Brightness Temperature Difference Research Articles

Combined dust detection algorithm by using MODIS infrared channels over East Asia

A new dust detection algorithm is developed by combining the results of multiple dust detection methods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTD methods have limitations in identifying the offset values of the BTD to discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10×10pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

Intercalibrating Microwave Satellite Observations for Monitoring Long-Term Variations in Upper- and Midtropospheric Water Vapor*

Abstract This paper analyzes the growing archive of 183-GHz water vapor absorption band measurements from the Advanced Microwave Sounding Unit B (AMSU-B) and Microwave Humidity Sounder (MHS) on board polar-orbiting satellites and document adjustments necessary to use the data for long-term climate monitoring. The water vapor channels located at 183.31 ± 1 GHz and 183.31 ± 3 GHz are sensitive to upper- and midtropospheric relative humidity and less prone to the clear-sky sampling bias than infrared measurements, making them a valuable but underutilized source of information on free-tropospheric water vapor. A method for the limb correction of the satellite viewing angle based upon a simplified model of radiative transfer is introduced to remove the scan angle dependence of the radiances. Biases due to the difference in local observation time between satellites and spurious trends associated with satellite orbital drift are then diagnosed and adjusted for using synthetic radiative simulations based on the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim). The adjusted, cloud-filtered, and limb-corrected brightness temperatures are then intercalibrated using zonal-mean brightness temperature differences. It is found that these correction procedures significantly improve consistency and quantitative agreement between microwave radiometric satellite observations that can be used to monitor upper- and midtropospheric water vapor. The resulting radiances are converted to estimates of the deep-layer-mean upper- and midtropospheric relative humidity, and can be used to evaluate trends in upper-tropospheric relative humidity from reanalysis datasets and coupled ocean–atmosphere models.

USING AEROSOL REFLECTANCE FOR DUST DETECTION

Abstract. In this study we propose an approach for dust detection by aerosol reflectance over arid and urban region in clear sky condition. In urban and arid areas surface reflectance in red and infrared spectral is bright and hence shorter wavelength is required for this detections. Main step of our approach can be mentioned as: cloud mask for excluding cloudy pixels from our calculation, calculate Rayleigh path radiance, construct a surface reflectance data base, estimate aerosol reflectance, detect dust aerosol, dust detection and evaluations of dust detection. Spectral with wavelength 0.66, 0.55, 0.47 μm has been used in our dust detection. Estimating surface reflectance is the most challenging step of obtaining aerosol reflectance from top of atmosphere (TOA) reflectance. Hence for surface estimation we had created a surface reflectance database of 0.05 degree latitude by 0.05 degree longitude resolution by using minimum reflectivity technique (MRT). In order to evaluate our dust detection algorithm MODIS aerosol product MOD04 and common dust detection method named Brightness Temperature Difference (BTD) had been used. We had implemented this method to Moderate Resolution Imaging Spectroradiometer (MODIS) image of part of Iran (7 degree latitude and 8 degree longitude) spring 2005 dust phenomenon from April to June. This study uses MODIS LIB calibrated reflectance high spatial resolution (500 m) MOD02Hkm on TERRA spacecraft. Hence our dust detection spatial resolution will be higher spatial resolution than MODIS aerosol product MOD04 which has 10 × 10 km2 and BTD resolution is 1 km due to the band 29 (8.7 μm), 31 (11 μm), and 32 (12 μm) spatial resolutions.

