Moderate Resolution Imaging Spectroradiometer Cloud Research Articles

Analysis of improvements in MOPITT observational coverage over Canada

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument has been measuring global tropospheric carbon monoxide (CO) since March 2000, providing the longest nearly continuous record of CO from space. During its long mission, the data processing algorithms have been updated to improve the quality of CO retrievals and the sensitivity to the lower troposphere. Currently, MOPITT retrievals are only performed for clear-sky observations or over low clouds for ocean scenes. The cloud detection scheme was modified in the new V9 product, resulting in an improvement in observational coverage, especially over land. Comparison of the spatial and seasonal variations of the data coverage in V9 and V8 shows differences with significant geographical and temporal variability, with some regions such as Canada and the Amazon exhibiting a doubling of data in winter. Here we conducted an analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud heights and cloud mask products along with MOPITT retrieval cloud flag descriptors to understand the impact of cloud conditions on the MOPITT observational coverage, with a particular focus on observations over Canada. The MOPITT CO total column (TC) data were modified by turning off the cloud detection scheme to allow for a CO retrieval result, regardless of their cloud status. Analyses of the standard V8 CO TC product (cloud filtered) and non-standard product (non-cloud-masked) were conducted for selected days. Results showed some coherent structures that were observed frequently in the non-masked CO product that was not present in the V8 product and could potentially be actual CO features. Many times, these CO plumes were also seen in the Infrared Atmospheric Sounding Interferometer (IASI) CO TC product. The MODIS cloud height analysis revealed that a significant number of low-cloud CO retrievals were discarded in the V8 product. Most of the missed CO plumes in the V8 product are now detected in the new V9 product as a result of the dependence of the MOPITT radiance ratio (MRT) test over land. Comparisons of the MRT and MODIS cloud height data indicate a remarkable negative correlation. As a result of the modified V9 cloud detection algorithm, a significant portion of the low-cloud CO retrievals is now incorporated in the new V9 MOPITT product. Consequently, the observational coverage over Canada is significantly improved, which benefits analyses of regional CO variability, especially during extreme pollution events. We also conducted a comparison of MOPITT and IASI CO TC and found generally good agreement, with about a 5 %–10 % positive bias that is more pronounced in highly polluted scenes.

A Multi-Year Study of GOES-13 Droplet Effective Radius Retrievals for Warm Clouds over South America and Southeast Pacific

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (5∘ N–30∘ S; 20∘–70∘ W), the Southeast Pacific (5∘ N–30∘ S; 70∘–120∘ W), and the Amazon (2∘ N–7∘ S; 54∘–73∘ W), for four months in each year from 2014–2017. Results were compared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Abstract. Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.

Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields

AbstractClouds and shadows pose a significant barrier for land surface optical and infrared remote sensing image processing and their various applications. The detection and removal of clouds and shadows from satellite images have always been critical preprocessing steps. To date, a variety of methods have been designed to solve this problem. Some require particular channels, while others are heavily dependent on the availability of temporally adjacent images (reference images). Moreover, many methods are too complex to use by common users. For those reasons, in this paper an alternative scheme for detecting clouds and shadows is proposed based on simulated top‐of‐atmosphere radiance fields. At the same time, a simple approach to remove clouds and shadows is also provided. The results indicate that the new method can properly identify both clouds and shadows in satellite images. Especially, it shows obvious advantage over the Moderate Resolution Imaging Spectroradiometer cloud product (MOD35) for shadow detection. Although the proposed cloud removal method is simple, the radiances of a contaminated image can be reasonably reconstructed with root‐mean‐square error < 3.0 W/m2·sr·μm and mean bias < 1.0 W/m2·sr·μm for all seven Moderate Resolution Imaging Spectroradiometer reflective bands for our case studies. These results prove the effectiveness of the proposed scheme in identifying and removing clouds and shadows from remotely sensed images. Meanwhile, these findings provide some new ideas for the remote sensing community, especially in the fields of cloud detection and image processing.

Regional Regime‐Based Evaluation of Present‐Day General Circulation Model Cloud Simulations Using Self‐Organizing Maps

AbstractGlobal clusters are derived by applying the self‐organizing map technique to the Moderate Resolution Imaging Spectroradiometer cloud top pressure‐cloud optical thickness joint histograms. These cloud clusters are then used to classify Cloud Feedback Model Intercomparison Project Observation Simulator Package output from the HadGEM3 (Global Atmosphere version 7) atmosphere‐only climate model. Discrepancies in the Global Atmosphere version 7 representation of particular clusters can be established by examining the two sets of cluster's occurrence rate and radiative effect. The overall differences in the occurrence rates show major discrepancies in several of the clusters, resulting in a shift from five dominant clusters in Moderate Resolution Imaging Spectroradiometer (above 10% occurrence rate) to two dominant clusters in the model. A comparison of the geographic distributions of occurrence rate shows that the differences are strongly regional and unique to each cluster. While comparisons of the global mean longwave and shortwave cloud radiative effect (CRE) show strong agreement, examination of the CRE of individual cloud types reveals larger errors that highlight the role of compensating errors in masking model deficiencies. CRE data for each of the clusters is further partitioned into regions. This establishes that the bias associated with a cluster is highly variable globally, with no clusters showing consistent biases across all regions. Therefore, regional level phenomena likely play an important role in the creation of these errors.

Precipitation rate climatology related to different cloud types using satellite imagery over Iran

Cloud types have a substantial influence on precipitation. This paper presents a study of the monthly variations of daytime different cloud types over Iran using data collected from Moderate-resolution Imaging Spectroradiometer (MODIS) aboard Terra during 2001–2015, MODIS aboard Aqua during 2002–2015, International Satellite Cloud Climatology Project (ISCCP) H-series cloud type data during 2001–2009 and precipitation rate associated with different cloud types using Tropical Rainfall Measuring Mission (TRMM) satellite products during 2001–2009. Different cloud types were determined using MODIS cloud optical thickness and cloud top pressure data based on ISCCP algorithm. The results showed that stratocumulus and cumulus clouds have maximum occurrence frequency over marine areas especially southern seas. The maximum frequency of nimbostratus and deep convective occurrence occurred over mountainous regions particularly at the time of Aqua overpass and cirrus and cirrostratus are observed over southeast of Iran during warm months due to monsoon system. Altostratus cloud is extended in each month except January, at the time of Terra overpass while nimbostratus is seen at the time of Aqua overpass during warm months in the study area. Cumulus and altocumulus clouds have shown remarkable frequency in all months especially over marine regions during warm and fall months. The higher value of precipitation rate is related to altostratus with a rate approximately 7 mm/h at the time of Terra overpass during April. Altostratus has the maximum recorded precipitation rate except in Nov., Dec., Sep., and Jan. at the time of Terra overpass, whereas the maximum precipitation rate is linked to nimbostratus cloud activity (up to 5 mm/h) except for March, April, and Sep. at the time of Aqua overpass. Deep convective (up to 1.32 mm/h), cirrostratus (up to 1.11 mm/h), and cirrus (0.02 mm/h) are observed only in warm months. The results were compared with ISCCP cloud types so that precipitation rate classified from low to high and Spearman rank correlation was calculated. The results showed good agreement between these two cloud type data; however, there were few notable difference between them.

Developing MODIS-based cloud climatologies to aid species distribution modeling and conservation activities

Author(s): Douglas, Michael William; Beida, Rahama; Mejia, John F.; Fuentes, Marcia Vetromilla | Abstract: WorldClim (Hijmans et al. 2005) has been the de-facto source of basic climatological analyses for most species distribution modeling research and conservation science applications because of its global coverage and fine (l1 km) spatial resolution. However, it has been recognized since its development that there are limitations in data-poor regions, especially with regard to the precipitation analyses. Here we describe procedures to develop a satellite-based daytime cloudiness climatology that better reflects the variations in vegetation cover in many regions of the globe than do the WorldClim precipitation products. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from the National Aeronautics and Space Administration (NASA) Terra and Aqua sun-synchronous satellites have recently been used to develop multi-year climatologies of cloudiness. Several procedures exist for developing such climatologies. We first discuss a simple procedure that uses brightness thresholds to identify clouds. We compare these results with those from a more complex procedure: the MODIS Cloud Mask product, recently averaged into climatological products by Wilson and Jetz (2016). We discuss advantages and limitations of both approaches. We also speculate on further work that will be needed to improve the usefulness of these MODIS-based climatologies of cloudiness. Despite limitations of current MODIS-based climatology products, they have the potential to greatly improve our understanding of the distribution of biota across the globe. We show examples from oceanic islands and arid coastlines in the subtropics and tropics where the MODIS products should be of special value in predicting the observed vegetation cover. Some important applications of reliable climatologies based on MODIS imagery products will include 1) helping to restore long-degraded cloud-impacted environments; 2) improving estimations of the spatial distribution of cloud-impacted species; and 3) helping to identify areas for rapid biological assessments. The last application can even benefit from qualitative perusal of the current MODIS climatologies.

