Cloudy Scenes Research Articles

CLOUDET: A Cloud Detection and Estimation Algorithm for Passive Microwave Imagers and Sounders Aided by Naïve Bayes Classifier and Multilayer Perceptron

CLOUDET, a cloud detection and estimation algorithm for passive microwave imagers and sounders is presented. CLOUDET is based on a naive Bayes classifier and multilayer perceptron. It is applied to the special sensor microwave imager/sounder (SSMIS), and the ECMWF integrated forecast system (IFS) cloud liquid water information has been used to train the algorithm. CLOUDET is applicable to both ocean and land-surface types. CLOUDET has been developed and evaluated by employing two different groups of radiometric information, namely, the humidity channels near 183 GHz (humidity algorithm) and the window channels between 19 and 91 GHz (window algorithm). It has been revealed that both humidity and window algorithms can detect cloudy scenes over ocean at a confidence level of more than 90% (80% over land). The analysis indicates that the humidity algorithm has a better ability in detecting cloudy scenes over ocean than the window algorithm ( ${\bf CSI=0.98}$ vs. ${\bf CSI=0.93}$ ). The opposite is true over land-surface type, revealing a CSI of 0.85 by humidity algorithm as opposed to CSI of 0.88 by window algorithm. The estimation of cloud by the CLOUDET algorithm has also been very promising during the validation effort. In particular, the correlation coefficient obtained over ocean through the use of the window algorithm is 0.70 (MAE 0.04 mm and RMSE 0.09 mm). The presented algorithm CLOUDET can be served as a stand-alone tool to reject and identify the cloudy scenes as well as to estimate the cloud liquid water path amount prior to assimilating the radiances into numerical weather prediction model.

Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

Abstract. This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud-top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud-top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band and a nearby window channel. Both CTT and CTP are converted to cloud-top height (CTH) using atmospheric profiles from a numerical weather prediction model. First, a sensitivity study using radiative transfer simulations in the near-infrared and thermal infrared was performed to demonstrate, in a quantitative manner, the larger impact of the assumed cloud vertical extinction profile, described in terms of shape and vertical extent, on MERIS than on AATSR top-of-atmosphere measurements. Consequently, cloud vertical extinction profiles will have a larger influence on the MERIS than on the AATSR cloud height retrievals for most cloud types. Second, the difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE), as observed by ground-based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. Similarly to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is stronger for single-layer clouds than for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE provide an additional parameter, next to CTH, that can be obtained from passive imager measurements and can be used to further describe cloud vertical distribution, thus contributing to the characterization of a cloudy scene. To further demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, possibly providing enhanced information on cloud vertical distributions.

Deriving Arctic Cloud Microphysics at Barrow, Alaska: Algorithms, Results, and Radiative Closure

AbstractCloud phase and microphysical properties control the radiative effects of clouds in the climate system and are therefore crucial to characterize in a variety of conditions and locations. An Arctic-specific, ground-based, multisensor cloud retrieval system is described here and applied to 2 yr of observations from Barrow, Alaska. Over these 2 yr, clouds occurred 75% of the time, with cloud ice and liquid each occurring nearly 60% of the time. Liquid water occurred at least 25% of the time, even in winter, and existed up to heights of 8 km. The vertically integrated mass of liquid was typically larger than that of ice. While it is generally difficult to evaluate the overall uncertainty of a comprehensive cloud retrieval system of this type, radiative flux closure analyses were performed in which flux calculations using the derived microphysical properties were compared with measurements at the surface and the top of the atmosphere. Radiative closure biases were generally smaller for cloudy scenes relative to clear skies, while the variability of flux closure results was only moderately larger than under clear skies. The best closure at the surface was obtained for liquid-containing clouds. Radiative closure results were compared with those based on a similar, yet simpler, cloud retrieval system. These comparisons demonstrated the importance of accurate cloud-phase and cloud-type classification, and specifically the identification of liquid water, for determining radiative fluxes. Enhanced retrievals of liquid water path for thin clouds were also shown to improve radiative flux calculations.

Classification of cloud scenes by Argus spectral data

The mini-spectrometer Argus 1000 being in space, continuously monitors the sources and sinks of the trace gases. This paper presents a methodology of classification of cloud scene by Argus Spectral Data (CCSArSD) by applying radiance enhancement (RE) technique within 900-1700 nm of wavelength bands at infrared sounder along with GENSPECT line by line radiative transfer code for different weeks per passes. Argus was launched on aboard CanX-2 micro-satellite on 28th April 2008 as part of a technology demonstration mission. The algorithm describes a method to detect the cloudy or non-cloudy scenes. We have collected more than 300 weeks per passes with each have more than 200 spectra. The REi within the selected wavelength bands of Argus, provides a promising results to classify the cloud scene. We moreover worked on the shortwave upwelling radiative flux (W/m2) to improve the CCSArSD model, which needs further study to jump up to higher rank.

Insights into Tikhonov regularization: application to trace gas column retrieval and the efficient calculation of total column averaging kernels

Abstract. Insights are given into Tikhonov regularization and its application to the retrieval of vertical column densities of atmospheric trace gases from remote sensing measurements. The study builds upon the equivalence of the least-squares profile-scaling approach and Tikhonov regularization method of the first kind with an infinite regularization strength. Here, the vertical profile is expressed relative to a reference profile. On the basis of this, we propose a new algorithm as an extension of the least-squares profile scaling which permits the calculation of total column averaging kernels on arbitrary vertical grids using an analytic expression. Moreover, we discuss the effective null space of the retrieval, which comprises those parts of a vertical trace gas distribution which cannot be inferred from the measurements. Numerically the algorithm can be implemented in a robust and efficient manner. In particular for operational data processing with challenging demands on processing time, the proposed inversion method in combination with highly efficient forward models is an asset. For demonstration purposes, we apply the algorithm to CO column retrieval from simulated measurements in the 2.3 μm spectral region and to O3 column retrieval from the UV. These represent ideal measurements of a series of spaceborne spectrometers such as SCIAMACHY, TROPOMI, GOME, and GOME-2. For both spectral ranges, we consider clear-sky and cloudy scenes where clouds are modelled as an elevated Lambertian surface. Here, the smoothing error for the clear-sky and cloudy atmosphere is significant and reaches several percent, depending on the reference profile which is used for scaling. This underlines the importance of the column averaging kernel for a proper interpretation of retrieved column densities. Furthermore, we show that the smoothing due to regularization can be underestimated by calculating the column averaging kernel on a too coarse vertical grid. For both retrievals, this effect becomes negligible for a vertical grid with 20–40 equally thick layers between 0 and 50 km.

Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

AbstractThe objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the “truth” with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60–75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s−1). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.

SST와 CALIPSO 자료를 이용한 DCD 방법으로 정의된 안개화소 분석

Nighttime sea fog detection from satellite is very hard due to limitation in using visible channels. Currently, most widely used method for the detection is the Dual Channel Difference (DCD) method based on Brightness Temperature Difference between 3.7 and 11 <TEX>${\mu}m$</TEX> channel (BTD). However, this method have difficulty in distinguishing between fog and low cloud, and sometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profile measurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from various satellite remote sensing algorithms and suggested the direction for the improvement of the algorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCD method excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are real foggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The 514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposed new algorithm detects foggy pixels by comparing the difference between cloud top temperature and underneath sea surface temperature from assimilated data along with the DCD method. We have used two types of cloud top temperature, which obtained from 11 <TEX>${\mu}m$</TEX> brightness temperature (B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and 1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear, and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysis of the operational COMS fog detection algorithm reveals that the cloud screening process was strictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggy pixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Our analysis suggests that there is no winner for nighttime sea fog detection algorithms, but loser because real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. This overwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudged information, which are mostly originated from difficulty in distinguishing between clear and cloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgently required to reduce the enormous error in nighttime sea fog detection from satellite.

