Search Results

You are looking at 1 - 10 of 17 items for

  • Author or Editor: Donald W. Hillger x
  • All content x
Clear All Modify Search
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.

Full access
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.

Full access
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.

Full access
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.

Full access
Donald W. Hillger and Thomas H. Vonder Haar

Abstract

A statistical analysis of satellite infrared sounding data from the Vertical Temperature Profile Radiometer (VTPR) on NOAA 4 was performed in conjunction with the National Severe Storms Laboratory (NSSL) mesoscale sounding period (10 May-12 June 1976). Satellite radiances, retrieved temperatures and moisture information in the form of radiance residuals at a resolution of ∼70 km were examined for a 14-day composite period using structure and correlation functions. A structure analysis as a function of data separation distance for a field of measured values can detect the mean nondirectional gradient in the field. Estimates of the relative noise level in the measurements were also obtained by extrapolating the obtained structure to zero separation distance. The rms radiance noise levels for the VTPR channels were found to be close to the design specifications for the VTPR instrument. For retrieved temperatures, the noise level was determined to be ∼0.5°C at the three pressure levels examined.

The structure functions for all available satellite-derived temperatures in the composite period compared favorably to similar results computed using high-resolution NSSL rawinsonde data. However, moisture correlation results demonstrated that the satellite-derived moisture in the integrated sense is not an equivalent substitute for mesoscale rawinsonde soundings. The structure-function analysis was also applied to each of the 14 individual days of available satellite data. The structure as a function of distance for VTPR channels 6 and 7 reflect mainly lower tropospheric temperature and moisture gradients, respectively. On days with both large temperature and moisture gradients as detected by the satellite these gradients appeared to be associated with severe storms later in the day. A structure-function analysis of satellite-derived 500 mb temperature fields was also found to detect existing upper air patterns on individual days.

The information content of individual temperature and moisture fields derived from the satellite soundings was interpreted by comparison with similar fields from conventional rawinsonde soundings. Fields of both temperature and moisture from the two instruments were compared by direct correlation of the two sets of measurements. Discrepancies existed between the compared fields, but they were to a large extent explained by differences between the two measuring systems and time and space scale differences between the measurements. According to the analysis, temperatures were retrieved from the satellite data with reasonable success on most of the days at both 300 and 500 mb, but with much less success at 700 mb. Again, the moisture information extracted from the satellite data was not as promising due to its integrated effect and due to the small observed moisture gradients on some days.

Structure-function analysis of satellite temperature soundings was shown to provide a means of interpreting these satellite data. It demonstrated that high-resolution satellite soundings provide information about spatial variations of temperature structure equivalent to that provided by high-density rawinsondes.

Full access
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.

Full access
Donald W. Hillger and Thomas H. Vonder Haar

Abstract

A technique is presented whereby the noise level of satellite measurements of the atmosphere and earth can be estimated. The technique analyzes a spatial array of data measured by a satellite instrument. A minimum of about 200 satellite measurements is required, preferably in a regular pattern. Statistical structure analysis is used to describe a combination of the mean gradient and noise in the data. The noise level is then estimated by separating out the gradient information and leaving only the noise. Results are presented for four satellite sounding instruments, and effective blackbody or brightness temperature noise levels were compared to prelaunch specifications or inflight calibrations for each instrument. Comparisons showed that in the absence of cloud-contaminated measurements (in the case of infrared data) and away from the highly variable ground surface, the noise level of various satellite instruments can be obtained without the need for calibration data. The noise levels imply how much spatial averaging is possible, without smearing the detected geophysical gradient, and how much is necessary, to meet the absolute signal accuracy requirements for the intended use of the satellite measurements.

Full access
Donald W. Hillger and Timothy J. Schmit

Abstract

No Abstract available.

Full access
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.

Full access
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.

Full access