Study of Temperature /Moisture Retrieval Capabilities of DMSP /SSH Sensor Channels.

Abstract

A number of retrieval algorithms were examined for efficacy in inferring atmospheric temperature and humidity structure from upwelling Earth-atmospheric radiances measured in the sensing bands of DMSP/SSH. Inversions of synthetic radiances for cloud-free conditions were performed to determine the limits on retrieval information. It is shown that the CO2 channels yield fairly accurate temperature retrievals, especially when statistical data are used in constructing a guess profile and in regularizing the solutions. Maximum errors below the 70 mb pressure level are typically 3 to 4 C at middle-to-high latitudes, and 4 to 5 C in the tropics. The analysis used noise-free synthetic radiances, but regularization parameter values were assigned on the basis of measured DMSP noise-equivalent spectral radiances. The humidity retrieval capabilities of the six SSH H2O channels are marginal because three of the channels are redundant, and because the channels are too strongly absorbing to infer low-level moisture except under moderately dry conditions. It was found that substitute window channels near 795 and 900/cm are too opaque to provide an independent determination of total precipitable water, although they are probably good candidates as sounding channels to replace the redundant DMSP channels.

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Document Details

Document Type
Technical Report
Publication Date
Sep 25, 1978
Accession Number
ADA073144

Entities

People

  • Alexander S. Zachor

Organizations

  • Honeywell International, Inc.

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Temperature
  • Algorithms
  • Altitude
  • Atmospheric Temperature
  • Databases
  • Equations
  • Grids
  • High Latitudes
  • Inversion
  • Lapse Rate
  • Latitude
  • Measurement
  • Radiative Transfer
  • Statistics
  • Surface Temperature
  • Water Vapor
  • Weighting Functions

Readers

  • Atmospheric Remote Sensing.
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference