MLS Data Overview

The MLS instrument has been taking data from orbit aboard the Aura platform since July 2004 and measures radiances centered around 5 spectral bands in the vicinity of 118, 190, 240, 640, and 2520 GHz. Several channels per spectral band at approximately 5 GHz spacing record data with a limb-scan oriented along-track. Data are recorded above 215 hPa with a vertical resolution of better than 3 km. Along-track resolution is approximately 500 km with a cross-track footprint of 5 km. More advanced product versions claim a spatial resolution footprint of 3 km X 300 km X 7 km. Measurements at 118 and 2520 GHz are polarization sensitive, while the

radiometers for the other bands are double-sideband receivers, meaning they are insensitive to the polarization of the signal. These measurements lead to about two dozen standard products over the course of normal operations, including radical species. These products have led to a greatly increased understanding of upper-tropospheric and lower-stratospheric processes. The below figure depicts high-resolution radiances over the MLS spectral bands along with the instrument’s channel coverage:

Figure 6.4: Depiction of folded-sideband high-resolution radiances at several tangent heights in the 5 spectral bands over which the MLS instrument measures. Diagram from

http://mls.jpl.nasa.gov/images/folded-big.png

While the MLS instrument was primarily designed to characterize gaseous stratospheric constituents, one of the most scientifically interesting results generated from the instrument’s

measurements is the Ice Water Content (IWC) product. As described by Wu et al. [2006], this product is derived from a combination of radiance data from the MLS spectral bands with most of the information coming from the window channels in the 240 GHz band. Some extra information is derived from the 118 GHz and the 2.5 THz because they are sensitive to the polarization signal from clouds. The figure below provides a cartoon of the MLS data measurement technique for the IWC product.

Figure 6.5: Schematic of cloud IWC observations derived from limb-viewing geometry from Wu et al. [2006]. At pressures above 215 mbar, IWC observations are derived from single-channel radiances in window channels.

The IWC product is derived by first retrieving the temperature and gaseous quantities from the measurements and then utilizing the discrepancies in window-channel radiance between the clear- sky forward model calculations [Read et al.,2006] and the measured radiance data to produce cloud-induced radiance values (Tcir). The Tcir values are then used with the cloudy-sky forward model to estimate cloud IWC predicated on several assumptions that are discussed below. Figure 6.6 indicates that between 5 and 30 mg/m3_{, the relationship between IWC and Tcir is very robust}

and that IWC values of up to 50 mg/m3 _{can be retrieved through this technique. The resulting}

IWC product is reported at 215, 178, 147, 121, 100, 83, and 68 hPa. The data can be gridded on monthly timescales with at 4° latitude by 8° longitude resolution to produce maps of vertically resolved cloud ice-water content.

Figure 6.6: Solid line denotes sensitivity of MLS window channel brightness temperatures to cloud IWC. Dashed line indicates the fit line utilized in the retrievals. From Wu et al.[2006]. The following figure realizes the IWC vertical structure in a way that has not been achieved from previous remote sensing measurements.

Figure 6.7: Monthly-mean IWC values at 4x5° resolution at several pressure levels for December 2004 retrieved from MLS, version 1.0. From http://mls.jpl.nasa.gov/products/iwc_product.php Several features are indicated in this figure. First, the areas of the planet with strong convection tend to produce high IWC values up to 147 hPa. Second, cloud ice extent outside of regions of

strong convection is sparse above 215 hPa. Furthermore, this figure gives an indication of the extent of cloud ice in the tropics and extra-tropics.

There are many sources of uncertainty in the derivation of the MLS IWC product, and several of the most significant sources arise from assumptions made in the retrieval process. First, there are radiance and forward model uncertainties. While the former is almost entirely random, the latter depends on the clear-sky gas retrievals, inducing a Tcir uncertainty of 2K at 100 hPa and 10K at 300 hPa which translates to 10–50% IWC product uncertainty. More systematic uncertainties are present in the assumptions utilized in the translation from Tcir to IWC. In particular, the ice particle size distribution and shape are mostly unconstrained by the retrieval, and variations in the IWC product may lead to uncertainties as large as a factor of 2. Finally, due to its viewing geometry, this IWC product covers an enormously large horizontal footprint, thereby thwarting standard validation efforts.

However, efforts by Wu et al.[2008] have explored how MLS data compare with estimates of cloud ice from other A-Train measurements, including MODIS [Justice et al., 1998] and CloudSat [Stephens et al.,2002]. These authors found that the MLS product is consistent to within 50% of results from other datasets, and assumptions regarding the particle size distribution within ice clouds contribute substantially to these discrepancies. Even with 50% nominal

uncertainty in cloud ice, scientifically meaningful results can still be derived from the MLS IWC product. For example, Li et al.[2007] found that significant differences existed between the distribution of cloud ice fields from the ECMWF analysis product and the IWC distributions derived from the MLS measurements. The spatial patterns of this disagreement between the two characterizations of UT IWC suggested that the modeling of deep convection over equatorial land masses was deficient in the analysis calculations. Therefore, while caution must be exercised in the use of the MLS IWC product, it presents an unprecedented and scientifically valuable record of UT cloud layering.