Derivation of a threshold function for the Advanced Very High Resolution Radiometer 3.75 µm channel and its application in automatic cloud discrimination over snow/ice surfaces

The distinct contrast between the reflectance of solar radiation in Advanced Very High Resolution Radiometer (AVHRR) channel 3 (3.75?µm) by clouds and by bright surfaces provides an effective means of cloud discrimination over snow/ice surfaces. A threshold function for the top-of-atmosphere (TOA) albedo in channel 3 (r ₃) is derived and used to develop an improved method for cloud discrimination over snow/ice surfaces that makes explicit use of TOA r ₃. Corrections for radiance anisotropy and temperature effects are required to derive accurate values of r ₃ from satellite measurements and to utilize the threshold function. It has been used to retrieve cloud cover fractions from National Oceanic and Atmospheric Administration (NOAA)-14 AVHRR data over the Arctic Ocean and over the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska. The retrieved cloud fractions are in good agreement with SHEBA (Surface HEat Budget of the Arctic Ocean) surface visual observations and with NSA cloud radar and lidar observations, respectively. This method can be utilized to improve cloud discrimination over snow/ice surfaces for any satellite sensor with a channel near 3.7?µm.     

An improved look-up table technique for geophysical parameters from SSM/I

An improved look-up table technique is developed to calculate meteorological parameters from Special Sensor Microwave/Imager (SSM/I) measurements. The method, which is based on a look-up table and an extrapolation and interpolation technique used in the weather prediction model, gives results comparable to or better than the regression method for the total precipitable water (TPW), surface wind speed (V), and cloud liquid water path (LWP). Applied to a noise-free data set (dependent test) TPW, V and LWP are retrieved with a rms. accuracy of 0.26 kg m^-2, 0.28 m s^-1 and 0.002 kg m^-2, respectively. If the random noise of the SSM/I radiometer is taken into account in the retrieval, the r.m.s. increases to 0.84 kg m^-2, 1.08 m s^-1 and 0.013 kg m^-2, respectively. The method is applied to a set of over 520 SSM/I measurements from the DMSP-F8 satellite for which collocated radiosondes launched from ships are available. The rms. (bias) of TPW and V was 2.91 (-0.61) kg m^-2 and 2.75 (-0.13) m s^-1, respectively. We use the improved look-up table technique to calculate the monthly mean global distribution of surface wind for August 1989 and compare the results with the Comprehensive Ocean-Atmosphere Data Set (COADS) for the same month. The rms. error and mean differences for the monthly mean values of sea surface wind speed between the retrievals and COADS data are 1.01 m s^-1 and 0.03 m s^-1, respectively. We also calculate LWP for October 1987 and compare it with the LWP derived from cloud optical thicknesses of International Satellite Cloud Climatology Project (ISCCP) dataset. Good agreement is obtained. The extension of the method to calculate cloud water and water vapour profiles is discussed.     

Validation of cloud property retrievals from MTSAT-1R imagery using MODIS observations

A cloud property retrieval algorithm optimized for five channels (centred at 0.6, 3.7, 6.7, 10.8, and 12.0 μm) has been explored for application to onboard meteorological radiometers on geostationary satellites; however, its validity remains to be established. Here, we present validation results for the cloud properties retrieved by the developed algorithm from the full-disk imagery of the Multi-functional Transport Satellite (MTSAT-1R) for August 2006. The considered cloud properties include cloud phase (CP), cloud optical thickness (COT), effective radius (ER) and cloud top pressure (CTP). Their one-month averages, daily variations, and respective collocated values are compared with the Moderate Resolution Imaging Spectroradiometer cloud data. Our validation results show that an additional 6.7 μm brightness temperature test in CP retrieval identifies water and ice phases that may be overlooked in the 10.8- and 12.0-μm bands. Our method to extract cloud-reflected radiances at the 0.6- and 3.7-μm bands contributes to the accuracy of the COT for values between 5 and 60, and the ER for values less than 40 μm. Estimating high-cloud top pressure from the radiance ratio in the 6.7- and 10.8-μm bands remarkably reduces (by up to 70%) large uncertainties in the CTP, which may be found in the presence of high thin cirrus clouds.     

Cloud climatology over the oceanic regions adjacent to the Indian Subcontinent: inter-comparison between passive and active sensors

Understanding the cloud vertical structure and its variation in space and time is important to reduce the uncertainty in climate forcing. Here, we present the cloud climatology over the oceanic regions (Arabian Sea, Bay of Bengal, and South Indian Ocean) adjacent to the Indian subcontinent using data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), GCM-Oriented CALIPSO Cloud Product (GOCCP), and International Satellite Cloud Climatology Project (ISCCP). Fractional cloud cover (f_c) shows stronger seasonal variations over the Arabian Sea (mean annual f_c lies in the range 0.5–0.61) and Bay of Bengal (mean annual f_c lies in the range 0.69–0.75) relative to the South Indian Ocean (mean annual f_c lies in the range 0.64–0.71). Inter-comparison of statistics from passive (MISR, MODIS and ISCCP) and active (GOCCP) sensors reveals the challenges in interpreting satellite data for climate implications. While MISR detects more low clouds because of its stereo technique, MODIS and ISCCP detect more high clouds because of their radiometric techniques. Therefore, a combination of these two techniques in passive sensors may lead to more realistic understanding of the cloud vertical structure. GOCCP (active sensor) can detect multilayer cloud, but accuracy reduces if the high clouds are optically thick. A dominance of low and high clouds throughout the year is observed in these regions, where cumulus and cirrus dominate among low and high clouds, respectively.     

Cloud liquid water path derived from AVHRR data using APOLLO

Abstract

Cloud liquid water path (LWP) is an important parameter to validate forecasts obtained from circulation models of high spatial resolution. At present, it is not measured on a routine basis. Being part of the Advanced Very High Resolution Radiometer (AVHRR) Processing scheme Over cLoud, Land and Ocean (APOLLO) a parametrization scheme to derive optical thickness from cloud reflectance and, further, the LWP has been adopted and adjusted to AVHRR channel-1 counts. From these counts, top-of-atmosphere bidirectional reflectance data are obtained. Using APOLLO-derived fully cloudy pixels only, the directional hemispherical cloud reflectance is derived to which the parametrization scheme refers. The LWP of each fully cloudy pixel is determined. As a first application, the mean LWP of 63-5 × 63-5 km² boxes is computed and compared to a 14-hour forecast of the LWP made with the Europa-Modell of the Deutsche Wetterdienst. The location of the clouds seems to be forecasted rather well by the model. However, the LWP computed by the model is higher (by a factor of 4 or 5) than that derived from AVHRR data using APOLLO. A first validation by means of aircraft measurements shows that the APOLLO-derived LWP is too low by about 50 per cent. This reduces the discrepancy to the computed data but the model predicted LWP still seems to be too high by a factor of 3.     

Mesoscale cloud pattern classification over ocean with a neural network using a new index of cloud variability

The purpose of this study is to determine the feasibility of a mesoscale (<300 km) cloud classification using infrared radiance data of satellite‐borne instruments. A new method is presented involving an index called the diversity index (DI), derived from a parameter commonly used to describe ecosystem variability. In this respect, we consider several classes of value ranges of standard deviation of the brightness temperature at 11 µm (σ_BT). In order to calculate DI for 128×128 km² grids, subframes of 8 km×8 km are superimposed to the satellite image, and then σ_BT is calculated for all 256 subframes and assigned to one of the classes. Each observed cloud pattern is associated with an index characterized by the frequency of σ_BT classes within the scene, representative of a cloud type. Classification of different clouds is obtained from Advanced Very High Resolution Radiometer (AVHRR)‐NOAA 16 data at 1 km resolution. Stratus, stratocumulus and cumulus are specifically recognized by this window analysis using a DI threshold. Then, a six‐class scheme is presented, with the standard deviation of the infrared brightness temperature of the entire cloud scene (σ_c) and DI as inputs of a neural network algorithm. This neural network classifier achieves an overall accuracy of 77.5% for a six‐class scheme, and 79.4% for a three‐class scheme, as verified against the analyses of nephanalists as verified against a cloud classification from Météo France. As an application of the proposed methodology, regional cloud variability over Pacific is examined using cloud patterns derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard Earth Observing System (EOS) Terra polar orbiter platform, for February 2003 and 2004. The comparison shows regional change in monthly mean cloud types, associated with 2003 El Niño and 2004 neutral events. A significant increase in the occurrence of convective clouds (+15%) and a decrease in stratiform clouds (?10%) are observed between the two months.     