Acceleration of radiative transfer model calculations for the retrieval of trace gases under cloudy conditions

In the independent pixel approximation (IPA), radiative transfer computations involving cloudy scenes require two separate calls to the radiative transfer model (RTM), one call for a clear sky scenario, the other for an atmosphere containing clouds. In this paper, clouds are considered as an optically homogeneous layer. We present two novel methods for RTM performance enhancement with particular application to trace gas retrievals under cloudy conditions. Both methods are based on reusing results from clear-sky RTM calculations to speed up corresponding calculations for the cloud-filled scenario.The first approach is numerically exact, and has been applied to the discrete-ordinate with matrix exponential (DOME) RTM. Results from the original clear sky computation can be saved in the memory and reused for the non-cloudy layers in the second computation. In addition, for the whole-atmosphere boundary-value approach to the determination of the intensity field, we can exploit a ’telescoping technique’ to reduce the dimensionality (and hence the computational effort for the solution) of the boundary value problem in the absence of Rayleigh scattering contributions for higher azimuthal components of the radiation field.The second approach is (for the cloudy scenario) to generate a spectral correction applied to the radiation field from a fast two-stream RTM. This correction is based on the use of principal-component analysis (PCA) applied to a given window of spectral optical property data, in order to exploit redundancy in the data and confine the number of full-stream multiple scatter computations to the first few EOFs (Empirical Orthogonal Functions) arising from the PCA. This method has been applied to the LIDORT RTM; although the method involves some approximation, it provides accuracy better than 0.2%, and a speed-up factor of approximately 2 compared with two calls of RTM.

The impact of horizontal heterogeneities, cloud fraction, and liquid water path on warm cloud effective radii from CERES-like Aqua MODIS retrievals

Abstract. The impact of horizontal heterogeneities, liquid water path (LWP from AMSR-E), and cloud fraction (CF) on MODIS cloud effective radius (re), retrieved from the 2.1 μm (re2.1) and 3.8 μm (re3.8) channels, is investigated for warm clouds over the southeast Pacific. Values of re retrieved using the CERES algorithms are averaged at the CERES footprint resolution (∼20 km), while heterogeneities (Hσ) are calculated as the ratio between the standard deviation and mean 0.64 μm reflectance. The value of re2.1 strongly depends on CF, with magnitudes up to 5 μm larger than those for overcast scenes, whereas re3.8 remains insensitive to CF. For cloudy scenes, both re2.1 and re3.8 increase with Hσ for any given AMSR-E LWP, but re2.1 changes more than for re3.8. Additionally, re3.8–re2.1 differences are positive (&lt;1 μm) for homogeneous scenes (Hσ &lt; 0.2) and LWP &gt; 45 gm−2, and negative (up to −4 μm) for larger Hσ. While re3.8–re2.1 differences in homogeneous scenes are qualitatively consistent with in situ microphysical observations over the region of study, negative differences – particularly evinced in mean regional maps – are more likely to reflect the dominant bias associated with cloud heterogeneities rather than information about the cloud vertical structure. The consequences for MODIS LWP are also discussed.

Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data

Land surface temperature (LST) is of primary importance in understanding global environment change, urban climatology, and land–atmosphere energy exchange. Analysis of long-term remotely sensed LST data remains a challenge for researchers. Most previous studies explored urban thermal pattern over space and time using a limited number of clear-sky images or by removing cloud-contaminated pixels. The limitation in the number of image scenes prevents from deriving long-term LST climatology for a particular region. Moreover, simply eliminating cloudy pixels inevitably obscures the spatial and temporal patterns of LST. This research attempts to characterize the annual and seasonal temperature behaviors during the period of year 2000 to year 2010 in Los Angeles by employing an annual temperature cycle (ATC) model. All 115 image scenes (path 41, row 36) of less than 30% of cloud cover available from the Landsat archive were utilized for the analysis. Three ATC parameters, i.e., mean annual surface temperature (MAST), yearly amplitude surface temperature (YAST), and the phase shift, were optimized with the Levenberg–Marquardt minimization scheme to understand the annual and seasonal characteristics of LST. The overall RMSE of 7.36K was achieved for the optimization for all 115 images; while for the median monthly composite, the RMSE reached 2.85K. The monthly median composite partly removed day-specific anomalies and the impact of cloud cover and reflected LSTs largely under clear sky conditions, leading to the improvement in the modeling result. The MAST and YAST derived from the modeling result of the monthly median composite data were further analyzed to relate to three normalized indices, i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). The results showed that the mean temperature of urban areas in LA was as high as in the barren land area, reaching almost 310K, but the urban areas possessed a less fluctuation. Seasonal analysis suggested that in winter, the urban areas observed a higher temperature than the barren land/desert, implying that urban materials held a larger amount of heat for a longer time than the barren land. The separation of cloudy scenes into cloud percentage groups made it possible for the evaluation of the effect of cloud cover on the ATC modeling process. The sensitivity analysis indicated that the inclusion of cloudy images brought about a decrease in MAST ranging from 0.18 to 2.0K, depending on the percentage of cloud cover and the number of cloudy scenes used for the modeling. The decrease is due likely to the inclusion of cloud temperatures in analysis rather than shaded or other land surface temperatures. When all cloudy images were included in the modeling, a decrease of 2K in MAST and an increase of 0.15 in YAST were observed. The regression analysis demonstrated that NDBI and NDVI were the main factors influencing the spatial variations of MAST and YAST with the R2 value of 0.63 and 0.49, respectively. In addition, the spatial variation of YAST was found more complex than that of MAST, since the three indices can only explain up to 53% of its variance.

