Ground-based two-channel microwave radiometers (MWRs) have been used for over 15 years by the Atmospheric Radiation Measurement (ARM) program to provide observations of downwelling emitted radiance from which precipitable water vapor (PWV) and liquid water path (LWP) - two geophysical parameters critical for many areas of atmospheric research - are retrieved. An algorithm that incorporates output from two advanced retrieval techniques, namely, a physical-iterative approach and a computationally efficient statistical method, has been developed to retrieve these parameters. The forward model used in both methods is the monochromatic radiative transfer model MonoRTM. An important component of this MWR RETrieval (MWRRET) algorithm is the determination of small (< 1 K) offsets that are subtracted from the observed brightness temperatures before the retrievals are performed. Accounting for these offsets removes systematic biases from the observations and/or the model spectroscopy necessary for the retrieval, significantly reducing the systematic biases in the retrieved LWP. The MWRRET algorithm significantly provides more accurate retrievals than the original ARM statistical retrieval, which uses monthly retrieval coefficients. By combining the two retrieval methods with the application of brightness temperature offsets to reduce the spurious LWP bias in clear skies, the MWRRET algorithm significantly provides better retrievals of PWV and LWP from the ARM two-channel MWRs compared to the original ARM product.

The direct radiative effect (DRE) of aerosols, which is the instantaneous radiative impact of all atmospheric particles on the Earth's energy balance, is sometimes confused with the direct radiative forcing (DRF), which is the change in DRE from pre-industrial to present-day (not including climate feedbacks). In this study we couple a global chemical transport model (GEOS-Chem) with a radiative transfer model (RRTMG) to contrast these concepts. We estimate a global mean all-sky aerosol DRF of −0.36 Wm⁻² and a DRE of −1.83 Wm⁻² for 2010. Therefore, natural sources of aerosol (here including fire) affect the global energy balance over four times more than do present-day anthropogenic aerosols. If global anthropogenic emissions of aerosols and their precursors continue to decline as projected in recent scenarios due to effective pollution emission controls, the DRF will shrink (−0.22 Wm⁻² for 2100). Secondary metrics, like DRE, that quantify temporal changes in both natural and anthropogenic aerosol burdens are therefore needed to quantify the total effect of aerosols on climate.

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern G... Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern G...

Observations of atmospheric ammonia are important in understanding and modelling the impact of ammonia on both human health and the natural environment. We present a detailed description of a robust retrieval algorithm that demonstrates the capabilities of utilizing Cross-track Infrared Sounder (CrIS) satellite observations to globally retrieval ammonia concentrations. Initial ammonia retrieval results using both simulated and real observations show that (i) CrIS is sensitive to ammonia in the boundary layer with peak vertical sensitivity typically around ~ 850–750 hPa (~ 1.5 to 2.5 km), which can dip down close to the surface (~ 900 hPa) under ideal conditions, (ii) it has a minimum detection limit of ~ 1 ppbv (peak profile value typically at the surface), and (iii) the information content can vary significantly with maximum values of ~ 1 degree-of-freedom for signal. Comparisons of the retrieval with simulated "true" profiles show a small positive retrieval bias of 6% with a standard deviation of ~ ± 20% (ranging from ± 12 to ± 30% over the vertical profile). Note that these uncertainty estimates are considered as lower bound values as no potential systematic errors are included in the simulations. The CrIS NH₃ retrieval applied over the Central Valley in CA, USA, demonstrates that CrIS correlates well with the spatial variability of the boundary layer ammonia concentrations seen by the nearby Quantum Cascade-Laser (QCL) in situ surface and the Tropospheric Emission Spectrometer (TES) satellite observations as part of the DISCOVER-AQ campaign. The CrIS and TES ammonia observations show quantitatively similar retrieved boundary layer values that are often within the uncertainty of the two observations. Also demonstrated is CrIS's ability to capture the expected spatial distribution in the ammonia concentrations, from elevated values in the Central Valley from anthropogenic agriculture emissions, to much lower values in the unpolluted or clean surrounding mountainous regions. These initial results demonstrate the capabilities of the CrIS satellite to measure ammonia.