Assessment of the importance of moderate spatial resolution cloud climatology data in solar radiation modeling

Improving solar radiation models is critical for supporting the increase in solar energy usage and modeling ecosystem dynamics. However, coarse spatial resolutions of solar radiation models overlook the impacts resulting from spatial variability of clouds at meso- and micro-scales. To address this problem, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud climatology developed by the National Severe Storms Laboratory was used to relate cloudiness to surface solar radiation observations. We developed a linear regression model between the surface solar radiation and MODIS cloud climatology and used the model to estimate average radiation across Oklahoma. Furthermore, the study compared the average error and coefficient of determination to measured ground radiation. Error analysis of the regression model showed that the differences between observed radiation and estimated radiation were spatially autocorrelated for the Aqua MODIS satellite scan. This suggests cloudiness alone is not sufficient to predict surface solar radiation. This study found that simple cloud datasets alone can account for approximately 50% of the variation in observed solar radiation at 250-m spatial resolution, but additional datasets such as optical depth, elevation, and slope are needed to accurately explain spatial distributions of incoming shortwave radiation.

Regime-based evaluation of cloudiness in CMIP5 models

The concept of cloud regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating in each grid cell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product [long-term average total cloud amount (TCA)], cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our results support previous findings that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is still not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer cloud observations evaluated against ISCCP like another model output. Lastly, contrasting cloud simulation performance against each model’s equilibrium climate sensitivity in order to gain insight on whether good cloud simulation pairs with particular values of this parameter, yields no clear conclusions.

The influence of winter cloud on summer sea ice in the Arctic, 1983–2013

AbstractArctic sea ice extent has declined dramatically over the last two decades, with the fastest decrease and greatest variability in the Beaufort, Chukchi, and East Siberian Seas. Thinner ice in these areas is more susceptible to changes in cloud cover, heat and moisture advection, and surface winds. Using two climate reanalyses and satellite data, it is shown that increased wintertime surface cloud forcing contributed to the 2007 summer sea ice minimum. An analysis over the period 1983–2013 reveals that reanalysis cloud forcing anomalies in the East Siberian and Kara Seas precondition the ice pack and, as a result, explain 25% of the variance in late summer sea ice concentration. This finding was supported by Moderate Resolution Imaging Spectroradiometer cloud cover anomalies, which explain up to 45% of the variance in sea ice concentration. Results suggest that winter cloud forcing anomalies in this area have predictive capabilities for summer sea ice anomalies across much of the central and Eurasian Arctic.

Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train

Abstract The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu–Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.

An Assessment of Diurnal and Seasonal Cloud Cover Changes over the Hawaiian Islands Using Terra and Aqua MODIS*

Abstract A comprehensive understanding of the spatial, seasonal, and diurnal patterns in cloud cover frequency over the Hawaiian Islands was developed using high-resolution image data from the National Aeronautics and Space Administration’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellites. The Terra and Aqua MODIS cloud mask products, which provide the confidence that a given 1-km pixel is unobstructed by cloud, were obtained for the entire MODIS time series (10-plus years) over the main Hawaiian Islands. Monthly statistics were generated from the daily cloud mask data, including mean cloud cover frequency at the four daily overpass times. The derived mean cloud cover frequency showed patterns that were generally consistent with the known distribution of mean rainfall and with the results from previous studies. Cloud cover frequency was the highest over land areas with elevations between the lifting condensation level (~600 m) and the mean height of the trade wind inversion (TWI) base (~2200 m), especially for the windward (northeastern) mountain slopes. Above the TWI, cloud frequency decreased sharply with elevation. Irrespective of season, cloud cover frequency was generally higher in the afternoon than in the morning and higher in daytime than at nighttime although these trends varied spatially. The dry season months (May–October) were less cloudy than the wet season months (November–April) at nighttime. The analysis also revealed a local December–January minimum in the annual cycle of cloud cover frequency. The monthly time series produced in this study is the first high-spatial-resolution cloud cover dataset in Hawaii.

