scholarly journals Implementation of the Community Radiative Transfer Model in Advanced Clear-Sky Processor for Oceans and validation against nighttime AVHRR radiances

2009 ◽  
Vol 114 (D6) ◽  
Author(s):  
Xing-Ming Liang ◽  
Alexander Ignatov ◽  
Yury Kihai
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

scholarly journals Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

2010 ◽  
Vol 27 (10) ◽  
pp. 1609-1623 ◽  
Author(s):  
B. Petrenko ◽  
A. Ignatov ◽  
Y. Kihai ◽  
A. Heidinger
Keyword(s):  
High Resolution ◽  
Radiative Transfer ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Spatial Uniformity ◽  
Uniformity Test ◽  
Cross Platform

Abstract The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

scholarly journals Correction of UVS-AB-T UV Radiometers Using a Radiative Transfer Model

2014 ◽  
Vol 31 (5) ◽  
pp. 1098-1103 ◽  
Author(s):  
Dong Xia ◽  
Haobo Tan ◽  
Ling Chen ◽  
Weiqiang Mo ◽  
Zhiyang Yuan ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Quadratic Function ◽  
Reaction Rates ◽  
Correction Method ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Data ◽  
Clear Sky ◽  
Corrected Data ◽  
Decrement Rate

AbstractObservation of UV radiation is of major importance to human health and to the calculation of photochemical reaction rates. However, the sensitivity of UV radiometers decays because of equipment aging. A correction method is therefore proposed by using a decrement formula that is approximately a quadratic function of time and is obtained by fitting the clear-sky observation data from an aged UVS-AB-T UV radiometer with the data simulated by the Tropospheric Ultraviolet and Visible (TUV) radiative transfer model. The corrected data from the older radiometer are verified by the data from another newer radiometer on selected clear-sky days. The results show a high correlation and a low bias between the radiometers, and the mean of the corrected data from the older radiometer is 94.5% of that from the newer radiometer. After a long time of use, the decrement of the observation data would increase dramatically and errors of the data after correction would still be significant. In Dongguan, China, a recommendation is made that a UV radiometer should not be used for more than 5 years when the decrement rate reaches 50%.

scholarly journals Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

2017 ◽  
Vol 17 (22) ◽  
pp. 13559-13572 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Loretta J. Mickley ◽  
Eric M. Leibensperger ◽  
Michael J. Iacono
Keyword(s):  
United States ◽  
Radiative Transfer ◽  
Southeastern United States ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58–1.0 Wm−2 a−1 over the 2000–2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3–5.0  ×  10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000–2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear-sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm−2 a−1 at Goodwin Creek, MS, and +0.93 Wm−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990–2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk.

scholarly journals Longwave Radiative Effect of the Cloud-Aerosol Transition Zone Based on CERES Observations

2021 ◽  
Author(s):  
Babak Jahani ◽  
Hendrik Andersen ◽  
Josep Calbó ◽  
Josep-Abel González ◽  
Jan Cermak
Keyword(s):  
Radiative Transfer ◽  
Transition Zone ◽  
Radiant Energy ◽  
Energy System ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radiative Effect ◽  
Radiative Effects ◽  
Clear Sky ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study presents an approach for quantification of cloud-aerosol transition zone broadband longwave radiative effects at the top of the atmosphere (TOA) during daytime over the ocean, based on satellite observations and radiative transfer simulation. Specifically, we used several products from MODIS (Moderate Resolution Imaging Spectroradiometer) and CERES (Clouds and the Earth’s Radiant Energy System) sensors for identification and selection of CERES footprints with horizontally homogeneous transition zone and clear-sky conditions. For the selected transition zone footprints, radiative effect was calculated as the difference between the instantaneous CERES TOA upwelling broadband longwave radiance observations and corresponding clear-sky radiance simulations. The clear-sky radiances were simulated using the Santa Barbara DISORT Atmospheric Radiative Transfer model fed by the hourly ERA5 reanalysis (fifth generation ECMWF reanalysis) atmospheric and surface data. The CERES radiance observations corresponding to the clear-sky footprints detected were also used for validating the simulated clear-sky radiances. We tested this approach using the radiative measurements made by the MODIS and CERES instruments onboard Aqua platform over the south-eastern Atlantic Ocean during August 2010. For the studied period and domain, transition zone radiative effect (given in flux units) is on average equal to 8.0 ± 3.7 W m−2 (heating effect; median: 5.4 W m−2), although cases with radiative effects as large as 50 W m−2 were found.

