Atmospheric Radiative Transfer Simulator Research Articles

PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations

Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

The Microwave Temperature and Humidity Profiler: Description and Preliminary Results.

This manuscript presents the Microwave Temperature and Humidity Profiler (MTHP), a dual-band spectroradiometer designed for measuring multi-incidence angle temperature and humidity atmospheric profiles from an aircraft platform. The MTHP bands are at 60 GHz for measuring the oxygen complex lines, therefore at this band, MTHP has a hyperspectral radiometer able to provide 2048 channels over an 8 GHz bandwidth, and 183 GHz for measuring water vapor, which only uses four channels since this absorption band's spectral richness is simpler. The MTHP builds upon the Microwave Temperature Profiler (MTP) with the inclusion of the hyperspectral radiometer. The instrument's design, components, and calibration methods are discussed in detail, with a focus on the three-point calibration scheme involving internal calibration loads and static air temperature readings. Preliminary results from the Technological Innovation into Iodine and GV aircraft Environmental Research (TI3GER) campaign are presented, showcasing the instrument's performance during flights across diverse geographical regions. The manuscript presents successful antenna temperature measurements at 60 GHz and 183 GHz. The hyperspectral measurements are compared with a simulated antenna temperature using the Atmospheric Radiative Transfer Simulator (ARTS) showing an agreement better than R2 > 0.88 for three of the flights analyzed. Additionally, the manuscript draws attention to potential Radio Frequency Interference (RFI) effects observed during a specific flight, underscoring the instrument's sensitivity to external interference. This is the first-ever airborne demonstration of a broadband and hyperspectral multi-incidence angle 60 GHz measurement. Future work on the MTHP could result in an improved spatial resolution of the atmospheric temperature vertical profile and, hence, help in estimating the Planetary Boundary Layer (PBL) with better accuracy. The MTHP and its hyperspectral multi-incidence angle at 60 GHz have the potential to be a valuable tool for investigating the PBL's role in atmospheric dynamics, offering insights into its impact on Earth's energy, water, and carbon cycles.

Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600–1650 cm−1 Region

The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of <0.1 K. The comparison results for Jacobians showed that the Jacobians calculated by ARTS and LBLRTM were close for temperature (can be used for Numerical Weather Prediction application) and O3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community.

A Novel Ice Cloud Retrieval Algorithm for Submillimeter Wave Radiometers: Simulations and Application to an Airborne Experiment

A retrieval methodology based on the Bayesian neural network (BNN) is presented that inverts the ice water path (IWP), mean mass-weighted diameter (D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</sub> ), and cloud height of ice clouds from sub-millimeter radiometer observations. The training dataset was created using collecting cloud profiles from the DARDAR (raDAR/liDAR) database and running simulations by the Atmospheric Radiative Transfer Simulator (ARTS) model. Since the effective radius (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ) is the size descriptor of ice particles in the DARDAR database, a look-up table of ice water content (IWC), D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</sub> , and r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> was constructed to convert r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> profiles into D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</sub> profiles. In addition, random noises corresponding to the measurement uncertainties of the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 experiment were added to the simulated brightness temperatures before training the BNN. The proposed retrieval method was first applied to the simulated testing database, and then to the observations of CoSSIR. Moreover, the retrieved IWP and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</sub> were compared to the retrievals of the Bayesian Monte Carlo Integration (BMCI) method. The retrieved cloud height was assessed by cloud height extracted from the reflectivity data of the Cloud Radar System (CPS) flow on the same aircraft with CoSSIR. The comparison showed that the correlation coefficients of the retrieved IWP and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">me</sub> from the two methods are above 0.8, and the retrieved cloud height also showed good agreement with that extracted from the CPS.

Sensitivity Analysis of Microwave Spectrometer for Atmospheric Temperature and Humidity Sounding on the New Generation Fengyun Satellite

The vertical profiles, spatiotemporal distribution, and trends of temperature and humidity in the middle atmosphere are significant for numerical weather prediction and the analysis of global climate change. To better design and apply spaceborne microwave spectrometer on the new generation Fengyun satellite, the sensitivities of the spectrometers at 22.235, 50–60, 118.75, 183.31, 325.153, 380.33, 424.77, 448.0, and 556.94 GHz are analyzed, respectively. Qpack2 included in the atmospheric radiative transfer simulator is used. The results show that the retrieval accuracy of the 50.0–60.0 GHz spectrometer is obviously better than other spectrometers, and the effective detection height (EDH) is the highest, reaching 0.233 hPa. The humidity profiles bigger than 500 hPa are well detected by the seven channels of the Humidity and Temperature Profiler or 22.0–32.0 GHz spectrometer. The retrieval accuracy is better than 6%, which greatly improves the retrieval performance of humidity profiles in the lower troposphere over the sea. In addition, this frequency band is not affected by errors in sea surface temperature or wind speed. The humidity profiles over the sea and land in the middle atmosphere under clear-sky and cloudy-sky conditions are well detected by the 183.31 and 556.94 GHz spectrometer. The EDH can be increased to 1.14 hPa by the 556.94 GHz spectrometer. In the design and application of future spaceborne microwave spectrometers for temperature and humidity profiles detection, the spectrometers mentioned previously are good candidates, and the parameter configuration of these spectrometers can be used as a reference.

