Correcting Geolocation Errors for Microwave Instruments Aboard NOAA Satellites

Moradi, Isaac; Meng, Huan; Ferraro, Ralph; Bilanow, S.

doi:10.1109/tgrs.2012.2225840

Cited by 41 publications

(24 citation statements)

References 18 publications

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“…This subsection shows the results of geolocation error correction. Moradi [15] proposed that the geolocation errors of satellite data caused by various factors can be corrected by adjusting the satellite attitude angle. When the geolocation error is obtained, it needs to be converted into a satellite attitude angle error.…”

Section: Geolocation Error Correctionmentioning

confidence: 99%

“…It is called geolocation error, which is one of the most important factors affecting the quantitative application of satellite remote sensing data [4][5][6][7][8]. Up to now, several methods have been proposed for the geolocation error estimation of MWRI, such as coastline inflection point method (CIM) [9][10][11][12] and node differential method (NDM) [13][14][15][16]. The NDM estimates satellite attitude angle error and corrects geolocation error by minimizing the brightness temperature difference between ascending and descending orbit data in the same region.…”

Section: Introductionmentioning

confidence: 99%

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ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Zhao

Chen

et al. 2019

Remote Sensing

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Known as input in the Numerical Weather Prediction (NWP) models, Microwave Radiation Imager (MWRI) data have been widely distributed to the user community. With the development of remote sensing technology, improving the geolocation accuracy of MWRI data are required and the first step is to estimate the geolocation error accurately. However, the traditional method, such as the coastline inflection method (CIM), usually has the disadvantages of low accuracy and poor anti-noise ability. To overcome these limitations, this paper proposes a novel ℓ p iterative closest point coastline inflection method ( ℓ p -ICP CIM). It assumes that the field of views (FOVs) across the coastline can degenerate into a step function and employs an ℓ p ( 0 ≤ p < 1 ) sparse regularization optimization model to solve the coastline point. After estimating the coastline points, the ICP algorithm is employed to estimate the corresponding relationship between the estimated coastline points and the real coastline. Finally, the geolocation error can be defined as the distance between the estimated coastline point and the corresponding point on the true coastline. Experimental results on simulated and real data sets show the effectiveness of our method over CIM. The accuracy of the geolocation error estimated by ℓ p -ICP CIM is up to 0 . 1 pixel, in more than 90 % of cases. We also show that the distribution of brightness temperature near the coastline is more consistent with the real coastline and the average geolocation error is reduced by 63 % after geolocation error correction.

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Section: Geolocation Error Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Zhao

Chen

et al. 2019

Remote Sensing

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“…Most of the methods applied to assess the on-orbit geolocation error of microwave sensors rely on Earth targets, such as coastline inflection point method (CIP) (Hoffman et al, 1987;Smith et al, 2009;Gregorich and Aumann, 2003;Currey, 2002), image co-registration method (Wang et al, 2013(Wang et al, , 2017Wolfe et al, 2002Wolfe et al, , 2013Khlopenkov et al, 2010), land-sea fraction method (LFM) (Bennartz, 1999), and ascending and descending observation comparison (Moradi et al, 2013). Recent study (Zhou et al, 2019) disclosed that antenna beam misalignment is a major error source in ATMS total geolocation error budget.…”

Section: Introductionmentioning

confidence: 99%

On Study of Two-Dimensional Lunar Scan for Advanced Technology Microwave Sounder Geometric Calibration

Zhou¹,

Yang²

2019

Preprint

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Abstract. The NOAA-20 satellite was successfully launched on 18 November 2017. It carries five key instruments including Advanced Technology Microwave Sounder (ATMS). On January 31, 2018, the spacecraft performed a pitch-over maneuver operation, during which the two-dimensional lunar scan observations were collected. In this study, a technique has been developed by which the ATMS on-orbit geometric calibration accuracy can be validated based on this lunar scan dataset. The fully calibrated data are fitted in the antenna pattern coordinate by Gaussian function. The deviation of the center of the fit function from the origin of the frame is taken to be the boresight pointing error of the instrument. This deviation is further transformed to the Euler angle roll and pitch defined in spacecraft coordinate system. The estimated ATMS boresight pointing Euler angle roll (pitch) is 0.05°, (0.22°) at K-band, −0.07°, (0.25°) at Ka-band, 0.02°, (0.24°) at V-band, −0.07°, (−0.08°) at W-band, and −0.04°, (0.02°) at G-band. The results are validated by comparing with those derived from the coastline inflection point method. It shows that the Euler angles derived from these two independent methods are consistent very well. For the sounding channels where the coastline method is inapplicable, the lunar scan method is still capable of delivering reasonable estimations of their geometric calibration errors.

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“…One main feature of radiometric errors is that the errors are normally scene dependent and change with the scene brightness temperatures (Tb) and polarization. Over the years some alternative methods have been developed to determine the relative accuracy of microwave measurements, including validation using measurements from similar instruments on board airborne platforms (e.g., Wilheit, 2013), comparison with simulations conducted using a radiative transfer model and atmospheric profiles (Saunders et al, 2013;Kerola, 2006;Moradi et al, 2013b), and intercomparison with respect to similar instruments on board space-borne platforms (Moradi et al, 2015a;Sapiano et al, 2013;John et al, 2012). Although comparing observed and simulated brightness temperatures can to some extent reveal errors in microwave satellite measurements, the application is very limited due to the biases in NWP fields and radiosonde sensor biases, as well as errors in the RT models and inputs provided to the RT models such as surface emissivity.…”

Section: Introductionmentioning

confidence: 99%

Radiometric correction of observations from microwave humidity sounders

Moradi

Beauchamp

Ferraro³

2018

Atmos. Meas. Tech.

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Abstract. The Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) are total power microwave radiometers operating at frequencies near the water vapor absorption line at 183 GHz. The measurements of these instruments are crucial for deriving a variety of climate and hydrological products such as water vapor, precipitation, and ice cloud parameters. However, these measurements are subject to several errors that can be classified into radiometric and geometric errors. The aim of this study is to quantify and correct the radiometric errors in these observations through intercalibration. Since the bias in the calibration of microwave instruments changes with scene temperature, a two-point intercalibration correction scheme was developed based on averages of measurements over the tropical oceans and nighttime polar regions. The intercalibration coefficients were calculated on a monthly basis using measurements averaged over each specified region and each orbit, then interpolated to estimate the daily coefficients. Since AMSU-B and MHS channels operate at different frequencies and polarizations, the measurements from the two instruments were not intercalibrated. Because of the negligible diurnal cycle of both temperature and humidity fields over the tropical oceans, the satellites with the most stable time series of brightness temperatures over the tropical oceans (NOAA-17 for AMSU-B and NOAA-18 for MHS) were selected as the reference satellites and other similar instruments were intercalibrated with respect to the reference instrument. The results show that channels 1, 3, 4, and 5 of AMSU-B on board NOAA-16 and channels 1 and 4 of AMSU-B on board NOAA-15 show a large drift over the period of operation. The MHS measurements from instruments on board NOAA-18, NOAA-19, and MetOp-A are generally consistent with each other. Because of the lack of reference measurements, radiometric correction of microwave instruments remain a challenge, as the intercalibration of these instruments largely depends on the stability of the reference instrument.

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Correcting Geolocation Errors for Microwave Instruments Aboard NOAA Satellites

Cited by 41 publications

References 18 publications

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

On Study of Two-Dimensional Lunar Scan for Advanced Technology Microwave Sounder Geometric Calibration

Radiometric correction of observations from microwave humidity sounders

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