Infrared Imaging Radiometer Research Articles

Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods

Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning–based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The random forest (RF) and neural network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively sensed cloud boundaries from the radar and lidar systems on board the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors. Significance Statement Using satellites to detect the heights of clouds in the atmosphere is important for a variety of weather applications, including aviation weather forecasting. However, detecting low clouds can be challenging if there are other clouds above them. To address this, we have developed machine learning–based models that can be used with passive satellite instruments. These models use satellite observations at visible and infrared wavelengths, an estimate of relative humidity in the atmosphere, and geographic and surface-type information to predict whether low clouds are present. Our results show that these models have significant skill at predicting low clouds, even in the presence of higher cloud layers.

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

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

Skin Sea Surface Temperatures From the GOES-16 ABI Validated With Those of the Shipborne M-AERI

Derived from satellite measurements, the skin sea surface temperature (SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> ) is a critical variable in research into the climate and other aspects of the ocean–atmosphere system As the new generation of visible and infrared imaging radiometers, the Advanced Baseline Imager (ABI) onboard the current U.S. geostationary satellites series provides marked improvements to remote sensing of the ocean surface from geostationary orbit, including a wider range of spectral bands and more rapid sampling. This research compares SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> derived from the ABI onboard the Geostationary Operational Environmental Satellite (GOES)-16 satellite from January 2018 to October 2019 with the independent data acquired from Marine-Atmospheric Emitted Radiance Interferometers (M-AERIs) deployed on ships. The result shows an averaged SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> mean bias of 0.086 K and a robust standard deviation of 0.220 K. The properties of the bias are determined based on various aspects such as geographical distribution, day/night, aerosol effect, and satellite zenith angle. This study also shows that the ABI sensor-specific error statistics (SSES) are effective to reduce the errors by slant-path water vapor and satellite zenith angle. The main result of this research is that the SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> products derived from the ABI compare well with independent, International System of Units (SI)-traceable radiometric skin temperatures taken from ships, and this should give confidence in the use of these remotely-sensed SST <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">skin</sub> fields for many scientific endeavors. Some remaining challenges have been identified for algorithm developers to address, and we hope these will be pursued in due course.

Cloud Detection From Paired CrIS Water Vapor and CO₂ Channels Using Machine Learning Techniques

Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> bands.

CALIPSO (IIR-CALIOP) Retrievals of Cirrus Cloud Ice Particle Concentrations.

A new satellite remote sensing method is described whereby the sensitivity of thermal infrared wave resonance absorption to small ice crystals is exploited to estimate cirrus cloud ice particle number concentration N, effective diameter De, and ice water content IWC. This method uses co-located observations from the Infrared Imaging Radiometer (IIR) and from the CALIOP (Cloud and Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) polar orbiting satellite, employing IIR channels at 10.6 μm and 12.05 μm. Using particle size distributions measured over several flights of the TC4 (Tropical Composition, Cloud and Climate Coupling) and the mid-latitudes SPARTICUS (Small Particles in Cirrus) field campaigns, we show for the first time that N/IWC is tightly related to βeff; the ratio of effective absorption optical depths at 12.05 μm and 10.6 μm. Relationships developed from in situ aircraft measurements are applied to βeff derived from IIR measurements to retrieve N. This satellite remote sensing method is constrained by measurements of βeff from the IIR and is by essence sensitive to the smallest ice crystals. Retrieval uncertainties are discussed, including uncertainties related to in situ measurement of small ice crystals (D < 15 µm), which are studied through comparisons with IIR βeff. The method is applied here to single-layered semi-transparent clouds having a visible optical depth between about 0.3 and 3, where cloud base temperature is ≤ 235 K. Two years of CALIPSO data have been analyzed for the years 2008 and 2013, with the dependence of cirrus cloud N and De on altitude, temperature, latitude, season (winter vs. summer) and topography (land vs. ocean) described. The results for the mid-latitudes show a considerable dependence on season. In the high latitudes, N tends to be highest and De smallest, whereas the opposite is true for the tropics. The frequency of occurrence of these relatively thick cirrus clouds exhibited a strong seasonal dependence in the high latitudes, with the occurrence frequency during Arctic winter being at least twice that of any other season. Processes that could potentially explain some of these micro-and macroscopic cloud phenomena are discussed.

