A Time-Varying Radiometric Bias Correction for the TRMM Microwave Imager

Gopalan, Kaushik; Jones, W. Linwood; Biswas, Moanaro; Bilanow, S.; Wilheit, T. T.; Kasparis, Takis

doi:10.1109/tgrs.2009.2028882

Cited by 50 publications

(17 citation statements)

References 23 publications

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“…Solardependent anomalies are present in the data (e.g. Gopalan et al, 2009) but they are accounted for in the ECMWF bias correction (Geer et al, 2010a).…”

Section: Observationsmentioning

confidence: 99%

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

2014

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Abstract. To simulate passive microwave radiances in allsky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30-50 GHz) and too little scattering at high frequencies (e.g. 150-183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60 %. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

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“…Solardependent anomalies are present in the data (e.g. Gopalan et al, 2009) but they are accounted for in the ECMWF bias correction (Geer et al, 2010a).…”

Section: Observationsmentioning

confidence: 99%

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

2014

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“…In practice, the intercomparisons are themselves sensitive tests for such problems and sometimes backtracking is necessary. The along track bias in TMI described by Gopalan et al [10] was discovered this way and then corrected on a single satellite basis. An extensive effort by Colorado State University to produce Fundamental Climate Data Records for SSM/I and SSMIS, described in companion papers in this volume, will reduce much of this sort of problem for those sensors.…”

Section: Introductionmentioning

confidence: 97%

Comparing Calibrations of Similar Conically Scanning Window-Channel Microwave Radiometers

Wilheit¹

2013

IEEE Trans. Geosci. Remote Sensing

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The Global Precipitation Measuring Mission requires the ability to compare the calibrations of similar, but not identical, orbiting microwave radiometers. A fitting algorithm has been developed which adjusts a set of geophysical parameters to match the radiances of a source sensor. The adjusted parameters are then used to compute the radiances for a target sensor. For comparison purposes, a simple (weather forecast) analysis-based algorithm has also been implemented. The algorithms have been tested on two pairs of sensors, TMI/Windsat and TMI/AMSR-E. The differences in the results between the two algorithms are generally small. The contribution of various error sources has also been evaluated. The error analysis suggests similar quality for both algorithms. A comparison of the observed variability in the differences between the two sensors in each pair shows very similar variability for the TMI/AMSR-E pair as for the TMI/Windsat pair. It is also shown that the fitting algorithm partially compensates for shortcomings in the radiative transfer models by introducing spurious correlations among the retrieved parameters.

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“…The purpose of the double difference technique is to find a linear calibration transfer function between two instruments, or, alternatively, to find the inter-sensor biases independent of instrument and measurement artifacts [22][23][24]. When extended to scatterometry, this technique improves the direct comparison by including σ 0 models to replace the brightness temperature models in the radiometer case.…”

Section: Calibration Methodsmentioning

confidence: 99%

RapidScat Cross-Calibration Using the Double Difference Technique

et al. 2017

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RapidScat is a National Aeronautics and Space Administration (NASA) Ku-Band scatterometer that was operated onboard the International Space Station between September 2014 and August 2016 when the mission effectively ended after an irrecoverable instrument failure. A unique non-Sun-synchronous orbit facilitated global contiguous geographical sampling between the ±56 • latitude. For the first time, such an orbit enabled an overlap with other scatterometers flying in Sun-synchronous orbits. The double-difference technique was developed and successfully used for microwave radiometer calibration at the Remote Sensing Laboratory at the University of Central Florida, USA. This paper presents the extension of the double difference methodology to scatterometry. The methodology has been adopted for the cross-instrument calibration between RapidScat and QuikScat scatterometers simultaneously orbiting the Earth on-board two independent satellite platforms. The double-difference technique was deployed to compare measurements from these two scatterometers, as a more accurate alternative to the classic single difference approach. The work summarized in this paper addressed a cross-calibration algorithm developed and applied to RapidScat and QuikScat data in the period from January 2015 to March 2016. The initial results of the statistical analysis and biases between the two scatterometers are presented. Calculated biases may be used for measurement correction and reprocessing.

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A Time-Varying Radiometric Bias Correction for the TRMM Microwave Imager

Cited by 50 publications

References 23 publications

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Comparing Calibrations of Similar Conically Scanning Window-Channel Microwave Radiometers

RapidScat Cross-Calibration Using the Double Difference Technique

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