Linearized Discrete Ordinate Radiative Transfer Research Articles

Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals

Abstract. In this work, we apply a principal component analysis (PCA)-based approach combined with lookup tables (LUTs) of corrections to accelerate the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model used in the retrieval of ozone profiles from backscattered ultraviolet (UV) measurements by the Ozone Monitoring Instrument (OMI). The spectral binning scheme, which determines the accuracy and efficiency of the PCA-RT performance, is thoroughly optimized over the spectral range 265 to 360 nm with the assumption of a Rayleigh-scattering atmosphere above a Lambertian surface. The high level of accuracy (∼ 0.03 %) is achieved from fast-PCA calculations of full radiances. In this approach, computationally expensive full multiple scattering (MS) calculations are limited to a small set of PCA-derived optical states, while fast single scattering and two-stream MS calculations are performed, for every spectral point. The number of calls to the full MS model is only 51 in the application to OMI ozone profile retrievals with the fitting window of 270–330 nm where the RT model should be called at fine intervals (∼ 0.03 nm with ∼ 2000 wavelengths) to simulate OMI measurements (spectral resolution: 0.4–0.6 nm). LUT corrections are implemented to accelerate the online RT model due to the reduction of the number of streams (discrete ordinates) from 8 to 4, while improving the accuracy at the level attainable from simulations using a vector model with 12 streams and 72 layers. Overall, we speed up our OMI retrieval by a factor of 3.3 over the previous version, which has already been significantly sped up over line-by-line calculations due to various RT approximations. Improved treatments for RT approximation errors using LUT corrections improve spectral fitting (2 %–5 %) and hence retrieval errors, especially for tropospheric ozone by up to ∼ 10 %; the remaining errors due to the forward model errors are within 5 % in the troposphere and 3 % in the stratosphere.

A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

Abstract. Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements

Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single-scattering albedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) v2.7, which contains 1 459 200 unique mappings. From the simulations, 75 % were randomly selected for training and the remaining 25 % for validation. The overall error of estimated aerosol properties (1) for total aerosol optical depth (AOD) is -1.4±10.1 %, (2) for the single-scattering albedo is 0.1±3.6 %, and (3) for the asymmetry factor is -0.1±2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

Development and Application of HECORA Cloud Retrieval Algorithm Based On the O2-O2 477 nm Absorption Band

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI.

Interpreting the ultraviolet aerosol index observed with the OMI satellite instrument to understand absorption by organic aerosols: implications for atmospheric oxidation and direct radiative effects

Abstract. Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOS-Chem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (−0.32 to −0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from −0.57 to −0.09 over West Africa in January, from −0.32 to +0.0002 over South Asia in April, from −0.97 to −0.22 over southern Africa in July, and from −0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Ångström exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV–Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30 % over South America in September, up to 20 % over southern Africa in July, and up to 15 % over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03 W m−2 and all-sky surface DRE by −0.08 W m−2. Regional changes of up to +0.3 W m−2 at TOA and down to −1.5 W m−2 at the surface are found over major biomass burning regions.

Space-based retrieval of NO<sub>2</sub> over biomass burning regions: quantifying and reducing uncertainties

Abstract. The accuracy of space-based nitrogen dioxide (NO2) retrievals from solar backscatter radiances critically depends on a priori knowledge of the vertical profiles of NO2 and aerosol optical properties. This information is used to calculate an air mass factor (AMF), which accounts for atmospheric scattering and is used to convert the measured line-of-sight "slant" columns into vertical columns. In this study we investigate the impact of biomass burning emissions on the AMF in order to quantify NO2 retrieval errors in the Ozone Monitoring Instrument (OMI) products over these sources. Sensitivity analyses are conducted using the Linearized Discrete Ordinate Radiative Transfer (LIDORT) model. The NO2 and aerosol profiles are obtained from a 3-D chemistry-transport model (GEOS-Chem), which uses the Fire Locating and Monitoring of Burning Emissions (FLAMBE) daily biomass burning emission inventory. Aircraft in situ data collected during two field campaigns, the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) and the Dust and Biomass-burning Experiment (DABEX), are used to evaluate the modeled aerosol optical properties and NO2 profiles over Canadian boreal fires and West African savanna fires, respectively. Over both domains, the effect of biomass burning emissions on the AMF through the modified NO2 shape factor can be as high as −60%. A sensitivity analysis also revealed that the effect of aerosol and shape factor perturbations on the AMF is very sensitive to surface reflectance and clouds. As an illustration, the aerosol correction can range from −20 to +100% for different surface reflectances, while the shape factor correction varies from −70 to −20%. Although previous studies have shown that in clear-sky conditions the effect of aerosols on the AMF was in part implicitly accounted for by the modified cloud parameters, here it is suggested that when clouds are present above a surface layer of scattering aerosols, an explicit aerosol correction would be beneficial to the NO2 retrieval. Finally, a new method that uses slant column information to correct for shape-factor-related AMF error over NOx emission sources is proposed, with possible application to near-real-time OMI retrievals.

Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe

Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov–Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40% in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 21% precision for direct comparisons).

The GODFIT algorithm: a direct fitting approach to improve the accuracy of total ozone measurements from GOME

We present the total ozone retrieval algorithm GODFIT (GOME Direct-FITting). Applied to nadir backscattered measurements from the Global Ozone Monitoring Experiment (GOME), it is based on a direct-fitting approach by which spectral radiances simulated using the radiative transfer model LIDORT v3.3 (Linearized Discrete Ordinate Radiative Transfer) are adjusted to measurements in the 325–335 nm interval. Total O3 columns retrieved from GOME spectra have been compared not only to columns retrieved from Ozone Monitoring Instrument (OMI) measurements using the TOMS v8.5 algorithm, but also to correlative ground-based measurements from the GAW/NDACC networks (Global Atmosphere Watch/Network for the Detection of Atmospheric Composition Change). We show that GODFIT produces a significant reduction of the GOME ground-based differences and some of the associated dependencies, compared with the GOME Data Processor (GDP) 4.1 product. Version 5 of GDP, based on the GODFIT algorithm, will be released in spring 2010.

Ten years of GOME/ERS‐2 total ozone data—The new GOME data processor (GDP) version 4: 1. Algorithm description

The Global Ozone Monitoring Instrument (GOME) was launched on European Space Agency's ERS‐2 platform in April 1995. The GOME data processor (GDP) operational retrieval algorithm has generated total ozone columns since July 1995. In 2004 the GDP system was given a major upgrade to version 4.0, a new validation was performed, and the 10‐year GOME level 1 data record was reprocessed. In two papers, we describe the GDP 4.0 retrieval algorithm and present an error budget and sensitivity analysis (paper 1) and validation of the GDP total ozone product and the overall accuracy of the entire GOME ozone record (paper 2). GDP 4.0 uses an optimized differential optical absorption spectroscopy (DOAS) algorithm, with air mass factor (AMF) conversions calculated using the radiative transfer code linearized discrete ordinate radiative transfer (LIDORT). AMF computation is based on the TOMS version 8 ozone profile climatology, classified by total column, and AMFs are adjusted iteratively to reflect the DOAS slant column result. GDP 4.0 has improved wavelength calibration and reference spectra and includes a new molecular Ring correction to deal with distortion of ozone absorption features due to inelastic rotational Raman scattering effects. Preprocessing for cloud parameter estimation in GDP 4.0 is done using two new cloud correction algorithms: OCRA and ROCINN. For clear and cloudy scenes the precision of the ozone column product is better than 2.4 and 3.3%, respectively, for solar zenith angles up to 80°. Comparisons with ground‐based data are generally at the 1–1.5% level or better for all regions outside the poles.

An improved retrieval of tropospheric nitrogen dioxide from GOME

We present a retrieval of tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument that improves in several ways over previous retrievals, especially in the accounting of Rayleigh and cloud scattering. Slant columns, which are directly fitted without low‐pass filtering or spectral smoothing, are corrected for an artificial offset likely induced by spectral structure on the diffuser plate of the GOME instrument. The stratospheric column is determined from NO2 columns over the remote Pacific Ocean to minimize contamination from tropospheric NO2. The air mass factor (AMF) used to convert slant columns to vertical columns is calculated from the integral of the relative vertical NO2 distribution from a global 3‐D model of tropospheric chemistry driven by assimilated meteorological data (Global Earth Observing System (GEOS)‐CHEM), weighted by altitude‐dependent scattering weights computed with a radiative transfer model (Linearized Discrete Ordinate Radiative Transfer), using local surface albedos determined from GOME observations at NO2 wavelengths. The AMF calculation accounts for cloud scattering using cloud fraction, cloud top pressure, and cloud optical thickness from a cloud retrieval algorithm (GOME Cloud Retrieval Algorithm). Over continental regions with high surface emissions, clouds decrease the AMF by 20–30% relative to clear sky. GOME is almost twice as sensitive to tropospheric NO2 columns over ocean than over land. Comparison of the retrieved tropospheric NO2 columns for July 1996 with GEOS‐CHEM values tests both the retrieval and the nitrogen oxide radical (NOx) emissions inventories used in GEOS‐CHEM. Retrieved tropospheric NO2 columns over the United States, where NOx emissions are particularly well known, are within 18% of GEOS‐CHEM columns and are strongly spatially correlated (r = 0.78, n = 288, p < 0.005). Retrieved columns show more NO2 than GEOS‐CHEM columns over the Transvaal region of South Africa and industrial regions of the northeast United States and Europe. They are lower over Houston, India, eastern Asia, and the biomass burning region of central Africa, possibly because of biases from absorbing aerosols.

A linearized discrete ordinate radiative transfer model for atmospheric remote-sensing retrieval

The radiative transfer forward model simulation of intensities and associated parameter derivatives (weighting functions) is a vital part of the retrieval of earth atmospheric constituent information from measurements of backscattered light. The discrete ordinate method is the most commonly used approach for the determination of solutions to the radiative transfer equation. In this paper, we carry out an internal perturbation analysis of the complete discrete ordinate solution in a plane parallel multi-layered multiple-scattering atmosphere. Perturbations in layer atmospheric quantities will translate into small changes in the single-scatter albedos and optical depth values. In addition, we consider perturbations in layer thermal emission source terms and in the surface albedo. It is shown that the solution of the boundary value problem for the perturbed intensity field leads in a natural way to the weighting function associated with the parameter causing the perturbation. We have developed a numerical model LIDORT (linearized discrete ordinate radiative transfer) for the simultaneous generation of backscatter intensities and weighting function output at arbitrary elevation angles, for a user-defined set of atmospheric variations. Results for a 5-layer test atmosphere with two scatterers and thermal emission terms are shown. Intensities are validated against benchmark discrete ordinate results, while weighting functions are checked for consistency against finite difference results based on external perturbations. A second example is presented for a 60-layer terrestrial atmosphere with molecular and aerosol scattering and ozone trace gas absorption in the UV spectral range; weighting functions are shown to correspond closely with results derived from another radiative transfer model.