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Donald W. Hillger

Abstract

Mesoscale moisture fields are retrieved from TOVS (TIROS Operational Vertical Sounder) infrared radiances from two polar-orbiting satellites. A special feature of the retrieval process is the determination of the surface skin temperature independently of the temperature profile above the surface. This allows temperature inversions in retrieved temperature profiles, thereby more closely matching rawinsonde (RAOB) temperature profiles in inversion situations. A modification to the moisture feedback equation is required for such surface temperature inversion cases. Resulting satellite-derived total moisture values are compared both to RAOB-measured moisture at the RAOB scale (>250 km) and to total moisture estimated from surface dew points at the surface weather observation scale (<250 km). One finding is that mesoscale features are detected by the increased density satellite measurements which remain undetected by observations at the RAOB scale. Secondly, differences between satellite-derived and surface-estimated total moisture can indicate vertical moisture extent. Finally, time-changes in the satellite-derived total moisture fields are shown to be similar in pattern to moisture changes estimated from surface observations. Verification of such temporal changes can be found both in comparison to surface-estimated total moisture and by advection of moisture by the 700 mb wind.

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Donald W. Hillger

Abstract

The current Geostationary Operational Environmental Satellite (GOES) series was inaugurated in 1994 with the launch of GOES-8 and will continue with two more satellites (GOES-O and -P) after the most recent GOES-13 launched in 2006. The next-generation GOES (beginning with GOES-R) will be launched in the 2015 time frame. This new series of satellites will include improved spatial, temporal, spectral, and radiometric resolution. The last two characteristics are manifest by an increased number of spectral bands and increased precision for measurements from those bands. To take advantage of the lead time needed to design, build, and test this new and complex satellite system, work is going into developing image products to be implemented as soon as GOES-R becomes operational.

Preparations for GOES-R image products for applications to various weather events, especially mesoscale events, are well underway. The approach used for these “risk reduction” activities is to apply data from existing operational and experimental satellites (both polar orbiting and geostationary) to create image products that will emulate those to be available from GOES-R as closely as possible. Those image products can either be new products or improvements leveraged on existing operational products. In this article, the new GOES-R Advanced Baseline Imager is briefly reviewed, and the evolutionary development of two qualitative products—one for the detection of fog and stratus, and the other for blowing dust—is presented. Emphasis is on the evolutionary development of these mesoscale products and possible quantitative discrimination among the various image features that are seen.

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Alan E. Lipton
and
Donald W. Hillger

Abstract

In retrieval of atmospheric temperature and moisture soundings from satellite infrared radiance measurements the raw data commonly used consist of dense fields of radiances interrupted by data-free gaps. This note reports an objective analysis procedure which was developed to specifically handle data fields of a discontinuous nature. The method is a correlation-weighted interpolation scheme and includes an oval-extension gap filling feature. Test cases demonstrate the ability of the program to fill gaps caused by instrument calibration periods and by data contamination due to clouds. The procedure is shown to produce much better results within a data-free region than does a similar method without the gap filling feature. An application of this method is also shown in a comparison of satellite-derived atmospheric parameters with conventional observations on a point-to-point basis. However, applications of the procedure are not limited to satellite data analysis, but could include analyses of aircraft data and data from ocean buoys.

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Donald W. Hillger
and
James D. Clark

Abstract

In Part I of this paper, the infrared bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed for volcanic ash signals using principal component image analysis. Target volcanoes included Popocatepetl volcano near Mexico City and Cleveland volcano in the Aleutian Islands. The analyses were performed to determine the MODIS bands that contribute the most to detecting volcanic ash. Even though the explained variance and signal-to-noise ratio of most of these new images are generally small, several of them provide views of volcanic ash with good contrast to the image background and other image features. Both day and night examples indicate that volcanic ash can be readily detected by combinations of MODIS bands to determine not only the ash extent but also qualitative variations in the concentration and height of the ash. The 36 bands on MODIS give much more flexibility for ash detection than the 4 bands on the current Geostationary Operational Environmental Satellite (GOES) Imager. In particular, MODIS bands 28–32, in the water vapor and longwave infrared portions of the spectrum, contributed most frequently to the detection of airborne volcanic ash. These include bands 28–30 in the 7.3–9.7-μm portion of the spectrum known for volcanic signals. Several of the MODIS bands that proved useful are bands projected for inclusion in the next major upgrade to the GOES Imager (scheduled for 2012). However, band 30 (9.7 μm) is neither available on the current GOES series nor planned for future GOES Imagers. In Part II of this paper, MODIS data for the same volcanic cases examined in Part I are used for specific simulations of current and near-term GOES imagery. The purpose of the simulations is to assess the impact of changes that will occur in the spectral bands of the GOES-M Imager (launched in 2001 and renamed GOES-12) when it becomes operational.