Improved Quantitative Precipitation Forecasts by MHS Radiance Data Assimilation with a Newly Added Cloud Detection Algorithm

Abstract Satellite microwave humidity sounding data are assimilated through the gridpoint statistical interpolation (GSI) analysis system into the Advanced Research core of the Weather Research and Forecasting (WRF) model (ARW) for a coastal precipitation event. A detailed analysis shows that uses of Microwave Humidity Sounder (MHS) data from both NOAA-18 and MetOp-A results in GSI degraded precipitation threat scores in a 24-h model forecast. The root cause for this degradation is related to the MHS quality control algorithm, which is supposed to remove cloudy radiances. Currently, the GSI cloud detection is based on the brightness temperature differences between observations and the model background state at two MHS window channels. It is found that the GSI quality control algorithm fails to identify some MHS cloudy radiances in cloud edges where the ARW model has no cloud and the water vapor amount is low. A new MHS cloud detection algorithm is developed based on a statistical relationship between three MHS channels and the Geostationary Operational Environmental Satellite (GOES) imager channel at 10.7 μm. The 24-h quantitative precipitation forecast is improved rather than degraded by MHS radiance data assimilation when the new cloud detection algorithm is added to the GSI MHS quality control process. The temporal evolution of 3-h accumulative rainfall distributions compared favorably with that of multisensor NCEP observations and GOES-12 imager observations. The precipitation threat scores are increased by more than 50% after 3–6 h of model forecasts for 3-h rainfall thresholds exceeding 1.0 mm.

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.

Dust detection over desert surfaces with thermal infrared bands using dynamic reference brightness temperature differences

The brightness temperature difference (BTD) between two thermal infrared bands is a common index for dust detection. However, the BTD is sensitive to the observed temperature, which hinders its use in automatic dust detection, especially over desert land surfaces. In this paper, a dynamic reference brightness temperature differences (DRBTD) algorithm was developed to detect dust by removing the influence of the observed temperature on the BTD. Using long‐term MODIS observations, the algorithm establishes the clear‐sky linear relationships pixel by pixel between the brightness temperatures (BTs) at 12 and 11 µm channels and the relationships between the BTs at 8.6 and 11 µm channels. From these relationships, the reference BTDs are dynamically generated according to the observed brightness temperatures. Next, the DRBTDI, which is the difference of the observed BTD and the reference BTD, is created and used to separate the dust from other observed objects. This algorithm is applied to MODIS observations to detect several dust events during the daytime and the nighttime over Mongolia and northwestern and northern China. The results are compared with Ozone Monitoring Instrument aerosol index (OMI AI), MODIS Deep Blue aerosol optical depth (AOD), and Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The comparisons indicate that the DRBTD algorithm can effectively distinguish dust from clouds and land surface. During the daytime, the DRBTDI is correlated with the OMI AI and MODIS AOD with a correlation coefficient of Pearson (r) of 0.79 and 0.77, respectively. At night, the DRBTDI is correlated with the CALIOP dust AOD with an r of 0.78.

Discriminating sea ice from low-level water clouds in split-window, mid-wavelength IR imagery

A technique is demonstrated to enhance the contrast between sea ice and low-level water clouds. The approach uses the brightness temperature difference (BTD) feature from data collected in the split-window, mid-wavelength infrared (IR) region (i.e. two bands at 3.7 μm and 4.0 μm). These spectral data are available with Visible Infrared Imager Radiometer Suite (VIIRS) moderate-resolution bands M12 and M13, respectively. Under daytime conditions, the data collected in these bands contain energy that originates from both the sun and the Earth–atmosphere system. Due to the small wavelength difference between these, the terrestrial energy component in the bands is typically quite similar as are the surface reflectances for sea ice and ocean surfaces. Thus, the enhanced contrast between sea ice and water clouds, evident in a M12–M13 BTD image, results from differences in the solar energy, which decreases rapidly across this atmospheric window. Observed BTD values for water clouds can exceed 30K, while those for snow-ice fields are typically much smaller (e.g. 0–5K). Thus, water clouds appear bright in the image while sea ice, oceans, and most land surfaces are very dark. The enhanced contrast in the split-window, mid-wave IR BTD image makes it valuable for both image analysis and use in cloud algorithms. In addition, these images support the creation of manually generated cloud masks that have been shown useful for quantitatively evaluating the performance of automated cloud analysis algorithms and cloud forecast models. In this article, the value of 3.7 μm minus 4.0 μm BTD imagery for distinguishing between sea ice and low-level water clouds is shown using VIIRS data collected over the Beaufort Sea on 31 May 2012. Manually generated cloud masks, derived in part from these data, are then used to quantitatively evaluate the effectiveness of various cloud tests, including those used in the VIIRS cloud mask algorithm and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm. The results strongly suggest that split-window, mid-wavelength IR imagery provides valuable information to help differentiate between clouds and sea ice. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring, especially over the polar regions.

