An Overview of the Joint Polar Satellite System (JPSS) Science Data Product Calibration  and Validation

Zhou, Lihang; Divakarla, Murty; Liu, Xingpin

doi:10.3390/rs8020139

Cited by 57 publications

(34 citation statements)

References 13 publications

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“…CrIS onboard SNPP is a well‐calibrated instrument (Han et al, ; Zavyalov et al, ; Zhou et al, ), with noise below specification (Zavyalov et al, ), high spectral and radiometric accuracy (Strow et al, ; Tobin et al, ; L. Wang et al, ), and high geolocation accuracy (L. Wang et al, ; L. Wang et al, ). With its long‐term stability (Chen et al, , ; Han et al, ; Moeller et al, ; Strow et al, ; Tobin et al, ; Zavyalov et al, ; Zhou et al, ), CrIS radiances are used to identify DCCs and help monitor the calibration stabilities of the VIIRS RSBs. This study provides a chance to better understand the VIIRS RSB radiometric stabilities, using the collocated infrared sounder instrument for detecting more confident DCCs.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements

Gong

et al. 2019

JGR Atmospheres

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As uniform and stable objects, deep convective clouds (DCCs) are often used to monitor the calibration stabilities of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership. Traditionally, DCCs are identified by the legacy 11-μm brightness temperature (BT11) threshold method. With the collocated Cross-track Infrared Sounder (CrIS), a method of combining the BT difference between a water vapor absorption channel and a window channel to its measurement noise ratio (BNR) is adopted and applied to DCC identification. The BNR method improves the DCC detections over the legacy method because it is less contaminated with high clouds not thick and bright enough. Using observations from 2017 to 2018, the results show BNR has better performances than BT11 for identifying DCCs and monitoring RSBs. When comparing to BT11, BNR has more robust and invariant time series of monthly reflectance for all RSBs; that is, the standard deviation and total variation range (maximum-minimum) are up to 47% smaller. Because BNR affects more on the left tails (less reflective) than the modes of the histograms of reflectance, the improvement is more significant on the mean reflectance than the mode. BNR detects fewer DCCs than BT11, but with more confidence. This allows the weekly and daily time series for monitoring RSBs in higher temporal frequencies. This method can be applied to other imagers with collocated advanced infrared sounders for detecting DCCs and monitoring the calibration stabilities of RSBs.

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Section: Introductionmentioning

confidence: 99%

Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements

Gong

et al. 2019

JGR Atmospheres

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“…The Ozone Mapping Profiler Suite (OMPS) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in October 2011, consists of a combined nadir mapper (OMPS-NM) and nadir profiler (OMPS-NP) and a separate limb profiler (OMPS-LP), which provide total column, partial column profile, and limb profile products, respectively. A second suite was placed onboard the Joint Polar Satellite System JPSS-1 satellite (Zhou et al, 2016), renamed NOAA-20 and launched in November 2017. The retrieved data used in this study are from the OMPS S-NPP nadir measurements and are considered to be at a provisional product maturity level.…”

