Todd Berendes

Shortwave direct radiative forcing of biomass burning aerosols estimated using VIRS and CERES data

Using collocated data from the Visible Infrared Scanner (VIRS) and the Clouds and the Earths Radiant Energy Budget Scanner (CERES) from the Tropical Rainfall Measuring (TRMM) satellite, observational estimates of the instantaneous Shortwave Aerosol Radiative Forcing (SWARF) of smoke aerosols at the top-of-atmosphere (TOA) are obtained for four days in May 1998 during a biomass-burning episode in Central America. The detection of smoke aerosols is demonstrated using VIRS imagery. Assuming a single scattering albedo (ωo) of 0.86 (at 0.63 µm) that is representative of absorbing aerosols, smoke optical thickness (τ0.63) is retrieved over ocean areas. The average τ0.63 for these four days was 1.2 corresponding to a SWARF value of −68 Wm−2. The SWARF changes from −24 to −99 Wm−2 as τ0.63 changes from 0.2 to 2.2. Global observational estimates of biomass burning aerosol radiative forcing can be obtained by combining data sets from TRMM and Terra satellites.

Cumulus Cloud Field Morphology and Spatial Patterns Derived from High Spatial Resolution Landsat Imagery

Abstract Detailed observations of cumulus cloud scales and processes are an essential ingredient in models that deal with (i) high spatial resolution cumulus ensembles; and (ii) parameterization of cloud radiative processes. The present investigation focuses on three aspects of the morphology of cumulus clouds: 1) the inhomogeneity as represented by the size distribution of clouds and cloud “holes,” 2) the nearest-neighbor relationships regarding their sizes and mutual distances, and 3) the scales of their clustering. Distributionwise, cloud size can best be represented by a mixture of two power laws. Clouds of diameter below 1 km have the slope parameter ranging from about 1.4 to 2.3, while larger clouds have slopes ranging from 2.1 to 4.75. Furthermore, these clouds are bifractal in nature. The break in power law and fractal dimension occurs at a size critical to the cloud-scale processes in the following sense. First, this is the cloud size that makes the largest contribution to the extent of cloud cov...

The 1985 Biomass Burning Season in South America: Satellite Remote Sensing of Fires, Smoke, and Regional Radiative Energy Budgets

Using satellite imagery, more than five million square kilometers of the forest and cerrado regions over South America are extensively studied to monitor fires and smoke during the 1985 biomass burning season. The results are characterized for four major ecosystems, namely, 1) tropical rain forest, 2) tropical broadleaf seasonal, 3) savanna/grass and seasonal woods (SGW), and 4) mild/warm/hot grass/shrub (MGS). The spatial and temporal distribution of fires are examined from two different methods using the multispectral Advanced Very High Resolution Radiometer Local Area Coverage data. Using collocated measurements from the instantaneous scanner Earth Radiation Budget Experiment data, the direct regional radiative forcing of biomass burning aerosols is computed. The results show that more than 70% of the fires occur in the MGS and SGW ecosystems due to agricultural practices. The smoke generated from biomass burning has negative instantaneous net radiative forcing values for all four major ecosystems within South America. The smoke found directly over the fires has mean net radiative forcing values ranging from 225.6 to 233.9 W m22. These results confirm that the regional net radiative impact of biomass burning is one of cooling. The spectral and broadband properties for clear-sky and smoke regions are also presented that could be used as input and/or validation for other studies attempting to model the impact of aerosols on the earth‐atmosphere system. These results have important applications for future instruments from the Earth Observing System (EOS) program. Specifically, the combination of the Visible Infrared Scanner and Clouds and the Earth’s Radiant Energy System (CERES) instruments from the Tropical Rainfall Measuring Mission and the combination of Moderate Resolution Imaging Spectrometer and CERES instruments from the EOS morning crossing mission could provide reliable estimates of the direct radiative forcing of aerosols on a global scale, thereby reducing the uncertainties in current global aerosol radiative forcing values.