Use of spaceborne lidar for the evaluation of thin cirrus contamination and screening in the Aqua MODIS Collection 5 aerosol products

Cloud contamination from subvisual thin cirrus clouds is still a challenging issue for operational satellite aerosol retrievals. In the A‐Train constellation, concurrent high‐sensitivity cirrus observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) provide us with an unprecedented opportunity to examine the susceptibility of the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrievals to thin cirrus contamination and to evaluate the robustness of various cirrus screening techniques. Quantitative evaluations indicate that the current cirrus screening schemes in the MODIS Dark Target and Deep Blue Collection 5 aerosol retrievals can effectively remove most cirrus signals while some residual thin cirrus signals still exist with strong spatial and seasonal variability. Results also show significant linkage between thin cirrus occurrence frequency and the susceptibility of aerosol retrievals to thin cirrus contamination. Using the CALIPSO cirrus observations as a reference, we also examined the effectiveness and robustness of eight MODIS‐derived cirrus screening parameters. These parameters include apparent reflectance at 1.38 µm (R1.38), cirrus reflectance at 0.66 µm (CR0.66), CR0.66 cirrus flag (CF), reflectance ratio between 1.38 µm and 0.66 µm (RR1.38/0.66), reflectance ratio between 1.38 µm and 1.24 µm (RR1.38/1.24), brightness temperature difference between 8.6 µm and 11 µm (BTD8.6–11), brightness temperature difference between 11 µm and 12 µm (BTD11–12), and cloud phase infrared approach (CPIR). Among these parameters, RR1.38/0.66 achieves the best overall performance, followed by the BTD11–12. Results from several test cases suggest that the cirrus screening schemes in the operational MODIS aerosol retrieval algorithms can be further improved to reduce thin cirrus contamination.

An enhanced dust index for Asian dust detection with MODIS images

An enhanced dust index (EDI) for Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance bands is proposed that provides a means to detect the dust status of the atmosphere. The EDI utilizes only solar reflectance channels and may therefore be applied consistently to the entire MODIS time series records (1999 to present) for daytime dust observation, producing a higher spatial resolution (500 m) dust result than that from thermal-infrared records (1000 m), which were developed previously and are currently being used. The index introduces dust optical density (α), which can be simply estimated by spectral unmixing, into the normalized difference between reflectance at near-infrared (2.13 μm) and blue (0.469 μm). Dust severity can thus be rated from weak to severe within a standard range of –1 to 1. The index was applied to 11 typical dust events during 2000–2010 in East Asia, where it showed good coherence with meteorological station-observed visibility (R 2 = 0.7909, p < 0.05) and standardized visibility (R 2 = 0.7128, p < 0.05). Further comparison with the commonly used normalized difference dust index (NDDI) and brightness temperature difference (BTD) between MODIS bands 31 and 32 also indicated a better performance of the EDI in identifying the spatial and density distributions of dust. Previously applied satellite-based dust indices, particularly for the visible and near-infrared, can therefore be improved for a better quantification of dust aerosols.