Cloud Detection Method Based on Spectral Area Ratios in MODIS Data

A variety of clouds are present in almost all moderate-resolution imaging spectroradiometer (MODIS) images. To extract accurate information from MODIS data, a key preprocessing step is to detect the cloudy pixels. This article proposes a new algorithm to distinguish between cloudy and cloud-free pixels in MODIS images. This algorithm is based on the differences in the spectral areas between clouds and other surface features. It uses as many as 24 of the 36 MODIS spectral bands to obtain the integrated spectral information. The method has been illustrated by an example of 76 MODIS images recorded from 2011 to 2013. The results show that the algorithm is capable of correctly identifying most of the cloud-contaminated pixels except for some thin cloud pixels. We compared the new method with the MODIS Cloud Mask algorithm and found that the new algorithm performs better than the MODIS MOD35 Cloud Mask in some situation, such as coastal area, sun glint, and data with invalid values.

Evaluation of modeled surface ozone biases as a function of cloud cover fraction

Abstract. A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a negative correlation with the MODIS CFs, showing around 1 ppb decrease for 10 % MODIS CF change over the contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 % CF change, implying that spatial and temporal misplacement of the modeled cloud field might have biased modeled surface ozone level. Current NAQFC cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of surface-ozone bias in the current NAQFC system.

Understanding the synoptic variability of stratocumulus cloud liquid water path over the Southeastern Pacific

The spatial and temporal variance of stratocumulus cloud liquid water path (CLWP) over Southeastern Pacific has been investigated by combining satellite moderate resolution imaging spectroradiometer cloud products, CLWP from advanced microwave scanning radiometer-EOS observations and NCEP final analysis atmospheric products with empirical orthogonal function (EOF) Analysis. CLWP variance is the most complicated factor among three fundamental cloud quantities (the microphysical cloud droplet concentration, and the macrophysical CLWP and cloud fractional cover). The results show that EOF/PC1 of CLWP represents the variation of domain-averaged CLWP, which is mainly controlled by surface meteorological factors. Sea surface temperature and cold advection drive the synoptic and seasonal scales of variance of CLWP, while surface wind speed plays a fundamental role in stratocumulus cloud formation and daily variance of CLWP. EOF/PC2 of CLWP describes the spatial variance of CLWP. This daily spatial variance of CLWP is controlled by the factors of lower tropospheric stability and cloud top relative humidity, which determine cloud thickness and, consequently, CLWP through thermodynamic and entrainment processes. Further study indicates a twofold interaction of the surface wind speed on stratocumulus CLWP: (1) dynamically through modulation of surface latent heat and sensible heat fluxes and (2) microphysically through enhanced marine aerosol production.

Evapotranspiration estimation over agricultural plains using MODIS data for all sky conditions

Agricultural, water resource, and forest management require evapotranspiration (ET) data over a range of spatial and temporal scales. Several methods, with reasonable accuracy, exist for computing ET for clear-sky conditions using satellite remote sensing data. However, computation of ET for cloudy pixels remains a challenge. Here, we have developed an ET estimation algorithm for all sky conditions using primarily remote sensing data. The approach is based on an extension of the Priestley–Taylor model with contextual interpretation of remotely sensed surface temperature and vegetation index. It requires estimation of evaporative fraction, soil heat flux, and net radiation that can be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). For cloudy pixels, a regression-based algorithm utilizes MODIS cloud data product for cloud-top temperature, cloud fraction, cloud emissivity, cloud optical thickness, and land-surface temperature (LST) to calculate net radiation and thereafter ET. The algorithm was tested and validated using data from the South Great Plains in the USA with reasonable accuracy of ranging between 54.5% and 69.6% and an average root mean square error of 40.3 . The methodology was tested to estimate ET over the agricultural plains of the Ganges Basin in India. The proposed ET algorithm has capabilities to estimate ET over a range of space and time scales in data-scarce regions.

