Engineering topics

Rosenfield, J.

Publications and source records attributed to Rosenfield, J..

5 records

NASA NTRS1984

Remote sensing of weather and climate parameters from HIRS2/MSU on Tiros-N

At the Goddard Laboratory for Atmospheric Sciences (GLAS) a physically based satellite temperature sounding retrieval system, involving the simultaneous analysis of HIRS2 and MSU sounding data, has been developed for determining atmospheric and surface conditions which are consistent with the observed radiances. In addition to determining accurate atmospheric temperature profiles even in the presence of cloud contamination, the system provides global estimates of day and night sea or land surface temperatures, snow and ice cover, and parameters related to cloud cover. The inverse radiative transfer equation approach to the multi-spectral analysis of the data, and details of its implementation, are described. Ice, snow, and cloud fields derived for January 1979 are consistent with other measures of similar parameters obtained from AVHRR and SMMR. Monthly mean sea-surface temperature fields agree with those derived from ship and buoy measurements to 0.5 C.

Susskind, J.↗

NASA NTRS1983

An accurate radiative transfer model for use in the direct physical inversion of HIRS2 and MSU temperature sounding data

The direct computation of atmospheric transmittance and clear column radiances for the channels of HIRS2 and MSU as a function of atmospheric and surface conditions is described in detail. A comparison is made between the observations and the calculated radiances derived from colocated oceanic radiosondes. It is found that under clear conditions, calculated brightness temperatures for the HIRS2 have a standard deviation of the order of 0.7 C compared with observations, whereas MSU channels have a standard deviation of approximately 1 C. In some channels, small biases are found that can be removed by an empirical 'tuning' with coefficients that can be successfully transferred from one season to another. Less satisfactory agreement is obtained from a comparison of calculations with 'reconstructed' clear radiances, which are used in analyzing sounding data under partially cloudy conditions.

Susskind, J.↗

NASA NTRS1982

The GLAS physical inversion method for analysis of HIRS2/MSU sounding data

Goddard Laboratory for Atmospheric Sciences has developed a method to derive atmospheric temperature profiles, sea or land surface temperatures, sea ice extent and snow cover, and cloud heights and fractional cloud, from HIRS2/MSU radiance data. Chapter 1 describes the physics used in the radiative transfer calculations and demonstrates the accuracy of the calculations. Chapter 2 describes the rapid transmittance algorithm used and demonstrates its accuracy. Chapter 3 describes the theory and application of the techniques used to analyze the satellite data. Chapter 4 shows results obtained for January 1979.

Susskind, J.↗

NASA NTRS1981

Comparison of observed and computed brightness temperatures for the HIRS2 and MSU sounders on TIROS-N

Given the radiosonde temperature and humidity profile, brightness temperatures for the temperature sounding IR channels on HIRS 2 were calculated. The temperature profile was interpolated to 53 levels between 1000 and 30 mb assuming temperature to be linear in the logarithm of the pressure between the mandatory levels. The 11 levels above 30 mb were extrapolated according to climatology. The surface was taken to be the climatological sea surface temperature. The specific humidity was interpolated between mandatory levels assuming a p(n) dependence. A specific humidity corresponding to a climatological water vapor mixing ratio of 2 ppmv was assumed at and above 100 mb. Zonally averaged climatological ozone profiles were used to compute the ozone component of the transmittance.

Susskind, J.↗

NASA NTRS1980

The GLAS physical inversion method for analysis of TIROS N data

The Goddard Laboratory for Atmospheric Sciences approach to temperature sounding from satellite observations is fundamentally different from that of NESS in that heavy reliance is placed on the ability to model accurately the instrumental response to atmospheric and surface conditions, while no use is made of statistical relationships between satellite observations and atmospheric temperature profiles. The method involves starting with a guess set of atmospheric and surface conditions from which expected brightness temperatures for the satellite observations are computed. Then, iterative relaxation of atmospheric and surface conditions is performed according to the difference between observed and computed brightness temperatures until sufficient agreement is reached. In the absence of sufficient agreement, no retrieval is produced for that location.

Susskind, J.↗