Influence of clouds on OMI satellite total daily UVA exposure over a 12-year period at a southern hemisphere site

ABSTRACT

This research investigated and evaluated the influence of clouds on the total daily UVA (320–400 nm) exposures calculated from the three Ozone Monitoring Instrument (OMI) UV spectral irradiances at solar noon. These evaluated satellite total daily UVA exposure data were compared to the total daily UVA exposures of a ground-based instrument over the period of October 2004 to December 2014 at a sub-tropical Australian site (27.5°S, 151.9°E) under all cloud cover conditions including sun obscured and not obscured conditions. The aim was to evaluate the influence of clouds on the total daily UVA. When the sun was not obscured by clouds, there was good agreement between satellite and ground-based daily UVA exposure measurements with coefficient of determination (R²) between 0.80 and 0.84, for the cloud conditions 0 to 2, >2 to 4, >4 to 6 and >6 to 8 oktas. For sun obscured by clouds, the R² was 0.71, 0.64 and 0.75, respectively, for >2 to 4, >4 to 6 and >6 to 8 oktas. The method was validated using total daily UVA exposures from ground measurements taken in 2015 and 2016 giving a mean absolute error of 84.2 kJ m^?2 (10%) and 138.8 kJ m^?2 (30%) respectively, for the cases of sun not obscured cloudy days and sun obscured by cloud cover. Total daily UVA exposures were able to be calculated from the OMI satellite spectral irradiance for all cloud conditions, including cases where the sun was obscured, demonstrating the potential application of the technique to be applied in locations that do not record surface UVA measurements directly.     

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m^?2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.     

Cloud motion in the GOCI/COMS ocean colour data

The Geostationary Ocean Colour Imager (GOCI) instrument, on Korea’s Communications, Oceans, and Meteorological Satellite (COMS), can produce a spectral artefact arising from the motion of clouds – the cloud is spatially shifted and the amount of shift varies by spectral band. The length of time it takes to acquire all eight GOCI bands for a given slot (portion of a scene) is sufficient to require that cloud motion be taken into account to fully mask or correct the effects of clouds in all bands. Inter-band correlations can be used to measure the amount of cloud shift, which can then be used to adjust the cloud mask so that the union of all shifted masks can act as a mask for all bands. This approach reduces the amount of masking required versus a simple expansion of the mask in all directions away from clouds. Cloud motion can also affect regions with unidentified clouds – thin or fractional clouds that evade the cloud identification process – yielding degraded quality in retrieved ocean colour parameters. Areas with moving and unidentified clouds require more elaborate masking algorithms to remove these degraded retrievals. Correction for the effects of moving fractional clouds may also be possible. The cloud shift information can be used to determine cloud motion and thus wind at the cloud levels on sub-minute timescales. The beneficial and negative effects of moving clouds should be considered for any ocean colour instrument design and associated data processing plans.     

Satellite‐derived cloud top pressure product validation using aircraft‐based cloud physics lidar data from the ATReC field campaign

The primary objective of this study was to assess the accuracy of satellite‐derived estimates of cloud‐top height (CTH). These estimates were derived using hourly data from the Geostationary Operational Environmental Satellite (GOES‐12) Imager and Sounder instruments. In addition, CTHs were derived using data from the MODerate resolution Imaging Spectrometer (MODIS), located on the polar‐orbiting Aqua platform. Cloud physics lidar (CPL) data taken during the Atlantic‐THORPEX Regional Campaign (ATReC) were used as the reference data set. Two cases were examined, one containing clouds at many different levels (5 December 2003) and one consisting entirely of mid‐level clouds (between 4 and 10 km, 28 November 2003). For the first case, 19.4% of the Sounder pixels and 28.0% of the Imager pixels were within ±0.5 km of the CPL measurement, while 51.5% of the Sounder pixels and 64.3% of the Imager pixels were within ±1.5 km. For the second case, 29.7% of the Sounder pixels and 39.9% of the Imager pixels were within ±0.5 km of the CPL measurement, while 85.2% of the Sounder pixels and 85.1% of the Imager pixels were within ±1.5 km. The results indicate that MODIS CTH retrievals may provide an improvement over heights derived using geostationary instruments, especially for cases where cloud heights are not highly variable.     

