Entity

Time filter

Source Type


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. Source


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. Source


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. Source


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. Source


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. Source

Discover hidden collaborations