1D-VAR Retrieval of Temperature and Humidity Profiles From a Ground-Based Microwave Radiometer

A variational method to retrieve profiles of temperature, humidity, and cloud is described, which combines observations from a 12-channel microwave radiometer, an infrared radiometer, and surface sensors with background from shortrange numerical weather prediction (NWP) forecasts in an optimal way,...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2007-07, Vol.45 (7), p.2163-2168
Main Author: Hewison, T.J.
Format: Article
Language:English
Subjects:
Applied geophysics
Atmospheric measurements
Atmospheric modeling
Channels
Clouds
Earth sciences
Earth, ocean, space
Error analysis
Error detection
Exact sciences and technology
Humidity
Infrared sensors
Instruments
Internal geophysics
Mathematical models
Meteorology
Microwave radiometers
Microwave radiometry
Microwave theory and techniques
remote sensing
Retrieval
Sensor phenomena and characterization
Studies
Temperature sensors
variational methods
Weather forecasting
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Description
Summary:A variational method to retrieve profiles of temperature, humidity, and cloud is described, which combines observations from a 12-channel microwave radiometer, an infrared radiometer, and surface sensors with background from shortrange numerical weather prediction (NWP) forecasts in an optimal way, accounting for their error characteristics. An analysis is presented of the error budget of the background and observations, including radiometric, modeling, and representativeness errors. Observation errors of some moisture channels are found to be dominated by representativeness, due to their sensitivity to atmospheric variability on smaller scales than the NWP model grid, whereas channels providing information on temperature in the lowest 1 km are dominated by instrument noise. Profiles of temperature and a novel total water control variable are retrieved from synthetic data using Newtonian iteration. An error analysis shows that these are expected to improve mesoscale NWP, retrieving temperature and humidity profiles up to 4 km with uncertainties of 1 K and 40% and 2.8 and 1.8 degrees of freedom for signal, respectively, albeit with poor vertical resolution. A cloud classification scheme is introduced to address convergence problems and better constrain the retrievals. This Bayesian retrieval method can be extended to incorporate observations from other instruments to form a basis for future integrated profiling systems.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2007.898091