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Robert M. Rabin
and
Timothy J. Schmit

Abstract

In this note, the relationship between the observed daytime rise in surface radiative temperature, derived from the Geostationary Operational Environmental Satellites (GOES) sounder clear-sky data, and modeled soil moisture is explored over the continental United States. The motivation is to provide an infrared (IR) satellite–based index for soil moisture, which has a higher resolution than possible with the microwave satellite data. The daytime temperature rise is negatively correlated with soil moisture in most areas. Anomalies in soil moisture and daytime temperature rise are also negatively correlated on monthly time scales. However, a number of exceptions to this correlation exist, particularly in the western states. In addition to soil moisture, the capacity of vegetation to generate evapotranspiration influences the amount of daytime temperature rise as sensed by the satellite. In general, regions of fair to poor vegetation health correspond to the relatively high temperature rise from the satellite. Regions of favorable vegetation match locations of lower-than-average temperature rise.

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Donald W. Hillger
and
Timothy J. Schmit

Abstract

No Abstract available.

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Christopher M. Hayden
and
Timothy J. Schmit

One aspect of the modernized observational capability of the National Weather Service will be geostationary sounding of temperature and moisture from GOES-I, currently scheduled for launch in 1992. The capability has evolved from ten years of research with the VISSR (Visible Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS). This article summarizes that evolution and describes the initial operational sounder products expected of GOESI. The effect on products caused by problems in meeting performance specifications are discussed. The outlook for follow-on sounders in the next series (circa 2000) of GOES is also briefly treated.

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William H. Raymond
and
Timothy J. Schmit
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John F. Dostalek
and
Timothy J. Schmit

Abstract

Statistics are compiled comparing calculations of total precipitable water (TPW) as given by GOES sounder derived product imagery (DPI) to that computed from radiosonde data for the 12-month period March 1998–February 1999. In order to investigate the impact of the GOES sounder data, these results are evaluated against statistics generated from the comparison between the first guess fields used by the DPI (essentially Eta Model forecasts) and the radiosonde data. It is found that GOES data produce both positive and negative results. Biases in the first guess are reduced for moist atmospheres, but are increased in dry atmospheres. Time tendencies in TPW as measured by the DPI show a higher correlation to radiosonde data than does the first guess. Two specific examples demonstrating differences between the DPI and Eta Model forecasts are given.

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Christopher M. Hayden
,
Gary S. Wade
, and
Timothy J. Schmit

Abstract

Derived product imagery (DPI) is a method of presenting quantitative meteorological information, derived from satellite measurements, as a color-coded image at single-pixel resolution. Its intended use is as animated sequences to observe trends in the displayed quantities, which for the GOES-8 are total precipitable water, lifted index, and surface skin temperature. Those products are produced once per hour, over the continental United States and the Gulf of Mexico. This paper reviews the development of the DPI and details the algorithm used for GOES-8. The quality of the products is discussed, and an example is given. The greatest value of the DPI probably lies in comparing a sequence of the satellite product with a sequence derived from a numerical forecast. In this way, deviation of the forecast from reality is readily exposed.

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Xia L. Ma
,
Timothy J. Schmit
, and
William L. Smith

Abstract

A nonlinear physical retrieval algorithm is developed and applied to the GOES-8/9 sounder radiance observations. The algorithm utilizes Newtonian iteration in which the maximum probability solution for temperature and water vapor profiles is achieved through the inverse solution of the nonlinear radiative transfer equation. The nonlinear physical retrieval algorithm has been tested for one year. It has also been implemented operationally by the National Oceanic and Atmospheric Administration National Environmental Satellite, Data and Information Service during February 1997. Results show that the GOES retrievals of temperature and moisture obtained with the nonlinear algorithm more closely agree with collocated radiosondes than the National Centers for Environmental Prediction (NCEP) forecast temperature and moisture profile used as the initial profile for the solution. The root-mean-square error of the total water vapor from the solution first guess, which is the NCEP 12-h forecast (referred to as the “background”), is reduced approximately 20% over the conventional data-rich North American region with the largest changes being achieved in areas of sparse radiosonde data coverage.

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Youri Plokhenko
,
W. Paul Menzel
,
Gail Bayler
, and
Timothy J. Schmit

Abstract

The spatial and temporal continuity of the infrared measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder data are investigated, and an experimental processing approach is presented. Spatial filtering and cloud detection are performed in a joint algorithm: the preparation of the data for sounding analysis starts with spatial smoothing, followed by cloud detection, followed by averaging the clear-sky (cloud free) subsamples. Analysis of the sounder images reveals the presence of coherent noise on large spatial scales in some of the spectral bands. Analysis of a temporal sequence of spatially smoothed sounder images reveals regions of unphysical hourly change likely induced by instrument noise. A nonlinear temporal–spatial filtering algorithm is presented and tested that improves the noise filtering for the sounder spectral measurements and the thermodynamical spatial and temporal consistency of the sounding retrievals in the troposphere.

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W. Paul Menzel
,
Timothy J. Schmit
,
Peng Zhang
, and
Jun Li

Abstract

Atmospheric sounding of the vertical changes in temperature and moisture is one of the key contributions from meteorological satellites. The concept of using satellite infrared radiation observations for retrieving atmospheric temperature was first proposed by Jean I. F. King. Lewis D. Kaplan noted that the radiation from different spectral regions are primarily emanating from different atmospheric layers, which can be used to retrieve the atmospheric temperature at different heights in the atmosphere. The United States launched the first meteorological satellite Television Infrared Observation Satellite-1 (TIROS-1) on 1 April 1960, opening a new era of observing the Earth and its atmosphere from space. Since then, hundreds of meteorological satellites have been launched by space agencies, including those in Europe, China, Japan, Russia, India, Korea, and others. With the rapid development of atmospheric sounding technology and radiative transfer models, it became possible to determine the atmospheric state from combined satellite- and ground-based measurements. With advances in computing power, forecast model development, data assimilation, and observing technologies, numerical weather prediction (NWP) has achieved consistently better results and thereby improved the prediction and early warning of severe weather events as well as fostered the initial monitoring of global climate change. The purpose of this paper is to summarize and discuss the development of satellite vertical sounding capability, quantitative profile retrieval theory, and applications of satellite-based atmospheric sounding measurements, with a focus on infrared sounding.

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Yong-Keun Lee
,
Zhenglong Li
,
Jun Li
, and
Timothy J. Schmit

Abstract

A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

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