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  1. null (Ed.)
    Abstract. Temperature, H2O, and O3 profiles, as well as CO2, N2O, CH4, chlorofluorocarbon-12 (CFC-12), and sea surface temperature (SST) scalar anomalies are computed using a clear subset of AIRS observations over ocean for the first 16 years of NASA's Earth-Observing Satellite (EOS) Aqua Atmospheric Infrared Sounder (AIRS) operation. The AIRS Level-1c radiances are averaged over 16 d and 40 equal-area zonal bins and then converted to brightness temperature anomalies. Geophysical anomalies are retrieved from the brightness temperature anomalies using a relatively standard optimal estimation approach. The CO2, N2O, CH4, and CFC-12 anomalies are derived by applying a vertically uniform multiplicative shift to each gas in order to obtain an estimate for the gas mixing ratio. The minor-gas anomalies are compared to the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) in situ values and used to estimate the radiometric stability of the AIRS radiances. Similarly, the retrieved SST anomalies are compared to the SST values used in the ERA-Interim reanalysis and to NOAA's Optimum Interpolation SST (OISST) product. These intercomparisons strongly suggest that many AIRS channels are stable to better than 0.02 to 0.03 K per decade, well below climate trend levels, indicating that the AIRS blackbody is not drifting. However, detailed examination of the anomaly retrieval residuals (observed – computed) shows various small unphysical shifts that correspond to AIRS hardware events (shutdowns, etc.). Some examples are given highlighting how the AIRS radiance stability could be improved, especially for channels sensitive to N2O and CH4. The AIRS shortwave channels exhibit larger drifts that make them unsuitable for climate trending, and they are avoided in this work. The AIRS Level 2 surface temperature retrievals only use shortwave channels. We summarize how these shortwave drifts impacts recently published comparisons of AIRS surface temperature trends to other surface climatologies. 
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  2. null (Ed.)
    Abstract. A fast pseudo-monochromatic radiative transfer package using asingular value decomposition (SVD) compressed atmosphericoptical depth database has been developed, primarily for simulatingradiances from hyperspectral sounding instruments (resolution ≥0.1 cm−1). The package has been tested extensively for clear-skyradiative transfer cases, using field campaign data and satelliteinstrument data. The current database uses HITRAN 2016 lineparameters and is primed for use in the spectral region spanning 605 to 2830 cm−1. Optical depths for other spectral regions (15–605 and 2830–45 000 cm−1) can also be generated for use by kCARTA. Theclear-sky radiative transfer model computes the background thermalradiation quickly and accurately using a layer-varying diffusivityangle at each spectral point; it takes less than 30 s (on a2.8 GHz core using four threads) to complete a radiance calculationspanning the infrared. The code can also compute non-localthermodynamic equilibrium effects for the 4 µm CO2 region, as wellas analytic temperature, gas and surface Jacobians. The package alsoincludes flux and heating rate calculations and an interface to aninfrared scattering model. 
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  3. null (Ed.)
  4. Abstract. One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90%, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

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