Remote sensing of volcanic ash clouds using special sensor microwave imager data

Measurements from the satellite-based special sensor microwave imager (SSM/I) were used to collect passive microwave radiation (19–85 GHz) for the August 19, 1992 (UT date), Crater Peak/Spurr volcanic cloud. This eruption was also imaged by a ground-based C-band radar system at Kenai, Alaska, 80 km away, and by the thermal infrared channels of the polar-orbiting advanced very high resolution radiometer (AVHRR). The SSM/I sensor detects scattering of Earth-emitted radiation by millimeter size volcanic ash particles. The size of ash particles in a volcanic ash cloud can be estimated by comparing the scattering at different microwave frequencies. The mass of particles in the volcanic ash cloud can be estimated by using a theoretical method based on Mie theory or by adapting the empirical methods used for estimating rainfall rates and accounting for the different dielectric constants of volcanic ash and raindrops. For the August 19, 1992, Crater Peak/Spurr eruption, the SSM/I-based estimate of ash fallout mass (1.3 × 109 − 3 × 1010 kg) was 4%–85% of the mass fallout measured in the field. Like weather radar systems, the SSM/I offers the ability to sense young volcanic ash clouds during and immediately following (within 30 min) actual eruptions. Because most volcanoes are out of range of weather radar systems, the SSM/I may be an important tool for determining the magnitude, initial trajectory, and potential fallout mass of eruptions. The SSM/I may therefore play a role in mitigating volcanic cloud hazards for aircraft, determining masses where ground sampling is not possible, and in issuing fallout warnings for communities downwind of volcanic eruptions

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)

Temperature trends at the surface and in the troposphere

[1] This paper incorporates the latest improvements in intersatellite calibration, along with a new statistical technique, to determine the diurnal and seasonal cycles and climatic trends of 1978–2004 tropospheric temperature using Microwave Sounding Unit measurements. We also compare the latitudinal distribution of temperature trends from the surface and troposphere with each other and with model simulations for the past 26 years. The observations at the surface and in the troposphere are consistent with climate model simulations. At middle and high latitudes in the Northern Hemisphere, the zonally averaged temperature at the surface increased faster than in the troposphere while at low latitudes of both hemispheres the temperature increased more slowly at the surface than in the troposphere. The resulting global averaged tropospheric trend is +0.20 K/10 yr, with a standard error of 0.05 K/10 yr, which compares very well with the trend obtained from surface reports