuously and accurately as possible from space by missions such as the SOlar Radiation and Climate Experiment (SORCE) whose Project Scientist is a CRL member.

The goal of solar radiation transport studies is to track the fate of the radiant energy entering at the top of the Earth’s atmosphere. The radiation can be either reflected back to space (with clouds playing a critical role in this mechanism), transmitted to the surface (where it is either absorbed or reflected) or absorbed by one of many atmospheric constituents (that can be either in gaseous, liquid or solid phase). This process of energy-driven computation also comprises the prediction of signals of a variety of so-called “passive” sensors which is the basis of physics-based atmospheric remote sensing in the solar and thermal IR spectra. CRL radiation scientists are involved in studies of all types of radiation components: reflected and emitted to space, using satellite observations; transmitted and emitted towards the surface, using ground-based measurements; and absorbed, using simultaneous collocated satellites and ground observations and even aircraft measurements. Radiative transfer models help to find links between such observations.

In order to interpret remote sensing observations, researchers need computational tools, namely numerical radiative transfer models.  The CRL is very active in the development of these tools. For effective and reliable simulation of the atmospheric radiation processes, the models should be computationally fast, yet theoretically well-grounded. Some of the radiation models co-developed by CRL scientists are publicly available and have thousands of users around the world. One example is the widely-used Mie scattering code to calculate accurate extinction and scattering characteristics of spherical particles. Another is the DISORT (Discrete Ordinates Radiative Transfer) code for radiative transfer in a multi-layered plane-parallel media, one of the most widely used codes in the atmospheric radiation community that continues to be refined by CRL staff. Recently, a Monte Carlo radiative transfer model supported by CRL scientists became publicly available as an open-source tool for studying radiative transfer in three-dimensional atmospheres and for intercomparing 3D radiative transfer codes. The development of radiation codes capable of simulating polarized atmospheric radiation is also an area of CRL expertise with potential for operational use in remote sensing applications in the very near future. More accurate remote detection and retrieval of aerosols, clouds and surface properties was also the driving behind radiative transfer modeling efforts that led to the development in CRL of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm operating on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. Finally, CRL scientists have great interest in how approximate broadband radiative transfer codes use in Global Climate Models perform and have led initiatives to test the accuracy of such codes against more accurate (but less computationally efficient) standards. 

Contact: Alexander Marshak