CO2 Ocean Bistability on Terrestrial Exoplanets.

Cycling of carbon dioxide between the atmosphere and interior of rocky planets can stabilize global climate and enable planetary surface temperatures above freezing over geologic time. However, variations in global carbon budget and unstable feedback cycles between planetary sub-systems may destabilize the climate of rocky exoplanets toward regimes unknown in the Solar System. Here, we perform clear-sky atmospheric radiative transfer and surface weathering simulations to probe the stability of climate equilibria for rocky, ocean-bearing exoplanets at instellations relevant for planetary systems in the outer regions of the circumstellar habitable zone. Our simulations suggest that planets orbiting G- and F-type stars (but not M-type stars) may display bistability between an Earth-like climate state with efficient carbon sequestration and an alternative stable climate equilibrium where CO2 condenses at the surface and forms a blanket of either clathrate hydrate or liquid CO2. At increasing instellation and with ineffective weathering, the latter state oscillates between cool, surface CO2-condensing and hot, non-condensing climates. CO2 bistable climates may emerge early in planetary history and remain stable for billions of years. The carbon dioxide-condensing climates follow an opposite trend in pCO2 versus instellation compared to the weathering-stabilized planet population, suggesting the possibility of observational discrimination between these distinct climate categories.

Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data

The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth’s system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (r = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (r = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (r = 0.7607, RMSE = 0.8239), which proves the feasibility of this method.

Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals

The accurate simulation of microwave observations of clouds and precipitation are computationally challenging. A common simplification is the assumption of totally random orientation (TRO); however, studies have revealed that TRO occurs relatively infrequently in reality. A more appropriate assumption is that of azimuthally random orientation (ARO), but so far it has been a computationally expensive task. Recently a fast approximate approach was introduced that incorporates hydrometeor orientation into the assimilation of data from microwave conically scanning instruments. The approach scales the extinction in vertical (V) and horizontal (H) polarised channels to approximate ARO. In this study, the application of the approach was extended to a more basic radiative transfer perspective using the Atmospheric Radiative Transfer Simulator and the high-frequency channels of the Global Precipitation Measurement Microwave Imager (GMI). The comparison of forward simulations and GMI observations showed that with a random selection of scaling factors from a uniform distribution between 1 and 1.4–1.5, it is possible to mimic the full distribution of observed polarisation differences at 166 GHz over land and water. The applicability of this model at 660 GHz was also successfully demonstrated by means of existing airborne data. As a complement, a statistical model for polarised snow emissivity between 160 and 190 GHz was also developed. Combining the two models made it possible to reproduce the polarisation signals that were observed over all surface types, including snow and sea ice. Further, we also investigated the impact of orientation on the ice water path (IWP) retrievals. It has been shown that ignoring hydrometeor orientation has a significant negative impact (∼20% in the tropics) on retrieval accuracy. The retrieval with GMI observations produced highly realistic IWP distributions. A significant highlight was the retrieval over snow covered regions, which have been neglected in previous retrieval studies. These results provide increased confidence in the performance of passive microwave observation simulations and mark an essential step towards developing the retrievals of ice hydrometeor properties based on data from GMI, the Ice Cloud Imager (ICI) and other conically scanning instruments.

Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018

Abstract. A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r=0.95) and snow water equivalent rate (r=0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (−31 % and −8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r=0.74 and r=0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r=0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies

With the new generation of microwave instruments and, especially, the Ice Cloud Imager covering submillimeter frequencies, it is necessary to evaluate the performance of the operational Radiative Transfer model for TOVS (RTTOV). Thus, an intercomparison study has been conducted between RTTOV and the reference model ARTS (Atmospheric Radiative Transfer Simulator), with an emphasis on cloudy and precipitating conditions, covering frequencies between ≈53.6 and ≈664.0 GHz. Overall a rather good agreement is found between the δ-Eddington solution embedded in the scattering solver of RTTOV, RTTOV-SCATT, and the discrete ordinate solution embedded in ARTS. Under clear-sky conditions, given a consistent spectroscopy, the agreement is within 0.4 K over all frequencies considered. When idealized, homogeneous cloudy conditions are employed, the agreement is mostly ±2 K; this range is exceeded only at high scattering conditions. However, the following weaknesses are identified: the δ-Eddington solution fails to produce deep enough brightness temperature depressions at increasingly high scattering conditions and is not sufficient to capture the phase function structures at size parameters above 2–3; conditions typically found at around 664.0 GHz. When realistic hydrometeor profiles are employed, δ-Eddington leads to a root mean squared error of 1 K, with individual errors between 0 and 4 K. Infrequently, and in localized areas, larger discrepancies are identified, exceeding 10 K. However, these inaccuracies stemming from the simplified physics of RTTOV-SCATT were found at least an order of magnitude smaller than the cloud and precipitation representation errors assigned in data assimilation. Thus, we support the use of RTTOV-SCATT at submillimeter frequencies for operational purposes.

Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems

Abstract. Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally generated, mid-latitude cloud systems in order to validate the retrieval and assess its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 µm is found for one of the flights for the large plate aggregate and the six-bullet rosette shapes. The variational retrieval fits the observations well, although small systematic deviations are observed for some of the sub-millimeter channels pointing towards issues with the sensor calibration or the modeling of gas absorption. For one of the flights the quality of the fit to the observations exhibits a weak dependency on the assumed ice particle shape, indicating that the employed combination of observations may provide limited information on the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations may improve remote-sensing measurements of ice hydrometeors and thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increase confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.

Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0

Abstract. Satellite observations of radiation in the microwave and sub-millimetre spectral regions (broadly from 1 to 1000 GHz) can have strong sensitivity to cloud and precipitation particles in the atmosphere. These particles (known as hydrometeors) scatter, absorb, and emit radiation according to their mass, composition, shape, internal structure, and orientation. Hence, microwave and sub-millimetre observations have applications including weather forecasting, geophysical retrievals and model validation. To simulate these observations requires a scattering-capable radiative transfer model and an estimate of the bulk optical properties of the hydrometeors. This article describes the module used to integrate single-particle optical properties over a particle size distribution (PSD) to provide bulk optical properties for the Radiative Transfer for TOVS microwave and sub-millimetre scattering code, RTTOV-SCATT, a widely used fast model. Bulk optical properties can be derived from a range of particle models including Mie spheres (liquid and frozen) and non-spherical ice habits from the Liu and Atmospheric Radiative Transfer Simulator (ARTS) databases, which include pristine crystals, aggregates, and hail. The effects of different PSD and particle options on simulated brightness temperatures are explored, based on an analytical two-stream solution for a homogeneous cloud slab. The hydrometeor scattering “spectrum” below 1000 GHz is described, along with its sensitivities to particle composition (liquid or ice), size and shape. The optical behaviour of frozen particles changes in the frequencies above 200 GHz, moving towards an optically thick and emission-dominated regime more familiar from the infrared. This region is little explored but will soon be covered by the Ice Cloud Imager (ICI).

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.

Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP &lt; 100 g/m2, Dme &lt; 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.

Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm

Ice clouds play a critical role in the balance of the earth–atmosphere radiation system, but there are some limitations in the existing remote sensing methods for ice clouds. Terahertz wave is expected to be the best waveband for retrieving ice clouds, with terahertz wavelengths in the order of the size of typical ice cloud particles. An inversion method for the remote sensing of ice clouds at terahertz wavelengths based on genetic algorithm is proposed in this paper. First, suitable channel sets in the terahertz band, which are mainly a combination of absorption lines and window regions, are determined. Then, to improve the efficiency of the generation of the retrieval database, based on the brightness temperature simulated by the atmospheric radiative transfer simulator (ARTS) for different cloud parameters, a fast forward operator is constructed using three-dimensional interpolation to simulate the brightness temperature difference between clear sky and a cloudy scene. Finally, an inversion model to retrieve the ice cloud base height, the effective particle diameter and the ice water path is established based on the genetic algorithm, and an analysis of the inversion errors is performed. The results show that the forward operator, constructed by the nearest interpolation, can accurately calculate the brightness temperature difference at a high speed. The proposed inversion method at terahertz wavelengths based on the genetic algorithm can achieve the expected scientific requirement. The absolute error of the cloud height is around 0.2 km, and the absolute error of the low ice water path (below 20 g/m2) is small, while the relative error of the high ice water path is generally maintained at around 10%, and the absolute error of the effective particle diameter is mostly around 4 μm.

A Parameterization of the Cloud Scattering Polarization Signal Derived From GPM Observations for Microwave Fast Radative Transfer Models

Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well.

