scholarly journals Time Scales of Submesoscale Flow Inferred from a Mooring Array

2020 ◽  
Vol 50 (4) ◽  
pp. 1065-1086
Author(s):  
Jörn Callies ◽  
Roy Barkan ◽  
Alberto Naveira Garabato
Keyword(s):  
Spatial Scale ◽  
Root Mean Square ◽  
Time Scales ◽  
Spatial Scales ◽  
Helmholtz Decomposition ◽  
Rossby Number ◽  
Mean Square ◽  
Leading Order ◽  
Rotational Component ◽  
Insight Into

AbstractWhile the distribution of kinetic energy across spatial scales in the submesoscale range (1–100 km) has been estimated from observations, the associated time scales are largely unconstrained. These time scales can provide important insight into the dynamics of submesoscale turbulence because they help quantify to what degree the flow is subinertial and thus constrained by Earth’s rotation. Here a mooring array is used to estimate these time scales in the northeast Atlantic. Frequency-resolved structure functions indicate that energetic wintertime submesoscale turbulence at spatial scales around 10 km evolves on time scales of about 1 day. While these time scales are comparable to the inertial period, the observed flow also displays characteristics of subinertial flow that is geostrophically balanced to leading order. An approximate Helmholtz decomposition shows the order 10-km flow to be dominated by its rotational component, and the root-mean-square Rossby number at these scales is estimated to be 0.3. This rotational dominance and Rossby numbers below one persist down to 2.6 km, the smallest spatial scale accessible by the mooring array, despite substantially superinertial Eulerian evolution. This indicates that the Lagrangian evolution of submesoscale turbulence is slower than the Eulerian time scale estimated from the moorings. The observations therefore suggest that, on average, submesoscale turbulence largely follows subinertial dynamics in the 1–100-km range, even if Doppler shifting produces superinertial Eulerian evolution. Ageostrophic motions become increasingly important for the evolution of submesoscale turbulence as the scale is reduced—the root-mean-square Rossby number reaches 0.5 at a spatial scale of 2.6 km.

scholarly journals Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262009
Author(s):  
Rui Zhang ◽  
Hejia Song ◽  
Qiulan Chen ◽  
Yu Wang ◽  
Songwang Wang ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Time Scales ◽  
Hemorrhagic Fever ◽  
Early Warning Systems ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Forecasting Models ◽  
Best Fitting ◽  
Different Time Scales ◽  
Better Than

Objectives This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results ARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

scholarly journals Constraining tropospheric mixing timescales using airborne observations and numerical models

2003 ◽  
Vol 3 (4) ◽  
pp. 1023-1035 ◽  
Author(s):  
P. Good ◽  
C. Giannakopoulos ◽  
F. M. O’Connor ◽  
S. R. Arnold ◽  
M. de Reus ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Time Scales ◽  
Transport Model ◽  
Numerical Models ◽  
Spatial Scales ◽  
Lagrangian Model ◽  
Small Scale ◽  
Tracer Transport ◽  
Trajectory Length ◽  
Eulerian Grid