Aviation Applications for Satellite-Based Observations of Cloud Properties, Convection Initiation, In-Flight Icing, Turbulence, and Volcanic Ash

Abstract Advanced Satellite Aviation Weather Products (ASAP) was jointly initiated by the NASA Applied Sciences Program and the NASA Aviation Safety and Security Program in 2002. The initiative provides a valuable bridge for transitioning new and existing satellite information and products into Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP) efforts to increase the safety and efficiency of the airspace system. The ASAP project addresses hazards such as convective weather, turbulence (clear air and cloud induced), icing, and volcanic ash, and is particularly applicable in extending the monitoring of weather over data-sparse areas, such as the oceans and other observationally remote locations. ASAP research is conducted by scientists from NASA, the FAA AWRPs Product Development Teams (PDT), NOAA, and the academic research community. In this paper we provide a summary of activities since the inception of ASAP that emphasize the use of current-generation satellite technologies ...

Cumulus cloud base height estimation from high spatial resolution Landsat data: a Hough transform approach

This study develops a semiautomated methodology for estimating cumulus cloud base heights using high-spatial-resolution Landsat multispectral scanner data. The approach employs a variety of image processing techniques to match cloud edges with their corresponding shadow edges. Cloud base height is then estimated by computing the separation between the corresponding generalized Hough transform reference points. Sixteen subregions, each 30 km*30 km in size, are selected for four Landsat scenes. Standard deviations of cloud base height within each of the subregion range from about 100 m to 150 m. Differences between cloud base heights computed using the Hough transform and a manual verification technique are small. It is estimated that cloud base height accuracies of 50-70 m may be possible using HIRIS and ASTER instruments in the Earth Observation Satellite (EOS) Global Climate Change program. >

Cloud cover comparisons of the MODIS daytime cloud mask with surface instruments at the north slope of Alaska ARM site

This paper compares daytime cloud fraction derived from the Moderate Resolution Imaging Spectrometer (MODIS), an imager on the National Aeronautics and Space Administrations Earth Observing System Aqua and Terra platforms, to observations from a suite of surface-based instrumentation located at the Department of Energys atmospheric radiation measurement (ARM) program North Slope of Alaska (NSA) Clouds and Radiation Testbed site. In this systematic comparison of satellite-to-surface measurements, 3650 cases are analyzed from February through September 2001. The surface instruments used in these comparisons include the Vaisala Ceilometer (VCEIL), the Micropulse Lidar (MPL), the Active Remote Sensing of Clouds (ARSCL) composite laser-derived data product, the Whole-Sky Imager (WSI), and the Normal Incidence Pyrheliometer (NIP). In terms of the active sensors, VCEIL cloud cover results compare to within /spl plusmn/20% of MODIS results 77% of the time. As expected, VCEIL is found to be insensitive to optically thin high-level clouds. MPL results are consistent with MODIS in 83% of the cases; however, the MPL preliminary.cbh variable reports spurious clouds in clear-sky conditions. The ARSCL composite laser-derived data product agrees with MODIS in 81% of the cases, improving upon high cloud detection of the VCEIL, while eliminating the spurious clear-sky cloud detections in the MPL preliminary.cbh variable. For the passive WSI, cloud cover agrees with the MODIS cloud fraction in 74% of the cases, with the difference primarily caused by the insensitivity of the WSI to thin clouds. Detailed analysis of individual cases shows that the MODIS cloud mask generally detects more thin cirrus than the surface-based instruments, but it sometimes fails to detect low-level cumulus and fog over the ARM NSA site.

A comparison of paired histogram, maximum likelihood, class elimination, and neural network approaches for daylight global cloud classification using AVHRR imagery

