Minji Seo

Long-Term Variability of Surface Albedo and Its Correlation with Climatic Variables over Antarctica

The cryosphere is an essential part of the earth system for understanding climate change. Components of the cryosphere, such as ice sheets and sea ice, are generally decreasing over time. However, previous studies have indicated differing trends between the Antarctic and the Arctic. The South Pole also shows internal differences in trends. These phenomena indicate the importance of continuous observation of the Polar Regions. Albedo is a main indicator for analyzing Antarctic climate change and is an important variable with regard to the radiation budget because it can provide positive feedback on polar warming and is related to net radiation and atmospheric heating in the mainly snow- and ice-covered Antarctic. Therefore, in this study, we analyzed long-term temporal and spatial variability of albedo and investigated the interrelationships between albedo and climatic variables over Antarctica. We used broadband surface albedo data from the Satellite Application Facility on Climate Monitoring and data for several climatic variables such as temperature and Antarctic oscillation index (AAO) during the period of 1983 to 2009. Time series analysis and correlation analysis were performed through linear regression using albedo and climatic variables. The results of this research indicated that albedo shows two trends, west trend and an east trend, over Antarctica. Most of the western side of Antarctica showed a negative trend of albedo (about −0.0007 to −0.0015 year−1), but the other side showed a positive trend (about 0.0006 year−1). In addition, albedo and surface temperature had a negative correlation, but this relationship was weaker in west Antarctica than in east Antarctica. The correlation between albedo and AAO revealed different relationships in the two regions; west Antarctica had a negative correlation and east Antarctica showed a positive correlation. In addition, the correlation between albedo and AAO was weaker in the west. This suggests that the eastern area is influenced by the atmosphere, but that the western area is influenced more strongly by other factors.

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2). Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Evapotranspiration (ET) from the land surface is an important hydrometeorological factor in the exchange of energy between the atmosphere and land surface. The accurate quantification for management of water resources and understanding of climate change are crucial, requiring continuous temporal and spatial monitoring. The objective of this study is to apply and estimate daily actual ET using semiempirical method, B-method, which is based on surface energy balance over heterogeneous area, Korea. To estimate daily ET, we used geostationary meteorological satellite data (Communication, Ocean and Meteorological Satellite, COMS) and polar-orbiting satellite data (Systeme Pour ľObservation de la Terre, SPOT). Estimated daily ET using only satellite data was relatively accurate and reflects land surface characteristics. It had high periodicity and spatial resolution over a wide area on clear-sky days. The daily ET was overestimated by about 1 mm/day at the two flux tower measurements sites, but the simulated seasonal variation and pattern were in good agreement with flux tower measurements. In the mixed forest, the root-mean-square error (RMSE) was 0.94 mm/day and the bias was 1.05 mm/day, while, in the rice paddy, RMSE was 1.12 mm/day and bias was 1.21 mm/day.

Surface albedo from the geostationary Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager (MI) observation system

The surface albedo is an essential climate variable that is considered in many applications used for predicting climate and understanding the mechanisms of climate change. In this study, surface albedo was estimated using a bidirectional reflectance distribution function model based on Communication, Ocean and Meteorological Satellite/Meteorological Imager data. Geostationary orbiting satellite data are suitable for a level 2 product like albedo, which requires a synthetic process to estimate. The authors modified established methods to consider the geometry of the solar-surface-sensor of COMS/MI. Of note, the viewing zenith angle term was removed from the kernel integration used for estimating spectral albedo. Finally, the spectral (narrow) albedo was converted into the broadband albedo with shortwave length (approximately 0.3–2.5 μm). This study determined conversion coefficients using only one spectral albedo of visible channel. The estimated albedo had a relatively high correlation with Satellite Pour l’Observation de la Terre/Vegetation and low unweighted error values specific for land types or times. The validation results show that estimated albedo has a root mean square error of 0.0134 at Jeju flux site that indicates accuracy similar to that of other satellite-based products.

Analysis on long-term variability of sea ice albedo and its relationship with sea ice concentration over Antarctica

Sea ice is an important factor for understanding Antarctic climate change. Especially, annual change of sea ice shows different trend in Antarctica and Arctic. This different variation need to continuously observe the Polar Regions. Sea Ice Albedo (SIA) and Sea Ice Concentration (SIC) are an indicator of variation on sea ice. In addition, albedo is key parameter to understand the energy budget in Antarctica. This being so, it is important to analyze long-term variation of the two factors for observing of change of Antarctic environment. In this study, we analyzed long-term variability of SIC and SIA to understand the changes of sea ice over Antarctic and researched the relationship with two factors. We used the SIA data at The Satellite Application Facility on Climate Monitoring (CM SAF) and the SIC data provided by Ocean and Sea Ice Satellite Application Facility (OSI-SAF) from 1982 to 2009. The study period was selected to Antarctic summer season due to polar nights. We divided study periods into two terms, Nov-Dec(ND) and Jan-Feb(JF) in order to reflect the characteristics of sea ice area, which minimum extend occurred in September and maximum extend occurred in February. We analyzed the correlation between SIA and SIC. As a results, two variables have a strong positive correlation (each correlation coefficients are 0.91 in Nov-Dec and 0.90 in Jan-Feb). We performed time series analysis using linear regression to understand the spatial and temporal tendency of SIA and SIC. As a results, SIA and SIC have a same spatial trend such as Weddle sea and Ross sea sections show the positive trend and Bellingshausen Amundsen sea shows the negative trend of two factors. Moreover, annual SIA change rate is 0.26% ~ 0.04% yr-1 over section where represent positive trend during two study periods. And annual SIA change rate is - 0.14 ~ - 0.25 % yr-1 of in the other part where represent negative trend during two study periods.

Correlation analysis between variability pattern of TPW and climate variables

Water vapor is main absorption factor of outgoing longwave radiation. Because increase of water vapor accelerate to become high land surface temperature, it is essential to monitoring the changes in the amount of water vapor and to investigating the causes of such changes. This paper, we monitor variability pattern of Total Precipitable Water (TPW) which observed by satellite. But long-term investigation of climate over Korea peninsula is very difficult due to climatic characteristic in middle latitude of instable atmospheric. El Nino that is one of climate variables appears regularly when compared to the others. Also, precipitation of all climate variables play an important part to analyze variability pattern of water vapor because it is produced by water vapor. Therefore, if we know climatic variability by them, correlation analysis between TPW and climate variables can be improved. In this study, we analyze long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) and precipitation change in middle area of Korea peninsula quantitatively and El Nino was compared to relation of TPW and precipitation. The aim of study is to investigate precipitation and El Nino has an impact on variability pattern of TPW. First, time series analysis is used to calculate TPW and precipitation quantitatively, and anomaly analysis is performed to analyze their correlation. From the results obtained, TPW and precipitation has correlation mostly but the part had inverse correlation was found. We compare it with El Nino of anomaly results. As a result, after El Nino occurred, TPW and precipitation had inverse correlation.

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth’s energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 ~ 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer’s Accuracy), UA (User’s Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Long-term variability of Total PrecipitableWater using a MODIS over Korea

Abstract : Water vapor leading various scale of atmospheric circulation and accounting for about60% of the naturally occurring warming effect is important climate variables. Using the Total PrecipitableWater (TPW) from Moderate Resolution Imaging Spectroradiometer (MODIS) operating on both Terraand Aqua, we study long-term Variation of TPW and define relationship among TPW and climaticparameters such as temperature and precipitation to quantitatively demonstrate the impact on climatechange over East Asia focusing on the Korea peninsula. In this study, we used linear regression analysisto detect the correlation of TPW and temperature/precipitation and harmonic analysis to analyzechangeable aspects of periodic characteristics. A result of analysis using linear regression analysis betweenTPW and climate elements, TPW shows a high determination coefficient (R 2 ) with temperature andprecipitation (determination coefficient between TPW and temperature: 0.94, determination coefficientbetween TPW anomaly and temperature anomaly: 0.8, determination coefficient between TPW andprecipitation: 0.73, determination coefficient between TPW anomaly and precipitation anomaly: 0.69). Aresult of harmonic analysis of TPW and precipitation of two-year to five-year cycle, amplitude contributionratio of 3.5-year cycle are much higher and two phases are similar in 3.5-year cycle.Key Words : MODIS, TPW, Anomaly, Harmonic analysis요약 :