Detection of Optically Thin Mineral Dust Aerosol Layers over the Ocean Using MODIS

Abstract Analyses show that several existing Moderate Resolution Imaging Spectroradiometer (MODIS) dust detection techniques, including an approach based on simple brightness temperature difference thresholds, the D-parameter method, and the multichannel image (MCI) algorithm, may be more effective for detection of highly concentrated dust plumes than for thin dust layers. Using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud and aerosol classification as a reference, the sensitivities of six MODIS radiative parameters (including brightness temperature differences, and standard deviation and ratios of reflectances) to cloud, clear sky, and dust layers are examined in this paper. Reflectance ratios and the standard deviation of reflectances were confirmed to be useful in the discrimination of dust from cloud and underlying ocean surface, while brightness temperature differences alone were not sufficient to separate dust from cloud and clear sky over the ocean surface. Using a collocated MODIS and CALIPSO training dataset from 2008, visible and infrared MODIS radiative parameters from six latitude bands and four seasons were combined using linear and quadratic discriminant analyses to develop a new algorithm for the detection of optically thin dust over the ocean. The validation using collocated MODIS and CALIPSO data from 2009 shows that the present algorithm is effective in detecting thin dust layers having optical thicknesses between 0.1 and 2.0, but that it tends to misclassify optically thicker dust layers as clouds.

Noninvasive Analysis of Paintings by Mid‐infrared Hyperspectral Imaging

In recent years, in situ noninvasive spectroscopy has played an increasingly important role in art conservation. Spectroscopic methods can be used to gain a deep understanding of the material composition of art objects while fully respecting their integrity and value. Considerable improvements in detector technology, instrument–computer interfacing, focusing optics, and the performance of radiation sources have been made in the spectroscopic field and have led to the development and successful application of a series of noninvasive and portable analytical tools for point examination. In this context, current scientific interest is focused on the development of mapping/imaging multi-/hyperspectral methods, since area examination naturally meets the demands of a holistic art-historical approach by revealing not only the chemical composition of painting materials but also their semiquantitative spatial distribution with respect to what is visible to the naked eye. Recently, the possibility of mapping elemental distribution on paintings by means of a portable scanning macro X-ray fluorescence device was demonstrated to be useful for the investigation of the materials used by artists. The molecular identification and spatial distribution of a number of pigments can be inferred from reflection imaging in the visible (Vis: 400–750 nm) and near-infrared regions (NIR: 750–2500 nm) by combining information on electronic transitions in the visible range with overtone and combination vibrational bands in the near-infrared range which are associated with inorganic pigments containing hydroxy groups, such as lead white, azurite, and gypsum. The identification and mapping of organic compounds is a challenge for the noninvasive analysis of artworks. In comparison with inorganic pigments and fillers, there is a larger variety of organic compounds, and they are present in smaller amounts and more subject to chemical alteration. If both traditional and modern art are taken into consideration, just some of the organic compounds to be identified and localized may include: drying oils, proteins, acrylics, alkyds, polyvinyl acetates, natural and synthetic waxes, terpene resins, and natural and synthetic dyes. Recently, Ricciardi et al. demonstrated the potential of hyperspectral imaging in the NIR region (10000–4000 cm ) to discriminate between lipid and proteinaceous binders on illuminated manuscripts on the basis of the vibrational combination/overtone bands of methylene groups and amides. In contrast, the mid-infrared (MIR) range has not yet been exploited for the spatially resolved remote study of artworks, although the MIR range (4000–600 cm ) has proved to be very informative for the identification of artists materials by noninvasive point analysis 12] or microinvasively by cross-section imaging. Herein, we describe the potential of imaging MIR spectroscopy for painting analysis through the application of a novel hyperspectral imaging system (HI90, Bruker Optics). The system developed for the remote identification and mapping of hazardous compounds was adapted in this study for reflection measurements of paintings by the use of an external infrared radiation source. The subject of our study was a painting by Alberto Burri, Sestante 10 (1982), which is currently exhibited at the Ex-Seccatoi del Tabacco (Perugia, Italy). Three areas (ca. 9 9 cm) of the large painting (250 360 cm) were analyzed on site with the HI90 system. We also investigated several points within the same areas noninvasively with a portable FTIR spectrometer (Alpha-R, Bruker Optics) to validate the assignment made on the basis of the HI90 spectral data. In Figure 1a, the visible image of the investigated area I of Sestante 10 is shown. The resulting brightness temperature difference images in Figure 1b,c clearly highlight the use of a different binder for the orange sector to that used for the other sectors. More precisely, the image depicted in Figure 1b shows the false-color representation of the difference in the mean brightness temperature for a peak in the signature of the orange area (1154–1167 cm ) and the mean brightness temperature for a frequency range in which the reflectance is lower (1197–1209 cm ; see range (b) in Figure 1d). The area in the brightness temperature image in Figure 1b indicates the presence of an acrylic binder, as suggested by a comparison with the spectrum recorded with the HI90 instrument [*] Dr. F. Rosi, Dr. C. Miliani Istituto di Scienze e Tecnologie Molecolari CNR-ISTM, SMAArt Via Elce di sotto, 9 Perugia 06123 (Italy) E-mail: costanza.miliani@cnr.it