The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing

Abstract. The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is an eight-band (355, 380, 445, 470, 555, 660, 865, 935 nm) pushbroom camera, measuring polarization in the 470, 660, and 865 nm bands, mounted on a gimbal to acquire multiangular observations over a ±67° along-track range. The instrument has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. AirMSPI employs a photoelastic modulator-based polarimetric imaging technique to enable accurate measurements of the degree and angle of linear polarization in addition to spectral intensity. A description of the AirMSPI instrument and ground data processing approach is presented. Example images of clear, hazy, and cloudy scenes over the Pacific Ocean and California land targets obtained during flights between 2010 and 2012 are shown, and quantitative interpretations of the data using vector radiative transfer theory and scene models are provided to highlight the instrument's capabilities for determining aerosol and cloud microphysical properties and cloud 3-D spatial distributions. Sensitivity to parameters such as aerosol particle size distribution, ocean surface wind speed and direction, cloud-top and cloud-base height, and cloud droplet size is discussed. AirMSPI represents a major step toward realization of the type of imaging polarimeter envisioned to fly on NASA's Aerosol-Cloud-Ecosystem (ACE) mission in the next decade.

Retrieval of aerosol microphysical and optical properties above liquid clouds from POLDER/PARASOL polarization measurements

Abstract. Most of the current aerosol retrievals from passive sensors are restricted to cloud-free scenes, which strongly reduces our ability to monitor the aerosol properties at a global scale and to estimate their radiative forcing. The presence of aerosol above clouds (AAC) affects the polarized light reflected by the cloud layer, as shown by the spaceborne measurements provided by the POlarization and Directionality of Earth Reflectances (POLDER) instrument on the PARASOL satellite. In a previous work, a first retrieval method was developed for AAC scenes and evaluated for biomass-burning aerosols transported over stratocumulus clouds. The method was restricted to the use of observations acquired at forward scattering angles (90–120°) where polarized measurements are highly sensitive to fine-mode particle scattering. Non-spherical particles in the coarse mode, such as mineral dust particles, do not much polarize light and cannot be handled with this method. In this paper, we present new developments that allow retrieving also the properties of mineral dust particles above clouds. These particles do not much polarize light but strongly reduce the polarized cloud bow generated by the liquid cloud layer beneath and observed for scattering angles around 140°. The spectral attenuation can be used to qualitatively identify the nature of the particles (i.e. accumulation mode versus coarse mode, i.e. mineral dust particles versus biomass-burning aerosols), whereas the magnitude of the attenuation is related to the optical thickness of the aerosol layer. We also use the polarized measurements acquired in the cloud bow to improve the retrieval of both the biomass-burning aerosol properties and the cloud microphysical properties. We provide accurate polarized radiance calculations for AAC scenes and evaluate the contribution of the POLDER polarization measurements for the simultaneous retrieval of the aerosol and cloud properties. We investigate various scenes with mineral dust particles and biomass-burning aerosols above clouds. For clouds, our results confirm that the droplet size distribution is narrow in high-latitude ocean regions and that the droplet effective radii retrieved from both polarization measurements and from total radiance measurements are generally close for AAC scenes (departures smaller than 2 μm). We found that the magnitude of the primary cloud bow cannot be accurately estimated with a plane parallel transfer radiative code. The errors for the modeling of the polarized cloud bow are between 4 and 8% for homogenous cloudy scenes, as shown by a 3-D radiative transfer code. These effects only weakly impact the retrieval of the Aerosol Optical Thickness (AOT) performed with a mineral dust particle model for which the microphysical properties are entirely known (relative error smaller than 6%). We show that the POLDER polarization measurements allow retrieving the AOT, the fine-mode particle size, the Ångström exponent and the fraction of spherical particles. However, the complex refractive index and the coarse-mode particle size cannot be accurately retrieved with the present polarization measurements. Our complete and accurate algorithm cannot be applied to process large amounts of data, so a simpler algorithm was developed to retrieve the AOT and the Ångström exponent above clouds in an operational way. Illustrations are provided for July–August 2008 near the African coast. Large mean AOTs above clouds at 0.865 μm (&gt;0.3) are retrieved for oceanic regions near the coasts of South Africa that correspond to biomass-burning aerosols, whereas even larger mean AOTs above clouds for mineral dust particles (&gt;0.6) are also retrieved near the coasts of Senegal. For these regions and time period, the direct AAC radiative forcing is likely to be significant. The final aim of this work is the global monitoring of the AAC properties and the estimation of the direct aerosol radiative forcing in cloudy scenes.

A global analysis on the view‐angle dependence of plane‐parallel oceanic liquid water cloud optical thickness using data synergy from MISR and MODIS

AbstractWe examine the viewing zenith angle dependent bias (VZA bias) in warm cloud optical thickness (τ) retrieved from a plane‐parallel approach applied to fused Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging SpectroRadiometer (MISR) data for the months of January and July between 2001 and 2008. The near‐simultaneous multiple view‐angle observations from MISR offers many advantages over previous τ‐VZA bias studies: 1) The analysis no longer requires seasonal and latitudinal cloud invariant assumptions, 2) consistent cloudy scene identification with VZA, 3) stratification of VZA‐bias with scene characteristics, and 4) a greater range of sun‐view geometries. Contrasting results between previous studies are resolved through careful consideration of the relative azimuth angle (RAZ) between sun and view. Relative to nadir‐retrieved τ, τ increases in both forward‐ and backscatter directions with higher value in backscatter directions for solar zenith angle (SZA) &lt; ~40°. For SZA &gt; ~40°, τ increases with increasing VZA in backscatter directions and strongly decreases in forward‐scatter directions. For the most oblique views, ~40–100% absolute monthly mean differences relative to nadir‐retrieved τ is common. This behavior is strongly tied to the sampled RAZ and explained based on three factors tied to the spatial heterogeneity of clouds. These factors also explain the behavior of the τ‐VZA bias when stratified by nadir‐retrieved τ and spatial heterogeneity, even in the thin‐cloud limit where sun‐glint effects are evident. We also observe an underestimation of τ relative to nadir in the rainbow‐scattering directions and attribute it to an overestimation of the cloud‐drop effective radius retrieved from MODIS due to cloud heterogeneity. There remains a need to quantify the bias in nadir‐retrieved τ as a function of SZA and spatial heterogeneity as a step toward providing bias correction over a wide range of sun‐view geometries.

