Asia Pacific Economic Cooperation Climate Center

Haeundae gu, South Korea

Asia Pacific Economic Cooperation Climate Center

Haeundae gu, South Korea
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Lee S.-S.,University of Hawaii at Manoa | Moon J.-Y.,University of Hawaii at Manoa | Wang B.,University of Hawaii at Manoa | Wang B.,Nanjing University of Information Science and Technology | Kim H.-J.,Asia Pacific Economic Cooperation Climate Center
Journal of Climate | Year: 2017

The boreal summer intraseasonal oscillation (BSISO) is one of the most prominent modes in the tropical climate system. For better subseasonal prediction of extreme precipitation the relationship between BSISO activity and extreme precipitation events (days with daily precipitation exceeding the local 90th percentile) over Asia is investigated, especially the dependence of extreme precipitation occurrence on BSISO precipitation anomaly pattern (phase) and intensity (amplitude) in each month. At a given area and month, the probability of extreme precipitation changes from less than 10% to over 40%-50% according to BSISO phases, and it tends to be high when BSISO amplitude is large. The extreme precipitation probability estimated by BSISO activity is generally higher over ocean than over land. Over some land regions, however, occurrence of extreme precipitation is notably modulated by BSISO activity. In May, the extreme precipitation probability over southeastern China can reach about 30%-40% when BSISO precipitation anomaly arrives over the region. Similarly, in September the extreme precipitation probability over western China can reach 40%-50% when BSISO precipitation anomaly arrives there. The BSISO activity provides useful information in narrowing down the area and timing of high probability of extreme precipitation occurrence. Using real-time BSISO monitoring and forecast data provided by the Asia-Pacific Economic Cooperation (APEC) Climate Center, it is shown that 1) the best model (ECMWF) can predict the leading BSISO modes about 20 days ahead with bivariate correlation skills higher than 0.5 except in May, and 2) the empirical probability distributions of extreme precipitation that are based on BSISO activity can be captured by the BSISO forecasts for lead times longer than 2 weeks. © 2017 American Meteorological Society.

Park S.,Ulsan National Institute of Science and Technology | Im J.,Ulsan National Institute of Science and Technology | Rhee J.,Asia Pacific Economic Cooperation Climate Center | Shin J.,National Meteorological Satellite Center | Park J.D.,National Meteorological Satellite Center
Water (Switzerland) | Year: 2017

Soil moisture is a key part of Earth's climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m3·m−3, and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m3·m−3) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model. © 2017 by the authors.

Jeong D.,Asia Pacific Economic Cooperation Climate Center | Min K.-H.,Kyungpook National University | Min K.-H.,Purdue University | Lee G.,Kyungpook National University | Kim K.-E.,Kyungpook National University
Advances in Atmospheric Sciences | Year: 2014

This paper presents a case study of mesoscale convective band (MCB) development along a quasi-stationary front over the Seoul metropolitan area. The MCB, which initiated on 1500 UTC 20 September 2010 and ended on 1400 UTC 21 September 2010, produced a total precipitation amount of 259.5 mm. The MCB development occurred during a period of tropopause folding in the upper level and moisture advection with a low-level jet. The analyses show that the evolution of the MCB can be classified into five periods: (1) the cell-forming period, when convection initiated; (2) the frontogenetic period, when the stationary front formed over the Korean peninsula; (3) the quasi-stationary period, when the convective band remained over Seoul for 3 h; (4) the mature period, when the cloud cover was largest and the precipitation rate was greater than 90 mm h-1; and (5) the dissipating period, when the MCB diminished and disappeared. The synoptic, thermodynamic, and dynamic analyses show that the MCB maintained its longevity by a tilted updraft, which headed towards a positive PV anomaly. Precipitation was concentrated under this area, where a tilted ascending southwesterly converged with a tilted ascending northeasterly, at the axis of cyclonic rotation. The formation of the convective cell was attributed in part by tropopause folding, which enhanced the cyclonic vorticity at the surface, and by the low-level convergence of warm moist air and upperlevel divergence. The southwesterly flow ascended in a region with high moisture content and strong relative vorticity that maintained the development of an MCB along the quasi-stationary front. © 2014 Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg.

Stefanova L.,Florida State University | Misra V.,Florida State University | O'Brien J.J.,Florida State University | Chassignet E.P.,Florida State University | And 2 more authors.
Climate Dynamics | Year: 2012

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean-atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean-atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i. e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates. © 2011 Springer-Verlag.

Bundel A.Y.,Russian Hydrometeorological Research Center | Kryzhov V.N.,Russian Hydrometeorological Research Center | Min Y.-M.,Asia Pacific Economic Cooperation Climate Center | Khan V.M.,Russian Hydrometeorological Research Center | And 2 more authors.
Russian Meteorology and Hydrology | Year: 2011

The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T850 and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid- and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above- and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia. © 2011 Allerton Press, Inc.

