Atmospheric Window Region Research Articles

Estimation of Nighttime Aerosol Optical Depths Using Atmospheric Infrared Sounder Longwave Radiances

AbstractAerosol remote sensing typically relies on reflected shortwave radiation and thus lacks nighttime retrievals. Here we made an original attempt to retrieve nighttime aerosol optical depth (AOD) by utilizing longwave measurements in the atmospheric window region from the Atmospheric InfraRed Sounder (AIRS) instrument. A machine‐learning based algorithm is developed using AIRS longwave radiance and auxiliary data as the input and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) as well as reanalysis surface temperature as the output. Independent validation indicates good agreement with lunar AOD derived from surface photometers. An overall increase in nighttime AOD compared to daytime is also uncovered, which is further corroborated by surface and space lidar measurements. The theoretical basis of the algorithm is further verified using radiative transfer simulations. Our approach substantially extends the potential of hyperspectral longwave measurements and offers valuable insights into nighttime aerosol properties.

Scratch-resistant antireflective coating for mid-wave infrared band

In the 3–5 μm mid-wave infrared (MWIR) atmospheric transparency window region, infrared detectors are widely applied in spectral imaging, remote sensing and other fields, and their rapid development has put forward higher requirements for infrared optical windows. Common mid-wave infrared optical materials such as germanium and zinc sulfide have poor mechanical properties, causing them difficult to meet various harsh environments. In this paper, a 3-layer film stack is proposed to achieve high transmission and high hardness by radio-frequency reactive magnetron sputtering with the single aluminum target. Two types of AlN/Al2O3/AlN stack with top AlN thicknesses of 50 and 100 nm are studied to realize the best optical and mechanical characteristics. The average transmission increases up to 94.0% for 3–5 μm with the double-side AlN/Al2O3/AlN stacks on the silicon substrate, while the hardness is significantly improved to ∼16.6 GPa for both types. After annealing at 500 °C and 750 °C for 6 h in a vacuum environment, the spectra of the two film stacks move towards the shorter wavelength slightly, and the hardness of both are obviously enhanced. The highest hardness of the film system reaches ∼20.8 GPa, making it far more resistant to sand erosion than ordinary antireflection films. The result shows that this film stack can withstand high temperature and gravel impact tests, meeting the service requirements of the harsh environment of the MWIR optical system.

Assessment of a physics-based retrieval of exoplanet atmospheric temperatures from infrared emission spectra

Abstract Atmospheric temperatures are to be estimated from thermal emission spectra of Earth-like exoplanets orbiting M-stars as observed by current and future planned missions. To this end, a line-by-line radiative transfer code is used to generate synthetic thermal infrared (TIR) observations. The range of ‘observed’ intensities provides a rough hint of the atmospheric temperature range without any a priori knowledge. The equivalent brightness temperature (related to intensities by Planck’s function) at certain wavenumbers can be used to estimate the atmospheric temperature at corresponding altitudes. To exploit the full information provided by the measurement we generalize Chahine’s original approach and infer atmospheric temperatures from all spectral data using the wavenumber-to-altitude mapping defined by the weighting functions. Chahine relaxation allows an iterative refinement of this ‘first guess’. Analysis of the $4.3\rm \, \mu m$ and $15\rm \, \mu m$ carbon dioxide TIR bands enables an estimate of atmospheric temperatures for rocky exoplanets even for low signal to noise ratios of 10 and medium resolution. Inference of Trappist-1e temperatures is, however, more challenging especially for CO2 dominated atmospheres: the ‘standard’ $4.3\rm \, \mu m$ and $15\rm \, \mu m$ regions are optically thick and an extension of the spectral range towards atmospheric window regions is important. If atmospheric composition (essentially CO2 concentration) is known temperatures can be estimated remarkably well, quality measures such as the residual norm provide hints on incorrect abundances. In conclusion, temperature in the mid atmosphere of Earth-like planets orbiting cooler stars can be quickly estimated from thermal IR emission spectra with moderate resolution.

Infrared Gasochromic Devices Based on Metal Thin Films

AbstractVariable infrared emissive materials and devices are effective for the dynamic modulation of infrared radiation. Herein, a new idea of dynamic emissivity modulation based on the hydrogen‐induced metal–insulator phase transition from metallic yttrium (or yttrium dihydride) to dielectric yttrium trihydride is proposed, and infrared gasochromic devices based on yttrium/rhodium metal thin films and infrared‐transparent covers are developed. By alternately injecting 4% hydrogen–argon gas mixture and air into the gasochromic device, the infrared emissivity of the device is dynamically modulated reversibly. The emissivity changes in the 340‐nm‐yttrium/6‐nm‐rhodium device are 0.25 and 0.39 in the 3–5 and 7.5–14 µm atmospheric window regions, respectively, and the response time is <40 s. The emissivity change is affected by the surface morphology and thickness of the yttrium/rhodium film. Moreover, the infrared gasochromic device operates effectively over 100 cycles, and subsequent failure is attributed to oxidation of the yttrium film. The proposed device is easily enlarged and patterned because of its simple structure, thereby indicating that such metal thin films provide a new material platform for dynamic infrared radiation modulation, which has great application prospects in the fields of adaptive thermal camouflage, smart thermal management, and infrared information display.

Phase-change materials reinforced intelligent paint for efficient daytime radiative cooling

SummaryPassive cooling of buildings has become increasingly important for green and low-carbon development, especially in the near decade where daytime radiative cooling technology (DRCT) has drawn attention with big breakthroughs. However, irresistibly importing heat from sunlight and surroundings results in notable temperature rise, thus limiting the cooling effect. Here, we report a radiative paint with latent heat storage capacity to store imported heat by coupling randomly-distributed phase-change materials (PCMs) based microcapsules with acrylic resin to enhance cooling performance. The bifunctional paint shows good performance in selected-suitable phase transition temperature, high enthalpy, high reflectivity in the sunlight region and strong emissivity in the atmospheric window region. The temperature measurements demonstrate that the paint possesses enhanced cooling performance of temperature drop and time buffering effect compared with the pure radiative cooling paint. This work offers the potential to broaden the applications of PCMs and DRCT for energy saving and environment protection.

Construction of efficient passive radiative cooling emitter with selective emission in the whole atmospheric window and durable anti-contamination performance

Polymer-based radiative cooling emitters (RCE) can drop the surface temperature lower than that of ambient without any external energy input, while the optical performance and application is limited. In this work, with the aim of enhancing the selective emission within atmospheric window and anti-contamination of the polymer coating in RCE, a super-hydrophobic bilayer RCE has been designed and prepared. Firstly, series of selected particles were embedded into a thin Polymethylpentene (TPX) composite polymer film. Then, the film was coated onto a high-reflective aluminum sheet. Next, the surface of the RCE was modified by particles (mica and TiO2) deposition and hexadecyltrimethoxysilane (HDTMS). As a result, the absorption peak was broadened to the whole atmospheric window region, and the anti-contamination performance was achieved with enhanced water contact angle of 159°. Moreover, the contact angle showed negligible change after continuous contaminated and cleaned processes. Besides, the RCE can also retain the selective emission and super-hydrophobic performance after exposed to UV-irradiation. During outdoor measurement, the RCE can drop the temperature below the ambient with a temperature difference of ~6 °C during the whole day. Super-hydrophobicity also can gain the RCE better cooling performance under high relative humidity, with a temperature of 2–3 °C larger than the hydrophilic RCE. The successful combination of cooling and anti-contamination functions endows our designed the polymer-based RCE the potential of practical application to serve as a multifunctional and long life passive RCE.

Inter-Comparison of Field- and Laboratory-Derived Surface Emissivities of Natural and Manmade Materials in Support of Land Surface Temperature (LST) Remote Sensing

Correct specification of a target’s longwave infrared (LWIR) surface emissivity has been identified as one of the greatest sources of uncertainty in the remote sensing of land surface temperature (LST). Field and laboratory emissivity measurements are essential for improving and validating LST retrievals, but there are differing approaches to making such measurements and the conditions that they are made under can affect their performance. To better understand these impacts we made measurements of fourteen manmade and natural samples under different environmental conditions, both in situ and in the laboratory. We used Fourier transform infrared (FTIR) spectrometers to deliver spectral emissivities and an emissivity box to deliver broadband emissivities. Field- and laboratory-measured spectral emissivities were generally within 1–2% in the key 8–12 micron region of the LWIR atmospheric window for most samples, though greater variability was observed for vegetation and inhomogeneous samples. Differences between laboratory and field spectral measurements highlighted the importance of field methods for these samples, with the laboratory setup unable to capture sample structure or inhomogeneity. The emissivity box delivered broadband emissivities with a consistent negative bias compared to the FTIR-based approaches, with differences of up to 5%. The emissivities retrieved using the different approaches result in LST retrieval differences of between 1 and 4 °C, stressing the importance of correct emissivity specification.

Spectrally Resolved Fluxes from IASI Data: Retrieval Algorithm for Clear-Sky Measurements

AbstractSpace-based measurements of the outgoing longwave radiation (OLR) are essential for the study of Earth’s climate system. While the CERES instrument provides accurate measurements of this quantity, its measurements are not spectrally resolved. Here we present a high-resolution OLR product (sampled at 0.25 cm−1), derived from measurements of the IASI satellite sounder. The applied methodology relies on precalculated angular distribution models (ADMs). These are usually calculated for tens to hundreds of different scene types (characterized by surface and atmosphere parameters). To guarantee accurate results in the range 645–2300 cm−1 covered by IASI, we constructed ADMs for over 140 000 scenes. These were selected from one year of CAMS reanalysis data. A dissimilarity-based selection algorithm was applied to choose scenes as different from each other as possible, thereby maximizing the performance on real data, while keeping the number of scenes manageable. A comparison of the IASI OLR integrated over the 645–2300 cm−1 range was performed with the longwave broadband OLR products from CERES and the AIRS instrument. The latter are systematically higher due to the contribution of the far infrared to the total IR spectral range, but as expected exhibit generally high spatial correlations with the IASI OLR, except for some areas in the tropical region. We also compared the IASI OLR against the spectrally resolved OLR derived from AIRS. A good agreement was found above 1200 cm−1 while AIRS OLR appeared to be systematically higher in the atmospheric window region, likely related to differences in overpass time or to the use of a different cloud detection algorithm.

Climate Metrics for C1-C4 Hydrofluorocarbons (HFCs).

Hydrofluorocarbons (HFCs) are potent greenhouse gases that are potential substitutes for ozone depleting substances. The Kigali amendment lists 17 HFCs that are currently in commercial use to be regulated under the Montreal Protocol. Future commercial applications may explore the use of other HFCs, most of which currently lack an evaluation of their climate metrics. In this work, atmospheric lifetimes, radiative efficiencies (REs), global warming potentials (GWPs), and global temperature change potentials (GTPs) for all saturated HFCs with fewer than 5 carbon atoms are estimated to help guide future usage and policy decisions. Atmospheric lifetimes were estimated using a structure activity relationship (SAR) for OH radical reactivity and estimated O(1D) reactivity. Radiative metrics were obtained using theoretically calculated infrared absorption spectra that were presented in a previous work. Calculations for some additional HFCs not included in the previous work were performed in this work. The HFCs display unique infrared spectra with strong absorption in the Earth's atmospheric infrared window region, primarily due to the C-F stretching vibration. Results from this study show that the HFC global atmospheric lifetimes and REs are dependent upon their H atom content and molecular structure. Therefore, the HFC radiative metric evaluation requires a case-by-case evaluation. A thorough experimental evaluation of a targeted HFC's atmospheric lifetime and climate metrics is always highly recommended. However, in cases where it is experimentally difficult to separate isomers, the new results from this study should help guide the experiments, as well as provide relevant climate metrics with uncertainties and policy relevant data.

DFT Study of Photochemical Properties and Radiative Forcing Efficiency Features of the Stereoisomers cis- and trans-CHCl═CH-CF3.

The accurate assessment of radiative forcing efficiency (RFE) of a greenhouse gas is based on the precise knowledge of its structure and infrared absorption spectrum. The present work investigates the UV-vis absorption spectra and IR absorption spectra that are used for the determination of RFE of the short-lived compounds, cis- and trans-CHCl═CHCF3 (CTFP). These investigations were carried out with six different density functional theory (DFT) methods B3LYP, CAM-B3LYP, M06, M06-2X, TPSS0, and ωB97X-D associated to the basis set 6-31G(3df). Therefrom, the relative populations of the two states cis and trans for temperatures over the range 220-370 K at 1 atm and along the atmospheric altitude were assessed. It turns out that trans-CTFP is the abundant component between the two states. This review reveals that B3LYP and M06 reproduce well the experimental results of UV-vis spectra of trans-CTFP. As for cis-CTFP, EOM-CCSD is not well fitted by DFT methods. The cis- to trans-CTFP isomerization leads to the red shift for DFT methods and to the blue shift in regard to EOM-CCSD and experimental results. The IR absorption spectra are well fitted by B3LYP over the range 500-1600 cm-1 and TPSS0 over 1300-2000 cm-1 for both stereoisomers. Moreover, the root-mean-square errors (RMSEs) of frequencies from experimental data are lower for B3LYP and TPSS0 for both systems. The computed IR absorption band strengths over 500-2000 cm-1 for cis- and over 600-1800 cm-1 for trans-CTFP are consistent with the experiment. The relevant descriptor RFEs of the climate effect were calculated using a narrow band model for a constant vertical profile and then corrected with a lifetime factor for different computational methods. The computed values correlate well with the experimental results for both stereoisomers except M06-2X and TPSS0. It is worth noting that, for both systems, the intense radiative forcing spectra are located at frequencies ranging in 1000-1200 cm-1. The lower forcings of trans-CTFP lying in the atmospheric window region 800-1000 cm-1 are greater than those of cis-CTFP. Therefore, RFE(trans-CTFP) = 1.127 RFE(cis-CTFP).

Mitigating and comparing the striping noise in FengYun‐3B/C/D microwave radiation imager brightness temperature observations

AbstractThe Microwave Radiation Imager (MWRI) on board the FengYun‐3 (FY‐3) series of satellites can provide global observations four times daily, which significantly improves the ability to monitor large‐scale natural disasters. There is noticeable striping noise in brightness temperature observations from the MWRIs on board the FY‐3B, FY‐3C and FY‐3D satellites. The central frequencies of all MWRI channels are in the atmospheric window region. In this article, a modified principal component analysis/ensemble empirical mode decomposition method proposed by Zou et al. in 2017 was applied to deal with the striping noise in MWRI measurements. Results reveal that the magnitude of the striping noise in the brightness temperature observations of MWRI channels 10 are about ±1.2 K for FY‐3B, ±1.1 K for FY‐3C and ± 0.8 K for FY3D. The striping noise in FY‐3D MWRI measurements is significantly reduced, compared with the striping noise in FY‐3B and FY‐3C MWRI measurements. Striping features will appear in the liquid water path (LWP) distribution if LWP is retrieved from brightness temperatures without removing the striping noise. The differences between LWP retrieved from the MWRI observations with and without striping noise mitigation could be as large as 0.07 kg·m−2. It is thus necessary to remove striping noise before any MWRI data applications.

Metamaterial-Based Radiative Cooling: Towards Energy-Free All-Day Cooling

In the light of the ever increasing dangers of global warming, the efforts to reduce energy consumption by radiative cooling techniques have been designed, but are inefficient under strong sunlight during the daytime. With the advent of metamaterials and their selective control over optical properties, radiative cooling under direct sunlight is now possible. The key principles of metamaterial-based radiative cooling are: almost perfect reflection in the visible and near-infrared spectrum (0.3–3 µm) and high thermal emission in the infrared atmospheric window region (8–13 µm). Based on these two basic principles, studies have been conducted using various materials and structures to find the most efficient radiative cooling system. In this review, we analyze the materials and structures being used for radiative cooling, and suggest the future perspectives as a substitute in the current cooling industry.