scholarly journals Re-Examining the First Climate Models: Climate Sensitivity of a Modern Radiative–Convective Equilibrium Model

2019 ◽  
Vol 32 (23) ◽  
pp. 8111-8125 ◽  
Author(s):  
Lukas Kluft ◽  
Sally Dacie ◽  
Stefan A. Buehler ◽  
Hauke Schmidt ◽  
Bjorn Stevens
Keyword(s):  
Relative Humidity ◽  
Radiative Transfer ◽  
Temperature Profile ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Lapse Rate ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Humidity Profile ◽  
Clear Sky

Abstract We revisit clear-sky one-dimensional radiative–convective equilibrium (1D-RCE) and determine its equilibrium climate sensitivity to a CO2 doubling (ECS) and associated uncertainty. Our 1D-RCE model, named konrad, uses the Rapid Radiative Transfer Model for GCMs (RRTMG) to calculate radiative fluxes in the same way as in comprehensive climate models. The simulated radiative feedbacks are verified by a line-by-line radiative transfer model, with which we also investigate their spectral distribution. Changing the model configuration of konrad enables a clear separation between the water vapor and the lapse rate feedbacks, as well as the interaction between the two. We find that the radiative feedback and ECS are sensitive to the chosen relative humidity profile, resulting in an ECS range of 2.09–2.40 K. Using larger CO2 forcings we find that the radiative feedback changes up to 10% for surface temperatures of 291–299 K. Although the ECS is similar to previous studies, it arises from the compensation of a larger clear-sky forcing (4.7 W m−2) and more strongly negative feedbacks (−2.3 W m−2 K−1). The lapse rate feedback and the feedback from the interaction of lapse rate and humidity compensate each other, but the degree of compensation depends on the relative humidity profile. Additionally, the temperature profile is investigated in a warming climate. The temperature change at the convective top is half as large as at the surface, consistent with the proportionally higher anvil temperature hypothesis, as long as the humidity is consistently coupled to the temperature profile.

scholarly journals A clear-sky radiation closure study using a one-dimensional radiative transfer model and collocated satellite-surface-reanalysis data sets

2016 ◽  
Vol 121 (22) ◽  
pp. 13,698-13,714 ◽  
Author(s):  
Erica K. Dolinar ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Jonathan H. Jiang ◽  
Norman G. Loeb
Keyword(s):  
Radiative Transfer ◽  
Reanalysis Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Sets ◽  
One Dimensional ◽  
Clear Sky

scholarly journals Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask

2021 ◽  
Vol 13 (2) ◽  
pp. 222
Author(s):  
Xingming Liang ◽  
Quanhua Liu
Keyword(s):  
Radiative Transfer ◽  
High Efficiency ◽  
Infrared Imaging ◽  
Thermal Emission ◽  
Model Performance ◽  
Global Ocean ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Stability ◽  
Clear Sky

A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M). The objectives were to (1) enhance FCDN_CSM performance to include daytime analysis, and improve model stability, accuracy, and efficiency; and (2) further understand the model performance based on a combination of the statistics and physical interpretation. According to the analyses of the F-score, the prediction result showed ~96% and ~97% accuracy for day and night, respectively. The type Cloud was the most accurate, followed by Clear-Sky. The O-M mean biases are comparable to the ACSPO CSM for all bands, both day and night. The standard deviations (STD) were slightly degraded in long wave IRs (M14, M15, and M16), mainly due to contamination by a 3% misclassification of the type Cloud, which may require the model to be further fine-tuned to improve prediction accuracy in the future. However, the consistent O-M means and STDs persist throughout the prediction period, suggesting that FCDN_CSM version 2 is robust and does not have significant overfitting. Given its high F-scores, spatial and long-term stability for both day and night, high efficiency, and acceptable O-M means and STDs, FCDN_CSM version 2 is deemed to be ready for use in the FCDN_CRTM.

scholarly journals Aerosol trends as a potential driver of regional climate in the central United States: Evidence from observations

