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 |
Wen L.,Asia Pacific Economic Cooperation Climate Center |
Nagabhatla N.,Leibniz University of Hanover |
Nagabhatla N.,Asia Pacific Economic Cooperation Climate Center |
And 3 more authors.
Chinese Journal of Oceanology and Limnology | Year: 2015
In this paper, we introduced parameterizations of the salinity effects (on heat capacity, thermal conductivity, freezing point and saturated vapor pressure) in a lake scheme integrated in the Weather Research and Forecasting model coupled with the Community Land Model (WRF-CLM). This was done to improve temperature simulation over and in a saline lake and to test the contributions of salinity effects on various water properties via sensitivity experiments. The modified lake scheme consists of the lake module in the CLM model, which is the land component of the WRF-CLM model. The Great Salt Lake (GSL) in the USA was selected as the study area. The simulation was performed from September 3, 2001 to September 30, 2002. Our results show that the modified WRF-CLM model that includes the lake scheme considering salinity effects can reasonably simulate temperature over and in the GSL. This model had much greater accuracy than neglecting salinity effects, particularly in a very cold event when that effect alters the freezing point. The salinity effect on saturated vapor pressure can reduce latent heat flux over the lake and make it slightly warmer. The salinity effect on heat capacity can also make lake temperature prone to changes. However, the salinity effect on thermal conductivity was found insignificant in our simulations. © 2015, Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag Berlin Heidelberg.
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.