A downscaling method for simulating deep current interactions with topography – Application to the Sigsbee Escarpment

2013 ◽  
Vol 69 ◽  
pp. 50-63 ◽  
Author(s):  
Steven L. Morey ◽  
Dmitry S. Dukhovskoy
2007 ◽  
Vol 37 (3) ◽  
pp. 708-726 ◽  
Author(s):  
Peter Hamilton

Abstract Observations of deep currents, from a closely spaced array deployed at the base of the Sigsbee Escarpment, south of the Mississippi Delta, are dominated by energetic, short-period (∼10 days) topographic Rossby waves. Over a 2-yr interval, distinct trains of waves occurred, with differing characteristic periods, wavelengths, and horizontal energy distribution, usually initiated with a burst of large-amplitude current fluctuations that slowly decay over the next 2–3 months. Two of the wave trains were associated with the shedding and westward passage of major Loop Current anticyclones; however, one event occurred when upper-layer currents were quiescent. This latter wave train showed that bottom-trapped topographic wave motions can be traced to within 300 m of the surface in a 2000-m water column. Ray tracing showed that a likely source region of the short-period waves was the west side of an extended Loop Current. The data and ray paths suggest that, through refraction and reflection, the steep escarpment keeps the energetic waves confined to the deep water. These mechanisms help to explain why short-period fluctuations are not observed farther west in the central and western gulf basin.


Author(s):  
Xin Ma ◽  
Haowei Zhang ◽  
Ge Han ◽  
Feiyue Mao ◽  
Hao Xu ◽  
...  
Keyword(s):  

Water ◽  
2017 ◽  
Vol 9 (12) ◽  
pp. 995 ◽  
Author(s):  
Shen Tan ◽  
Bingfang Wu ◽  
Nana Yan ◽  
Weiwei Zhu
Keyword(s):  

Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.


2013 ◽  
Vol 17 (10) ◽  
pp. 4189-4208 ◽  
Author(s):  
S. Radanovics ◽  
J.-P. Vidal ◽  
E. Sauquet ◽  
A. Ben Daoud ◽  
G. Bontron

Abstract. Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east–west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east–west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.


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