The effect of soil moisture and wind speed on aerosol optical thickness retrieval in a desert environment using SEVIRI thermal channels

Dust emission and deposition are associated with several factors such as surface roughness, land cover, soil properties, soil moisture (SM), and wind speed (WS). A combination of land surface and remote-sensing models has recently been investigated for dust detection and monitoring. The thermal bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) satellite are widely used for qualitative detection of dust over desert because of their high spectral and temporal resolutions. In this work, the contribution of ground-measured WS data and satellite-measured SM data on aerosol optical thickness (AOT) retrieval was investigated using an artificial neural network (ANN) model. ANNs have been applied in similar applications and have shown a higher performance than simple multiple-regression models. This performance is mainly due to the ANN's ability to capture complex and non-linear relationships between inputs and outputs. A combination of MSG/SEVIRI brightness temperature (BT)/brightness temperature differences (BTDs), BTD3.9–10.8, BTD8.7–10.8, BTD10.8–12, and BT3.9, was used as input to the base ANN model while Aerosol Robotic Network (AERONET) AOT (level 2) data at 0.5 μm were used as output. These input/output sets were obtained from two stations (Hamim and Mezaira) lying in the inland desert of the United Arab Emirates (UAE). About 3800 observations were collected, of which two-thirds were used to train the ANN model and the remaining third was kept as an independent set to assess the accuracy of the trained model. Later, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) SM data and ground-measured WS data were used as additional inputs to the base model to investigate their contribution to the AOT retrieval. SM data consist of daytime AMSR-E-derived daily and collected from a National Snow and Ice Data Centre (NSIDC)-archived database. Hourly average WS data were also collected at 10 m height in the same AERONET sites from two stations managed by the UAE National Centre of Meteorology and Seismology. All ground and satellite measurements were extracted for the closest time to AERONET measurements. The use of these additional inputs has been shown to have a positive impact on the accuracy of simulated AOT. The addition of these inputs to the base ANN increased R 2 from 0.68 to 0.76 and reduced root mean square error from 0.113 to 0.09.

Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8–3.9, 6.2–10.8, 8.7–10.8, and 10.8–12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8–3.9 and 8.7–10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2–10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively.

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

The upcoming Landsat Data Continuity Mission (LDCM) will include new channels centered around 1.38 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> and 12 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu \hbox{m}$</tex></formula> . This work studies the potential impact of these new channels on LDCM's cloud detection capabilities by using MODerate resolution Imaging Spectroradiometer (MODIS) data as a proxy. Thresholds for the 1.38 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> band and the so-called “split window” technique (using the brightness temperature difference of bands centered at 11 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \mu\hbox{m}$</tex></formula> and 12 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> ) are derived using atmospheric profiles from the ECMWF ERA-40 reanalysis and a MODIS-band radiance simulator. The thresholds are incorporated into a previously published cloud mask scheme and applied on low- and mid-latitude (60 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{\circ}$</tex></formula> S to 60 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$^{\circ}\ \hbox{N}$</tex></formula> ) MODIS radiance data from two different days, six months apart. While the previous scheme yields agreement rates to the MODIS cloud mask just below 80 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\%$</tex></formula> , the addition of the 1.38 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> and split window tests increases the agreement by 7–9%. The earlier scheme is still appropriate for cloud masking of historical Landsat images and for carrying consistent cloud detection into the future. The enhanced scheme of this paper, on the other hand, with its improved masking of primarily high thin clouds, can be either used independently or combined with other masking techniques for generating reliable LDCM cloud mask products that can potentially include confidence indicators based on the degree of agreement between multiple cloud masks.

