Fast Radiative Transfer Model Research Articles

A Fast Computing Model for the Oxygen A-Band High-Spectral-Resolution Absorption Spectra Based on Artificial Neural Networks

A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This paper established a fast computing model (ANN-HASFCM) for high-spectral-resolution absorption spectra by using artificial neural networks and PCA (principal component analysis) spectral reconstruction technology. This paper chose the line-by-line radiative transfer model (LBLRTM) as the comparative model and simulated training spectral data in the oxygen A-band (12,900–13,200 cm−1). Subsequently, ANN-HASFCM was applied to the retrieval of the atmospheric density profile with the data of the Global Ozone Monitoring by an Occultation of Stars (GOMOS) instrument. The results show that the relative error between the optical depth spectra calculated by LBLRTM and ANN-HASFCM is within 0.03–0.65%. In the process of using the global-fitting algorithm to invert GOMOS-measured atmospheric samples, the inversion results using Fast-LBLRTM and ANN-HASFCM as forward models are consistent, and the retrieval speed of ANN-HASFCM is more than 200 times faster than that of Fast-LBLRTM (reduced from 226.7 s to 0.834 s). The analysis shows the brilliant application prospects of ANN-HASFCM in limb remote sensing.

Global evaluation of fast radiative transfer model coefficients for early meteorological satellite sensors

Abstract. RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independent dataset of 25 000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historical satellite instruments: the InfraRed Interferometer Spectrometer D (IRIS-D), Satellite Infrared Spectrometer B (SIRS-B), Medium Resolution Infrared Radiometer (MRIR) and High Resolution Infrared Radiometer (HRIR) for the infrared part of the spectrum, and the Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave – Humidity (SSM/T-2), Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager/Sounder (SSMI/S) for the microwave. Simulated channel brightness temperatures show similar statistics for both the independent and the 83-profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between the RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0.4 ± 0.2 K for the infrared and 0.04 ± 0.13 K for the microwave. Examination of the latitudinal dependence of the bias reveals different patterns of variability for similar channels on different instruments, such as the channel centred at 679 cm−1 on both IRIS-D and SIRS-B, showing the importance of the specification of the instrumental spectral response functions (ISRFs). Maximum differences of up to several kelvin are associated with extremely non-typical profiles, such as those in polar or very hot regions.

Unifying radiative transfer models in computer graphics and remote sensing, Part II: A differentiable, polarimetric forward model and validation

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 2 of this work, we demonstrate that Monte Carlo forward models in computer graphics are capable of sufficient accuracy for remote sensing by extending Mitsuba 3, a forward and inverse modeling framework recently developed in the computer graphics community, to simulate simple atmosphere-ocean systems and show that our framework is capable of achieving error on par with codes currently used by the remote sensing community on benchmark results.

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.

Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.

Fast Calculation on Atmospheric Rayleigh Scattering Extinction Optical Depth for Day/Night Band Radiative Transfer Model With Periodic Lunar Illumination

AbstractRayleigh scattering effect inspired by tiny atmospheric molecules generates significant extinction at relatively short wavelength, such as Day/Night Band (DNB) covering visible (VIS, 0.4–0.76 μm) and near‐infrared (NIR, 0.76–1.0 μm) wavelengths. This salient extinction effect coupled with periodic changes in lunar illumination and wide DNB will further lead to increased complexity in nocturnal fast radiative transfer model (RTM) calculation of DNB. For these reasons, this study is aimed at developing a precise and robust fast calculation method on Rayleigh scattering extinction optical depth (RayOD) for pertinent fast RTM calculation of DNB. Firstly, we coupled the lunar spectral irradiance model and the parameterized Rayleigh scattering extinction calculation module into a RTM for forward simulations. Moreover, we find that the errors in DNB‐averaged RayOD calculated from the classical fast algorithm from RTTOV (Radiative Transfer for TOVS) of training the DNB instrument coefficients (about 20% of relative error in reflectance) and the parameterization method using the central wavelength of DNB (about 5%) are too large to be tolerable in fast RTM. Finally, to address this issue, we skillfully conceptualize a new equivalent optimal central wavelength algorithm to retrieve a dynamically wavelength in DNB using observation scattering and lunar phase angles for rapidly calculating DNB‐averaged RayOD. The relative errors in simulated reflectance from coupled RTM aforementioned are subtle nuances, which are about 10−5∼10−6 order of magnitude. Besides, this robust algorithm can also correct the relative error of about 1% at dark surface caused by periodic changes in lunar illumination.

Multi-Layer Cloud Motion Vector Forecasting for Solar Energy Applications

Real-time forecasting of solar radiation posses several benefits and has huge potential for industrial applications. However, the intermittent nature of solar radiation makes it difficult to forecast accurately. Cloud cover is one of the major influencing factors of solar radiation. Thus, forecasting cloud motion effectively can help to improve the accuracy of short-term solar radiation forecasts. In this study, a novel Multi-Layer Cloud Motion Vector (referred as 3D-CMV) forecasting technique was introduced, which combined with the fast radiative transfer model (FRTM) produces forecasts up to 3 h ahead at 15 min intervals over 5km × 5km grids across Europe and North Africa. The cloud microphysics obtained from the Support to Nowcasting and Very Short Range Forecasting (SAFNWC) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) was used as input to the forecasting system. The results obtained improvements in forecasts as compared to the conventional cloud motion vector techniques, across all seasons and sky conditions. Comparisons against ground-based measurements from the Baseline Surface Radiation Network (BSRN) revealed an overall maximum percentage difference of less than 12%, bias under -20 Wm−2 and a root mean square error (RMSE) under 80 Wm−2. Performance evaluations of Multi-Layer Cloud Motion Vector has been performed against several state-of-the-art techniques and presented in this study. Short-term solar energy forecasting has an established market and rising demand. Accurate forecasts from Multi-Layer CMV hence pose a high potential for real world applications.

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

Optimized Wavelength Sampling for Thermal Radiative Transfer in Numerical Weather Prediction Models

In the thermal spectral range, there are millions of individual absorption lines of water vapor, CO2, and other trace gases. Radiative transfer calculations of wavelength-integrated quantities, such as irradiance and heating rate, are computationally expensive, requiring a high spectral resolution for accurate numerical weather prediction and climate modeling. This paper introduces a method that could highly reduce the cost of integration in the thermal spectrum by employing an optimized wavelength sampling method. Absorption optical thicknesses for various trace gases were calculated from the HITRAN 2012 spectroscopic dataset using the ARTS line-by-line model as input to a fast Schwarzschild radiative transfer model. Using a simulated annealing algorithm, different optimized sets of wavelengths and corresponding weights were identified, which allowed for accurate integrated quantities to be computed as a weighted sum, reducing the computational time by several orders of magnitude. For each set of wavelengths, a lookup table, including the corresponding weights and absorption cross-sections, is created and can be applied to any atmospheric setups for which it was trained. We applied the lookup table to calculate irradiances and heating rates for a large set of atmospheric profiles from the ECMWF 91-level short-range forecast. Ten wavelength nodes are sufficient to obtain irradiances within an average root mean square error (RMSE) of upward and downward radiation at any height below 1 Wm−2 while 100 wavelengths allowed for an RSME of below 0.05 Wm−2. The applicability of this method was confirmed for irradiances and heating rates in clear conditions and for an exemplary cloud at 3.2 km height. Representative spectral gridpoints for integrated quantities in the thermal spectrum (REPINT) is available as absorption parameterization in the libRadtran radiative transfer package, where it can be used as an efficient molecular absorption parameterization for a variety of radiative transfer solvers.

Finding point method and re-optimized method for the brightness temperature simulation from Fengyun 4A AGRI in infrared channels.

The correlated k-distribution (CKD) is a fast radiative transfer model and is often used in atmospheric absorption simulation. In the paper, we apply two automatic CKD methods to satellite brightness temperature simulations from the Fengyun 4A Advanced Geostationary Radiation Imager (AGRI) in infrared channels, namely, the finding point method (FPM) and the re-optimized method (ROM). In the calculation, we used Radiative Transfer for the Television Observation Satellite Operational Vertical Sounder (RTTOV) as the comparison, and we use line-by-line (LBL) integration as the reference. Compared with LBL in the brightness temperature simulation of real profiles, the errors of FPM in 7.1µm and 13.5µm channels are 0.22K, -0.13K for mean error and 0.3128K, 0.2184K for root mean square error (RMSE), respectively, which are larger than that of RTTOV, with 0.16K, 0.02K, 0.2144K, and 0.1226K, respectively. In the other channels, the results show that of ROM has the highest accuracy and RTTOV has the lowest accuracy. In general, FPM and ROM can achieve very good accuracy in satellite infrared remote sensing.

Assessment of Retrieved GMI Emissivity over Land, Snow, and Sea Ice in the GEOS System

Abstract Directly assimilating microwave radiances over land, snow, and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System–Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow, and ice could provide a needed source of data for numerical weather prediction particularly in the planetary boundary layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. published a 5-yr climatological database for retrieved microwave emissivity from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) aboard the GPM mission. In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called stand-alone mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al. The simulated GMI observations using Munchak et al. agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

Computation of longwave radiative flux and vertical heating rate with 4A-Flux v1.0 as an integral part of the radiative transfer code 4A/OP v1.5

Abstract. Based on advanced spectroscopic databases, line-by-line and layer-by-layer radiative transfer codes numerically solve the radiative transfer equation with very high accuracy. Taking advantage of its pre-calculated optical depth lookup table, the fast and accurate radiative transfer model Automatized Atmospheric Absorption Atlas OPerational (4A/OP) calculates the transmission and radiance spectra for a user-defined layered atmospheric model. Here, we present a module called 4A-Flux, which is developed and implemented into 4A/OP in order to include the calculation of the clear-sky longwave radiative flux profiles and heating rate profiles at a very high spectral resolution. Calculations are performed under the assumption of local thermodynamic equilibrium, a plane-parallel atmosphere, and specular reflection on the surface. The computation takes advantage of pre-tabulated exponential integral functions that are used instead of a classic angular quadrature. Furthermore, the sub-layer variation of the Planck function is implemented to better represent the emission of layers with a high optical depth. Thanks to the implementation of 4A-Flux, 4A/OP models have participated in the Radiative Forcing Model Intercomparison Project (RFMIP-IRF) along with other state-of-the-art radiative transfer models. 4A/OP hemispheric flux profiles are compared to other models over the 1800 representative atmospheric situations of RFMIP, yielding an outgoing longwave radiation (OLR) mean difference between 4A/OP and other models of −0.148 W m−2 and a standard deviation of 0.218 W m−2, showing a good agreement between 4A/OP and other models. 4A/OP is applied to the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database to analyze the response of the OLR and vertical heating rate to several perturbations of temperature or gas concentration. This work shows that 4A/OP with 4A-Flux module can successfully be used to simulate accurate flux and heating rate profiles and provide useful sensitivity studies including sensitivities to minor trace gases such as HFC134a, HCFC22, and CFC113. We also highlight the interest for the modeling community to extend intercomparison between models to comparisons between spectroscopic databases and modeling to improve the confidence in model simulations.

An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method

Abstract. We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high-quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. For 15 min means of DSSI, results from our preliminary implementation of Heliosat-V and ground-based measurements show a bias of 20 W m−2, a root-mean-square difference of 93 W m−2, and a correlation coefficient of 0.948. The statistics, except for the bias, are similar to operational and corrected satellite-based data products HelioClim3 version 5 and the CAMS Radiation Service.