An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

Combining atmospheric and snow radiative transfer models to assess the solar radiative effects of black carbon in the Arctic

Abstract. The magnitude of solar radiative effects (cooling or warming) of black carbon (BC) particles embedded in the Arctic atmosphere and surface snow layer was explored on the basis of case studies. For this purpose, combined atmospheric and snow radiative transfer simulations were performed for cloudless and cloudy conditions on the basis of BC mass concentrations measured in pristine early summer and more polluted early spring conditions. The area of interest is the remote sea-ice-covered Arctic Ocean in the vicinity of Spitsbergen, northern Greenland, and northern Alaska typically not affected by local pollution. To account for the radiative interactions between the black-carbon-containing snow surface layer and the atmosphere, an atmospheric and snow radiative transfer model were coupled iteratively. For pristine summer conditions (no atmospheric BC, minimum solar zenith angles of 55∘) and a representative BC particle mass concentration of 5 ng g−1 in the surface snow layer, a positive daily mean solar radiative forcing of +0.2 W m−2 was calculated for the surface radiative budget. A higher load of atmospheric BC representing early springtime conditions results in a slightly negative mean radiative forcing at the surface of about −0.05 W m−2, even when the low BC mass concentration measured in the pristine early summer conditions was embedded in the surface snow layer. The total net surface radiative forcing combining the effects of BC embedded in the atmosphere and in the snow layer strongly depends on the snow optical properties (snow specific surface area and snow density). For the conditions over the Arctic Ocean analyzed in the simulations, it was found that the atmospheric heating rate by water vapor or clouds is 1 to 2 orders of magnitude larger than that by atmospheric BC. Similarly, the daily mean total heating rate (6 K d−1) within a snowpack due to absorption by the ice was more than 1 order of magnitude larger than that of atmospheric BC (0.2 K d−1). Also, it was shown that the cooling by atmospheric BC of the near-surface air and the warming effect by BC embedded in snow are reduced in the presence of clouds.

Jacobian matrix for near-infrared remote sensing based on vector radiative transfer model

Due to the polarization effects of the Earth’s surface reflection and atmospheric particles’ scattering, high-precision retrieval of atmospheric parameters from near-infrared satellite data requires accurate vector atmospheric radiative transfer simulations. This paper presents a near-infrared vector radiative transfer model based on the doubling and adding method. This new model utilizes approximate calculations of the atmospheric transmittance, reflection, and solar scattering radiance for a finitely thin atmospheric layer. To verify its accuracy, the results for four typical scenarios (single molecular layer with Rayleigh scattering, single aerosol layer scattering, multi-layer Rayleigh scattering, and true atmospheric with multi-layer molecular absorption, Rayleigh and aerosol scattering) were compared with benchmarks from a well-known model. The comparison revealed an excellent agreement between the results and the reference data, with accuracy within a few thousandths. Besides, to fulfill the retrieval algorithm, a numerical differentiation-based Jacobian calculation method is developed for the atmospheric and surface parameters. This is coupled with the adding and doubling process for the radiative transfer calculation. The Jacobian matrix produced by the new algorithm is evaluated by comparison with that from the perturbation method. The relative Jacobian matrix deviations between the two methods are within a few thousandths for carbon dioxide and less than 1.0×10−3% for aerosol optical depth. The two methods are consistent for surface albedo, with a deviation below 2.03×10−4%. All validation results suggest that the accuracy of the proposed radiative transfer model is suitable for inversion applications. This model exhibits the potential for simulating near-infrared measurements of greenhouse gas monitoring instruments.

Coupled retrieval of the three phases of water from spaceborne imaging spectroscopy measurements

Measurements of reflected solar radiation by imaging spectrometers can quantify water in different states (solid, liquid, gas) thanks to the discriminative absorption shapes. We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data, such as those from the German EnMAP mission. The retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer-Lambert law. The model is inverted on a per-pixel basis using a maximum likelihood estimation formalism. Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end-to-end simulation tool EeteS, we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R2 of 0.991 for water vapor and 0.965 for liquid water. Furthermore, we applied the algorithm to airborne AVIRIS-C data to demonstrate the ability to map snow/ice extent as well as to a CHRIS-PROBA dataset for which concurrent field measurements of canopy water content were available. The comparison between the retrievals and the ground measurements showed an overall R2 of 0.80 for multiple crop types and a remarkable clustering in the regression analysis indicating a dependency of the retrieved water content from the physical structure of the vegetation. In addition, the algorithm is able to produce smoother and more physically-plausible water vapor maps than the ones from the band ratio approaches used for multispectral data, since biases due to background reflectance are reduced. The demonstrated potential of imaging spectroscopy to provide accurate quantitative measures of water from space will be further exploited using upcoming spaceborne imaging spectroscopy missions like PRISMA or EnMAP.