Infrared radiation characterization of several stainless steel textiles in the 3.5–5.1 μm infrared range

The infrared emission of four stainless steel textiles was characterized in the mid-infrared range, i.e. 3.6÷5.1 μm, by observing their temperature evolution under heating regime. The investigated steel textiles differ by the type of fabrics, resulting in some variation in the infrared emission. Standard test method for measuring emissivity was applied to the set of metallic textiles using infrared imaging radiometers as well as a reference surface of known high emissivity. The obtained experimental results allowed to retrieve the infrared emissivity at different applied temperatures. The experimental data were interpreted by means of Plank's theory of black-body radiation. Finally, all the investigated textiles appear to be suitable for thermal shielding applications.

Retrieving the Polar Mixed‐Phase Cloud Liquid Water Path by Combining CALIOP and IIR Measurements

AbstractMixed‐phase cloud (MC) is the dominant cloud type over the polar region, and there are challenging conditions for remote sensing and in situ measurements. In this study, a new methodology of retrieving the stratiform MC liquid water path (LWP) by combining Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) and infrared imaging radiometer (IIR) measurements was developed and evaluated. This new methodology takes the advantage of reliable cloud‐phase discrimination by combining lidar and radar measurements. An improved multiple‐scattering effect correction method for lidar signals was implemented to provide reliable cloud extinction near cloud top. Then with the adiabatic cloud assumption, the MC LWP can be retrieved by a lookup‐table‐based method. Simulations with error‐free inputs showed that the mean bias and the root mean squared error of the LWP derived from the new method are −0.23 ± 2.63 g/m2, with the mean absolute relative error of ~4%. Simulations with erroneous inputs suggested that the new methodology could provide reliable retrieval of LWP to support the statistical or climatology analysis. Two‐month A‐train satellite retrievals over Arctic region showed that the new method can produce very similar cloud top temperature (CTT) dependence of LWP to the ground‐based microwave radiometer measurements, with a bias of −0.78 g/m2 and a correlation coefficient of 0.95 between the two mean CTT‐LWP relationships. The new approach can also produce reasonable pattern and value of LWP in spatial distribution over the Arctic region.

Ice particle morphology and microphysical properties of cirrus clouds inferred from combined CALIOP‐IIR measurements

AbstractIce particle morphology and microphysical properties of cirrus clouds are essential for assessing radiative forcing associated with these clouds. We develop an optimal estimation‐based algorithm to infer cirrus cloud optical thickness (COT), cloud effective radius (CER), plate fraction including quasi‐horizontally oriented plates (HOPs), and the degree of surface roughness from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Infrared Imaging Radiometer (IIR) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) platform. A simple but realistic ice particle model is used, and the relevant bulk optical properties are computed using state‐of‐the‐art light‐scattering computational capabilities. Rigorous estimation of uncertainties related to surface properties, atmospheric gases, and cloud heterogeneity is performed. The results based on the present method show that COTs are quite consistent with other satellite products and CERs essentially agree with the other counterparts. A 1 month global analysis for April 2007, in which CALIPSO off‐nadir angle is 0.3°, shows that the HOP has significant temperature‐dependence and is critical to the lidar ratio when cloud temperature is warmer than −40°C. The lidar ratio is calculated from the bulk optical properties based on the inferred parameters, showing robust temperature dependence. The median lidar ratio of cirrus clouds is 27–31 sr over the globe.