Abstract. A technique is demonstrated for estimating atmospheric mixing time-scales from in-situ data, using a Lagrangian model initialised from an Eulerian chemical transport model (CTM). This method is applied to airborne tropospheric CO observations taken during seven flights of the Mediterranean Intensive Oxidant Study (MINOS) campaign, of August 2001. The time-scales derived, correspond to mixing applied at the spatial scale of the CTM grid. They are relevant to the family of hybrid Lagrangian-Eulerian models, which impose Eulerian grid mixing to an underlying Lagrangian model. The method uses the fact that in Lagrangian tracer transport modelling, the mixing spatial and temporal scales are decoupled: the spatial scale is determined by the resolution of the initial tracer field, and the time scale by the trajectory length. The chaotic nature of lower-atmospheric advection results in the continuous generation of smaller spatial scales, a process terminated in the real atmosphere by mixing. Thus, a mix-down lifetime can be estimated by varying trajectory length so that the model reproduces the observed amount of small-scale tracer structure. Selecting a trajectory length is equivalent to choosing a mixing timescale. For the cases studied, the results are very insensitive to CO photochemical change calculated along the trajectories. That is, it was found that if CO was treated as a passive tracer, this did not affect the mix-down timescales derived, since the slow CO photochemistry does not have much influence at small spatial scales. The results presented correspond to full photochemical calculations. The method is most appropriate for relatively homogeneous regions, i.e. it is not too important to account for changes in aircraft altitude or the positioning of stratospheric intrusions, so that small scale structure is easily distinguished. The chosen flights showed a range of mix-down time upper limits: a very short timescale of 1 day for 8 August, due possibly to recent convection or model error, 3 days for 3 August, probably due to recent convective and boundary layer mixing, and 6-9 days for 16, 17, 22a, 22c and 24 August. These numbers refer to a mixing spatial scale of 2.8°, defined here by the resolution of the Eulerian grid from which tracer fields were interpolated to initialise the Lagrangian model. For the flight of 3 August, the observed concentrations result from a complex set of transport histories, and the models are used to interpret the observed structure, while illustrating where more caution is required with this method of estimating mix-down lifetimes.

scholarly journals Space–Time Scales of Northward Propagation of Convection during Boreal Summer

2012 ◽  
Vol 140 (12) ◽  
pp. 3857-3866 ◽  
Author(s):  
Arindam Chakraborty ◽  
Ravi S. Nanjundiah
Keyword(s):  
Spatial Scale ◽  
Time Scales ◽  
Indian Monsoon ◽  
Spatial Scales ◽  
Tropical Rainfall Measuring Mission ◽  
Boreal Summer ◽  
Monsoon Precipitation ◽  
Intraseasonal Variation ◽  
Spatial And Temporal Scales ◽  
Northward Propagation

Abstract This study uses precipitation estimates from the Tropical Rainfall Measuring Mission to quantify the spatial and temporal scales of northward propagation of convection over the Indian monsoon region during boreal summer. Propagating modes of convective systems in the intraseasonal time scales such as the Madden–Julian oscillation can interact with the intertropical convergence zone and bring active and break spells of the Indian summer monsoon. Wavelet analysis was used to quantify the spatial extent (scale) and center of these propagating convective bands, as well as the time period associated with different spatial scales. Results presented here suggest that during a good monsoon year the spatial scale of this oscillation is about 30° centered around 10°N. During weak monsoon years, the scale of propagation decreases and the center shifts farther south closer to the equator. A strong linear relationship is obtained between the center/scale of convective wave bands and intensity of monsoon precipitation over Indian land on the interannual time scale. Moreover, the spatial scale and its center during the break monsoon were found to be similar to an overall weak monsoon year. Based on this analysis, a new index is proposed to quantify the spatial scales associated with propagating convective bands. This automated wavelet-based technique developed here can be used to study meridional propagation of convection in a large volume of datasets from observations and model simulations. The information so obtained can be related to the interannual and intraseasonal variation of Indian monsoon precipitation.

scholarly journals Constraining tropospheric mixing timescales using airborne observations and numerical models

2003 ◽  
Vol 3 (2) ◽  
pp. 1213-1245 ◽  
Author(s):  
P. Good ◽  
C. Giannakopoulos ◽  
F. M. O’Connor ◽  
S. R. Arnold ◽  
M. de Reus ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Time Scales ◽  
Transport Model ◽  
Numerical Models ◽  
Spatial Scales ◽  
Lagrangian Model ◽  
Small Scale ◽  
Tracer Transport ◽  
Transport Modelling ◽  
Trajectory Length