A Sea-Ice Lead Detection Algorithm for Use With High-Resolution Airborne Visible Imagery

The detection of leads, or cracks, in sea ice is critical for the derivation of sea-ice freeboard from altimetric measurements of sea-ice elevation. We present an approach for lead detection in sea ice using high-resolution visible imagery from airborne platforms. We develop a new algorithm, i.e., the sea-ice lead detection algorithm using minimal signal (SILDAMS), that detects clouds, extracts leads, and classifies ice types within leads from airborne visible imagery. Cloud detection is based on an assessment of local variances of pixel brightness across image scenes and where available coincident altimetric measurements are used to confirm suspected cloudy scenes. The lead extraction step computes affine time-frequency distributions (minimal signal) for the Red, Green, and Blue channels of each image. The transformed outputs are combined to take advantage of three channels simultaneously. Finally, lead pixel geolocations are extracted using a set of uniform thresholds for ice typing (including open water, thin ice, and gray ice) within leads along each flight line. SILDAMS was tested using data from the Digital Mapping System (DMS). DMS digital photographs represent the highest resolution ( ≈10 cm) visible imagery available over sea ice and were collected during NASA Operation IceBridge sea-ice flights in the Antarctic and the Arctic in 2009 and 2010, respectively. We demonstrate that SILDAMS has a high lead detection capability of 99%.

Towards the use of microphysical variables for the assimilation of cloud‐affected infrared radiances

AbstractThis article focuses on the simulation and the assimilation of satellite infrared observations in convective‐scale numerical weather prediction (NWP) systems. A radiative transfer model that includes profiles for liquid‐water content, ice‐water content and cloud fraction was used to simulate cloud‐affected radiances as background equivalents. This approach avoids the use of cloud parameters (cloud‐top pressure and effective cloud fraction) deduced from a CO2 slicing algorithm and the modelling of clouds by single‐layer clouds. The advanced radiative transfer model was evaluated using infrared observations measured by the Infrared Atmospheric Sounding Interferometer (IASI). The observation‐screening procedure that was developed to improve the selection of usable cloudy scenes led to a good agreement between observations and background equivalents. For that purpose, a radiance analysis of collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view was used. The goal of this preliminary work is to assess the feasibility of adding the cloud variables (liquid and ice‐water contents) to the state vector of the assimilation system. The approach is illustrated with one‐dimensional variational (1D‐Var) retrievals. The physical consistency of the 1D‐Var adjustments is verified with real observations. Then observing‐system simulation experiments (OSSE) are used to validate the 1D‐Var retrievals.

A method of retrieving cloud top height and cloud geometrical thickness with oxygen A and B bands for the Deep Space Climate Observatory (DSCOVR) mission: Radiative transfer simulations

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) was designed to measure the atmosphere and surface properties over the whole sunlit half of the Earth from the L1 Lagrangian point. It has 10 spectral channels ranging from the UV to the near-IR, including two pairs of oxygen (O2) A-band (779.5 and 764nm) and B-band (680 and 687.75nm) reference and absorption channels selected for the cloud height measurements. This paper presents the radiative transfer analysis pertinent to retrieving cloud top height and cloud geometrical thickness with EPIC A- and B-band observations. Due to photon cloud penetration, retrievals from either O2 A- or B-band channels alone gives the corresponding cloud centroid height, which is lower than the cloud top. However, we show both the sum and the difference between the retrieved cloud centroid heights in the A and B bands are functions of cloud top height and cloud geometrical thickness. Based on this fact, the paper develops a new method to retrieve cloud top height and cloud geometrical thickness simultaneously for fully cloudy scenes over ocean surface. First, cloud centroid heights are calculated for both A and B bands using the ratios between the reflectances of the absorbing and reference channels; then the cloud top height and the cloud geometrical thickness are retrieved from the two dimensional look up tables that relate the sum and the difference between the retrieved centroid heights for A and B bands to the cloud top height and the cloud geometrical thickness. This method is applicable for clouds thicker than an optical depth of 5.

Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressure contains information on aerosol layer pressure. For cloud free scenes, the derived FRESCO cloud pressure is close to the aerosol layer pressure, especially for optically thick aerosol layers. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressure may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO and AAI data, an estimate for the aerosol layer pressure can be given.

