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Journal ArticleDOI

Comparison of mixing layer heights determined using LiDAR, radiosonde, and numerical weather prediction model at a rural site in southern India

TL;DR: In this paper, the authors used ground-based elastic backscatter LiDAR (EBL) profiles for the determination of mixing layer height (MLH) and compared it with concurrent radiosonde (RS) observations [MLH (RS)] and numerical models.
Abstract: There is no agreed reference method for accurately determination of mixing layer height (MLH) in the existing literature. In part, this is due to different definitions of the atmospheric boundary layer exist, depending on the quantities and the physical processes invoked. In addition, MLH during late afternoon transition period is highly challenging to determine and perform model simulations because of the rapid variations in turbulent kinetic energy. For the first time, MLH has been determined at remote tropical site of Gadanki, India (13.45°N, 79.17°E, 360 masl) using ground-based elastic backscatter LiDAR (EBL). This article focuses on the late afternoon transition period and compares it with MLH obtained from the EBL to concurrent radiosonde (RS) observations [MLH (RS)] and numerical models. Five different techniques have been applied to the EBL backscatter profiles for the determination of MLH. The mean of the five methods agreed to within 15% with the RS-derived MLH under various synoptic co...
Citations
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01 Jan 1989
TL;DR: In this article, a two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea.
Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

3,813 citations

Journal ArticleDOI
TL;DR: In this paper, a comparison between proprietary (Jenoptik) and freely available (STRAT) algorithms to retrieve MLH diurnal cycle over an urban area was conducted in the summer season when MLH is above the full overlapping height of the ceilometer in order to minimize negative impact of the biaxial LiDAR's drawback.
Abstract: Mixing layer height (MLH) is a crucial parameter for air quality modelling that is still not routinely measured. Common methods for MLH determination use atmospheric profiles recorded by radiosonde but this process suffers from coarse temporal resolution since the balloon is usually launched only twice a day. Recently, cheap ceilometers are gaining popularity in the retrieval of MLH diurnal evolution based on aerosol profiles. This study presents a comparison between proprietary (Jenoptik) and freely available (STRAT) algorithms to retrieve MLH diurnal cycle over an urban area. The comparison was conducted in the summer season when MLH is above the full overlapping height of the ceilometer in order to minimize negative impact of the biaxial LiDAR’s drawback. Moreover, fogs or very low clouds which can deteriorate the ceilometer retrieval accuracy are very unlikely to be present in summer. The MLHs determined from the ceilometer were verified against those measured from the radiosonde, which were estimated using the parcel, lapse rate, and Richardson methods (the Richardson method was used as a reference in this study). We found that the STRAT and Jenoptik methods gave lower MLH values than radiosonde with an underestimation of about 150 m and 650 m, respectively. Additionally, STRAT showed some potential in tracking the MLH diurnal evolution, especially during the day. A daily MLH maximum of about 2000 m was found in the late afternoon (18–19 LT). The Jenoptik algorithm showed comparable results to the STRAT algorithm during the night (although both methods sometimes misleadingly reported residual or advected layers as the mixing layer (ML)). During the morning transition the Jenoptik algorithm outperformed STRAT, which suffers from abrupt changes in MLH due to integrated layer attribution. However, daytime performance of Jenoptik was worse, especially in the afternoon when the algorithm often cannot estimate any MLH (in the period 13–16 LT the method reports MLHs in only 15–30% of all cases). This makes day-to-day tracing of MLH diurnal evolution virtually impracticable. This problem is possibly due to its early version (JO-CloVis 8.80, 2009) and issues with real-time processing of a single profile combined with the low signal-to-noise ratio of the ceilometer. Both LiDAR-based algorithms have trouble in the evening transition since they rely on aerosol signature which is more affected by the mixing processes in the past hours than the current turbulent mixing.

8 citations

Journal ArticleDOI
TL;DR: Polarization lidar observations were made to study the transport of an elevated aerosol layer over Gadanki, India (13.45° N, 79.17° E) during the pre-monsoon period of the year 2009 as mentioned in this paper.
Abstract: Polarization lidar observations were made to study the transport of an elevated aerosol layer over Gadanki, India (13.45° N, 79.17° E) during the pre-monsoon period of the year 2009. Observations show significant aerosol layering within and above the boundary layer. Coordinated observations with radiosondes were carried out from 2 to 10 April 2009. Temporal and spatial variations of the parameters are studied for the boundary layer (≈ 2.5 km) and up to 5 km. The backscattering coefficient and the depolarization ratio are observed to increase and decrease with an increase in humidity, respectively. Clouds are not formed, indicating less efficiency of the aerosol in acting as condensation nuclei. The transport of the elevated aerosol layer is investigated using a back-trajectory analysis, revealing that the transported layer originating from the central Indian region has a depolarization ratio of at least 0.05. From model analysis and satellite fire-count data, it is inferred that the source of the aerosol layer is wildfire events over the central Indian region. The elevated smoke-aerosol layer (not mixing with the boundary layer) has implications for the altering of the temperature profile of the atmosphere and the suppression of cloud formation.

4 citations

Journal ArticleDOI
TL;DR: In this paper, the authors focused on the changes in the vertical structure of aerosol concentration and how those changes impacted radiation balance, the planetary boundary layer (PBL) height and surface meteorological parameters.

1 citations

References
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Journal ArticleDOI
TL;DR: The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible, except that the horizontal resolution is T62 (about 210 km) as discussed by the authors.
Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

28,145 citations


"Comparison of mixing layer heights ..." refers methods in this paper

  • ...The six-hourly National Centers for Environmental Prediction-Global Data Assimilation System analysis is used for initial and lateral boundary conditions for the WRF model (Kalnay et al. 1996)....

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Journal ArticleDOI
TL;DR: ERA-Interim as discussed by the authors is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which will extend back to the early part of the twentieth century.
Abstract: ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

22,055 citations


"Comparison of mixing layer heights ..." refers methods in this paper

  • ...For this purpose, WRF and ERA MLH values were taken 09:00 (UTC) and 12:00 (UTC), as seen in Table 4, the distinct correlation coefficients and slopes at 11:30 IST and 14:30 IST between MLH (LiDAR) and MLH (WRF) and MLH (ERA)....

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  • ...In this study, we also used MLH from ECMWF ERA-interim reanalysis (here onwards referred simply as ERA) (Dee et al. 2011) with MLH determined from LiDAR profiles....

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  • ...The horizontal resolution of the ERA fields is 0.70°, available at 60 vertical hybrid levels....

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  • ...There were also attempts to generate the climatology of MLH and its diurnal variation from reanalysis data sets, such as ERA (Von Engeln and Teixeira 2013)....

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  • ...These methods were discussed and then compared with concurrent RS observations, WRF model [MLH (WRF)] simulation, and ECMWF [ERA-Interim; MLH (ERA)] reanalysis results....