The accuracy and efficiency of four approaches to identifying clouds and aerosols in remote sensing imagery are compared. These approaches are as follows: a maximum likelihood classifier, a paired histogram technique, a hybrid class elimination approach, and a back-propagation neural network. Regional comparisons were conducted on advanced very high resolution radiometer (AVHRR) local area coverage (LAC) scenes from the polar regions, desert areas, and regions of biomass-burning, areas which are known to be particularly difficult. For the polar, desert, and biomass burning regions, the maximum likelihood classifier achieved 94–97% accuracy, the neural network achieved 95–96% accuracy, and the paired histogram approach achieved 93–94% accuracy. The primary advantage to the class elimination scheme lies in its speed; its accuracy of 94–96% is comparable to that of the maximum likelihood classifier. Experiments also clearly demonstrate the effectiveness of decomposing a single global classifier into separate regional classifiers, since the regional classifiers can be more finely tuned to recognize local conditions. In addition, the effectiveness of using composite features is compared to the simpler approach of using the five AVHRR channels and the reflectance of channel 3 treated as a sixth channel as the elements of the feature vector. The results varied, demonstrating that the features cannot be chosen independently of the classifier to be used. It is also shown that superior results can obtained by training the classifiers using subclass information and collapsing the subclasses after classification. Finally, ancillary data were incorporated into the classifiers, consisting of a land/water mask, a terrain map, and a computed sunglint probability. While the neural network did not benefit from this information, the accuracy of the maximum likelihood classifier improved by 1%, and the accuracy of the paired histogram method increased by up to 4%.

The ASTER polar cloud mask

This research is concerned with the problem of producing polar cloud masks for satellite imagery. The results presented are for Thematic Mapper (TM) data from the northern and southern polar regions, however, the techniques discussed will be applied to ASTER data when it becomes available. A series of classification techniques have been implemented and tested, the most promising of which is a neural network classifier. To use a neural network classifier, the pixels in the data must be transformed into feature vectors, some of which are used for training the network and the remainder of which are reserved for testing the final system. The first challenge is the identification of pure pixel samples from the imagery. The Interactive Visual Image Classification System (IVICS) was developed specifically for this project to make this task simpler for the human expert. After labeling the pixels, the feature vectors are generated. One hundred and forty potential vector elements, consisting of linear and nonlinear combinations of the satellite channel data, have been identified. Because smaller input vectors reduce the difficulty of training and can improve classification accuracy, the set of potential vector elements must be reduced. Two techniques have been tested: a histogram-based selection method and a fuzzy logic method. Both have proven effective for this task. Although the polar region is the only area considered in this work, a system that can produce cloud masks for all areas of the globe will be required. Thus, speed, extensibility, and flexibility requirements must be added to the accuracy constraint. To achieve these goals, a two-stage classification approach is used. The first stage uses a series of static and adaptive thresholds derived from statistical analysis of the polar scenes to reduce the set of possible classes to which a pixel may be assigned, once a cluster of classes has been selected, a neural network trained to distinguish between the classes in the cluster is used to make the ultimate classification.

Spectroradiometer with wedge interference filters (SWIF): measurements of the spectral optical depths at Mauna Loa Observatory

A spectroradiometer with wedge interference filters (SWIF) (the filters were produced by Carl Zeiss, Jena, Germany) and a CCD matrix (which was of Russian production) that functions as the sensor has been designed and built for use in ground-based optical sensing of the atmosphere and the Earths surface in the spectral range of 0.35-1.15 µm. Absolute calibration of this instrument was performed through a series of observations of direct solar radiation at Mauna Loa Observatory (MLO) in Hawaii in May and June 1993. Spectral optical depth (SOD) measurements that were made during these field experiments provided detailed spectral information about both aerosol extinction (scattering plus absorption) and molecular absorption in the atmosphere above the site at MLO. The aerosol-SOD measurements were compared with narrow-band radiometer measurements at wavelengths of 380, 500, and 778 nm The SWIF and narrow-band radiometer measurements are in agreement to within the experimental error. At a wavelength of 500 nm, the aerosol SOD was found to be approximately 0.045. Adescription of the SWIF instrument, its absolute calibration, and the determination of atmospheric SODs at MLO are presented.

Visualizations for the spyglass ontology-based information analysis and retrieval system

Spyglass is an ontology-based information retrieval system designed to help analysts explore very large collections of unstructured text documents. The tool includes two main components: server and client. The server is a web-based service that uses a specific domain ontology to index a collection of documents, answer queries from the client, and provide retrieval and visualization services based on the ontology and the resulting index. The client is a graphical user interface which allows analysts to explore the document collections, query single or multiple entities of interest of the ontology and retrieve the documents relevant to the query. The rich set of visualization tools in Spyglass will be presented in this paper.