Effects of Dynamic Range and Sampling Rate of an Infrared Thermometer to the Accuracy of the Cloud Detection

Cloud detection using downwelling radiation measured by infrared thermometer (IRT) has been utilized for many applications. The current study investigates the effects of disparate IRT specifications, including the dynamic range and sampling rates on the performance of cloud detection, which utilizes the spectral and temporal characteristics of cloudy radiation. To analyze the effects, the detection algorithm that was prepared with and applied to the IRT data with different specifications is compared with reference data, a ceilometer, and micro-pulse lidar (MPL). The comparison results show that the low-altitude clouds are detected with a sufficient accuracy: better than 97% probability of detection (POD). This is due to the much warmer brightness temperature (Tb) of the low clouds compared with the clear sky in the atmospheric window region where the IRT measurement was made. Conversely, the high-altitude cold clouds are hard to detect with the spectral test due to the much-reduced Tb contrast between cloudy and clear sky. Thus, the algorithm performance is largely dependent on the performance of the temporal test. Since the lower measurement noise provides a better estimation of the temporal variability of clear sky Tb with less estimation uncertainty, the IRT data having a better noise performance shows a better POD value by as much as 52.2% compared with the MPL result. However, the improvement is realized only when the dynamic range of IRT covers sufficiently cold Tb, such as −100 °C.

A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models.

The Role of Emissivity in the Detection of Arctic Night Clouds

Detection of clouds over polar areas from satellite radiometric measurements in the visible and IR atmospheric window region is rather difficult because of the high albedo of snow, possible ice covered surfaces, very low humidity, and the usual presence of atmospheric temperature inversion. Cold and highly reflective polar surfaces provide little thermal and visible contrast between clouds and the background surface. Moreover, due to the presence of temperature inversion, clouds are not always identifiable as being colder than the background. In addition, low humidity often causes polar clouds to be optically thin. Finally, polar clouds are usually composed of a mixture of ice and water, which leads to an unclear spectral signature. Single and bi-spectral threshold methods are sometimes inappropriate due to a large variability of surface emissivity and cloud conditions. The objective of this study is to demonstrate the crucial role played by surface emissivity in the detection of polar winter clouds and the potential improvement offered by infrared hyperspectral observations, such as from the Infrared Atmospheric Sounding Interferometer (IASI). In this paper a new approach for cloud detection is proposed and validated exploiting active measurements from satellite sensors, i.e., the CloudSat cloud profiling radar (CPR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For a homogenous IASI field of view (FOVs), the proposed cloud detection scheme tallies with the combined CPR and CALIOP product in classifying 98.11% of the FOVs as cloudy and also classifies 97.54% of the FOVs as clear. The Hansen Kuipers discriminant reaches 0.95.

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

AbstractAn established radiative transfer model (RTM) is adapted for simulating all‐sky infrared radiance spectra from the Canadian Global Environmental Multiscale (GEM) model in order to validate its forecasts at the radiance level against Atmospheric InfraRed Sounder (AIRS) observations. Synthetic spectra are generated for 2 months from short‐term (3–9 h) GEM forecasts. The RTM uses a monthly climatological land surface emissivity/reflectivity atlas. An updated ice particle optical property library was introduced for cloudy radiance calculations. Forward model brightness temperature (BT) biases are assessed to be of the order of ∼1 K for both clear‐sky and overcast conditions. To quantify GEM forecast meteorological variables biases, spectral sensitivity kernels are generated and used to attribute radiance biases to surface and atmospheric temperatures, atmospheric humidity, and clouds biases. The kernel method, supplemented with retrieved profiles based on AIRS observations in collocation with a microwave sounder, achieves good closure in explaining clear‐sky radiance biases, which are attributed mostly to surface temperature and upper tropospheric water vapor biases. Cloudy‐sky radiance biases are dominated by cloud‐induced radiance biases. Prominent GEM biases are identified as: (1) too low surface temperature over land, causing about −5 K bias in the atmospheric window region; (2) too high upper tropospheric water vapor, inducing about −3 K bias in the water vapor absorption band; (3) too few high clouds in the convective regions, generating about +10 K bias in window band and about +6 K bias in the water vapor band.