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Donald W. Hillger
and
James D. Clark

Abstract

In Part I of this paper the infrared bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) were analyzed using principal component image analysis for volcanic ash signals. The analyses performed determined that several of the thermal infrared bands of MODIS contributed significantly to detecting volcanic ash in the cases examined. Most, but not all, of these bands will be included in the next major upgrade to the Geostationary Operational Environmental Satellite (GOES) Imager scheduled for 2012. In Part II, MODIS data for the same volcanic cases examined in Part I (Popocatepetl near Mexico City and Cleveland in the Aleutian Islands) are used to simulate the impact of changes that will occur in spectral bands between current and near-term GOES imagery. The change from the 12.0-μm band to a 13.3-μm band on GOES-M (launched in 2001 and renamed GOES-12) was made to improve cloud-height determinations. However, when GOES-M becomes operational, the change in bands will have a potential negative impact on image products that are heavily utilized for volcanic ash detection. Image products generated from the three GOES infrared bands with the 13.3-μm band substituted for the 12.0-μm band indicate that volcanic ash can be detected but with diminished ability, especially for diffuse ash. For both day and night cases the increased contamination by clouds leads to increased chances of false ash detection.

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Donald W. Hillger
and
Gary P. Ellrod

Abstract

The detection of dust, fire hot spots, and smoke from the Geostationary Operational Environmental Satellite (GOES) is made easier by employing the principal component image (PCI) technique. PCIs are created by an eigenvector transformation of spectral band images from the five-band GOES Imager. The transformation is a powerful tool that provides a new set of images that are linear combinations of the original spectral band images. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. Whereas this multispectral technique is normally applied to high-spatial-resolution land remote sensing imagery, the application is herein made to lower-spatial-resolution weather satellite imagery for the purpose of feature detection and enhancement. Features used as examples include atmospheric dust as well as forest and range fire hot spots and their resulting smoke plumes. The applications of PCIs to GOES utilized the three infrared window images (bands 2, 4, and 5) in dust situations as well as the visible image (band 1) in smoke situations. Two conclusions of this study are 1) atmospheric and surface features are more easily identified in multiband PCIs than in the enhanced single-band images or even in some two-band difference images and 2) the elimination of certain bands can be made either directly by inspection of the PCIs, discarding bands that do not to contribute to the PCIs showing the desired features, or by including all available bands and letting the transformation process indicate the bands that are useful for detecting the desired features. This technique will be increasingly useful with the introduction of new and increased numbers of spectral bands with current and future satellite instrumentation.

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Donald W. Hillger
and
Timothy J. Schmit

Abstract

No Abstract available.

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Donald W. Hillger
and
James F. W. Purdom

Abstract

Clustering is used to enhance mesoscale meteorological detail in retrievals produced from satellite sounding measurements. By placing sounding fields-of-view (FOVs) into groups of similar measurements, mesoscale details are reinforced, compared to arbitrary grouping of FOVs into a fixed block size. Clustering takes advantage of similarity among the measurements to avoid smearing gradient information. A case study is presented showing the advantage of clustering as applied to the satellite sounding problem.

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Donald W. Hillger
and
Thomas H. Vonder Haar

Abstract

This is a study of environmental conditions prior to convective development on the Great Plains of the United States on four case study days in August 1975. The tool used was the High-resolution Infrared Radiation Sounder (HIRS) on Nimbus 6. A moisture-temperature retrieval scheme was developed to retrieve various lower tropospheric analysis and forecasting parameters from the HIRS radiances. Specifically, dew points and temperatures and other secondary parameters such as total precipitable water and static stability indices were derived and analyzed at a horizontal resolution of up to 30 km on these days. For the moisture parameters the comparisons to time-interpolated NWS rawinsonde values were especially good in spite of time and resolution differences. Comparisons with higher resolution synoptic surface observations of dew point and temperature were also good. The true quality of the mesoscale analyses, however, is only seen by examining the individual case study days. Small features at a scale of ∼100 km, below the resolution of upper air and surface observations, were detected by the high-resolution satellite data. For example, perturbations on the dry line usually seen at this time of the year were apparent in the satellite data, although only the general dry line position was picked up by synoptic surface observations. The time lead also was important. Convective development starting from 2–2.5 h after the satellite pass at local noon did correlate well with the local maxima of moisture and instability seen in the satellite-derived analyses. A statistical structure analysis of the satellite-derived parameters also gave the highest signal-to-noise values for the moisture and stability parameters, whereas the temperature parameters showed much less signal-to-noise content. Results from these case study days, therefore, show the quality of high-resolution satellite-derived parameters and the applicability of this method of retrieving and using satellite soundings at the mesoscale.

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Donald W. Hillger
and
Thomas H. Vonder Haar

Abstract

The ability to provide mesoscale temperature and moisture fields from operational satellite infrared sounding radiances over the United States is explored. High-resolution sounding information for mesoscale analysis and forecasting is shown to be obtainable in mostly clear areas. An iterative retrieval algorithm applied to NOAA-VTPR radiances uses a mean radiosonde sounding as a best initial guess profile. Temperature soundings are then retrieved at a horizontal resolution of ∼70 km, as is an indication of the precipitable water content of the vertical sounding columns. Derived temperature values may be biased in general by the initial guess sounding, or in certain areas by the cloud correction technique, but the resulting relative temperature changes across the field when not contaminated by clouds will be useful for mesoscale forecasting and models. The derived moisture, however, since affected only by high clouds, proves to be reliable to within 0.5 cm of precipitable water and contains valuable horizontal information. Present day applications from polar orbiting satellites as well as possibilities from upcoming temperature and moisture sounders on geostationary satellites are noted.

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