scholarly journals TanSat ACGS on-orbit spectral calibration by use of individual solar lines and entire atmospheric spectra

2020 ◽  
Author(s):  
Yanmeng Bi ◽  
Qian Wang ◽  
Zhongdong Yang ◽  
Chengbao Liu ◽  
Chao Lin ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Spectral Resolution ◽  
Atmospheric Carbon Dioxide ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Reference Spectrum ◽  
Center Wavelength

Abstract. The spectra measured by the Atmospheric Carbon dioxide Grating Spectrometer (ACGS) carried by the China global carbon dioxide observation satellite (TanSat) in the band of 0.76 μm, 1.61 μm and 2.06 μm can be used for the retrieval of carbon dioxide (CO2) concentrations by fitting the observations and simulations using the optimal estimation algorithm. Accurately detecting the change of the center wavelength is highly important because of its very high spectral resolution and accuracy requirement for product retrieval. The variations of center wavelength for all three bands of ACGS have been calculated on the locations of the individual solar absorption lines by comparing the solar-viewing measurements and the high resolution solar reference spectrum. The variations with magnitudes less than 10 % of the spectral resolution for each band have been detected. The changes are probably caused by vibration and the instrument status difference between the ground and space, especially temperature variation on orbit. In addition to solar lines, the entire atmospheric spectra simulated by radiative transfer model can be used as the reference spectrum to determine the wavelength change by fitting the measured and simulated spectra. The change of wavelength determined by atmospheric spectra is closely consistent with that by solar lines. Both schemes described here can be used not only for monitoring spectral stability but also to gain spectral knowledge prior to the level-2 product processing. These minor temporal changes of wavelength on orbit should be corrected in the product retrieval.

Dependence of Simulation Biases at AHI Surface-Sensitive Channels on Land Surface Emissivity over China

2018 ◽  
Vol 35 (6) ◽  
pp. 1283-1298 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou ◽  
F. Weng ◽  
M. Sun
Keyword(s):  
Brightness Temperature ◽  
Spectral Resolution ◽  
Land Surface ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Emissivity ◽  
Bare Rock ◽  
Land Surface Emissivity ◽  
M Channels

AbstractThis study compares the simulation biases of Advanced Himawari Imager (AHI) brightness temperature to observations made at night over China through the use of three land surface emissivity (LSE) datasets. The University of Wisconsin–Madison High Spectral Resolution Emissivity dataset, the Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer and Moderate Resolution Imaging Spectroradiometer Emissivity database over Land High Spectral Resolution Emissivity dataset, and the International Geosphere–Biosphere Programme (IGBP) infrared LSE module, as well as land skin temperature observations from the National Basic Meteorological Observing stations in China are used as inputs to the Community Radiative Transfer Model. The results suggest that the standard deviations of AHI observations minus background simulations (OMBs) are largely consistent for the three LSE datasets. Also, negative biases of the OMBs of brightness temperature uniformly occur for each of the three datasets. There are no significant differences in OMB biases estimated with the three LSE datasets over cropland and forest surface types for all five AHI surface-sensitive channels. Over the grassland surface type, significant differences (~0.8 K) are found at the 10.4-, 11.2-, and 12.4-μm channels if using the IGBP dataset. Over nonvegetated surface types (e.g., sandy land, gobi, and bare rock), the lack of a monthly variation in IGBP LSE introduces large negative biases for the 3.9- and 8.6-μm channels, which are greater than those from the two other LSE datasets. Thus, improvements in simulating AHI infrared surface-sensitive channels can be made when using spatially and temporally varying LSE estimates.

scholarly journals Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

2013 ◽  
Vol 52 (3) ◽  
pp. 710-726 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Steven Platnick ◽  
Andrew K. Heidinger ◽  
Bryan A. Baum ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Spectral Response ◽  
Thermal Infrared ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Retrieval Method ◽  
Ice Cloud ◽  
Cloud Properties

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

scholarly journals A synthetic data set of high-spectral resolution infrared spectra for the Arctic atmosphere

2016 ◽  
Author(s):  
C. J. Cox ◽  
P. M. Rowe ◽  
S. P. Neshyba ◽  
V. P. Walden
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Infrared Spectra ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Set ◽  
Atmospheric State

Abstract. Retrievals of cloud microphysical and macrophysical properties from ground-based and satellite-based infrared remote sensing instruments are critical for understanding clouds. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 222 cloudy cases from 50–3000 cm−1 (3.3 to 200 μm) at monochromatic (line-by-line) resolution at a spacing of ~ 0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the ACADIS data repository (doi:10.5065/D61J97TT).

scholarly journals Measuring Stratospheric H2O with an Airborne Spectrometer

2014 ◽  
Vol 31 (7) ◽  
pp. 1502-1515 ◽  
Author(s):  
Maziar Bani Shahabadi ◽  
Yi Huang
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Spectral Resolution ◽  
High Sensitivity ◽  
Far Infrared ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Small Scale ◽  
Transfer Model ◽  
Infrared Spectral

Abstract This study examines the ability of an infrared spectral sensor flying at the tropopause level for retrieving stratospheric H2O. Synthetic downwelling radiance spectra simulated by the line-by-line radiative transfer model are used for this examination. The potential of high-sensitivity water vapor retrieval is demonstrated by an ideal sensor with low detector noise, high spectral resolution, and full infrared coverage. A suite of hypothetical sensors with varying specifications is then examined to determine the technological requirements for a satisfactory retrieval. This study finds that including far infrared in the sensor’s spectral coverage is essential for achieving accurate H2O retrieval with an accuracy of 0.4 ppmv (1-sigma). The uncertainties in other gas species such as CH4, N2O, O3, and CO2 do not significantly affect the H2O retrieval. Such a hyperspectral instrument may afford an advantageous tool, especially for detecting small-scale lower-stratospheric moistening events.