Identification of raining clouds using a method based on optical and microphysical cloud properties from Meteosat second generation daytime and nighttime data

A new scheme for the delineation of raining and non-raining cloud areas applicable to mid-latitudes from daytime and nighttime multispectral satellite data is developed. The technique is based on optical and microphysical cloud properties using an artificial neural network. The tests have been conducted during the rainy season of 2006/2007. The proposed algorithm uses the spectral parameters of SEVIRI (Spinning Enhanced Visible and Infrared): brightness temperature TIR10.8 and brightness temperature differences ΔTIR10.8–IR12.1, ΔTIR8.7–IR10.8, ΔTIR3.9–IR10.8 and ΔTIR3.9–WV7.3 during the nighttime and reflectances RVIS0.6, RNIR1.6, brightness temperature TIR10.8, brightness temperature difference ΔTIR8.7–IR10.8 and ΔTIR10.8–IR12.0 during the daytime. The algorithm is calibrated by instantaneous meteorological radar using multilayer perceptron. Radar provided the ‘‘ground precipitation truth’’ for training and validation. The application shows interesting and encouraging results.

Night-time cloud detection for FY-3A/VIRR using multispectral thresholds

Night-time cloud detection using satellite data is a challenging area of research. This article presents a night-time cloud detection algorithm based on multispectral thresholds for the Visible and Infrared Radiometer (VIRR). VIRR is one of the keystone instruments on board the Chinese Feng Yun 3 (FY-3) polar-orbiting meteorological satellite. In this algorithm, three thermal infrared channels and other ancillary data are used to test for the presence of clouds according to different underlying surface types, and the four levels of possible cloud confidence are used to report whether a pixel is cloudy or clear. This algorithm strengthens the ability of identification of low cloud using the brightness temperature difference between the 3.7 and 12 μm channels. The comparisons of a new cloud mask with the official VIRR cloud mask product and with the official Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product are shown to illustrate and validate the effect of this new algorithm. In addition, this algorithm is applied to FY-3B/VIRR data to test the validity and accuracy of cloud detection.

Physical explanation of the weakened brightness temperature difference signal over the yellow sea during a dust event: Case study for March 15–16, 2009

This paper attempts to explain the cause of weakening or disappearing brightness temperature difference (BTD) signatures, in particular, over the Yellow Sea during the March 15–16, 2009 dust event. Using a simple correction approach that removes the effects of emissivity difference and water vapor effect difference, we confirmed that the weakening or disappearing BTD signatures noted over the Yellow Sea are largely due to the spectral emissivity contrast between land and ocean. The weakening or disappearing dust is hypothesized to be pronounced when the dust loading is weak because of the surface contribution to the top of atmosphere radiance, and that it is mainly due to the difference in spectral emissivity over the window band between land and ocean. It is further suggested that water vapor may be considered as a correction factor in spite of its smaller contribution.

A New Technique Using Infrared Satellite Measurements to Improve the Accuracy of the CALIPSO Cloud-Aerosol Discrimination Method

