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Robert P. d'Entremont

Abstract

A multispectral cloud analysis technique using NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) infrared imagery was developed and tested using the AFGL Man-computer Interactive Data Access System (McIDAS) and the AFGL Interactive Meteorological System (AIMS). Fractional cloud amount and cloud top heights are computed for low-level clouds at night, including subpixel resolution clouds (i.e., clouds which only partially fill a sensor's field of view). Multispectral analysis offers a technique for detecting low cloud, which is better than cloud analysis using single channel infrared imagery. Theoretical radiances are computed at the 3.7, 10.7 and 11.8 μm infrared spectral bands of the AVHRR as a function of cloud top altitude and cloud amount for a range of cloud conditions. Satellite-measured radiances are then compared to the theoretical values at each wavelength to determine the best cloud height/cloud amount match for a pixel. Test case comparisons using manually selected clear and partially cloud-filled regions of AVHRR imagery as displayed on AIMS showed good agreement between the multispectral analysis results and evaluation by human interpretation of the images, surface cloud observations and upper air soundings.

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Donald C. Norquist and Robert P. d'Entremont

Abstract

Vertical distributions of clouds have been a focus of many studies, motivated by their importance in radiative transfer processes in climate models. This study examines the horizontal distribution of cirrus clouds by means of satellite imagery analyses and numerical weather prediction model forecasts. A ground-truth dataset based on two aircraft mission periods flying particle probes through cirrus over a ground-based cloud radar is developed. Particle probe measurements in the cirrus clouds are used to compute ice water content and radar reflectivity averages in short time periods (25–30 s). Relationships for ice water content as a function of reflectivity are developed for 6-K ambient temperature categories. These relationships are applied to the radar-measured short-term-averaged reflectivities to compute vertical profiles of ice water content, which are vertically integrated over the depth of the observed cirrus clouds to form ice water path estimates. These and cloud-top height are compared with the same quantities as retrieved by the Geostationary Operational Environmental Satellite (GOES) level-2B algorithm applied to four channels of GOES-8 imagery measurements. The agreement in cloud-top height is reasonable (generally less than 2-km difference). The ice water path retrievals are smaller in magnitude than the radar estimates, and this difference grows with increasing cirrus thickness. Comparisons of a sequence of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) predictions and GOES level-2B retrievals of ice cloud tops for the convectively active second mission period showed that the MM5 cirrus areal extent was somewhat greater than the GOES depictions. Cloud-top height ranges were similar. MM5 is capable of producing ice water path magnitudes similar to the radar estimates, but the GOES retrievals are much more limited. Ninety-eight percent of the GOES grid points had ice water paths no greater than 60 g m−2, as compared with 74% for MM5. Ten percent of MM5 points had ice water content >200 g m−2, as compared with 0.07% for GOES retrievals. Based on this study, we conclude that GOES level-2B cloud-top retrievals are a reliable tool for prediction evaluations but the algorithm's retrievals of ice water path are not.

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Robert P. d'Entremont and Larry W. Thomason

An image-display technique is described that simultaneously combines three meteorological satellite images into a color-image product. The technique reveals many features of meteorological interest. It is frequently noted that interpretations of black-and-white “infrared” nighttime imagery are difficult to make when attempting to distinguish low clouds and fog from cloudfree land and ocean, thin from thick cirrus, and thick nonprecipitating clouds from nimbostratus clouds. It is found that a more-confident discrimination can be obtained between such features when the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Nimbus Scanning Multifrequency Microwave Radiometer (SMMR) data are combined into color-image products.

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Thomas M. Hamill, Robert P. D'Entremont, and James T. Buntin

Abstract

The Air Force Global Weather Central (AFGWC) Real-Time Nephanalysis (RTNEPH) is an automated cloud model that produces a 48-km gridded analysis of cloud amount, cloud type, and cloud height. Its primary input is imagery from polar-orbiting satellites.

Six main programs make up the RTNEPH. These are the satellite data mapper, the surface temperature analysis and forecast model, the satellite data processor, the conventional data processor, the merge processor, and the bogus processor. The satellite data mapper remaps incoming polar-orbiter imagery to a polar-stereographic database. The surface temperature model produces an analysis and forecast of shelter and skin temperatures for comparison to satellite-measured infrared (IR) brightness temperatures. The satellite data processor reads in the new satellite data and produces a satellite-derived cloud analysis. The conventional data processor retrieves and reformats cloud information from airport observations. The merge processor combines the satellite- and conventional-derived cloud analyses into a final nephanalysis. Finally, the bogus processor allows forecasters to manually correct the nephanalysis where appropriate.

The RTNEPH has been extensively redesigned, primarily to improve analyses of total and layered cloud amounts generated from IR data. Recent enhancements include the use of regression equations to calculate atmospheric water vapor attenuation, an improved definition of surface temperatures used to calculate cloud/no-cloud thresholds for IR data, and the use of Special Sensor Microwave/Imager (SSM/1) data to further improve the calculation of infrared cloud/no-cloud thresholds. Planned enhancements include the processing of geostationary satellite data, more sophisticated processing of visible data, and a higher-resolution satellite database for the archiving and processing of multispectral satellite data.

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David L. Mitchell, Robert P. d’Entremont, and R. Paul Lawson

Abstract

Since cirrus clouds have a substantial influence on the global energy balance that depends on their microphysical properties, climate models should strive to realistically characterize the cirrus ice particle size distribution (PSD), at least in a climatological sense. To date, the airborne in situ measurements of the cirrus PSD have contained large uncertainties due to errors in measuring small ice crystals (D ≲ 60 μm). This paper presents a method to remotely estimate the concentration of the small ice crystals relative to the larger ones using the 11- and 12-μm channels aboard several satellites. By understanding the underlying physics producing the emissivity difference between these channels, this emissivity difference can be used to infer the relative concentration of small ice crystals. This is facilitated by enlisting temperature-dependent characterizations of the PSD (i.e., PSD schemes) based on in situ measurements.

An average cirrus emissivity relationship between 12 and 11 μm is developed here using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and is used to “retrieve” the PSD based on six different PSD schemes. The PSDs from the measurement-based PSD schemes are compared with corresponding retrieved PSDs to evaluate differences in small ice crystal concentrations. The retrieved PSDs generally had lower concentrations of small ice particles, with total number concentration independent of temperature. In addition, the temperature dependence of the PSD effective diameter De and fall speed Vf for these retrieved PSD schemes exhibited less variability relative to the unmodified PSD schemes. The reduced variability in the retrieved De and Vf was attributed to the lower concentrations of small ice crystals in the retrieved PSD.

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K. Dieter Klaes, Robert P. d'Entremont, and Larry W. Thomason

The Geophysics Directorate's 8-bit multispectral color-composite image display technique has been installed on the Satellite Data Processing System at the German Military Geophysical Office in Traben-Trarbach, Germany. The technique simulates 24-bit full-color composites on 8-bit color workstations, combining image data from the NOAA multispectral Advanced Very High Resolution Radiometer. The real-time application of this technique to operational satellite data is discussed.

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Jeana Mascio, Stephen S. Leroy, Robert P. d’Entremont, Thomas Connor, and E. Robert Kursinski

Abstract

Radio occultation (RO) measurements have little direct sensitivity to clouds, but recent studies have shown that they may have an indirect sensitivity to thin, high clouds that are difficult to detect using conventional passive space-based cloud sensors. We implement two RO-based cloud detection (ROCD) algorithms for atmospheric layers in the middle and upper troposphere. The first algorithm is based on the methodology of a previous study, which explored signatures caused by upper-tropospheric clouds in RO profiles according to retrieved relative humidity, temperature lapse rate, and gradients in log-refractivity (ROCD-P), and the second is based on inferred relative humidity alone (ROCD-M). In both, atmospheric layers are independently predicted as cloudy or clear based on observational data, including high-performance RO retrievals. In a demonstration, we use data from 10 days spanning 7 months in 2020 of Formosa Satellite 7 (FORMOSAT-7)/COSMIC-2. We use the forecasts of NOAA GFS to aid in the retrieval of relative humidity. The prediction is validated with a cloud truth dataset created from the imagery of the GOES-16 Advanced Baseline Imager (ABI) satellite and the GFS three-dimensional analysis of cloud-state conditions. Given these two algorithms for the presence or absence of clouds, confusion matrices and receiver operating characteristic (ROC) curves are used to analyze how well these algorithms perform. The ROCD-M algorithm has a balanced accuracy, which defines the quality of the classification test that considers both the sensitivity and specificity, greater than 70% for all altitudes between 6 and 10.25 km.

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Robert P. d’Entremont, Richard Lynch, Gennadi Uymin, Jean-Luc Moncet, Ryan B. Aschbrenner, Mark Conner, and Gary B. Gustafson

Abstract

The Cloud Depiction and Forecast System version 2 (CDFS II) is the operational global cloud analysis and forecasting model of the 557th Weather Wing, formerly the U.S. Air Force Weather Agency. The CDFS II cloud-detection algorithms are threshold-based tests that compare satellite-observed multispectral reflectance and brightness temperature signatures with those expected for the clear atmosphere. User-prescribed quantitative differences between sensor observations and the expected clear-scene radiances denote cloudy pixels. These radiances historically have been modeled at 24-km resolution from a running 10-day statistical analysis of cloud-free pixels that requires the entire global cloud analysis to be executed twice in real time: once in operational cloud detection mode and a second time in a cloud-clearing mode that is designed explicitly for generating clear-scene statistics. Having to run the cloud analysis twice means the availability of fewer compute cycles for other operational models and requires costly interactive maintenance of distinct cloud-detection and cloud-clearing threshold sets. Additionally, this technique breaks down whenever a region is persistently cloudy. These problems are eliminated by means of the optimal spectral sampling (OSS) radiative transfer model of Moncet et al., optimized for execution in the CDFS run-time environment. OSS is particularly well suited for real-time remote sensing applications because of its user-tunable computational speed and numerical accuracy, with respect to a reference line-by-line model. The use of OSS has cut cloud model processing times in half, eliminated the influence of cloudy pixel artifacts in the statistical time series prescription of cloud-cleared radiances, and improved cloud-mask quality.

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