Multiyear satellite and surface observations of cloud fraction over China

Earth observation satellites can provide systematic and continuous monitoring of clouds. High spatial resolution (5 km) cloud fraction data are retrieved from the Moderate Resolution Imaging Spectroradiometer cloud mask products of Terra (12 years) and Aqua (9 years) over China. Long-term trends of cloud fraction for morning and afternoon observations clearly reflected seasonal variations. We found more clouds in the afternoon than in the morning. There is a strong correlation between satellite and surface observations of the daily cloud fraction for the period of 2012, with correlation coefficients of 0.678 and 0.7 for morning and afternoon, respectively. However, the analyses of the monthly mean cloud fraction between satellite and surface observations showed a larger discrepancy in the winter. In order to investigate the differences between satellite and ground-based cloud fraction over different underlying surfaces, a statistical test was carried out for six areas. The results indicated statistically significant increased correlations (p < 0.0001) between satellite and ground-based cloud fraction in northern China after removing winter data, especially in Northeastern Forest and Taklimakan Desert, while the correlation coefficients in southern China did not show significant changes.

Probability of cloud-free observation conditions across Great Britain estimated using MODIS cloud mask

Cloud cover is a major constraint on applications using optical remote sensing, particularly in temperate regions such as Great Britain (GB). This research explores the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product to calculate cloud probability across GB. MODIS 1 km cloud mask data for 2005 were downloaded and processed to map cloud-free probabilities. The results indicate an average yearly cloud-free probability of 21.3%, with maximum and minimum monthly mean probabilities of 33.3% and 12.9% in November and March, respectively. Temporal and spatial variations in cloud-free frequencies are apparent. Northern and western regions are generally cloudier than those in the south. The results of this research indicate that cloud probabilities can be identified at a relatively high spatial resolution.

FraRI: an algorithm to account for the discontinuity at 60° N in MODIS temperature products

The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites conducts continuous monitoring of the Earth's land surface and oceans. Recently, a sharp discontinuity (averaging 1.9°C) has been noticed at 60° N in both MODIS daytime and night-time land surface temperature (LST) products. This linear artefact arises because the CO2 high cloud test in the operational code for the generation of the MODIS cloud mask product is used only in the non-polar region (between 60° N and 60° S). The resulting discontinuity clearly has negative implications for any statistical applications of these temperature data. In this technical note we present a new algorithm, which minimizes this discontinuity. The method uses edge detection and elimination based on a mixture of Sobel and non-linear Laplacian filters (edge detection and quantification), cubic splines (edge modelling), and a controllable power function for image restoration. The implementation of this algorithm is demonstrated on an image of average minimum night-time LST between 2001 and 2008.

Synergetic use of POLDER and MODIS for multilayered cloud identification

The purpose of this study is to investigate the possibility of identifying overlapping clouds that contain thin cirrus overlying a lower-level water cloud by synergetic use of POLDER-3 (Polarization and Directionality of the Earth Reflectance) and MODIS (MODerate resolution Imaging Spectroradiometer) data. When thin cirrus clouds overlap the liquid cloud layer, the liquid information may be obtained by POLDER observations and the presence of the cirrus may be inferred from the MODIS CO 2-slicing technique. An initial comparison of the POLDER cloud phase and the MODIS cloud-top pressure for one scene over East Asia also shows that a large portion of clouds declared as liquid water clouds by POLDER-3 correspond to the lower cloud-top pressures derived from MODIS. As a result, an overlapped cloud identification method is proposed under the assumption that the multilayered cloud would be present if the POLDER cloud phase is liquid water and the MODIS cloud-top pressure is less than 500 hPa. For the studied scene, the comparison of the multilayered cloud identification results with CloudSat and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations illustrates that the proposed method could detect multilayered clouds when the upper cirrus has a visible optical thickness of less than 2.0. Then the identification results are compared with the MODIS Cloud_Multi_Layer_Flag. It is indicated that the consistency between the multilayered clouds from the proposed synergy and MODIS-operational algorithm increases gradually from over 40% to nearly 100% with the increase of the confidence level of the MODIS multilayered clouds from the lowest to the highest. Further analysis suggests that the majority of multilayered clouds falsely classified as single-layered clouds by the proposed method may correspond to relatively thick cirrus covering lower-level water clouds. Additionally, an index by using the multilayered cloud detection differences from the two methods is proposed to provide some information on the optical thickness of the cirrus covering lower-level water cloud. Finally, quantitative comparisons are extended to four other scenes at different locations by using active measurements. The results also show that the mean visible optical thickness of the high-level clouds of the multilayered clouds detected by both methods (1.57) is remarkably less than that by only MODIS-operational method (2.84), which means that the differences between the results from the two methods are mainly caused by the different sensitivities to the visible optical thickness of the high-level cloud and could be used to indicate the range of the visible optical thickness of the cirrus clouds covering the lower-level water clouds.