Improved retrievals from the DMSP wind speed algorithm under adverse weather conditions

Goodberlet, M.A.; Swift, C.T.

doi:10.1109/36.175343

Cited by 42 publications

(25 citation statements)

References 2 publications

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“…The retrieval errors are also comparable to the 1.52 m s Ϫ1 RMS error for a retrieval of TMI surface wind speed (compared to buoy) found by Connor and Chang (2000). Furthermore, Chang and Li (1998) used the operational Goodberlet and Swift (1992) surface wind speed retrieval algorithm for SSM/I F11 and found an RMS error of approximately 2.3 m s Ϫ1 . Quantification of the magnitudes and directions of the wind speed biases were not available in either study.…”

Section: Geophysical Parameter Retrieval Resultssupporting

confidence: 75%

Toward a Fully Parametric Retrieval of the Nonraining Parameters over the Global Oceans

Elsaesser

Kummerow

2008

Journal of Applied Meteorology and Climatology

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In light of the upcoming launch of the Global Precipitation Measurement (GPM) mission, a parametric retrieval algorithm of the nonraining parameters over the global oceans is developed with the ability to accommodate all currently existing and planned spaceborne microwave window channel sensors and imagers. The physical retrieval is developed using all available sensor channels in a full optimal estimation inversion. This framework requires that retrieved parameters be physically consistent with all observed satellite radiances regardless of the sensor being used. The retrieval algorithm has been successfully applied to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with geophysical parameter retrieval results comparable to independent studies using sensoroptimized algorithms. The optimal estimation diagnostics characterize the retrieval further, providing errors associated with each of the retrieved parameters, indicating whether the retrieved state is physically consistent with observed radiances, and yielding information on how well simulated radiances agree with observed radiances. This allows for the quantitative assessment of potential calibration issues in either the model or sensor. In addition, there is an expected, consistent response of these diagnostics based on the scene being observed, such as in the case of a raining scene, allowing for the emergence of a rainfall detection scheme providing a new capability in rainfall identification for use in passive microwave rainfall and cloud property retrievals.

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Section: Geophysical Parameter Retrieval Resultssupporting

confidence: 75%

Toward a Fully Parametric Retrieval of the Nonraining Parameters over the Global Oceans

Elsaesser

Kummerow

2008

Journal of Applied Meteorology and Climatology

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“…Wind measuring microwave sensors include the Scanning Multichannel Microwave Radiometer (SMMR) onboard the NIMBUS-7 satellite, the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E). Retrieval algorithms of wind speed include the traditional D-matrix (Lo 1983), regression (Goodberlet et al 1989;Goodberlet and Swift 1992), neural network (Krasnopolsky et al 1995), and physical approaches (Wentz et al 1986). Resolution of the ambiguity in wind direction (Wentz 1992) requires polarization measurements at AE45 in addition to the horizontal and vertical polarizations (Dzura et al 1992;Gasiewski and Kunkee 1993), or the use of variational analysis methods (Atlas et al 1991).…”

Section: Wind Stressmentioning

confidence: 99%

Satellite-Based Ocean Surface Turbulent Fluxes

Chiu

Shie

2013

Satellite-Based Applications on Climate Change

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Ocean surface turbulent fluxes of momentum, heat, and water vapor respond to and determine the coupling between the atmosphere and the ocean and are excellent indicators of air-sea interactions at most temporal and spatial scales. These fluxes can be determined from bulk properties at the sea surface. By combining satellite observations of bulk properties such as sea surface temperature, wind, and humidity, estimates of these fluxes are available globally. The bulk aerodynamic formulations of these fluxes are first reviewed. Satellite retrieval techniques of these bulk properties and operational or semi-operational ocean surface flux products such as the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Observations (HOAPS), the Japanese Oceanic Fluxes with the Use of Remote Observations (J-OFURO), and the US NASA Goddard Space Flight Center Satellite-Based Sea Surface Turbulent Fluxes (GSSTF), as well as merged approach of the Objectively Analyzed Air-Sea Fluxes for the global ocean (OAFlux) are described, and their error and uncertainties are briefly discussed.

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“…Naval Research Laboratory,4555 Overlook Avenue, SW,Washington,DC,20375 8. PERFORMING ORGANIZATION REPORT NUMBER…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

A nonlinear optimization algorithm for WindSat wind vector retrievals

Bettenhausen

Smith

Bevilacqua

et al. 2006

IEEE Trans. Geosci. Remote Sensing

111

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Abstract-WindSat is a space-based polarimetric microwave radiometer designed to demonstrate the capability to measure the ocean surface wind vector using a radiometer. We describe a nonlinear iterative algorithm for simultaneous retrieval of sea surface temperature, columnar water vapor, columnar cloud liquid water, and the ocean surface wind vector from WindSat measurements. The algorithm uses a physically based forward model function for the WindSat brightness temperatures. Empirical corrections to the physically based model are discussed. We present evaluations of initial retrieval performance using a six-month dataset of WindSat measurements and collocated data from other satellites and a numerical weather model. We focus primarily on the application to wind vector retrievals.

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Improved retrievals from the DMSP wind speed algorithm under adverse weather conditions

Cited by 42 publications

References 2 publications

Toward a Fully Parametric Retrieval of the Nonraining Parameters over the Global Oceans

Toward a Fully Parametric Retrieval of the Nonraining Parameters over the Global Oceans

Satellite-Based Ocean Surface Turbulent Fluxes

A nonlinear optimization algorithm for WindSat wind vector retrievals

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