Retrieving aerosol in a cloudy environment: aerosol product availability as a function of spatial resolution

Abstract. The challenge of using satellite observations to retrieve aerosol properties in a cloudy environment is to prevent contamination of the aerosol signal from clouds, while maintaining sufficient aerosol product yield to satisfy specific applications. We investigate aerosol retrieval availability at different instrument pixel resolutions using the standard MODIS aerosol cloud mask applied to MODIS data and supplemented with a new GOES-R cloud mask applied to GOES data for a domain covering North America and surrounding oceans. Aerosol product availability is not the same as the cloud free fraction and takes into account the techniques used in the MODIS algorithm to avoid clouds, reduce noise and maintain sufficient numbers of aerosol retrievals. The inherent spatial resolution of each instrument, 0.5×0.5 km for MODIS and 1×1 km for GOES, is systematically degraded to 1×1, 2×2, 1×4, 4×4 and 8×8 km resolutions and then analyzed as to how that degradation would affect the availability of an aerosol retrieval, assuming an aerosol product resolution at 8×8 km. The analysis is repeated, separately, for near-nadir pixels and those at larger view angles to investigate the effect of pixel growth at oblique angles on aerosol retrieval availability. The results show that as nominal pixel size increases, availability decreases until at 8×8 km 70% to 85% of the retrievals available at 0.5 km, nadir, have been lost. The effect at oblique angles is to further decrease availability over land but increase availability over ocean, because sun glint is found at near-nadir view angles. Finer resolution sensors (i.e., 1×1, 2×2 or even 1×4 km) will retrieve aerosols in partly cloudy scenes significantly more often than sensors with nadir views of 4×4 km or coarser. Large differences in the results of the two cloud masks designed for MODIS aerosol and GOES cloud products strongly reinforce that cloud masks must be developed with specific purposes in mind and that a generic cloud mask applied to an independent aerosol retrieval will likely fail.

Atmospheric water‐vapour profiling from passive microwave sounders over ocean and land. Part II: Validation using existing instruments

AbstractIn a companion article (Paper I), a water‐vapour retrieval algorithm has been developed using microwave sounding observations. An operational chain derived from this algorithm is being used for the Megha‐Tropiques mission, launched in autumn 2011. The water vapour is retrieved for clear and cloudy scenes, excluding precipitation cases, over ocean and land surfaces. By‐products are also calculated by the algorithm, including surface temperature and microwave emissivities over land.Evaluation of the water‐vapour products is the main objective of this article. The algorithm is tested using the HSB/AMSR‐E (resp. MHS/AMSU‐A) instruments on board the AQUA (resp. MetOp) platform, over the Tropics between ±30° in latitude. Results are compared with European Centre for Medium‐Range Weather Forecasts (ECMWF) analyses and data from radiosondes. The root‐mean‐square error for the total column water vapour is ∼5 kg m2 for both clear and cloudy scenes, compared with radiosondes over land. The atmospheric water‐vapour profile is retrieved for six atmospheric layers and the root‐mean‐square error is estimated to be lower than 20% in relative humidity, even for the lowest atmospheric layer over land. A posteriori validation tests on the brightness temperatures indicate an overall positive impact of the retrievals relative to a priori ECMWF analyses. Copyright © 2012 Royal Meteorological Society

Atmospheric water‐vapour profiling from passive microwave sounders over ocean and land. Part I: Methodology for the Megha‐Tropiques mission

AbstractA water‐vapour retrieval algorithm has been developed that uses satellite observations in the microwave region. It is based on neural‐network modelling and includes a dedicated calibration scheme for the satellite observations. The water vapour is retrieved for clear and cloudy scenes, over both ocean and land surfaces. Precipitation cases are excluded. The atmospheric relative humidity profile is retrieved on six atmospheric layers, together with the total column water vapour. By‐products are also retrieved by the algorithm, including surface temperature and microwave emissivities over the continents and surface wind speed over the ocean. A first version of a retrieval chain has been produced for the French–Indian Megha‐Tropiques mission launched on 12 October 2011. The algorithm has been further developed for the instruments AMSR‐E/HSB (resp. AMSU‐A/MHS) on board the AQUA (resp. MetOp) platform, in order to test it on existing satellite observations. In this article, the principles of the inversion method are presented and the theoretical retrieval uncertainties are assessed using direct tests on simulated data as well as estimations using the traditional information‐content analysis. Results of the retrieval algorithm will be evaluated in a companion article for AQUA and MetOp observations using comparisons with European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis and radiosonde measurements. Copyright © 2012 Royal Meteorological Society