[1] A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semiarid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restrictions as part of future development.Citation: Moncet, J.-L., P. Liang, J. F. Galantowicz, A. E. Lipton, G. Uymin, C. Prigent, and C. Grassotti (2011), Land surface microwave emissivities derived from AMSR-E and MODIS measurements with advanced quality control, J. Geophys. Res., 116,

This paper describes a rapid and accurate technique for the numerical modeling of band transmittances and radiances in media with nonhomogeneous thermodynamic properties (i.e., temperature and pressure), containing a mixture of absorbing gases with variable concentrations. The optimal spectral sampling (OSS) method has been designed specifically for the modeling of radiances measured by sounding radiometers in the infrared and has been extended to the microwave; it is applicable also through the visible and ultraviolet spectrum. OSS is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction models. The novel OSS approach is an extension of the exponential sum fitting of transmittances technique in that channel-average radiative transfer is obtained from a weighted sum of monochromatic calculations. The fact that OSS is fundamentally a monochromatic method provides the ability to accurately treat surface reflectance and spectral variations of the Planck function and surface emissivity within the channel passband, given that the proper training is applied. In addition, the method is readily coupled to multiple scattering calculations, an important factor for treating cloudy radiances. The OSS method is directly applicable to nonpositive instrument line shapes such as unapodized or weakly apodized interferometric measurements. Among the advantages of the OSS method is that its numerical accuracy, with respect to a reference line-by-line model, is selectable, allowing the model to provide whatever balance of accuracy and computational speed is optimal for a particular application. Generally only a few monochromatic points are required to model channel radiances with a brightness temperature accuracy of 0.05 K, and computation of Jacobians in a monochromatic radiative transfer scheme is straightforward. These efficiencies yield execution speeds that compare favorably to those achieved with other existing, less accurate parameterizations.

[1] A comparison of radiative transfer models for simulating radiances from the Atmospheric Infrared Sounder (AIRS), has been undertaken. Results from 14 line-by-line and fast parameterized infrared models were submitted. Several aspects of the models were compared. First, the forward model calculations for all 2378 AIRS channels for 52 diverse atmospheric profiles and one tropical Pacific profile coincident with AIRS data were performed for three local zenith viewing angles: nadir, 45, and 60 degrees. Second, for a subset of the models and only 20 AIRS channels the transmittances from each layer to space were provided. Finally, for some models the Jacobians with respect to temperature, water vapor, and ozone were also computed. For the forward model calculations, most models agree to within 0.02 K when compared to a reference lineby-line model averaged over a subset of profiles, with the exception of a few spectral regions. When compared with AIRS observations, however, the mean differences increase to 0.2 K, and for a few models even greater differences are seen. The transmittance differences highlighted regions of the spectrum where the spectroscopy of the models differs, particularly in the carbon dioxide absorption bands at 667 cm À1 and 2386 cm À1 . For the Jacobians all models have some profiles/channels that do not fit the reference well, and the main problems are documented here. The model differences only increase slightly for off-nadir viewing angles for both forward and Jacobian calculations.

Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H₂O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus important to improve the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Interferometer (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H₂O profiles. We show that the spectroscopy in the CO₂ ν₂ and ν₃ bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to mean changes of ~0.5 K in the retrieved vertical temperature profiles between the surface and 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO₂ bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H₂O spectroscopy in LBLRTM v12.1 substantially improves the a posteriori residuals in the P-branch of the H₂O ν₂ band, while the improvements in the R-branch are more modest. The H₂O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H₂O spectroscopy. We also find that the use of a fixed ratio of HDO to H₂O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch relative to the R-branch of the H₂O ν₂ band. There were no changes to O₃ spectroscopy between the two model versions, and so both versions give positive a posteriori residuals of ~ 0.3 K in the R-branch of the O₃ ν₃ band. While the updates to the H₂O self-continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO₂ ν₃ band head, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic a posteriori residuals remain in the ν₄ band of CH₄, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in...

[1] An analysis of land surface microwave emission time series shows that the characteristic diurnal signatures associated with subsurface emission in sandy deserts carry over to arid and semiarid regions worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (AMSR-E) measurements. The AMSR-E orbit has equator crossing times near 01:30 and 13:30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. AMSR-E measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from AMSR-E, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions.