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Daniel L. Smith

The significance of Percent Correct Scores for National Weather Service (NWS) probability of precipitation (PoP) forecasts is examined. It is shown that the areal variability of rainfall and the nature of PoP forecasts preclude the achievement of a score of 100%—even for the best possible forecasts. A maximum possible percent correct is defined and radar estimates of rainfall coverage are combined with actual forecasts to determine how closely NWS forecasters approached this limit. Day- and nighttime percent correct scores were 75% and 85%, respectively, for the data examined. These values were close to the respective maximum possible scores of 83% and 90%. Relatively small changes in forecasters' percent correct scores are considered in light of these findings.

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Daniel L. Smith

Abstract

No abstract available.

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Paul L. Moore
and
Daniel L. Smith

Abstract

An objective technique has been developed for modifying precipitation probability guidance forecasts received from the National Meteorological Center by means of radar information which becomes available subsequent to receipt of the guidance forecasts. Tests show improvement with respect to both the centralized guidance and the official subjective forecasts. The findings also carry implications as to the resolution necessary in radar data used in such a procedure.

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Neil L. Frank
and
Daniel L. Smith

Abstract

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Daniel L. Smith
and
Guenter Schwarz
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Paul H. Heinemann
,
J. David Martsolf
,
John F. Gerber
, and
Daniel L. Smith

Abstract

Manually Digitized Radar (MDR) and Geostationary Operational Environmental Satellite (GOES) thermal infrared (IR) data were merged to form a higher-resolution radar/IR product than that represented by the MDR. The combination MDR/IR maps were processed into a color coded map form and disseminated on a real-time basis through a computer network to users in the Florida agricultural community.

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Daniel M. Gilford
,
Shawn R. Smith
,
Melissa L. Griffin
, and
Anthony Arguez

Abstract

The daily temperature range (DTR; daily maximum temperature minus daily minimum temperature) at 290 southeastern U.S. stations is examined with respect to the warm and cold phases of the El Niño–Southern Oscillation (ENSO) for the period of 1948–2009. A comparison of El Niño and La Niña DTR distributions during 3-month seasons is conducted using various metrics. Histograms show each station’s particular distribution. To compare directly the normalized distributions of El Niño and La Niña, a new metric (herein called conditional ratio) is produced and results are evaluated for significance at 95% confidence with a bootstrapping technique. Results show that during 3-month winter, spring, and autumn seasons DTRs above 29°F (16.1°C) are significantly more frequent during La Niña events and that DTRs below 15°F (8.3°C) are significantly more frequent during El Niño events. It is hypothesized that these results are associated spatially with cloud cover and storm tracks during each season and ENSO phase. Relationships between DTRs and ENSO-related relative humidity are examined. These results are pertinent to the cattle industry in the Southeast, allowing ranchers to plan for and mitigate threats posed by periods of low DTRs associated with the predicted phase of ENSO.

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Daniel H. DeSlover
,
William L. Smith
,
Paivi K. Piironen
, and
Edwin W. Eloranta

Abstract

Knowledge of cirrus cloud optical depths is necessary to understand the earth’s current climate and to model the cloud radiation impact on future climate. Cirrus clouds, depending on the ratio of their shortwave “visible” to longwave “infrared” optical depth, can act to either cool or warm the planet. In this study, visible-to-infrared cirrus cloud optical depth ratios were measured using ground-based lidar and Fourier transform spectrometry. A radiosonde temperature profile combined with the 0.532-μm-high spectral resolution lidar vertical cloud optical depth profile provided an effective weighting to the cloud radiance measured by the interferometer. This allowed evaluation of cirrus cloud optical depths in 18 infrared microwindows between water vapor absorption lines within the 800–1200-cm−1 infrared atmospheric window. The data analysis was performed near the peak solar and terrestrial emission regions, which represent the effective radiative cloud forcing efficiency of the given cloud sample. Results are also presented that demonstrate the measurement of infrared optical depth using an assumed uniform cloud extinction cross section, which requires generic lidar cloud boundary data. The measured cloud extinction profile provided a more robust solution that would allow analysis of multiple-layer clouds and removed the uniform cloud extinction cross-section assumption. Mie calculations for ice particles were used to generate visible and infrared extinction coefficients; these were compared against the measured visible-to-infrared optical depth ratios. The results demonstrate strong particle size and shape sensitivity across the infrared atmospheric window.

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William L. Smith Sr.
,
Elisabeth Weisz
,
Stanislav V. Kireev
,
Daniel K. Zhou
,
Zhenglong Li
, and
Eva E. Borbas

Abstract

A fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression “clear trained” and “cloud trained” retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retrieval system. The improvement results from the fact that the nonlinear dependence of spectral radiance on the atmospheric variables, which is due to cloud altitude and associated atmospheric moisture concentration variations, is minimized as a result of the cloud-height-classification process. The detailed method and results from example applications of the DR retrieval algorithm are presented. The new DR method will be used to retrieve atmospheric profiles from Aqua AIRS, MetOp Infrared Atmospheric Sounding Interferometer, and the forthcoming Joint Polar Satellite System ultraspectral radiance data.

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Daniel K. Zhou
,
William L. Smith Sr.
,
Xu Liu
,
Allen M. Larar
,
Stephen A. Mango
, and
Hung-Lung Huang

Abstract

A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.

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