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 Infrared Continental Surface Emissivity Spectra Retrieved from AIRS Hyperspectral Sensor

2008 ◽  
Vol 47 (6) ◽  
pp. 1619-1633 ◽  
Author(s):  
E. Péquignot ◽  
A. Chédin ◽  
N. A. Scott
Keyword(s):  
Radiative Transfer ◽  
Skin Temperature ◽  
Land Surface ◽  
Thermal Emission ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Emissivity ◽  
Spectral Window ◽  
Emissivity Spectra

Abstract Atmospheric Infrared Sounder (AIRS; NASA Aqua platform) observations over land are interpreted in terms of monthly mean surface emissivity spectra at a resolution of 0.05 μm and skin temperature. For each AIRS observation, an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the thermodynamic initial guess retrieval (TIGR) climatological library of about 2300 representative clear-sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas (4A) line-by-line radiative transfer model. Then, surface temperature is evaluated by using a single AIRS channel (centered at 12.183 μm) chosen for its almost constant emissivity with respect to soil type. Emissivity is then calculated for a set of 40 atmospheric windows (transmittance greater than 0.5) distributed over the AIRS spectrum. The overall infrared emissivity spectrum at 0.05-μm resolution is finally derived from a combination of high-spectral-resolution laboratory measurements of various materials carefully selected within the Moderate-Resolution Imaging Spectroradiometer/University of California, Santa Barbara (MODIS/UCSB) and Advanced Spaceborne Thermal Emission and Reflection Radiometer/Jet Propulsion Laboratory (ASTER/JPL) emissivity libraries. It is shown from simulations that the accuracy of the method developed in this paper, the multispectral method (MSM), varies from about 3% around 4 μm to considerably less than 1% in the 10–12-μm spectral window. Three years of AIRS observations (from April 2003 to March 2006) between 30°S and 30°N have been processed and interpreted in terms of monthly mean surface skin temperature and emissivity spectra from 3.7 to 14.0 μm at a spatial resolution of 1° × 1°. AIRS retrievals are compared with the MODIS (also flying aboard the NASA/Aqua platform) monthly mean L3 products and with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies baseline-fit method (UW/CIMSS BF) global infrared land surface emissivity database.

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 Community Radiative Transfer Model for Stratospheric Sounding Unit

2011 ◽  
Vol 28 (6) ◽  
pp. 767-778 ◽  
Author(s):  
Yong Chen ◽  
Yong Han ◽  
Quanhua Liu ◽  
Paul Van Delst ◽  
Fuzhong Weng
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Spectral Resolution ◽  
Model Simulation ◽  
Spectral Response ◽  
Co2 Concentration ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Observation ◽  
The Impact

Abstract To better use the Stratospheric Sounding Unit (SSU) data for reanalysis and climate studies, issues associated with the fast radiative transfer (RT) model for SSU have recently been revisited and the results have been implemented into the Community Radiative Transfer Model version 2. This study revealed that the spectral resolution for the sensor’s spectral response functions (SRFs) calculations is very important, especially for channel 3. A low spectral resolution SRF results, on average, in 0.6-K brightness temperature (BT) errors for that channel. The variations of the SRFs due to the CO2 cell pressure variations have been taken into account. The atmospheric transmittance coefficients of the fast RT model for the Television and Infrared Observation Satellite (TIROS)-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-11, and NOAA-14 have been generated with CO2 and O3 as variable gases. It is shown that the BT difference between the fast RT model and line-by-line model is less than 0.1 K, but the fast RT model is at least two orders of magnitude faster. The SSU measurements agree well with the simulations that are based on the atmospheric profiles from the Earth Observing System Aura Microwave Limb Sounding product and the Sounding of the Atmosphere using Broadband Emission Radiometry on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. The impact of the CO2 cell pressures shift for SSU has been evaluated by using the Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA) model profiles. It is shown that the impacts can be on an order of 1 K, especially for SSU NOAA-7 channel 2. There are large brightness temperature gaps between observation and model simulation using the available cell pressures for NOAA-7 channel 2 after June 1983. Linear fittings of this channel’s cell pressures based on previous cell leaking behaviors have been studied, and results show that the new cell pressures are reasonable. The improved SSU fast model can be applied for reanalysis of the observations. It can also be used to address two important corrections in deriving trends from SSU measurements: CO2 cell leaking correction and atmospheric CO2 concentration correction.

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).

Hyper-resolution land surface modeling enables 30-m SMAP-based soil moisture at continental scales

2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Nathaniel W. Chaney ◽  
Hylke E. Beck ◽  
Ming Pan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Field Scale ◽  
Remotely Sensed Data

<p>Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Microwave-based satellite remote sensing offers unique opportunities for the large-scale monitoring of soil moisture at frequent temporal intervals. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. Several downscaling techniques based on high-resolution remotely sensed data proxies have been proposed (1 km to 100 m). Although these techniques yield aesthetically pleasing maps, by neglecting how the water and energy fluxes physically interact with the landscape, these approaches often fail to provide soil moisture estimates that are hydrologically consistent.</p><p>This work introduces a state-of-the-art framework that combines a process-based hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution brightness temperature to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). We demonstrate this framework by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission and subsequently merging the HydroBlocks-RTM and the SMAP L3-enhanced brightness temperature at the HRU scale. This allows for hydrologically consistent SMAP-based soil moisture retrievals at an unprecedented 30-m spatial resolution over continental domains. </p><p>We applied this framework to obtain 30-m SMAP-based soil moisture retrievals over the contiguous United States (2015-2018). When evaluated against sparse and dense in-situ soil moisture networks, the 30-m soil moisture retrievals showed substantial improvements in performance at field and watershed scales, outperforming both the SMAP L3-enhanced and the SMAP L4 soil moisture products. This work leads the way towards hydrologically consistent field-scale soil moisture retrievals and highlights the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. </p>

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).

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