In this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced ...Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.
An improved solution scheme is developed for the three-dimensional radiative transfer equation (RTE) in inhomogeneous cloudy atmospheres. This solution scheme is deterministic (explicit) and utilizes ...spherical harmonics series expansion and the finite-volume method for discretization of the RTE. The first-order upwind finite difference is modified to take into account bidirectional flow of radiance in spherical harmonics space, and an iterative solution method is applied. The multigrid method, which is generally employed to achieve rapid convergence in iterative calculation, is incorporated into the solution scheme. The present study suggests that the restriction and prolongation procedure for the multigrid method must be also modified to account for bidirectional flow, and proposes an efficient bidirectional restriction/prolongation procedure that does not increase the computational effort for coarser grids, resulting in a type of wavelet low-pass filter. Several calculation examples for various atmosphere models indicate that the proposed solution scheme is effective for rapid convergence and suitable for obtaining adequate radiation fields in inhomogeneous cloudy atmospheres, although a comparison with the Monte Carlo method suggests that the radiances obtained by this solution scheme at certain angles tends to be smoother.
•We develop a deterministic solution scheme for the 3-D radiative transfer.•The multigrid method is incorporated into an iterative solution scheme.•The multigrid method needs to be modified for the incorporation.•An ingenious procedure for the restriction and prolongation is proposed.•The scheme results in rapid convergence and obtains adequate radiation fields.
A new concept for cloud detection from observations by multispectral spaceborne imagers is proposed, and an algorithm comprising many pixel‐by‐pixel threshold tests is developed. Since in nature the ...thickness of clouds tends to vary continuously and the border between cloud and clear sky is thus vague, it is unrealistic to label pixels as either cloudy or clear sky. Instead, the extraction of ambiguous areas is considered to be useful and informative. We refer to the multiple threshold method employed in the MOD35 algorithm that is used for Moderate Resolution Imaging Spectroradiometer (MODIS) standard data analysis, but drastically reconstruct the structure of the algorithm to meet our aim of sustaining the neutral position. The concept of a clear confidence level, which represents certainty of the clear or cloud condition, is applied to design a neutral cloud detection algorithm that is not biased to either clear or cloudy. The use of the clear confidence level with neutral position also makes our algorithm structure very simple. Several examples of cloud detection from satellite data are tested using our algorithm and are validated by visual inspection and comparison to previous cloud mask data. The results indicate that our algorithm is capable of reasonable discrimination between cloudy and clear‐sky areas over ocean with and without Sun glint, forest, and desert, and is able to extract areas with ambiguous cloudiness condition.
The annual to monthly average of short‐term (hourly/minutely) measured global radiation (GR), hereafter called mean global radiation (MGR), has shown large secular trends (decadal or longer term) ...globally that are only attributable to clouds or aerosols. In order to investigate whether changes in cloud cover (CC) contribute to the MGR trend for a given location and period of interest, many studies compare secular trends of MGR and mean cloud cover (MCC, the annual to monthly average of CC). If these trends have opposite signs, such studies assume that the changes in CC cause the changes in MGR, and vice versa. However, this approach is misleading because MCC is not an appropriate measure of the averaged immediate effect of CC on GR for the year or month due to the solar‐elevation dependency and the inherent nonlinearity in the instantaneous effect. As a practical solution, we derived a more reasonable measure named MRRCC (mean radiation reduction by CC) to replace MCC. This is the annual to monthly average of the immediate reduction in GR arising from CC and is determined using a nonlinear regression model that relates immediate GR to the corresponding CC, solar elevation, and year. We carefully designed the model so that the calculation of MRRCC does not require any other variables (such as aerosol measurements). According to our tests at 42 observation stations with increasing MGR trends from 1990 to 2009, 24 stations had positive trends in MCC, indicating in the conventional approach that CC is not a cause of the MGR increase. However, 14 out of these 24 stations had negative MRRCC trends, indicating that CC is one cause of the MGR increase. This demonstrates that the use of MRRCC instead of MCC can alter our understanding of the causes of MGR trends globally.
Many climatology studies compare the secular trends in annual to monthly mean of global radiation (GR) and cloud cover (CC) to judge whether changes in CC cause changes in mean GR. This approach is misleading because mean CC is not an appropriate measure of the mean of the nonlinear and solar‐elevation‐dependent effect of CC on GR. We propose a widely applicable and reasonable measure to replace mean CC, which statistically evaluates the mean reduction in GR arising from CC.
Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation ...(TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.