Abstract. A technique is demonstrated for estimating atmospheric mixing time-scales from in-situ data, using a Lagrangian model initialised from an Eulerian chemical transport model (CTM). This method is applied to airborne tropospheric CO observations taken during seven flights of the Mediterranean Intensive Oxidant Study (MINOS) campaign, of August 2001. The time-scales derived, correspond to mixing applied at the spatial scale of the CTM grid. Specifically, they are upper bound estimates of the mix-down lifetime that should be imposed for a Lagrangian model to reproduce the observed small-scale tracer structure. They are relevant to the family of hybrid Lagrangian-Eulerian models, which impose Eulerian grid mixing to an underlying Lagrangian model. The method uses the fact that in Lagrangian tracer transport modelling, the mixing spatial and temporal scales are decoupled: the spatial scale is determined by the resolution of the initial tracer field, and the time scale by the trajectory length. The chaotic nature of lower-atmospheric advection results in the continuous generation of smaller spatial scales, a process terminated in the real atmosphere by mixing. Thus, a mix-down lifetime can be estimated by varying trajectory length so that the model reproduces the observed amount of small-scale tracer structure. Selecting a trajectory length is equivalent to choosing a mixing timescale. For the cases studied, the results are very insensitive to CO photochemical change calculated along the trajectories. The method is most appropriate for relatively homogeneous regions, i.e. it is not too important to account for changes in aircraft altitude or the positioning of stratospheric intrusions, so that small scale structure is easily distinguished. The chosen flights showed a range of mix-down time upper limits: 1 and 3 days for 8 August and 3 August, due to recent convective and boundary layer mixing respectively, and 7–9 days for 16, 17, 22a, 22c and 24 August. For the flight of 3 August, the observed concentrations result from a complex set of transport histories, and the models are used to interpret the observed structure, while illustrating where more caution is required with this method of estimating mix-down lifetimes.

scholarly journals Time Scales and Spatial Patterns of Passive Ocean–Atmosphere Decay Modes*

2008 ◽  
Vol 21 (10) ◽  
pp. 2187-2203 ◽  
Author(s):  
Benjamin R. Lintner ◽  
J. David Neelin
Keyword(s):  
Spatial Scale ◽  
Time Scales ◽  
Decay Time ◽  
Time Scale ◽  
Large Scale ◽  
Spatial Scales ◽  
Scale Dependence ◽  
Decay Modes ◽  
Decay Times ◽  
The Tropics

Abstract The decay characteristics of a mixed layer ocean passively coupled to an atmospheric model are important to the response of the climate system to stochastic or external forcing. Two salient features of such decay—the spatial-scale dependence of sea surface temperature anomaly (SSTA) decay time scales and the spatial inhomogeneities of SSTA decay modes—are addressed using intermediate-level complexity and simple analytic models of the tropical atmosphere. As expected, decay time scales increase with the spatial extent of the SSTA. Most modes decay rapidly—with characteristic decay times of 50–100 days for a 50-m mixed layer—with the decay determined by local surface flux adjustment. Only those modes with spatial scales approaching or larger than the tropical basin scale exhibit decay time scales distinctively longer than the local decay, with the decay time scale of the most slowly decaying mode of the order of 250–300 days in the tropics (500 days globally). Simple analytic prototypes of the spatial-scale dependence and the effect of basic-state inhomogeneities, especially the impact of nonconvecting regions, elucidate these results. Horizontal energy transport sets the transition between fast, essentially local, decay time scales and the slower decay at larger spatial scales; within the tropics, efficient wave dynamics accounts for the small number of slowly decaying modes. Inhomogeneities in the basic-state climate, such as the presence or absence of mean tropical deep convection, strongly impact large-scale SSTA decay characteristics. For nonconvecting regions, SSTA decay is slow because evaporation is limited by relatively slow moisture divergence. The separation of convecting- and nonconvecting-region decay times and the closeness of the slower nonconvecting-region decay time scale to the most slowly decaying modes cause a blending between local nonconvecting modes and the large-scale modes, resulting in pronounced spatial inhomogeneity in the slow decay modes.

The Relationship of Root Mean Square Value of Electromyography and Isometric Torque of Quadriceps in Normal Subjects

2016 ◽  
Vol 26 (1) ◽  
pp. 58
Author(s):  
Qiurong XIE ◽  
Zheng JIANG ◽  
Qinglu LUO ◽  
Jie LIANG ◽  
Xiaoling WANG ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Normal Subjects ◽  
Mean Square ◽  
Isometric Torque ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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