Chang H.,Portland State University | Johnson G.,Portland State University | Hinkley T.,Portland State University | Jung I.-W.,Portland State University | Jung I.-W.,Asia Pacific Economic Cooperation Climate Center
Journal of Hydrology | Year: 2014

This study examines the spatial patterns of annual runoff ratios and their variability and identifies the determinants of runoff indices for 238 reference basins with low levels of anthropogenic influence and 1352 non-reference basins with substantial levels of anthropogenic influence. Runoff ratios are high and runoff ratio coefficients of variation (CV) are low in coastal Pacific Northwest and Northeast basins, both humid temperate climates. The most significant variable that influences annual runoff ratio for both basin types is the average annual days of measurable precipitation. Snow percent of total precipitation and minimum watershed elevation are common predictors of runoff ratio for both types of basins. Slope percent and Horton overland flow are significant predictors for reference basin runoff ratio, while average annual precipitation, basin compactness, and dam storage are significant predictors for non-reference basin runoff ratio. The variables most significantly influencing runoff ratio CV in both types of basins are the average annual days of measurable precipitation, the precipitation seasonality index, and the base flow index. Horton overland flow is a significant predictor for reference basins, while minimum watershed elevation is a significant predictor for non-reference basins. Spatial autocorrelation of ordinary least squares estimated residuals are reduced by geographically weighted regression (GWR) for all models in both basin types. This study shows that GWR modeling, which takes into account spatial non-stationarity, can create more accurate representations of runoff ratio variability in both basin types. The spatially-varying coefficient values in GWR models also show local specific relationships between runoff indices and various climatic and landscape factors. © 2014 Elsevier B.V.

Kim G.,Pukyong National University | Kim D.-S.,Pukyong National University | Park K.-W.,Asia Pacific Economic Cooperation Climate Center | Cho J.,Pukyong National University | And 2 more authors.
Remote Sensing Letters | Year: 2014

Satellite remote sensing is a useful tool for monitoring wildfire by analysing the brightness temperature of medium and thermal infrared bands. This letter described a wildfire detection algorithm developed for the COMS (communication, ocean and meteorological satellite) and evaluated the applicability of the proposed method by comparing the detection results with the KFS (Korea Forest Service) wildfire survey data and ASTER (advanced spaceborne thermal emission and reflection radiometer) image. We detected various size of wildfires occurred in South Korea on 9 March 2013, which is a remarkable outcome when considering the limited channels of the COMS. For a more reliable algorithm, the characterization of subpixel fires using Doziers method or the multiple endmember spectral mixture analysis will be necessary as future work. In addition, more wildfire cases should be experimented for statistical assessment of the accuracy. © 2013 Taylor & Francis.

Tung Y.L.,City University of Hong Kong | Tam C.-Y.,City University of Hong Kong | Sohn S.-J.,Asia Pacific Economic Cooperation Climate Center | Chu J.-L.,National Science and Technology Center for Disaster Reduction
Journal of Geophysical Research: Atmospheres | Year: 2013

The performance of various seasonal forecast systems in predicting the station-scale summer rainfall in South China (SC) was assessed and was compared with that based on a statistical downscaling scheme. Hindcast experiments from 11 dynamical models covering the period of 1983-2003 were taken from the Asia-Pacific Economic Cooperation Climate Center multimodel ensemble. Based on observations, singular value decomposition analysis (SVDA) showed that SC precipitation is strongly related to the broad-scale sea level pressure (SLP) variation over Southeast Asia, western north Pacific, and part of the Indian Ocean. Analogous covariability was also found between model hindcasts and the observed station precipitation. Based on these results from SVDA, a statistical downscaling scheme for predicting SC station rainfall with model SLP as predictor was constructed. In general, the statistical scheme is superior to the original model prediction in two geographical regions, namely, western SC (near Guangxi) and eastern coastal SC (eastern Guangdong to part of Fujian). Further analysis indicated that dynamical models are able to reproduce the large-scale circulation patterns associated with the recurrent modes of SC rainfall, but not the local circulation features. This probably leads to erroneous rainfall predictions in some locations. On the other hand, the statistical scheme was able to map the broad-scale SLP patterns onto the station-scale rainfall anomalies, thereby correcting some of the model biases. Overall, our results demonstrate how SC summer rainfall predictions can be improved by tapping the source of predictability related to large-scale circulation signals from dynamical models. © 2013. American Geophysical Union. All Rights Reserved.

Swenson E.,Asia Pacific Economic Cooperation Climate Center | Swenson E.,George Mason University
Journal of Climate | Year: 2015

Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. Themost popular linearmethods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partiallywhitened variables. Similar to thewhitening transformation, the partialwhitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariancematrices prior to inversion, providing amore accurate estimate of the population covariance.CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction forMCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetricCPCCAsolutionsmay be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCAbased on the covariance estimate ofO. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications. © 2015 American Meteorological Society.

Wen L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Lv S.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Li Z.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Zhao L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | And 2 more authors.
Advances in Meteorology | Year: 2015

The Tibetan Plateau harbors thousands of lakes; however few studies focus on impacts of lakes on local climate in the region. To investigate and quantify impacts of the two biggest lakes (Ngoring Lake and Gyaring Lake) of the Yellow River source region in the Tibetan Plateau on local climate, two simulations (with and without the two large lakes) from May 2010 to July 2011 are performed and analyzed using the WRF-CLM model (the weather research and forecasting model coupled with the community land model). Differences between simulated results show that the WRF-CLM model could provide realistic reproduction of surface observations and has better simulation after considering lakes. Lakes mostly reduce the maximum temperature all year round and increase the minimum temperature except in March due to the large heat capacity that makes lakes absorb (release) more energy for the same temperature change compared to land. Lakes increase precipitation over the lake area and in the nearby region, mostly during 02-14 BT (Beijing Time) of July to October when the warm lake surface induces the low level horizontal convergence and updraft over lake and provides energy and vapor to benefit the development of the convection for precipitation. © 2015 Lijuan Wen et al.

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