The Prede POM sky radiometer is a filter radiometer deployed worldwide in the SKYNET international network. A new method, called Skyrad pack MRI version 2 (MRI v2), is presented here to retrieve ...aerosol properties (size distribution, real and imaginary parts of the refractive index, single-scattering albedo, asymmetry factor, lidar ratio, and linear depolarization ratio), water vapor, and ozone column concentrations from the sky radiometer measurements. MRI v2 overcomes two limitations of previous methods (Skyrad pack versions 4.2 and 5, MRI version 1). One is the use of all the wavelengths of 315, 340, 380, 400, 500, 675, 870, 940, 1020, 1627, and 2200 nm if available from the sky radiometers, for example, in POM-02 models. The previous methods cannot use the wavelengths of 315, 940, 1627, and 2200 nm. This enables us to provide improved estimates of the aerosol optical properties, covering almost all the wavelengths of solar radiation. The other is the use of measurements in the principal plane geometry in addition to the solar almucantar plane geometry that is used in the previous versions. Measurements in the principal plane are regularly performed; however, they are currently not exploited despite being useful in the case of small solar zenith angles when the scattering angle distribution for almucantars becomes too small to yield useful information. Moreover, in the inversion algorithm, MRI v2 optimizes the smoothness constraints of the spectral dependencies of the refractive index and size distribution, and it changes the contribution of the diffuse radiances to the cost function according to the aerosol optical depth. This overcomes issues with the estimation of the size distribution and single-scattering albedo in the Skyrad pack version 4.2. The scattering model used here allows for non-spherical particles, improving results for mineral dust and permitting evaluation of the depolarization ratio.
Abstract
In this work, the
Greenhouse Gases Observing Satellite
(
GOSAT
) Thermal and Near-infrared Sensor for Carbon Observation–Cloud and Aerosol Imager (TANSO-CAI) cloud screening results, which ...are necessary for the retrieval of carbon dioxide (CO
2
) and methane (CH
4
) gas amounts from
GOSAT
TANSO–Fourier Transform Spectrometer (FTS) observations, are compared with results from
Aqua
/Moderate Resolution Imaging Spectroradiometer (MODIS) in four seasons. A large number of pixels, acquired from both satellites with nearly coincident locations and times, are extracted for statistical comparisons. The same cloud screening algorithm was applied to both satellite datasets to focus on the performance of the individual satellite sensors, without concern for differences in algorithms. The comparisons suggest that CAI is capable of discriminating between clear and cloudy areas over water without sun glint and also may be capable of identifying thin cirrus clouds, which are generally difficult to detect without thermal infrared or near-infrared bands. On the other hand, cloud screening over land by CAI resulted in greater cloudy discrimination than that by MODIS, whereas detection of thin cirrus clouds tended to be more difficult over land than water, resulting in incorrect identification of thin cirrus as clear. The amount of missed thin cirrus had a seasonal variation, with the maximum occurring in summer. The cloudy tendency of CAI over half vegetation is caused by lack of an effective threshold test that can be applied to MODIS. The statistical results of the comparison clarified the important points to consider when using the results of CAI cloud screening.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper discusses the cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm (CAPCOM), which are used by the Second-generation GLobal Imager (SGLI) aboard ...the GCOM-C satellite, launched in December 2017. Also described are the preliminary results of cloud products’ validation. CLAUDIA was validated by comparing cloud fractions derived from satellite data against data from whole-sky images captured by ground-based fisheye cameras. User’s accuracy and producer’s accuracy were mostly high at around 90%, and the resulting overall accuracy was also high, ranging from 83 to 100% (average of all sites was 90.5%). CLAUDIA has proven to be sufficiently accurate to apply a cloud mask to measurements and meets the requirements for releasing data for SGLI cloud flag products (the minimum for a successful GCOM-C mission). CAPCOM was evaluated by comparing cloud properties obtained by SGLI products against data from MODIS collection 6 products (MOD06). This was done for both ocean and land in the low to middle latitudes (60° N–60° S) from August 22, 2018 to September 14, 2018. The comparison showed good correlation coefficients for cloud optical thickness, effective particle radius, and cloud-top temperature for water clouds: 0.88 (0.83), 0.92 (0.88), and 0.94 (0.92) for ocean (land), respectively. CAPCOM data for ice cloud optical thickness correlated well with data from MODIS products: 0.86 (ocean) and 0.82 (land).
To discuss the feasibility of the Himawari follow-on program, impacts of a hyperspectral sounder on a geostationary satellite (GeoHSS) is assessed using an observing system simulation experiment. ...Hypothetical GeoHSS observations are simulated by using an accurate reanalysis dataset for a heavy rainfall event in western Japan in 2018. The global data assimilation experiment demonstrates that the assimilation of clear-sky radiances of the GeoHSS improves the forecasts of the representative meteorological field and slightly reduces the typhoon position error. The regional data assimilation experiment shows that assimilating temperature and relative humidity profiles derived from the GeoHSS improves the heavy rainfall in the Chugoku region of western Japan as a result of enhanced southwesterly moisture flow off the northwestern coast of the Kyushu Island. These results suggest that the GeoHSS provides valuable information on frequently available vertically resolved temperature and humidity and thus improves the forecasts of severe events.
The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in October 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. ...Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO
2
and CH
4
have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO
2
emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO
2
and CO by TANSO-FTS-2.
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