scholarly journals A Methodology to Determine Recipe Adjustments for Multispectral Composites Derived from Next-Generation Advanced Satellite Imagers

2018 ◽  
Vol 35 (3) ◽  
pp. 643-664 ◽  
Author(s):  
Emily Berndt ◽  
Nicholas Elmer ◽  
Lori Schultz ◽  
Andrew Molthan
Keyword(s):  
Linear Regression ◽  
Brightness Temperature ◽  
Infrared Imaging ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Japan Meteorological Agency ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Next Generation ◽  
Temperature Offset

AbstractThe European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) began creating multispectral [i.e., red–green–blue (RGB)] composites in the early 2000s with the advent of the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI). As new satellite sensors—for example, the Himawari-8 Advanced Himawari Imager (AHI) and the Geostationary Operational Environmental Satellite Advanced Baseline Imager (ABI)—become available, there is a need to adjust the EUMETSAT RGB standard thresholds (i.e., recipes) to account for differences in spectral characteristics, spectral response, and atmospheric absorption in order to maintain an interpretation consistent with legacy composites. For the purpose of comparing RGB composites derived from nonoverlapping geostationary sensors, an adjustment technique was applied to the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to create an intermediate reference sensor (i.e., SEVIRI proxy). Brightness temperature offset values between each AHI and SEVIRI proxy band centered near 3.9, 8.6, 11.0, and 12.0 µm were determined with this technique and through line-by-line radiative transfer model simulations. The relationship between measured brightness temperature of AHI and the SEVIRI proxy was determined though linear regression similar to research by the Japan Meteorological Agency. The linear regression coefficients were utilized to determine the RGB recipe adjustments. Adjusting the RGB recipes to account for the differences in spectral characteristics results in RGB composites consistent with legacy EUMETSAT composites. The methodology was applied to an example of the Nighttime Microphysics RGB, confirming the Japan Meteorological Agency adjustments and demonstrating a simple methodology to determine recipe adjustments for RGB composites derived with next-generation sensors.

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 Mineralogical and chemical properties inversed from 21-lunar-day VNIS observations taken during the Chang’E-4 mission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qinghong Zeng ◽  
Shengbo Chen ◽  
Yuanzhi Zhang ◽  
Yongling Mu ◽  
Rui Dai ◽  
...  
Keyword(s):  
Impact Crater ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Chemical Properties ◽  
Landing Site ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Chemical Compositions ◽  
Impact Melt ◽  
And Chemical Properties

AbstractWe report on the mineralogical and chemical properties of materials investigated by the lunar rover Yutu-2, which landed on the Von Kármán crater in the pre-Nectarian South Pole–Aitken (SPA) basin. Yutu-2 carried several scientific payloads, including the Visible and Near-infrared Imaging Spectrometer (VNIS), which is used for mineral identification, offering insights into lunar evolution. We used 86 valid VNIS data for 21 lunar days, with mineral abundance obtained using the Hapke radiative transfer model and sparse unmixing algorithm and chemical compositions empirically estimated. The mineralogical properties of the materials at the Chang’E-4 (CE-4) site referred to as norite/gabbro, based on findings of mineral abundance, indicate that they may be SPA impact melt components excavated by a surrounding impact crater. We find that CE-4 materials are dominated by plagioclase and pyroxene and feature little olivine, with 50 of 86 observations showing higher LCP than HCP in pyroxene. In view of the effects of space weathering, olivine content may be underestimated, with FeO and TiO2 content estimated using the maturity-corrected method. Estimates of chemical content are 7.42–18.82 wt% FeO and 1.48–2.1 wt% TiO2, with a low-medium Mg number (Mg # ~ 55). Olivine-rich materials are not present at the CE-4 landing site, based on the low-medium Mg #. Multi-origin materials at the CE-4 landing site were analyzed with regard to concentrations of FeO and TiO2 content, supporting our conclusion that the materials at CE-4 do not have a single source but rather are likely a mixture of SPA impact melt components excavated by surrounding impact crater and volcanic product ejecta.

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 Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

scholarly journals Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres

2020 ◽  
Vol 28 (18) ◽  
pp. 25730
Author(s):  
Wenwen Li ◽  
Feng Zhang ◽  
Yi-Ning Shi ◽  
Hironobu Iwabuchi ◽  
Mingwei Zhu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Brightness

scholarly journals Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

2019 ◽  
Vol 11 (20) ◽  
pp. 2338 ◽  
Author(s):  
Liu ◽  
Chu ◽  
Yin ◽  
Liu
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Weather Prediction ◽  
Order Relation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radar Network ◽  
Intense Precipitation ◽  
Standard Deviations ◽  
Light Precipitation

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

scholarly journals Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands

2020 ◽  
Vol 12 (18) ◽  
pp. 2939
Author(s):  
Chang-Hwan Park ◽  
Thomas Jagdhuber ◽  
Andreas Colliander ◽  
Johan Lee ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Microwave Remote Sensing ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Time Varying ◽  
Vegetation Fraction ◽  
Omega Model

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.

scholarly journals 2018 Atmospheric Motion Vector (AMV) Intercomparison Study

2019 ◽  
Vol 11 (19) ◽  
pp. 2240 ◽  
Author(s):  
David Santek ◽  
Richard Dworak ◽  
Sharon Nebuda ◽  
Steve Wanzong ◽  
Régis Borde ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
Estimation Method ◽  
Japan Meteorological Agency ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
European Organization ◽  
Specific Configuration ◽  
Atmospheric Motion ◽  
Height Assignment

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

scholarly journals Local Tilt Optimization of Photovoltaic Solar Panels for Maximum Radiation Absorption

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Mauro Masili ◽  
Liliane Ventura
Keyword(s):  
Solar Radiation ◽  
Tilt Angle ◽  
Spectral Response ◽  
Solar Spectrum ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Radiation Absorption ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Solar Panels

Incident solar radiation on photovoltaic (PV) solar panels is not constant throughout the year. Besides dependence on the season, solar radiation is reliant on the location and weather conditions. For a given location on Earth, the best-fixed orientation of a PV panel can be determined by achieving the maximum incident solar irradiance throughout the year or for a predetermined period. In this paper, we use a sophisticated atmospheric radiative transfer model to calculate the direct and diffuse solar irradiation (radiant exposure) for the solar spectrum incident on PV solar panels to determine the best tilt angle of the panel in order to maximize absorption of solar radiation for selected periods. We used the Regula-Falsi numerical method to obtain the tilt angle at which the derivative of solar irradiation (concerning the tilt angle) approaches zero. Moreover, the spectral response of typical silicon cells is taken into account. These calculations were carried out in São Carlos (SP), a town in the southeast of Brazil. The best tilt angle was obtained for three selected periods. Additionally, we provide results for Southern latitudes ranging from 0° to −55° in steps of −5° for the meteorological seasons. We have shown that for each period, there is an increase in solar radiation absorption compared to the traditional installation angle based exclusively on the local latitude. These calculations can be extended to any location.

Empirical Polarization Distribution Models for CLARREO-Imager Intercalibration

2016 ◽  
Vol 33 (3) ◽  
pp. 439-451 ◽  
Author(s):  
D. Goldin ◽  
C. Lukashin
Keyword(s):  
Linear Polarization ◽  
Infrared Imaging ◽  
Solar Spectrum ◽  
National Research Council ◽  
Real Data ◽  
Radiative Transfer Model ◽  
Degree Of Polarization ◽  
Transfer Model ◽  
Distribution Models ◽  
Polarization Distribution

AbstractPolarization effects bias the performance of various existing passive spaceborne instruments, such as MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS), as well as geostationary imagers. It is essential to evaluate and correct for these effects in order to achieve the required accuracy of the total reflectance at the top of the atmosphere.In addition to performing highly accurate decadal climate change observations, one of the objectives of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission recommended by the National Research Council for launch by NASA is to provide the on-orbit intercalibration with the imagers over a range of parameters, including polarization. Whenever the on-orbit coincident measurements are not possible, CLARREO will provide the polarization distributions constructed using the adding–doubling radiative transfer model (ADRTM), which will cover the entire reflected solar spectrum. These ADRTM results need to be validated using real data. To this end the empirical polarization distribution models (PDMs) based on the measurements taken by the Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) mission were developed. Examples of such PDMs for the degree of polarization and the angle of linear polarization for the cloudless ocean scenes are shown here. These PDMs are compared across the three available PARASOL polarization bands, and the effect of aerosols on them is examined. The PDM-derived dependence of the reflectance uncertainty on the degree of polarization for imagers, such as MODIS or VIIRS, after their intercalibration with the CLARREO instrument is evaluated. The influence of the aerosols on the reflectance uncertainty is examined. Finally, the PDMs for the angle of linear polarization is cross-checked against the single-scattering approximation.

Sign in / Sign up

Export Citation Format

Share Document