Temperature dependent emissivity of different stainless steel textiles in the infrared range

We experimentally investigated the infrared properties of a set of steel textiles, prepared using different type of fabrics. Infrared emission of the textiles was characterized in the mid-infrared range, i.e. 8÷14 μm, by observing their temperature evolution under heating regime with a focal plane array (FPA) infrared camera. Standard test method for measuring and compensating emissivity using infrared imaging radiometers was applied to the set of metallic textiles. The obtained experimental results allowed to retrieve the infrared emissivity at different applied temperatures. Although their infrared emission show some differences depending on the specific fabric, all the investigated textiles composed of steel yarns appear to be suitable for thermal shielding applications. Finally, the measured data were interpreted by means of the finite-difference time-domain (FDTD) numerical simulations as well as using Plank's theory of black-body radiation.

Simulation of Airborne Radiometric Detection of Wake Vortices

This paper describes an analysis of the potential of using an airborne Fourier transform spectrometer (FTS) or radiometer to detect wake vortices. The goal was to determine the requirements for an infrared (IR) FTS to effectively detect wake vortices. Initially, a theoretical analysis of wake vortex detection by thermal radiation was realized in a series of simulations. The first stage used the Terminal Area Simulation System (TASS) dynamic model to simulate wake vortex temperature, moisture, and velocity fields. The second stage used these fields as input to the line-by-line radiative transfer model (LBLRTM) to simulate responses from both an imaging IR hyperspectral FTS and an IR imaging radiometer. These numerical simulations generated FTS and radiometer imagery that was compared with the original temperature data. This research supported an effort, using ground-based imaging FTS instruments, to make measurements of wake vortices of various landing aircraft. Results from two different field campaigns have been previously reported. Instrument specifications for wake vortex thermal detection are recommended for an imaging radiometer sensitive within the following two narrow spectral bands: 670–750 cm−1 and 2200–2350 cm−1 . The instrument must have at the very minimum a noise equivalent differential temperature < 2 mK and a spectral resolution of at least 32 cm−1.

Extension of radiative transfer code MOMO, matrix-operator model to the thermal infrared – Clear air validation by comparison to RTTOV and application to CALIPSO-IIR

1-D radiative transfer code Matrix-Operator Model (MOMO), has been extended from [0.2−3.65μm] the band to the whole [0.2−100μm] spectrum. MOMO can now be used for the computation of a full range of radiation budgets (shortwave and longwave). This extension to the longwave part of the electromagnetic radiation required to consider radiative transfer processes that are features of the thermal infrared: the spectroscopy of the water vapor self- and foreign-continuum of absorption at 12μm and the emission of radiation by gases, aerosol, clouds and surface. MOMO׳s spectroscopy module, Coefficient of Gas Absorption (CGASA), has been developed for computation of gas extinction coefficients, considering continua and spectral line absorptions. The spectral dependences of gas emission/absorption coefficients and of Planck׳s function are treated using a k-distribution. The emission of radiation is implemented in the adding–doubling process of the matrix operator method using Schwarzschild׳s approach in the radiative transfer equation (a pure absorbing/emitting medium, namely without scattering). Within the layer, the Planck-function is assumed to have an exponential dependence on the optical-depth. In this paper, validation tests are presented for clear air case studies: comparisons to the analytical solution of a monochromatic Schwarzschild׳s case without scattering show an error of less than 0.07% for a realistic atmosphere with an optical depth and a blackbody temperature that decrease linearly with altitude. Comparisons to radiative transfer code RTTOV are presented for simulations of top of atmosphere brightness temperature for channels of the space-borne instrument MODIS. Results show an agreement varying from 0.1K to less than 1K depending on the channel. Finally MOMO results are compared to CALIPSO Infrared Imager Radiometer (IIR) measurements for clear air cases. A good agreement was found between computed and observed radiance: biases are smaller than 0.5K and Root Mean Square Error (RMSE) varies between 0.4K and 0.6K depending on the channel. The extension of the code allows the utilization of MOMO as forward model for remote sensing algorithms in the full range spectrum. Another application is full range radiation budget computations (heating rates or forcings).