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.     

Determination of the cloud fraction in the SCIAMACHY ground scene using MERIS spectral measurements

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields.     

Inferences of all‐sky solar irradiance using Terra and Aqua MODIS satellite data

Solar irradiance is a key environmental control, and accurate spatial and temporal solar irradiance data are important for a wide range of applications related to energy and carbon cycling, weather prediction, and climate change. This study presents a satellite‐based scheme for the retrieval of all‐sky solar irradiance components, which links a physically based clear‐sky model with a neural network version of a rigorous radiative transfer model. The scheme exploits the improved cloud characterization and retrieval capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and employs a cloud motion tracking scheme for the production of hourly solar irradiance data throughout the day. The scheme was implemented for the Island of Zealand, Denmark (56° N, 12° E) and Southern Arizona, USA (31° N, 110° W) permitting model evaluation for two highly contrasting climates and cloud environments. Information on the atmospheric state was provided by MODIS data products and verifications against AErosol RObotic NETwork (AERONET) data demonstrated usefulness of MODIS aerosol optical depth and total precipitable water vapour retrievals for the delineation of spatial gradients. However, aerosol retrievals were significantly biased for the semi‐arid region, and water‐vapour retrievals were characterized by systematic deviations from the measurements. Hourly global solar irradiance data were retrieved with overall root mean square deviations of 11.5% (60 W m^?2) and 26.6% (72 W m^?2) for Southern Arizona and the Island of Zealand, respectively. For both regions, hourly satellite estimates were shown to be more reliable than pyranometer measurements from ground stations only 15 km away from the point of interest, which is comparable to the accuracy level obtainable from geostationary satellites with image acquisitions every 15–30 min. The proposed scheme is particularly useful for solar irradiance mapping in high‐latitude regions as data from geostationary satellites experience a gradual degradation in spatial resolution and overall quality with latitude and become unusable above approximately 60° latitude. However, in principle, the scheme can be applied anywhere on the globe, and a synergistic use of MODIS and geostationary satellite datasets may be envisaged for some applications.     

Atmospheric correction of hyperspectral airborne GCAS measurements over the Louisiana Shelf using a cloud shadow approach

As an image-driven method to correct for atmospheric effects, the cloud shadow (CS) approach does not require accurate radiometric calibration of the sensor, making it feasible to process remotely sensed data when radiometric calibration may contain non-negligible uncertainties. Using measurements from the Geostationary Coastal and Air Pollution Events Airborne Simulator and from the Moderate Resolution Imaging Spectroradiometer over the Louisiana Shelf, we evaluate the CS approach to airplane measurements in turbid-water environments. The original CS approach somehow produced remote-sensing reflectance (R_rs, sr^?1) with an abnormal spectral shape, likely a result of the assumption of identical path radiance for the pair of pixels in and out of the shadow, which is not exactly valid for measurements made from a low-altitude airplane. To overcome this limitation, an empirical scheme using an effective wavelength-dependent radiance reflectance for the cloud (γ, sr^?1) was developed and reasonable GCAS R_rs retrievals are then generated, which were further validated against in situ R_rs. Issues and challenges in applying CS to measurements of low-altitude airplanes are discussed.     