SSolar-GOA v1.0: a simple, fast, and accurate Spectral SOLAR radiative transfer model for clear skies

Abstract. The aim of this work is to describe the features of and to validate a simple, fast, accurate, and physically based spectral radiative transfer model in the solar wavelength range under clear skies. The model, named SSolar-GOA (the first “S” stands for “spectral”), was developed to evaluate the instantaneous values of spectral solar irradiances at ground level or at a given altitude of the atmosphere. The model requirements are designed based on the simplicity of the analytical expressions for the transmittance functions in order to be easily replicated and applied by a wide community of users for many different applications (atmospheric and environmental research studies, satellite remote sensing, solar energy, agronomy and forestry, ecology, and others). Although spectral, the model runs quickly and has sufficient accuracy for the evaluation of solar irradiances with a spectral resolution of 1–10 nm. The model assumes a single mixed molecule–aerosol scattering layer where the original Ambartsumian method of “adding layers” in a one-dimensional medium is applied, obtaining a parameterized expression for the total transmittance of scattering. Absorption by the different atmospheric gases follows “band model” parameterized expressions. The input parameters must be realistic and easily available since the spectral aerosol optical depth (AOD) is the main driver of the model. The validation of the SSolar-GOA model has been carried out through comparison with simulated irradiance data from the libRadtran package and with direct and global spectra measured by spectroradiometers. Thousands of spectra under clear skies have been compared for different atmospheric conditions and solar zenith angles (SZA). The SSolar-GOA is validated by a quantitative comparison with libRadtran, showing that it underestimates direct normal, global, and diffuse spectral components with relative differences of +1 % (RMSE % = 4.6–8), +3 % (RMSE % = 5.3–8), and 8 % (RMSE % = 9.3–9.6), respectively, when the SZA varies from 6 to 60∘. Compared with the measured irradiance data of the LI-1800 and ASD spectroradiometers, the relative differences of direct normal and global components are within the overall experimental error, about ±2 %–12 % (RMSE % = 5–8.3), with underestimated or overestimated values. The diffuse component presents the highest degree of relative difference that can reach ±20 %–30 % and RMSE of 25 %–50 %. The relative differences depend strongly on the spectral solar region analysed and the SZA, but the high values of RMSE are due to the artifice generated by the different spectral resolution of the absorption coefficients of both models. Model approach errors combined with calibration instrument errors may explain the observed differences. The SSolar-GOA v1.0 is implemented in Python and open-source licensing.

A deep learning approach to fast radiative transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Assimilation of satellite data in numerical weather prediction. Part II: Recent years

AbstractDevelopments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote‐sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud‐affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP‐based atmospheric reanalysis systems is also described.

The thermal infrared optical depth of mineral dust retrieved from integrated CALIOP and IIR observations

Recent studies have suggested that global climate models tend to underestimate dust particle size in particular, the very coarse mode, leading to an underestimated direct radiative effect (DRE) of dust in the longwave (LW) thermal infrared (TIR) region. However, the magnitude of LW DRE remains highly uncertain, because of limited observations of dust optical depth at the TIR (DAODTIR). This study presents a simple approach to retrieve the DAODTIR over the oceans during nighttime through synergistic use of observations from the Infrared Imaging Radiometer (IIR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), both onboard of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. For each cloud-free dust-laden profile identified by the IIR-CALIOP observation, a Lookup-Table (LUT) of the 10.6 μm IIR band brightness temperatures (BT) difference (dBT) under different DAODTIR with respect to their dust-free BTs is constructed based on the CALIOP retrieved dust vertical profile and a priori dust scattering properties using a fast radiative transfer model. Then the DAODTIR is retrieved by projecting the IIR-observed dBT on the LUT. Sensitivity studies show that the DAODTIR retrieval at 10.6 μm is more susceptible to the dust particle size distribution (PSD) assumption than dust refractive indices. To estimate the uncertainty caused by PSD assumption, two DAODTIR retrieval products, one based on the dust PSD from the AERONET at Cape Verde and the other on an in situ measured PSD from the recent Fennec campaign, are provided. The retrieval uncertainty is mainly contributed by the BT difference between the observation and simulation using auxiliary atmospheric data. The climatology of the retrieval from 2013 to 2019 shows reasonable spatiotemporal variations of DAODTIR with the global-averaged value of 0.008 and 0.013 based on different pre-assumed dust PSDs. Climatological results agree reasonably well with two independent DAODTIR retrieval products based on the Infrared Atmospheric Sounding Interferometer (IASI) over the active dust transport regions, such as North and Tropical Atlantic (r = 0.9 and r = 0.8) and Indian Ocean (r = 0.8). The seasonal and interannual variation is also well-compared (r = 0.76) with AERONET coarse-mode AOD at 97 selected sites. The synergic CALIOP observation allows the retrieved DAODTIR to directly compare with the extrapolated DAODTIR from DAOD in the visible (i.e., 532 nm), which helps evaluate the observational constraints on DAODTIR. Our study clearly reveals that the retrieved DAODTIR from IIR is much less susceptible to various uncertainties than converted results from CALIOP. It also offers a unique prospect of collocated active lidar and passive IR observations for retrieving dust DAODTIR.

Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES‐13/16 observations

AbstractSynthetic infrared GOES‐13 observations are generated from a high‐resolution (4 km) Weather Research and Forecasting (WRF) model run over Argentina for a meteorological event of deep moist convection using two different radiative transfer models. The fast operational Community Radiative Transfer Model (CRTM) and the physics‐based research model Atmospheric Radiative Transfer Simulator (ARTS) are compared with the available observations. CRTM shows good results at a low computational cost, and is a good candidate for operational use in the region. CRTM and ARTS synthetic satellite images show differences due to the treatment of the bulk scattering properties of the frozen hydrometeor species, especially around the cloud shield. With this WRF+CRTM configuration, a long‐term evaluation is conducted over 12 hr forecasts from one month of data in a region of the South American autumn with four different initializations at 0000, 0600, 1200 and 1800 UTC, as operational at the National Meteorological Service of Argentina. The simulated and observed GOES‐16 Advanced Baseline Imager (ABI) brightness temperatures (BTs) in the 6.9 m channel show good agreement with an averaged bias of K (RMSE 21.7 K), whereas the performances in the IR window channels show slightly larger averaged BT differences of 3.5 K (RMSE 15.2 K). BT differences between the 6.9 and 10.3 m channels indicate that the WRF+CRTM simulations underestimate low to mid‐level clouds and to a lesser extent high‐level clouds. In the BT (10.3 m) range between approximately 215 and 230 K, there is an overestimation of clouds with BT differences above 0 K between 6.9 and 10.3 m. This could be due to the misrepresentation of upper‐level clouds.

Fast observation simulation method based on XGBoost for visible bands over the ocean surface under clear-sky conditions

ABSTRACT The fast radiative transfer model has a wide range of applications in remote sensing, such as satellite L2 product retrieval and satellite L1 data quality monitoring and assessment. The traditional radiative transfer model, particularly reflective solar bands, has the disadvantage of being time-consuming. To improve the calculation efficiency and realize fast observation simulation over the global ocean under clear-sky conditions, machine learning technology is applied. A fast radiative transfer simulation method based on the extreme gradient boosting (XGBoost) algorithm is proposed. The polynomial regression and XGBoost regression models in the machine learning field were used in the fast simulation process. By comparing and analysing the experimental results of the two regression models, it was revealed that the prediction results of the XGBoost algorithm were better than those of the polynomial regression model. The shorter the wavelength, the better the prediction performance, with a small error and a larger determination coefficient. The blue bands demonstrated the best results, with an R2 (R-Squire) value of approximately 0.99. The deviation analysis between the predicted and simulated values demonstrated that there was no obvious functional dependence between the prediction error and the influencing factors.

A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using Shannon Entropy Method

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables’ (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS’s spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast.