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1.
Akula Venkatram 《Boundary-Layer Meteorology》1986,36(1-2):149-156
This paper examines the assumptions and derivations that govern commonly used methods of estimating the height of the thermal internal boundary layer (TIBL) that occurs near shorelines. We show that nearly all these methods require inputs that can be defined only in the very limited context of the data set used to derive the empirical equations for the boundary-layer height. This analysis suggests that the current formulations have little general applicability, and it points to the need for more reliable methods for estimating the TIBL height. 相似文献
2.
Hareesh Kumar Panangattu Viswanathan Mathew Basil Ramesh Kumar Madathiparambil Ranganatha Raghunadha Rao Akula Jagadeesh Puvvala Surya Venkata Radhakrishnan Kalarickal Gopalan Shyni Thiyyadi Nandakumar 《Ocean Dynamics》2013,63(11):1175-1180
Physical oceanography measurements reveal a strong salinity (0.18 psu km−1) and temperature (0.07 °C km−1) front off the east coast of India in December 1997. T–S diagrams suggest lateral mixing between the fresh water at the coast and the ambient warmer, saltier water. This front seems to be the result of southward advection of fresh and cool water, formed in the northern Bay of Bengal during the monsoon, by the East Indian Coastal Current, as suggested by the large-scale salinity structure in the SODA re-analysis and the anti-cyclonic gyre in the northwestern Bay of Bengal during winter. The data further reveals an offshore front in January, which appears to be the result of a meso-scale re-circulation around an eddy, bringing cold and freshwater from the northern Bay of Bengal further away from the shore. Our cruise data hence illustrates that very strong salinity fronts can appear in the Bay of Bengal after the monsoon, as a result of intense coastal circulation and stirring by eddies.
相似文献3.
Geochemistry and quality of groundwater of Gummanampadu sub-basin, Guntur District, Andhra Pradesh, India 总被引：3，自引：3，他引：0
N. Subba Rao A. Subrahmanyam S. Ravi Kumar N. Srinivasulu G. Babu Rao P. Surya Rao G. Venkatram Reddy 《Environmental Earth Sciences》2012,67(5):1451-1471
Groundwater survey has been carried out in the area of Gummanampadu sub-basin located in Guntur District, Andhra Pradesh, India for assessing the factors that are responsible for changing of groundwater chemistry and consequent deterioration of groundwater quality, where the groundwater is a prime source for drinking and irrigation due to non-availability of surface water in time. The area is underlain by the Archaean Gneissic Complex, over which the Proterozoic Cumbhum rocks occur. The results of the plotting of Ca2+ + Mg2+ versus HCO3 ? + CO3 2?, Ca2+ + Mg2+ versus total cations, Na+ + K+ versus total cations, Cl? + SO4 2? versus Na+ + K+, Na+ versus Cl?, Na+ versus HCO3 ? + CO3 2?, Na+ versus Ca2+ and Na+: Cl? versus EC indicate that the rock–water interaction under alkaline condition is the main mechanism in activating mineral dissociation and dissolution, causing the release of Ca2+, Mg2+, Na+, K+, HCO3 ?, CO3 2?, SO4 2? and F? ions into the groundwater. The ionic relations also suggest that the higher concentrations of Na+ and Cl? ions are the results of ion exchange and evaporation. The influences of anthropogenic sources are the other cause for increasing of Mg2+, Na+, Cl?, SO4 2? and NO3 ? ions. Further, the excess alkaline condition in water accelerates more effective dissolution of F?-bearing minerals. Moreover, the chemical data plotted in the Piper’s, Gibbs’s and Langelier–Ludwig’s diagrams, computed for the chloro-alkaline and saturation indices, and analyzed in the principal component analysis, support the above hypothesis. The groundwater quality is, thus, characterized by Na+ > Ca2+ > Mg2+ > K+: HCO3 ? + CO3 2? > Cl? > SO4 2? > NO3 ? > F? facies. On the other hand, majority of groundwater samples are not suitable for drinking with reference to the concentrations of TDS, TH, Mg2+ and F?, while those are not good for irrigation with respect to USSL’s and Wilcox’s diagrams, residual sodium carbonate, and magnesium hazard, but they are safe for irrigation with respect to permeability index. Thus, the study recommends suitable management measures to improve health conditions as well as to increase agricultural output. 相似文献
4.
Panangattu Viswanathan Hareesh Kumar Basil Mathew Madathiparambil Ranganatha Ramesh Kumar Akula Raghunadha Rao Puvvala Surya Venkata Jagadeesh Kalarickal Gopalan Radhakrishnan Thiyyadi Nandakumar Shyni 《Ocean Dynamics》2013,63(11-12):1175-1180
Physical oceanography measurements reveal a strong salinity (0.18 psu km?1) and temperature (0.07 °C km?1) front off the east coast of India in December 1997. T–S diagrams suggest lateral mixing between the fresh water at the coast and the ambient warmer, saltier water. This front seems to be the result of southward advection of fresh and cool water, formed in the northern Bay of Bengal during the monsoon, by the East Indian Coastal Current, as suggested by the large-scale salinity structure in the SODA re-analysis and the anti-cyclonic gyre in the northwestern Bay of Bengal during winter. The data further reveals an offshore front in January, which appears to be the result of a meso-scale re-circulation around an eddy, bringing cold and freshwater from the northern Bay of Bengal further away from the shore. Our cruise data hence illustrates that very strong salinity fronts can appear in the Bay of Bengal after the monsoon, as a result of intense coastal circulation and stirring by eddies. 相似文献
5.
A. Venkatram 《Boundary-Layer Meteorology》1978,15(4):447-452
A simple mixed layer model is used to derive the following expressions for the maximum (daily) convective velocity scale. w
*m
= AQ
m
1/2; w
*m
= Bz
im
The variables A and B are shown to vary within narrow limits thus allowing them to be treated as constants. This is very useful for routine computation of w
*m
, an important variable for dispersion under unstable conditions, from estimates of either the kinematic surface heat flux Q
m
(m-1) or the maximum mixed layer height z
im
.Analysis of observations made during the Minnesota boundary layer experiment shows that there is ample justification for assigning typical values to A and B in estimating w
*m. 相似文献
6.
A. Venkatram 《Boundary-Layer Meteorology》1980,19(4):481-485
Analysis of data collected during the Prairie Grass, Kansas and Minnesota experiments reveals the following empirical relationship between the Monin-Obukhov length L and the friction velocity u
*: L = Au
*
2, A = 1.1 × 103s2m-1. This result combined with the formulation for the height of the stable boundary layer h suggested by Zilitinkevich (1972) leads to h u
*
3/2
f–1/2 where f is the Coriolis parameter. Data from the Minnesota study (Caughey et al., 1979) provide ample support for this expression.These empirical equations for L and h are useful for routine dispersion estimates during stable conditions. 相似文献
7.
A. Venkatram 《Boundary-Layer Meteorology》1979,17(4):523-536
This paper examines the physical concepts underlying the measurement and modeling of pollutant concentrations caused by point sources. We show that the stochastic nature of concentration fields can impose severe limitations on our ability to predict short-term concentrations. Measurements represent averages over a limited number of concentration events and are thus expected to deviate from model predictions which correspond to ensemble averages. In this note we propose a method to estimate this deviation. We then illustrate the use of the technique by applying it to several cases of interest. We show that it is necessary to make an estimate of the expected deviation between predictions and observations before we can meaningfully compare them.We also briefly discuss the possible misinterpretation of model validation resulting from the use of non-dimensional concentrations. 相似文献
8.
Akula Venkatram 《Boundary-Layer Meteorology》1982,24(3):371-385
This paper describes a framework to evaluate air quality model predictions against observations. We propose the following relationship between observations and predictions from an adequate model% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabm4qayaaja% WaaSbaaSqaaiaaicdaaeqaamXvP5wqonvsaeHbfv3ySLgzaGqbaOGa% e8hkaGIaamiEamaaBaaaleaacaaIXaaabeaakiaacYcacaWG4bWaaS% baaSqaaiaaikdaaeqaaOGae8xkaKIaeyypa0Jabm4qayaajaWaaSba% aSqaaiaadchaaeqaaOGae8hkaGIaamiEamaaBaaaleaacaaIXaaabe% aakiab-LcaPiab-TcaRiabew7aLjab-HcaOiaadIhadaWgaaWcbaGa% aGOmaaqabaGccqWFPaqkaaa!4F93!\[\hat C_0 (x_1 ,x_2 ) = \hat C_p (x_1 ) + \varepsilon (x_2 )\],where x
1 refers to the inputs used in the model prediction C
p(x
1), and x
2denotes unknown variables which affect the observed concentration C
0. The hats associated with C
pand C
0denote transformations to convert the residual to a white noise sequence which is normally distributed. In this paper we assume % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabm4qayaaja% GaeyyyIORaciiBaiaac6gacaWGdbaaaa!3B39!\[\hat C \equiv \ln C\].The standard deviation of determines the expected deviation between model prediction and observation. The purpose of model improvement is to make this deviation as small as possible.The formalism we have proposed is applied to the evaluation of two models developed by this author. We show how careful analysis of residuals can lead to improvements in the model. We have also estimated for each of the models.In the last part of the part of the paper we show how the statistics of can be used to interpret model predictions. 相似文献
9.
Akula Venkatram 《Boundary-Layer Meteorology》1983,27(2):185-196
This paper examines the role of the definition of the ensemble in determining the uncertainty in air quality model predictions. We difine the ensemble in terms of the variables included in the model input set. Because observations are also governed by variables not included in this set, we can have an infinitely large set of observations associated with a single model prediction. The variation of these observations about the model prediction corresponds to all possible values of the unknown variables, and it represents the uncertainty in the model prediction. This concept is illustrated by estimating the uncertainty related to model predictions for two dispersion problems. 相似文献
10.
We examine the performance of two steady-state models, a numerical solution of the advection-diffusion equation and the Gaussian
plume-model-based AERMOD (the American Meteorological Society/Environmental Protection Agency Regulatory Model), to predict
dispersion for surface releases under low wind-speed conditions. A comparison of model estimates with observations from two
tracer studies, the Prairie Grass experiment and the Idaho Falls experiment indicates that about 50% of the concentration
estimates are within a factor of two of the observations, but the scatter is large: the 95% confidence interval of the ratio
of the observed to estimated concentrations is about 4. The model based on the numerical solution of the diffusion equation
in combination with the model of Eckman (1994, Atmos Environ 28:265–272) for horizontal spread performs better than AERMOD in explaining the observations. Accounting for
meandering of the wind reduces some of the overestimation of concentrations at low wind speeds. The results deteriorate when
routine one-level observations are used to construct model inputs. An empirical modification to the similarity estimate of
the surface friction velocity reduces the underestimation at low wind speeds. 相似文献