Impact of MODIS-derived cloud cover on aerosol optical depth measured with a sun photometer: a case study from Nanjing,China

Sun photometers have been used increasingly to monitor the atmospheric environment by measuring indicators such as aerosol optical depth (AOD). However, ground-measured AOD results are subject to the presence of clouds in the air. When cloud cover is not extensive, it is still possible to use sun photometry to determine AOD, even though accuracy is reduced by cloud contamination. This research aims to detect cloud cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data and then assess its impact on in situ-measured AOD. Normalized difference cloud index (NDCI) and linear spectral unmixing (LSU) were used to detect cloud cover from MODIS data. AOD at the time of acquisition of MODIS data was measured on the ground by sun photometry within 20 min of satellite overpasses (10 min before and 10 min after). Correlation analysis of NDCI- and LSU-derived cloud cover with in situ-measured AOD data demonstrates that LSU has a higher correlation coefficient with AOD than with NDCI. At 550 nm, a unit of cloud cover (e.g. 1%) raises ground-observed AOD by 0.0157. The findings of this study can be used to modify ground-derived AOD results to improve their reliability.     

Total and low cloud amounts over France and southern Britain in the summer of 1983: comparison of surface-observed and satellite-retrieved values

Abstract

Cloud observations from 124 surface stations for 12.00 hours G.M.T. for the 20 day period 22 July to 10 August 1983 have been compared with retrievals made using the cluster technique of Desbois et al. from METEOSAT radiances measured at 11.30hours G.M.T. The location, France and southern Britain, and time period, summer 1983, were selected to coincide with one of the regions designated for special study in the validation phase of the International Satellite Cloud Climatology Project (ISCCP). Total and low cloud amounts were compared. For total cloud amount 29 percent of the retrievals were fully in agreement with the surface observations and 64 per cent of differences were within ± 1 okta. In the case of low cloud, 33 per cent agreed fully and 64 per cent of the differences were within ± 1 okta. It must be noted that many of these successes (55 per cent in the total cloud amount and 71 per cent in the low cloud amount) were for cases of totally clear or totally cloudy skies and often when only one cloud layer was present.     

A methodology to evaluate the aerosol effective radius based on MODIS aerosol products applicable to other satellite platforms

A strategy to evaluate the effective radius (r _eff) as a function of aerosol retrievals is provided in this work. This methodology is based on the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products, using the 0.66 and 0.87 µm bands. These data have been studied from February 2000 to December 2005 in a grid situated at Subtropical North‐east Atlantic region. To reduce the number of MODIS useful variables a Factorial Analysis by Principal Components has been applied, decreasing the aerosol parameters from 18 to five. With these parameters, backscattering ratios and asymmetry factors at 0.66 and 0.87 µm besides the Ångström parameter, a lineal multivariate analysis technique has been applied to find the combination of variables that better evaluate the r _eff. The standard error for the predicted value of r _eff is ±0.09 µm. The expression obtained here has the advantage that it can be applied to other remote sensors like AVHRR/NOAA, HRV/SPOT, TM/LANDSAT, and so on, with long time series.     

Variability of bio-optical properties at sampling stations and implications for remote sensing: a case study in the north-east Gulf of Mexico

Variations of bio-optical properties at oceanographic sampling stations, although important for satellite data validation and algorithm development, have rarely been documented or studied. Using flow-through data and water samples collected from the flow-through system and Niskin bottles at ～260 stations between summer 1998 and spring 1999 in the north-east Gulf of Mexico (27.5° to 30.4°?N, 90° to 80°?W), we study the variability of several properties, including chlorophyll-a concentration and Gelbstoff absorption, at the sampling stations. It is found that the standard deviations for both Gelbstoff and chlorophyll are less than 10% of the mean values for more than 90% of the stations, including the coastal stations where water is turbid or Case II. High variations are found in the frontal regions near river plumes. At several stations chlorophyll-a and Gelbstoff vary by nearly two-fold due to spatial and/or temporal variations of the properties near the plume waters. This suggests that for water samples collected from moderately coloured waters (chlorophyll-a >0.25?mg?m^?3) or coastal river plume waters, special care should be taken to validate the sample data by using multiple samples, a continuous flow-through system, or a concurrent satellite data product map. Otherwise large uncertainties are likely to occur when these data are used to validate satellite estimates.     

A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery

ABSTRACT

This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient (κ) of 0.993 and a 95% confidence interval ranging from 0.990 to 0.997. The model also shows a very low commission error (sensitive to the presence of haze), which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution), leading to an increase in exploitable data for water quality studies.     

Determination of ice cloud models using MODIS and MISR data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from??0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.