Amy J. Braverman

Toward the characterization of upper tropospheric clouds using Atmospheric Infrared Sounder and Microwave Limb Sounder observations

[1]xa0We estimate the accuracy of cloud top altitude (Z) retrievals from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) observing suite (ZA) on board the Earth Observing System Aqua platform. We compare ZA with coincident measurements of Z derived from the micropulse lidar and millimeter wave cloud radar at the Atmospheric Radiation Measurement (ARM) program sites of Nauru and Manus islands (ZARM) and the inferred Z from vertically resolved Microwave Limb Sounder (MLS) ice water content (IWC) retrievals. The mean difference in ZA minus ZARM plus or minus one standard deviation ranges from −2.2 to 1.6 km ± 1.0 to 4.2 km for all cases of AIRS effective cloud fraction (fA) > 0.15 at Manus Island using the cloud radar only. The range of mean values results from using different approaches to determine ZARM, day/night differences, and the magnitude of fA; the variation about the mean decreases for increasing values of fA. Analysis of ZARM from the micropulse lidar at Nauru Island for cases restricted to 0.05 ≤ fA ≤ 0.15 indicates a statistically significant improvement in ZA − ZARM over the cloud radar-derived values at Manus Island. In these cases the ZA − ZARM difference is −1.1 to 2.1 km ± 3.0 to 4.5 km. These results imply that the operational ZA is quantitatively useful for constraining cirrus altitude despite the nominal 45 km horizontal resolution. Mean differences of cloud top pressure (PCLD) inferred from coincident AIRS and MLS ice water content (IWC) retrievals depend upon the method of defining AIRS PCLD (as with the ARM comparisons) over the MLS spatial scale, the peak altitude and maximum value of MLS IWC, and fA. AIRS and MLS yield similar vertical frequency distributions when comparisons are limited to fA > 0.1 and IWC > 1.0 mg m−3. Therefore the agreement depends upon the opacity of the cloud, with decreased agreement for optically tenuous clouds. Further, the mean difference and standard deviation of AIRS and MLS PCLD are highly dependent on the MLS tangent altitude. For MLS tangent altitudes greater than 146 hPa, the strength of the limb technique, the disagreement becomes statistically significant. This implies that AIRS and MLS “agree” in a statistical sense at lower tangent altitudes and “disagree” at higher tangent altitudes. These results provide important insights on upper tropospheric cloudiness as observed by nadir-viewing AIRS and limb-viewing MLS.

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

[1]xa0The Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation Systems Terra satellite have been measuring aerosol optical thickness (AOT) since early 2000. These remote-sensing platforms complement the ground-based Aerosol Robotic Network (AERONET) in better understanding the role of aerosols in climate and atmospheric chemistry. To date, however, there have been only limited attempts to exploit the complementary multiangle (MISR) and multispectral (MODIS) capabilities of these sensors along with the ground-based observations in an integrated analysis. This paper describes a geostatistical data fusion technique that can take advantage of the spatial autocorrelation of the AOT distribution, while making optimal use of all available data sets. Using Level 2.0 AERONET, MISR, and MODIS AOT data for the contiguous United States, we demonstrate that this approach can successfully incorporate information from multiple sensors and provide accurate estimates of AOT with rigorous uncertainty bounds. Cross-validation results show that the resulting AOT product is closer to the ground-based AOT observations than either of the individual satellite measurements.

Statistical data fusion of multi-sensor AOD over the Continental United States

This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N3), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States.

Sharing Satellite Observations with the Climate-Modeling Community: Software and Architecture

The disparate communities of climate modeling and remote sensing are finding economic, political, and societal benefit from the direct comparisons of climate model outputs to satellite observations, using these comparisons to help tune models and to provide ground truth in understanding the Earths climate processes. In the context of the Intergovernmental Panel on Climate Change (IPCC) and its upcoming 5th Assessment Report (AR5), the authors have been working with principals in both communities to build a software infrastructure that enables these comparisons. This infrastructure must overcome several software engineering challenges, including bridging heterogeneous data file formats and metadata formats, transforming swath-based remotely sensed data into globally gridded datasets, and navigating and aggregating information from the largely distributed ecosystem of organizations that house these climate model outputs and satellite data. The authors focus in this article is on the description of software tools and services that meet these stringent challenges, and on informing the broader communities of climate modelers, remote sensing experts, and software engineers on the lessons learned from their experience so that future systems can benefit and improve upon their existing results.

Entropy Constrained Clustering Algorithm Guided by Differential Evolution

Entropy constrained vector quantization (ECVQ) is a clustering technique (A. Philip et al., 1989) that has been successfully used to describe efficiently large amounts of data collected by the NASA Earth Observing System. The manipulation of this algorithm requires the user to set two parameters: the entropy Lagrange multiplier, and the initial guess for the number of clusters. In this work, we describe an integrated solution that uses a differential evolution algorithm to determine these two parameters. By optimizing two objective functions, entropy and distortion, we find that the solution that best describes the data is located at the inflection point in the Pareto front, i.e. at the point where the tradeoff between the two competing objectives does not favor either one.

Understanding architectural tradeoffs necessary to increase climate model intercomparison efficiency

NASAs Jet Propulsion Laboratory, in partnership with Lawrence Livermore National Laboratory, has been leading an effort to allow remote sensing data available from NASA satellites to be easily compared with climate model outputs available from the DOE-funded Earth System Grid, a national asset in climate science. This partnership is timely with the looming Intergovernmental Panel on Climate Change (IPCC)s 5th Assement Report (AR5) in active discussion, and the metrics to better understand Earths climate under formulation. JPLs project, titled the Climate Data eXchange (CDX) provides an easy-to-use software framework for cimate scientists to rapidliy integrate and evaluate the efficacy of observational data as applied to climate models.

Physical and Statistical Modeling of Saturn's Troposphere

The 5.2-μm atmospheric window on Saturn is dominated by thermal radiation and weak gaseous absorption, with a 20% contribution from sunlight reflected from clouds. The striking variability displayed by Saturns clouds at 5.2 μm and the detection of PH3 (an atmospheric tracer) variability near or below the 2-bar level and possibly at lower pressures provide salient constraints on the dynamical organization of Saturns atmosphere by constraining the strength of vertical motions at two levels across the disk. We analyse the 5.2-μm spectra of Saturn by utilising two independent methods: (a) physical models based on the relevant atmospheric parameters and (b) statistical analysis, based on principal components analysis (PCA), to determine the influence of the variation of phosphine and the opacity of clouds deep within Saturns atmosphere to understand the dynamics in its atmosphere.