Discrimination and validation of clouds and dust aerosol layers over the Sahara desert with combined CALIOP and IIR measurements

This study validates a method for discriminating between daytime clouds and dust aerosol layers over the Sahara Desert that uses a combination of active CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and passive IIR (Infrared Imaging Radiometer) measurements; hereafter, the CLIM method. The CLIM method reduces misclassification of dense dust aerosol layers in the Sahara region relative to other techniques. When evaluated against a suite of simultaneous measurements from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), CloudSat, and the MODIS (Moderate-resolution Imaging Spectroradiometer), the misclassification rate for dust using the CLIM technique is 1.16% during boreal spring 2007. This rate is lower than the misclassification rates for dust using the cloud aerosol discriminations performed for version 2 (V2-CAD; 16.39%) or version 3 (V3-CAD; 2.01%) of the CALIPSO data processing algorithm. The total identification errors for data from in spring 2007 are 13.46% for V2-CAD, 3.39% for V3-CAD, and 1.99% for CLIM. These results indicate that CLIM and V3-CAD are both significantly better than V2-CAD for discriminating between clouds and dust aerosol layers. Misclassifications by CLIM in this region are mainly limited to mixed cloud-dust aerosol layers. V3-CAD sometimes misidentifies low-level aerosol layers adjacent to the surface as thin clouds, and sometimes fails to detect thin clouds entirely. The CLIM method is both simple and fast, and may be useful as a reference for testing or validating other discrimination techniques and methods.

A variational approach for retrieving ice cloud properties from infrared measurements: application in the context of two IIR validation campaigns

Abstract. Cirrus are cloud types that are recognized to have a strong impact on the Earth-atmosphere radiation balance. This impact is however still poorly understood, due to the difficulties in describing the large variability of their properties in global climate models. Consequently, numerous airborne and space-borne missions have been dedicated to their study in the last decades. The satellite constellation A-Train has for instance proven to be particularly helpful for the study of cirrus. More particularly, the Infrared Imaging Radiometer (IIR) carried onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite shows a great sensitivity to the radiative and microphysical properties of these clouds. Our study presents a novel methodology that uses the thermal infrared measurements of IIR to retrieve the ice crystal effective size and optical thickness of cirrus. This methodology is based on an optimal estimation scheme, which possesses the advantage of attributing precise uncertainties to the retrieved parameters. Two IIR airborne validation campaigns have been chosen as case studies for illustrating the results of our retrieval method. It is observed that optical thicknesses could be accurately retrieved but that large uncertainties may occur on the effective diameters. Strong agreements have also been found between the products of our method when separately applied to the measurements of IIR and of the airborne radiometer CLIMAT-AV, which consolidates the results of previous validation studies of IIR level-1 measurements. Comparisons with in situ observations and with operational products of IIR are also discussed and appear to be coherent with our results. However, we have found that the quality of our retrievals can be strongly impacted by uncertainties related to the choice of a pristine crystal model and by poor constraints on the properties of possible liquid cloud layers underneath cirrus. Simultaneous retrievals of liquid clouds radiative and microphysical properties and/or the use of different ice crystal models should therefore be considered in order to improve the quality of the results.

Validation of IIR/CALIPSO Level 1 Measurements by Comparison with Collocated Airborne Observations during CIRCLE-2 and Biscay ‘08 Campaigns

Abstract In the frame of validation of the spatial observations from the radiometer IIR on board CALIPSO, the two airborne campaigns Cirrus Cloud Experiment (CIRCLE)-2 and Biscay ‘08 took place in 2007 and 2008 in the western part of France, over the Atlantic Ocean. During these experiments, remote sensing measurements were made over cirrus clouds, right under the track of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) in space and time collocation. For this purpose, a Falcon-20 aircraft was equipped with the Lidar pour l’Etude des Interactions Aérosols Nuages Dynamique Rayonnement et du Cycle de l’Eau (LEANDRE)-New Generation (NG) and the thermal infrared radiometer Conveyable Low-Noise Infrared Radiometer for Measurements of Atmosphere and Ground Surface Targets (CLIMAT)-Airborne Version (AV), whose spectral characteristics are strongly similar to those of the infrared imaging radiometer (IIR). In situ measurements were also taken in cirrus clouds during CIRCLE-2. After comparisons, consistent agreements are found between brightness temperatures measured by CLIMAT-AV and IIR. However, deviations in the brightness temperature measurements are still observed, mainly in the 8.6-μm channels. Simulations using a radiative transfer code are performed along a perfectly clear-sky area to show that these dissimilarities are inherent in slight differences between the spectral channels of both radiometers, and in differences between their altitudes. Cloudy and imperfectly clear areas are found to be harder to interpret, but the measurements are still coherent by taking into account experimental uncertainties. In the end, IIR measurements can be validated unambiguously.

