Geolocation assessment for CrIS sensor data records

Wang, Likun; Tremblay, Denis; Han, Yong; Esplin, Mark P.; Hagan, Denise E.; Predina, Joe; Suwinski, Lawrence; Jin, Xin; Chen, Yong

doi:10.1002/2013jd020376

Cited by 64 publications

(50 citation statements)

References 16 publications

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“…Spectrally resolved Outgoing Longwave Radiation (OLR) measured by satellite instruments such as the Cross-track Infrared Sounder (CrIS) onboard the JPSS [Wang et al, 2013], the Atmospheric Infrared Sounder (AIRS) onboard Aqua [Chahine et al, 2006], and the IASI onboard MetOp [Blumstein et al, 2004] is beneficial in improving weather forecast via data assimilation [e.g., Hilton et al, 2009;McNally et al, 2006] and in validating global climate models (GCMs) [Haskins et al, 1999;Huang and Yung, 2005;Huang and Ramaswamy, 2008;Huang et al, 2007;Bani Shahabadi and Huang, 2014;Turner and Tett, 2014]. Different approaches have been used for validating weather forecasts and climate simulations against satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

Shahabadi

Huang

Garand

et al. 2016

J Adv Model Earth Syst

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An established radiative transfer model (RTM) is adapted for simulating all‐sky infrared radiance spectra from the Canadian Global Environmental Multiscale (GEM) model in order to validate its forecasts at the radiance level against Atmospheric InfraRed Sounder (AIRS) observations. Synthetic spectra are generated for 2 months from short‐term (3–9 h) GEM forecasts. The RTM uses a monthly climatological land surface emissivity/reflectivity atlas. An updated ice particle optical property library was introduced for cloudy radiance calculations. Forward model brightness temperature (BT) biases are assessed to be of the order of ∼1 K for both clear‐sky and overcast conditions. To quantify GEM forecast meteorological variables biases, spectral sensitivity kernels are generated and used to attribute radiance biases to surface and atmospheric temperatures, atmospheric humidity, and clouds biases. The kernel method, supplemented with retrieved profiles based on AIRS observations in collocation with a microwave sounder, achieves good closure in explaining clear‐sky radiance biases, which are attributed mostly to surface temperature and upper tropospheric water vapor biases. Cloudy‐sky radiance biases are dominated by cloud‐induced radiance biases. Prominent GEM biases are identified as: (1) too low surface temperature over land, causing about −5 K bias in the atmospheric window region; (2) too high upper tropospheric water vapor, inducing about −3 K bias in the water vapor absorption band; (3) too few high clouds in the convective regions, generating about +10 K bias in window band and about +6 K bias in the water vapor band.

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Section: Introductionmentioning

confidence: 99%

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

Shahabadi

Huang

Garand

et al. 2016

J Adv Model Earth Syst

View full text Add to dashboard Cite

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“…Observations of the backscattered shortwave infrared solar spectra also enable the retrieval of CO 2 , CH 4 , and/or CO from SCIAMACHY (Buchwitz et al, 2005), GOSAT (Yoshida et al, 2011), OCO-2 (Eldering et al, 2017), and TROPOMI Hu et al, 2018). Moreover, many atmospheric species have strong spectroscopic signatures in the mid-infrared and can be retrieved from the Earth's thermal emission spectra col- 15 lected by satellite sensors such as MOPITT (Drummond et al, 2010), AIRS (Aumann et al, 2003), TES (Bowman et al, 2006), IASI (Clerbaux et al, 2009), and CrIS (Han et al, 2013). One species of particular significance to tropospheric chemistry and air quality is NH 3 (Baek et al, 2004;Paulot and Jacob, 2014), which has been successfully retrieved from TES (Shephard et al, 2011;Sun et al, 2015a), AIRS (Warner et al, 2016), IASI (Clarisse et al, 2010;Whitburn et al, 2016a;Van Damme et al, 2017), and CrIS (Shephard and Cady-Pereira, 2015;Dammers et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

Sun¹,

Zhu²,

Cady‐Pereira³

et al. 2018

Preprint

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Abstract.Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (Level 2) to a regular grid (Level 3). When averaging Level 2 data over a long time period to a target Level 3 grid significantly finer than Level 2 pixels, this process is referred to as "oversampling". An agile, physics-based oversampling approach is developed to represent each satellite obser-5 vation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous approaches. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOV) as polygons (tessellation 10 error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid resolution, the ground size of FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for high resolution oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grid resolutions finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the Level 3 results by decreasing the 15 shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO 2 products and IASI NH 3 products, showing substantially improved visualization of trace gas distribution and local gradients.1 Atmos. Meas. Tech. Discuss., https://doi

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“…It is important to ensure the quality of the reprocessed geolocation products before the reprocessed VIIRS radiance, reflectance, and brightness temperature products can be reliably evaluated. Moreover, VIIRS observations have the finest spatial resolutions among all instruments onboard the S-NPP satellite, accurate VIIRS geolocations are also critical for evaluating geolocation errors in the Cross-track Infrared Sounder [7], the Advanced Technology Microwave Sounder, and the Ozone Mapping and Profiler Suite.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the NOAA S-NPP VIIRS Geolocation Reprocessing Improvements

Wang¹,

Cao

Bai

et al. 2017

Remote Sensing

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Long-term time series analysis requires consistent data records from satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar orbiting Partner (S-NPP) satellite launched in 2011 requires a major effort to produce consistently calibrated sensor data records (SDR). Accurate VIIRS geolocation products are critical to other VIIRS products and products from other instruments on the S-NPP satellite. This paper presents methods for assessing major improvements to the VIIRS geolocation products in the ongoing National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) reprocessing that incorporates all corrections in calibration parameters and SDR algorithms since launch to present. In this study, we analyzed the history of VIIRS geometric calibration parameter updates to identify optimal parameters to account for geolocation errors in the early days of the mission. A sample area located in North Western Africa was selected for validation purposes after analyzing global VIIRS and Landsat control point matching results. Geolocation products over the study region were reprocessed and I-bands/M-bands geolocation improvements were characterized by comparing geolocation errors before and after the reprocessing. Our results indicate that all short-term geolocation anomalies before the latest operational geometric calibration parameter update on 22 August 2013 were effectively minimized after reprocessing, with geolocation errors reduced from −47.1 ± 83.8 m to −23.3 ± 51.1 m (along scan) and from −15.6 ± 43.6 m to −5.9 ± 37.7 m (along track). Terrain correction for the VIIRS Day-Night-Band (DNB) was not implemented in the NOAA operational processing until 22 May 2015. In the reprocessing, it will be implemented to the entire DNB geolocation data record. DNB reprocessing improvement due to this implementation was evaluated using nighttime observations over point sources at sea level and over high altitude. Our results show that the implementation of terrain correction will reduce DNB geolocation errors at off-nadir high elevation locations from up to 9 km to~0.5 pixel (0.375 km), comparable to those at sea level site. The reprocessed geolocation dataset will be distributed online for end-users to access.

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Geolocation assessment for CrIS sensor data records

Cited by 64 publications

References 16 publications

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

Assessment of the NOAA S-NPP VIIRS Geolocation Reprocessing Improvements

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