scholarly journals Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis

2019 ◽  
Vol 11 (22) ◽  
pp. 2710 ◽  
Author(s):  
Sihui Fan ◽  
Wei Han ◽  
Zhiqiu Gao ◽  
Ruoying Yin ◽  
Yu Zheng
Keyword(s):  
Principal Component Analysis ◽  
Spectral Resolution ◽  
Principal Component ◽  
Component Analysis ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Data ◽  
Observation Error ◽  
Array Position

The Geostationary Interferometric Infrared Sounder (GIIRS) is the first high-spectral resolution advanced infrared (IR) sounder onboard the new-generation Chinese geostationary meteorological satellite FengYun-4A (FY-4A). The GIIRS has 1650 channels, and its spectrum ranges from 700 to 2250 cm−1 with an unapodized spectral resolution of 0.625 cm−1. It represents a significant breakthrough for measurements with high temporal, spatial and spectral resolutions worldwide. Many GIIRS channels have quite similar spectral signal characteristics that are highly correlated with each other in content and have a high degree of information redundancy. Therefore, this paper applies a principal component analysis (PCA)-based denoising algorithm (PDA) to study simulation data with different noise levels and observation data to reduce noise. The results show that the channel reconstruction using inter-channel spatial dependency and spectral similarity can reduce the noise in the observation brightness temperature (BT). A comparison of the BT observed by the GIIRS (O) with the BT simulated by the radiative transfer model (B) shows that a deviation occurs in the observation channel depending on the observation array. The results show that the array features of the reconstructed observation BT (rrO) depending on the observation array are weakened and the effect of the array position on the observations in the sub-center of the field of regard (FOR) are partially eliminated after the PDA procedure is applied. The high observation and simulation differences (O-B) in the sub-center of the FOR array notably reduced after the PDA procedure is implemented. The improvement of the high O-B is more distinct, and the low O-B becomes smoother. In each scan line, the standard deviation of the reconstructed background departures (rrO-B) is lower than that of the background departures (O-B). The observation error calculated by posterior estimation based on variational assimilation also verifies the efficiency of the PDA. The typhoon experiment also shows that among the 29 selected assimilation channels, the observation error of 65% of the channels was reduced as calculated by the triangle method.

Model Calculations and Interferometer Measurements of Ice-Cloud Characteristics

2000 ◽  
Vol 39 (5) ◽  
pp. 634-644 ◽  
Author(s):  
Sunggi Chung ◽  
Steven Ackerman ◽  
Paul F. van Delst ◽  
W. Paul Menzel
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Gas Absorption ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Calculations ◽  
Ice Cloud ◽  
Macrophysical Properties ◽  
Ice Water

Abstract This paper investigates the relationship between high–spectral resolution infrared (IR) radiances and the microphysical and macrophysical properties of cirrus clouds. Through use of radiosonde measurements of the atmospheric state at the Department of Energy’s Atmospheric Radiation Measurement Program site, high–spectral resolution IR radiances are calculated by combining trace gas absorption optical depths from a line-by-line radiative transfer model with the discrete ordinate radiative transfer (DISORT) method. The sensitivity of the high–spectral resolution IR radiances to particle size, ice-water path, cloud-top location, cloud thickness, and multilayered cloud conditions is estimated in a multitude of calculations. DISORT calculations and interferometer measurements of cirrus ice cloud between 700 and 1300 cm−1 are compared for three different situations. The measurements were made with the High–Spectral Resolution Interferometer Sounder mounted on a National Aeronautics and Space Administration ER-2 aircraft flying at 20-km altitude during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS).

scholarly journals A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere

2016 ◽  
Vol 8 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Christopher J. Cox ◽  
Penny M. Rowe ◽  
Steven P. Neshyba ◽  
Von P. Walden
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Infrared Spectra ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Set ◽  
Atmospheric State

Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of  ∼  0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).

scholarly journals A High-Spectral-Resolution Radiative Transfer Model for Simulating Multilayered Clouds and Aerosols in the Infrared Spectral Region

2015 ◽  
Vol 72 (2) ◽  
pp. 926-942 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Xu Liu
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Present Model ◽  
Reference Model ◽  
Mineral Dust ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Multiple User ◽  
Infrared Measurements

Abstract A fast and flexible model is developed to simulate the transfer of thermal infrared radiation at wavenumbers from 700 to 1300 cm−1 with a spectral resolution of 0.1 cm−1 for scattering–absorbing atmospheres. In a single run and at multiple user-defined levels, the present model simulates radiances at different viewing angles and fluxes. Furthermore, the model takes into account complicated and realistic scenes in which ice cloud, water cloud, and mineral dust layers may coexist within an atmospheric column. The present model is compared to a rigorous reference model, the 32-stream Discrete Ordinate Radiative Transfer model (DISORT) code. For an atmosphere with three scattering layers (water, ice, and mineral dust), the root-mean-square error of the simulated brightness temperatures at the top of the atmosphere is approximately 0.05 K, and the relative flux errors at the boundary and internal levels are much smaller than 1%. Within the same computing environment, the fast model runs more than 10 000, 6000, and 4000 times faster than DISORT under single-layer, two-layer, and three-layer cloud–aerosol conditions, respectively. With its computational efficiency and accuracy, the present model may optimally facilitate the forward radiative transfer simulations involved in remote sensing implementations based on high-spectral-resolution and narrowband infrared measurements and in the data assimilation applications of the weather forecasting system. The selected 0.1-cm−1 spectral resolution is an obstacle to extending the present model to strongly absorptive bands (e.g., 600–700 cm−1). However, the present clear-sky module can be substituted by a more accurate model for specific applications involving spectral bands with strong absorption.

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