In this paper, we develop a new technique called the brightness temperature difference cloud and aerosol discrimination algorithm (BTD CAD) that uses thermal infrared satellite measurements to improve the accuracy of the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) CAD algorithm. It has been shown that the CALIPSO CAD algorithm can misclassify dense dust as cloud because the CALIPSO two-wavelength backscatter lidar operates at 532 and 1064 nm where very similar scattering properties are known to exist between dense dust and cloud. Therefore, we use the 11 and 12 μm thermal infrared channels from both the moderate resolution imaging spectroradiometer (MODIS) and the spinning enhanced visible and infrared imager (SEVIRI), which are very sensitive to dust concentration, in order to reduce the frequency of the dust misclassifications encountered by the CALIPSO CAD algorithm. For the two Saharan dust events presented in this paper, both the MODIS and SEVIRI BTD CAD techniques performed well but the MODIS BTD CAD correctly reclassified more CALIPSO CAD misclassifications as dust. After applying both techniques to all the daytime CALIPSO transects over North Africa during June 2007, the MODIS and SEVIRI BTD CAD increased the total number of detected aerosol layers by approximately 10% and 4%, respectively. Even though the Version 3 (V3) CAD algorithm is significantly more accurate in deciphering between dense dust and clouds than the Version 2 algorithm, the V3 still showed some dust misclassifications among the case studies. Thus, the BTD CAD technique can help reduce the frequency of dust misclassifications encountered by the V3 CAD algorithm.

Parameterization of contrail radiative properties for climate studies

The study of contrails and their impact on global climate change requires a cloud model that statistically represents contrail radiative properties. In this study, the microphysical properties of global contrails are statistically analyzed using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The MODIS contrail pixels are detected using an automated contrail detection algorithm and a manual technique using the brightness temperature differences between the MODIS 11 and 12 μm channels. The scattering and absorption properties of typical contrail ice crystals are used to determine an appropriate contrail model to minimize the uncertainties arising from the assumptions in a particular cloud model. The depolarization ratio is simulated with a variety of ice crystal habit fractions and matched to the collocated MODIS and CALIOP observations. The contrail habit fractions are determined and used to compute the bulk‐scattering properties of contrails. A parameterization of shortwave and longwave contrail optical properties is developed for the spectral bands of the Rapid Radiative Transfer Model (RRTM). The contrail forcing at the top of the atmosphere is investigated using the RRTM and compared with spherical and hexagonal ice cloud models. Contrail forcing is overestimated when spherical ice crystals are used to represent contrails, but if a hexagonal ice cloud model is used, the forcing is underestimated for small particles and overestimated for large particles in comparison to the contrail model developed in this study.

Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data

This paper aimed to investigate the potential of using spectral and textural features in the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data for developing techniques ca- pable of classifying convective and stratiform rain areas in the satellite image. Two different classification methods were introduced that use the brightness temperature (BT) T10.8 and brightness temperature differences T10.8-T12.1, T8.7-T10.8, T6.2-T10.8, T6.2-T7.3, T13.4-T10.8, T8.7-T12.1, and T9.7-T13.4 as spectral parameters, along with textural param- eters derived from the thermal infrared MSG-SEVIRI chan- nel to discriminate between convective and stratiform rainy clouds. The first is an algorithm based on the probability of convective rainfall (PCR) for each pixel of the MSG satellite data and the second is an artificial neural network multilayer perceptron (MLP) model that relies on the correlation of satellite data with convective and stratiform rain. Both schemes were trained using as reference convective/strati- form classification data from 88 stations in Greece for 20 rainy days with high convective activity and evaluated against an independent dataset of gauge data for six rainy days. Two PCR and two MLP algorithms were constructed based on the previous results: the PCR1 and MLP1 algo- rithms that use only spectral measures and the PCR2 and MLP2 algorithms based on both spectral and textural meas- ures. It was found that the introduction of textural parame- ters as additional information to a technique (PCR2 and MLP2 ) works in improving the delineation between convective and stratiform rainy pixels compared to the techniques based only on spectral information (PCR1 and MLP1). The comparison of the two schemes revealed a clear outperformance of the MLP techniques over the PCR tech- niques. Best skill is provided by MLP2 (POD074.5 %, FAR044.3 %, POFD022.5 %, CSI00.47, ETS00.31, bias01.34) followed by MLP1 and the two PCR techniques. All algorithms overestimate the convective rain occurrences observed by the rain gauge network. These findings showed that the combined use of spectral and textural features in the MSG-SEVIRI imagery can be beneficial for the classifica- tion of convective and stratiform precipitating clouds.