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Retrieving Temperature and Moisture Profiles from AERI Radiance Observations: AERIPROF Value-Added Product Technical Description Revision 1
This document explains the procedure to retrieve temperature and moisture profiles from high-spectral resolution infrared radiance data measured by the U.S. Department Of Energy (DOE) Atmospheric Radiation (ARM) Program’s atmospheric emitted radiance interferometer (AERI) instrument. The technique has been named the AERIPROF thermodynamic retrieval algorithm. The software has been developed over the last decade at the University of Wisconsin-Madison and has matured into an ARM Value-Added Procedure. This document will describe the AERIPROF retrieval procedure, outline the algorithm routines, discuss the software heritage, and, finally, provide references with further documentation
Observing convective aggregation
Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad a distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a ground-based observational network
Retrievals of atmospheric boundary layer temperature and moisture profiles by using Atmospheric Emitted Radiance Interferometer (AERI) measurements in Shouxian
Observational determination of surface radiative forcing by CO2 from 2000 to 2010.
The climatic impact of CO2 and other greenhouse gases is usually quantified in terms of radiative forcing, calculated as the difference between estimates of the Earth's radiation field from pre-industrial and present-day concentrations of these gases. Radiative transfer models calculate that the increase in CO2 since 1750 corresponds to a global annual-mean radiative forcing at the tropopause of 1.82 ± 0.19 W m(-2) (ref. 2). However, despite widespread scientific discussion and modelling of the climate impacts of well-mixed greenhouse gases, there is little direct observational evidence of the radiative impact of increasing atmospheric CO2. Here we present observationally based evidence of clear-sky CO2 surface radiative forcing that is directly attributable to the increase, between 2000 and 2010, of 22 parts per million atmospheric CO2. The time series of this forcing at the two locations-the Southern Great Plains and the North Slope of Alaska-are derived from Atmospheric Emitted Radiance Interferometer spectra together with ancillary measurements and thoroughly corroborated radiative transfer calculations. The time series both show statistically significant trends of 0.2 W m(-2) per decade (with respective uncertainties of ±0.06 W m(-2) per decade and ±0.07 W m(-2) per decade) and have seasonal ranges of 0.1-0.2 W m(-2). This is approximately ten per cent of the trend in downwelling longwave radiation. These results confirm theoretical predictions of the atmospheric greenhouse effect due to anthropogenic emissions, and provide empirical evidence of how rising CO2 levels, mediated by temporal variations due to photosynthesis and respiration, are affecting the surface energy balance