AERI 스펙트럼 분석을 통한 구름에 영향을 받은 스펙트럼 자료 제거 방법 개선

Abstract : The National Institute of Meteorological Research (NIMR) has operated the FourierTransform InfraRed (FTIR) spectrometer which is the Atmospheric Emitted Radiance Interferometer(AERI) in Anmyeon island, Korea since June 2010. The ground-based AERI with similar hyper-spectralinfrared sensor to satellite could be an alternative way to validate satellite-based remote sensing. In thisregard, the NIMR has focused on the improvement of retrieval quality from the AERI, particularly cloud-data filtering method. The AERI spectrum which is measured on a typical clear day is selected referencespectrum and we used region of atmospheric window. We performed test of threshold in order to selectvalid threshold. We retrieved methane using new method which is used reference spectrum, and the othermethod which is used KLAPS cloud cover information, each retrieved methane was compared with thatof ground-based in-situ measurements. The quality of AERI methane retrievals of new method wassignificantly more improved than method of used KLAPS. In addition, the comparison of vertical totalcolumn of methane from AERI and GOSAT shows good result.Key Words : AERI, Cloud Filtering, FTIR, Infrared, Remote sensing, Satellite.요약 :

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

Using longwave HIRS radiances to test climate models

A ‘model-to-radiance’ comparison of simulated brightness temperatures from the Hadley Centre Global Environmental Model 2 with measurements from the High Resolution Infrared Radiation Sounder/4 (HIRS/4) instrument onboard the MetOp-A satellite is presented. For the all-sky, the model overestimates brightness temperatures in the atmospheric window region with the greatest biases over areas associated with deep convective cloud. In contrast to many global climate models, much smaller clear-sky biases are found indicating that model clouds are the dominating source of error. Simulated values in upper atmospheric CO2 channels approximate observations better as a result of compensating cold biases at the poles and warm biases at lower latitudes, due to a poor representation of the Brewer Dobson circulation in the 38 level ‘low-top’ configuration of the model. Simulated all and clear-sky outgoing longwave radiation (OLR) evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) and HIRS OLR products reveal good agreement, in part due to cancellation of positive and negative biases. Through physical arguments relating to the spectral energy balance within a cloud, it is suggested that broadband agreement could be the result of a balance between positive window biases and unseen negative biases originating from the water vapour rotational band in the far infrared (not sampled by HIRS).

Scattering in infrared radiative transfer: A comparison between the spectrally averaging model JURASSIC and the line-by-line model KOPRA

The viability of a spectrally averaging model to perform radiative transfer calculations in the infrared including scattering by atmospheric particles is examined for the application of infrared limb remote sensing measurements. Here we focus on the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the European Space Agency's Envisat. Various spectra for clear air and cloudy conditions were simulated with a spectrally averaging radiative transfer model and a line-by-line radiative transfer model for three atmospheric window regions (825–830, 946–951, 1224–1228cm−1) and compared to each other. The results are rated in terms of the MIPAS noise equivalent spectral radiance (NESR). The clear air simulations generally agree within one NESR. The cloud simulations neglecting the scattering source term agree within two NESR. The differences between the cloud simulations including the scattering source term are generally below three and always below four NESR. We conclude that the spectrally averaging approach is well suited for fast and accurate infrared radiative transfer simulations including scattering by clouds. We found that the main source for the differences between the cloud simulations of both models is the cloud edge sampling. Furthermore we reasoned that this model comparison for clouds is also valid for atmospheric aerosol in general.