Detection of dust aerosol by combining CALIPSO active lidar and passive IIR measurements

Abstract. The version 2 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) dust layer detection method, which is based only on lidar measurements, misclassified about 43% dust layers (mainly dense dust layers) as cloud layers over the Taklamakan Desert. To address this problem, a new method was developed by combining the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and passive Infrared Imaging Radiometer (IIR) measurements. This combined lidar and IR measurement (hereafter, CLIM) method uses the IIR tri-spectral IR brightness temperatures to discriminate between ice cloud and dense dust layers, and lidar measurements alone to detect thin dust and water cloud layers. The brightness temperature difference between 10.60 and 12.05 μm (BTD11−12) is typically negative for dense dust and generally positive for ice cloud, but it varies from negative to positive for thin dust layers, which the CALIPSO lidar correctly identifies. Results show that the CLIM method could significantly reduce misclassification rates to as low as ~7% for the active dust season of spring 2008 over the Taklamakan Desert. The CLIM method also revealed 18% more dust layers having greatly intensified backscatter between 1.8 and 4 km altitude over the source region compared to the CALIPSO version 2 data. These results allow a more accurate assessment of the effect of dust on climate.

Multisource Data Integration for Fire Risk Management: The Local Test of a Global Approach

In this letter, we propose an algorithm to detect the presence of forest fires using data from both geostationary and polar-orbiting satellites. The very frequent acquisitions of the Spinning Enhanced Visible and Infrared Imager radiometer placed onboard the Meteosat Second Generation-9 satellite are used as main source for the algorithm, while the MEdium Resolution Imaging Spectrometer global vegetation index and the Advanced Along-Track Scanning Radiometer measurements are used to enhance the reliability of the detection. The problem is approached in a ¿global¿ way, providing the basis for an automated system that is not dependent on the local area properties. In cooperation with the Centre de Suivi E¿cologique (Dakar, Senegal), the proposed algorithm was implemented in a ¿Multisource Fire Risk Management System¿ for the Senegal area, as briefly described in this letter. A field campaign of one week was carried out in order to perform a validation of the system's detections, showing a good agreement with the fire coordinates measured on the ground. Furthermore, a consistency check was performed using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Rapid Response System, showing that more than 76% of high-confidence MODIS events are detected by the algorithm.

First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI nighttime data

Abstract. A new technique for process-oriented rain area classification using Meteosat Second Generation SEVIRI nighttime data is introduced. It is based on a combination of the Advective Convective Technique (ACT) which focuses on precipitation areas connected to convective processes and the Rain Area Delineation Scheme during Nighttime (RADS-N) a new technique for the improved detection of stratiform precipitation areas (e.g. in connection with mid-latitude frontal systems). The ACT which uses positive brightness temperature differences between the water vapour (WV) and the infrared (IR) channels (ΔTWV-IR) for the detection of convective clouds and connected precipitating clouds has been transferred from Meteosat First Generation (MFG) Metesoat Visible and Infra-Red Imager radiometer (MVIRI) to Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). RADS-N is based on the new conceptual model that precipitating cloud areas are characterised by a large cloud water path (cwp) and the presence of ice particles in the upper part of the cloud. The technique considers information about both parameters inherent in the channel differences ΔT3.9-10.8, ΔT3.9-7.3, ΔT8.7-10.8, and ΔT10.8-12.1, to detect potentially precipitating cloud areas. All four channel differences are used to gain implicit knowledge about the cwp. ΔT8.7-10.8 and ΔT10.8-12.1 are additionally considered to gain information about the cloud phase. First results of a comparison study between the classified rain areas and corresponding ground based radar data for precipitation events in connection with a cold front occlusion show encouraging performance of the new proposed process-oriented rain area classification scheme.