Section: Ompsmentioning

confidence: 99%

A study on harmonizing total ozone assimilation with multiple sensors

2019

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Bias estimations and corrections of total column measurements are applied and evaluated with ozone data from satellite instruments providing near-real-time products during summer 2014 and 2015 and winter 2015. The developed standalone bias-correction system can be applied in near-real-time chemical data assimilation and long-term reanalysis. The instruments to which these bias corrections were applied include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOp-B satellites (GOME-2A and GOME-2B), the total column ozone mapping instrument of the Ozone Mapping Profiler Suite (OMPS-NM) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Ozone Monitoring Instrument (OMI) instrument on the Aura research satellite. The OMI data set based on the TOMS version 8.5 retrieval algorithm was chosen as the reference used in the bias correction of the other satellite-based total column ozone data sets. OMI data were chosen for this purpose instead of groundbased observations due to OMI's significantly better spatial and temporal coverage, as well as interest in near-realtime assimilation. Ground-based Brewer and Dobson spectrophotometers, and filter ozonometers, as well as the Solar Backscatter Ultraviolet satellite instrument (SBUV/2), served as independent validation sources of total column ozone data. Regional and global mean differences of the OMI-TOMS data with measurements from the three groundbased instrument types for the three evaluated 2-month periods were found to be within 1 %, except for the polar regions, where the largest differences from the comparatively small data set in Antarctica exceeded 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6 % to 1.2 %, with smaller differ-ences than with ground-based observations over Antarctica. Bias corrections as a function of latitude and solar zenith angle were performed for GOME-2A/B and OMPS-NM using colocation with OMI-TOMS and three variants of differences with short-term model forecasts. These approaches were shown to yield residual biases of less than 1 %, with the rare exceptions associated with bins with less data. These results were compared to a time-independent bias-correction estimation that used colocations as a function of ozone effective temperature and solar zenith angle which, for the time period examined, resulted in larger residual biases for bins whose bias varies more in time.The impact of assimilating total column ozone data from single and multiple satellite data sources with and without bias correction was examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. Assimilation experiments for July-August 2014 show a reduction of global mean biases for short-term forecasts relative to ground-based Brewer and Dobson observations from a maximum of about 2.3 % in the absence of bias correction to less than 0.3 % in size when bias correction is included. Both temporally averaged and time-varying me...

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“…Along with the Atmospheric InfraRed Sounder (AIRS; Chahine et al, ) on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI; Chalon et al, ; Collard & McNally, ) on the European MetOp‐A/B satellites, CrIS provides needed information about global atmospheric profiles with high vertical resolution for numerical weather prediction models (Cucurull et al, ; Joo et al, ; Wang, Li, et al, ). Calibration and validation of the CrIS Sensor Data Records (SDRs) are performed before (Zavyalov et al, ) and after the launch (Han et al, ; Zhou et al, ) in order to determine the instrument noise characteristics (Zavyalov et al, ), the spectral/radiometric performance (Strow et al, ; Tobin, Revercomb, Knuteson, Best, et al, ; Tobin, Revercomb, Knuteson, Taylor, et al, ; Wang et al, ), and the geolocation accuracy (Wang et al, ; Wang, Zhang, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

Gong

et al. 2018

JGR Atmospheres

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The Cross‐track Infrared Sounder (CrIS) onboard the Suomi National Polar‐orbiting Partnership (SNPP) has high radiometric accuracy, which can be used to intercompare and understand the radiometric performance of the thermal emissive bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the same platform. Previous studies usually selected uniform scenes to avoid potential uncertainties due to geolocation errors. This study focuses on all scenes (including nonuniform scenes), as well as CrIS subpixel cloudiness, local zenith angle and surface type, and their impacts on VIIRS/CrIS intercomparisons. A geolocation error correction scheme is applied to mitigate the geolocation mismatch between CrIS and VIIRS. Intercomparison results of three days from 19 to 21 September 2016, a quarterly warm up/cool down calibration period, show overall good agreement in terms of radiance biases (less than 0.2 K in terms of brightness temperature) between VIIRS and CrIS, for scene temperatures between 220 and 300 K; however, larger biases are evident outside of this range. The three factors—CrIS subpixel cloudiness, local zenith angle, and surface type—show weak impacts on VIIRS radiance biases. Both clear‐sky and overcast conditions have slightly different radiance biases from other partially cloudy conditions because of more contributions from colder or warmer scenes. The time series show the impact of warm up/cool down and the effectiveness of the correction algorithm for reprocessing VIIRS M15 and M16 data sets. The methods and tools can be applied to monitor and validate other infrared imager/sounder duos onboard the same platform.

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An Overview of the Joint Polar Satellite System (JPSS) Science Data Product Calibration and Validation

Cited by 57 publications

References 13 publications

Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements

Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements

A study on harmonizing total ozone assimilation with multiple sensors

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

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