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111.
Seafloor sediment classification based on echo characteristics obtained from single-beam echosounder is very useful in remote
and instant sediment classification. Results of different classification techniques using such data provide robust results
when the acoustic beam has a normal incidence with the seabottom. This may not always be true and show poor classification,
with the data acquired during rough sea periods corresponding to both oblique and normal incidence of the acoustic pulse,
due to roll and pitch motion of the ship. In the present study, an attempt is made to exploit the artificial neural network
(ANN) techniques for better classification with such data. Learning Vector Quantisation (LVQ) is a supervised learning algorithm
of ANN that is found to be an effective tool and show good performance. The input data to the network include the roughness
index (E1) and hardness index (E2) derived from echo characteristics. The network utilizes the competitive learning, a distance
function in the first layer and a linear function in the second layer. The network was tried with a different size of hidden
neurons and training data size to see the influence on classification. It is found that with ten neurons in the first layer
and four neurons in the second layer good performance in classification for the data was achieved. 相似文献
112.
Sensitivity of high-resolution tropical cyclone intensity forecasts to surface flux parameterization
Chi-Sann Liou 《Natural Hazards》2007,41(3):387-399
Surface flux parameterization schemes used in current dynamic models are primarily based upon measurements at low and moderate
wind speeds. Recent studies show that these parameterization schemes may be incorrect at high wind speeds (e.g., tropical
cyclone forecasts). Five high-resolution numerical model experiments are designed to assess the sensitivity of tropical cyclone
intensity forecasts to changes in the surface flux parameterization. The sensitivity experiments are conducted by running
48 h forecasts of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) for six selected tropical cyclones with
individual modifications to surface flux calculation that include: (1) limiting the surface stress for wind speeds greater
than 33 m s−1, or 64 knots (kt); (2) computing the stress at the top of the model bottom grid layer (MBGL) by averaging results from surface
layer similarity and turbulence mixing parameterization for wind speeds greater than 33 m s−1; (3) increasing the roughness lengths for heat and moisture transfer by a factor of ten; (4) setting the roughness lengths
for heat and moisture transfer to 1/10 of the momentum roughness length; and (5) cooling the sea surface temperature (SST)
by a prescribed rate at high winds. Averaged responses for the six storms to these sensitivity tests show that: (i) the limit
on surface stress at high winds significantly increases the cyclone intensity in 48 h forecasts; (ii) the averaged surface
layer stress at high winds increases the cyclone intensity but to a much lesser degree than limiting the surface stress; (iii)
large increases in the roughness lengths for heat and moisture transfer are needed to significantly impact the intensity forecast;
(iv) the different roughness length formula for surface transfer coefficients notably increases C
h/C
d ratio from 0.59 to 0.79 for 25 m s−1 and 0.41 to 0.75 for 50 m s−1 that significantly increases the predicted cyclone intensity; and (v) cooling of the SST by −5.8°C in 48 h reduces the maximum
surface wind speed by −32 kt, or 16.5 m s−1, at 48 h forecast. These results suggest that a surface flux parameterization scheme suitable for tropical cyclone intensity
forecast must correctly model the leveling-off character of surface stress and C
h/C
d ratio at high winds. All modifications to surface flux calculation have little influence on 48 h track forecasts, even though
they may significantly impact the intensity forecasts.
相似文献
| Chi-Sann LiouEmail: |
113.
A Roughness Sublayer Wind Profile Above A Non-Uniform Surface 总被引：2，自引：0，他引：2
D. T. Mihailović Branislava Lalić B. Rajković I. Arsenić 《Boundary-Layer Meteorology》1999,93(3):425-451
In atmospheric models for different scales the underlying surface consists of patches of bare soil and plant communities with different morphological parameters. Experimental evidence indicates that there is a significant departure of the wind profile above a vegetative surface from that predicted by the logarithmic relationship, which gives values that are greater than those observed. This situation can seriously disturb the physical picture concerning the transfers of momentum, heat and water vapour from the surface into the atmosphere.The intention of this paper is to generalise the calculation of exchange of momentum between the atmosphere and a non-homogenous vegetative surface, and to derive a general equation for the wind speed profile in a roughness sublayer under neutral conditions. Furthermore, these results are extended to non-neutral cases. The suggested expression for the wind profile is compared with some earlier approaches and the observations obtained above a broad range of plant communities. 相似文献
114.
大气模式中表面水热通量计算的一些问题 总被引：1，自引：0，他引：1
对现有的大气模式中计算海面和大气间水、热通量的通量-廓线关系式进行了评论,提出一个理论上较完整的通量-廓线关系式。其中考虑了水汽对M－O参数的影响,并引进相应于虚位温的标量粗糙度。MoanaWave的实测资料表明,由于该资料相应于温度的粗糙度zoh和湿度粗糙度zoq相差不大,使现有的公式计算结果与文中提出的公式差别不大。当zoh,zoq差别大时,两种公式结果有一定的差别。而文中公式理论上更为合理。还将不稳定状态下计算通量的简化方法推广到海面。 相似文献
115.
116.
Accurate knowledge of the contacts between surface roughness and the resultant wind speed are important for climatic models, wind power meteorology, agriculture and erosion hazards especially on sand saltation in arid and semi-arid environments, where vegetation cover is scarce. In this study, synchronous measurements of three-dimensional wind speed below 5 m are carried out in three different surface roughness conditions in Minqin, China, and the difference in the turbulence statistics and the structure of the very large-scale motions (VLSMs) were revealed. The results show that the slope of the mean wind profile (MVP), the turbulent kinetic energy (TKE) and Reynolds stress increase with the surface roughness. The roughness seems to suppress the ejection events and the surface roughness will not only weaken the energy of the VLSMs, but also reduce the scale values of VLSMs near the wall. These influences may cause some changes regarding the dust transportation in streamwise and vertical directions during the sand and dust storm (SDS). That is, the decrease of the mean velocity near the ground will reduce the dust transportation in the streamwise direction and influence of the roughness on the ejection and sweep events will change the dust transportation in the vertical direction. Furthermore, the increase of roughness will weaken the scale and energy of VLSMs, which will lead to the decrease of the capacity of dust transportation. © 2019 John Wiley & Sons, Ltd. 相似文献
117.
Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation. 相似文献
118.
Surface soil moisture is an important parameter in hydrology and climate investigations. Current and future satellite missions with L-band passive microwave radiometers can provide valuable information for monitoring the global soil moisture. A factor that can play a significant role in the modeling and inversion of microwave emission from land surfaces is the surface roughness. In this study, an L-band parametric emission model for exponentially correlated surfaces was developed and implemented in a soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter of Hp in relation with the geometric roughness variables (root mean square height s and correlation length l) and incidence angle. The parameterization was developed based on a large set of simulations using an analytical approach incorporated in the advanced integral equation model (AIEM) over a wide range of geophysical properties. It was found that the effective roughness parameter decreases as surface roughness increases, but increases as incidence angle increases. In contrast to previous research, Hp was found to be expressed as a function of a defined slope parameter m = s2/l, and coefficients of the function could be well described by a quadratic equation. The parametric model was then tested with L-band satellite data in soil moisture retrieval algorithm over the Little Washita watershed, which resulted in an unbiased root mean square error of about 0.03 m3/m3 and 0.04 m3/m3 for ascending and descending orbits, respectively. 相似文献
119.
120.