Depth-resolved phase retardation measurements for laser-assisted non-ablative cartilage reshaping

Since polarization-sensitive optical coherence tomography (PS-OCT) is emerging as a new technique for determining phase retardation in biological materials, we measured phase retardation changes in cartilage during local laser heating for application to laser-assisted cartilage reshaping. Thermally-induced changes in phase retardation of nasal septal cartilage following Nd:YAG laser irradiation were investigated using a PS-OCT system. A PS-OCT system and infrared imaging radiometer were used to record, respectively, depth-resolved images of the Stokes parameters of light backscattered from ex vivo porcine nasal septal cartilage and radiometric temperature changes following laser irradiation. PS-OCT images of cartilage were recorded before (control), during and after laser irradiation. From the measured Stokes parameters (I, Q, U and V), an estimate of the relative phase retardation between two orthogonal polarizations was computed to determine birefringence in cartilage. Phase retardation images of light backscattered from cartilage show significant changes in retardation following laser irradiation. To investigate the origin of retardation changes in response to local heat generation, we differentiated two possible mechanisms: dehydration and thermal denaturation. PS-OCT images of cartilage were recorded after dehydration in glycerol and thermal denaturation in heated physiological saline. In our experiments, observed retardation changes in cartilage are primarily due to dehydration. Since dehydration is a principal source for retardation changes in cartilage over the range of heating profiles investigated, our studies suggest that the use of PS-OCT as a feedback control methodology for non-ablative cartilage reshaping requires further investigation.

Fast radiative transfer modeling for infrared imaging radiometry

Fast radiative transfer codes have been developed for simulating the outgoing radiance (and corresponding brightness temperature) to be measured by the Infrared Imaging Radiometer (IIR) of the space Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Two simple codes (FASRAD and FASAA), for which scattering is neglected, as well as an accurate code (FASDOM), accounting for scattering and absorption with the Discrete Ordinate Method (DOM), are presented. Their accuracy has been estimated with a reference code including a line-by-line model and the DOM. Simulations have shown that the accuracy is generally better than 0.3 K on the brightness temperature for clear or cloudy atmospheres. This accuracy agrees with the expected one of future IIR measurements. In addition, the impact of scattering on the brightness temperature has been evaluated for semi-transparent liquid clouds in the IIR spectral range. Especially, simulations have shown that cloud microphysics retrieval might be possible with the Brightness Temperature Difference (BTD) between two IIR bands, using the couple of wavelengths (8.7– 12 μ m ) or (10.6– 12 μ m ). However, scattering strongly influences the radiation for shorter wavelengths. The error on the BTD with (8.7– 12 μ m ) can reach 4 K when scattering is neglected, leading to large uncertainties in the retrieval of droplet effective radius.

気流下の着衣人体における各部位の対流熱伝達率と着衣抵抗

Convective heat transfer coefficients for each part of the clothed human body were evaluated under airflow conditions and compared to those of nude body. This was done by measuring clothing surface temperatures on seated and standing manikin using an infrared-imaging radiometer. The convective heat transfer coefficients for the clothed manikin were larger than for the nude manikin. The difference could be 100 to 200% for some individual parts, and for the overall body the difference was 30 to 50%. These results were consistent for both standing and sitting postures, or for facing upwind and facing downstream, although slightly larger differences between clothed and nude could be seen when the manikin was sitting and facing upwind than in the other conditions. Clothing insulation for each part was also estimated. Some differences between standing and sitting were observed at the body part level. However for the whole body the difference was small. Regression models for convective heat transfer coefficients and clothing insulation for all body parts were presented for use in human thermal modeling.