2017 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Loretta J. Mickley ◽  
Eric M. Leibensperger ◽  
Michael J. Iacono
Keyword(s):  
United States ◽  
Radiative Transfer ◽  
Southeastern United States ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (U.S.) has increased by 0.58–1.0 W m−2 a−1 over the 2000–2014 timeframe, simultaneously with reductions in U.S. aerosol optical depth (AOD) of 3.3–5.0 × 10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing U.S. aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000 2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear–sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 W m−2 a−1 at Goodwin Creek, MS, and +0.93 W m−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central U.S. The 1990–2015 trends in the NLDAS SWdn over the central U.S. are also of a similar magnitude as our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central U.S. are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the U.S., where improvements in air quality due to reductions in the aerosol burden could inadvertently increase vulnerability to drought.

scholarly journals kCARTA: a fast pseudo line-by-line radiative transfer algorithm with analytic Jacobians, fluxes, nonlocal thermodynamic equilibrium, and scattering for the infrared

2020 ◽  
Vol 13 (1) ◽  
pp. 323-339 ◽  
Author(s):  
Sergio DeSouza-Machado ◽  
L. Larrabee Strow ◽  
Howard Motteler ◽  
Scott Hannon
Keyword(s):  
Radiative Transfer ◽  
Thermodynamic Equilibrium ◽  
Local Thermodynamic Equilibrium ◽  
Singular Value ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Non Local ◽  
Value Decomposition ◽  
Current Database

Abstract. A fast pseudo-monochromatic radiative transfer package using a singular value decomposition (SVD) compressed atmospheric optical depth database has been developed, primarily for simulating radiances from hyperspectral sounding instruments (resolution ≥0.1 cm−1). The package has been tested extensively for clear-sky radiative transfer cases, using field campaign data and satellite instrument data. The current database uses HITRAN 2016 line parameters and is primed for use in the spectral region spanning 605 to 2830 cm−1. Optical depths for other spectral regions (15–605 and 2830–45 000 cm−1) can also be generated for use by kCARTA. The clear-sky radiative transfer model computes the background thermal radiation quickly and accurately using a layer-varying diffusivity angle at each spectral point; it takes less than 30 s (on a 2.8 GHz core using four threads) to complete a radiance calculation spanning the infrared. The code can also compute non-local thermodynamic equilibrium effects for the 4 µm CO2 region, as well as analytic temperature, gas and surface Jacobians. The package also includes flux and heating rate calculations and an interface to an infrared scattering model.

scholarly journals Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment

2020 ◽  
Vol 12 (22) ◽  
pp. 3825
Author(s):  
Xingming Liang ◽  
Quanhua Liu
Keyword(s):  
Radiative Transfer ◽  
Infrared Imaging ◽  
Thermal Emission ◽  
Model Simulation ◽  
Iterative Refinement ◽  
Sensor Data ◽  
Global Ocean ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model was trained and tested in the nighttime global ocean clear-sky domain, in which the VIIRS observation minus CRTM (O-M) biases have been well validated in recent years. The atmosphere profile from the European Centre for Medium-Range Weather Forecasts (ECMWF) and sea surface temperature (SST) from the Canadian Meteorology Centre (CMC) were used as FCDN_CRTM input, and the CRTM-simulated brightness temperatures (BTs) were defined as labels. Six dispersion days’ data from 2019 to 2020 were selected to train the FCDN_CRTM, and the clear-sky pixels were identified by an enhanced FCDN clear-sky mask (FCDN_CSM) model, which was demonstrated in Part 1. The trained model was then employed to predict CRTM BTs, which were further validated with the CRTM BTs and the VIIRS sensor data record (SDR) for both efficiency and accuracy. With iterative refinement of the model design and careful treatment of the input data, the agreement between the FCDN_CRTM and the CRTM was generally good, including the satellite zenith angle and column water vapor dependencies. The mean biases of the FCDN_CRTM minus CRTM (F-C) were typically ~0.01 K for all five bands, and the high accuracy persisted during the whole analysis period. Moreover, the standard deviations (STDs) were generally less than 0.1 K and were consistent for approximately half a year, before they significantly degraded. The validation with VIIRS SDR data revealed that both the predicted mean biases and the STD of the VIIRS observation minus FCDN_CRTM (V-F) were comparable with the VIIRS minus direct CRTM simulation (V-C). Meanwhile, both V-F and V-C exhibited consistent global geophysical and statistical distribution, as well as stable long-term performance. Furthermore, the FCDN_CRTM processing time was more than 40 times faster than CRTM simulation. The highly efficient, accurate, and stable performances indicate that the FCDN_CRTM is a potential solution for global and real-time monitoring of sensor observation minus model simulation, particularly for high-resolution sensors.

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