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Book
31 Jul 1988
TL;DR: In this article, the boundary layer is defined as the boundary of a boundary layer, and the spectral gap is used to measure the spectral properties of the boundary layers of a turbulent flow.
Abstract: 1 Mean Boundary Layer Characteristics.- 1.1 A boundary-layer definition.- 1.2 Wind and flow.- 1.3 Turbulent transport.- 1.4 Taylor's hypothesis.- 1.5 Virtual potential temperature.- 1.6 Boundaiy layer depth and structure.- 1.7 Micrometeorology.- 1.8 Significance of the boundary layer.- 1.9 General references.- 1.10 References for this chapter.- 1.11 Exercises.- 2 Some Mathematical and Conceptual Tools: Part 1. Statistics.- 2.1 The significance of turbulence and its spectrum.- 2.2 The spectral gap.- 2.3 Mean and turbulent parts.- 2.4 Some basic statistical methods.- 2.5 Turbulence kinetic energy.- 2.6 Kinematic flux.- 2.7 Eddy flux.- 2.8 Summation notation.- 2.9 Stress.- 2.10 Friction velocity.- 2.11 References.- 2.12 Exercises.- 3 Application of the Governing Equations to Turbulent Flow.- 3.1 Methodology.- 3.2 Basic governing equations.- 3.3 Simplifications, approximations, and scaling arguments.- 3.4 Equations for mean variables in a turbulent flow.- 3.5 Summary of equations, with simplifications.- 3.6 Case studies.- 3.7 References.- 3.8 Exercises.- 4 Prognostic Equations for Turbulent Fluxes and Variances.- 4.1 Prognostic equations for the turbulent departures.- 4.2 Free convection scaling variables.- 4.3 Prognostic equations for variances.- 4.4 Prognostic equations for turbulent fluxes.- 4.5 References.- 4.6 Exercises.- 5 Turbulence Kinetic Energy, Stability, and Scaling.- 5.1 The TKE budget derivation.- 5.2 Contributions to the TKE budget.- 5.3 TKE budget contributions as a function of eddy size.- 5.4 Mean kinetic energy and its interaction with turbulence.- 5.5 Stability concepts.- 5.6 The Richardson number.- 5.7 The Obukhov length.- 5.8 Dimensionless gradients.- 5.9 Miscellaneous scaling parameters.- 5.10 Combined stability tables.- 5.11 References.- 5.12 Exercises.- 6 Turbulence Closure Techniques.- 6.1 The closure problem.- 6.2 Parameterization rules.- 6.3 Local closure - zero and half order.- 6.4 Local closure - first order.- 6.5 Local closure - one-and-a-half order.- 6.6 Local closure - second order.- 6.7 Local closure - third order.- 6.8 Nonlocal closure - transilient turbulence theory.- 6.9 Nonlocal closure - spectral diffusivity theory.- 6.10 References.- 6.11 Exercises.- 7 Boundary Conditions and External Forcings.- 7.1 Effective surface turbulent flux.- 7.2 Heat budget at the surface.- 7.3 Radiation budget.- 7.4 Fluxes at interfaces.- 7.5 Partitioning of flux into sensible and latent portions.- 7.6 Flux to and from the ground.- 7.7 References.- 7.8 Exercises.- 8 Some Mathematical and Conceptual Tools: Part 2. Time Series.- 8.1 Time and space series.- 8.2 Autocorrelation.- 8.3 Structure function.- 8.4 Discrete Fourier transform.- 8.5 Fast Fourier Transform.- 8.6 Energy spectrum.- 8.7 Spectral characteristics.- 8.8 Spectra of two variables.- 8.9 Periodogram.- 8.10 Nonlocal spectra.- 8.11 Spectral decomposition of the TKE equation.- 8.12 References.- 8.13 Exercises.- 9 Similarity Theory.- 9.1 An overview.- 9.2 Buckingham Pi dimensional analysis methods.- 9.3 Scaling variables.- 9.4 Stable boundary layer similarity relationship lists.- 9.5 Neutral boundary layer similarity relationship lists.- 9.6 Convective boundary layer similarity relationship lists.- 9.7 The log wind profile.- 9.8 Rossby-number similarity and profile matching.- 9.9 Spectral similarity.- 9.10 Similarity scaling domains.- 9.11 References.- 9.12 Exercises.- 10 Measurement and Simulation Techniques.- 10.1 Sensor and measurement categories.- 10.2 Sensor lists.- 10.3 Active remote sensor observations of morphology.- 10.4 Instrument platforms.- 10.5 Field experiments.- 10.6 Simulation methods.- 10.7 Analysis methods.- 10.8 References.- 10.9 Exercises.- 11 Convective Mixed Layer.- 11.1 The unstable surface layer.- 11.2 The mixed layer.- 11.3 Entrainment zone.- 11.4 Entrainment velocity and its parameterization.- 11.5 Subsidence and advection.- 11.6 References.- 11.7 Exercises.- 12 Stable Boundary Layer.- 12.1 Mean Characteristics.- 12.2 Processes.- 12.3 Evolution.- 12.4 Other Depth Models.- 12.5 Low-level (nocturnal) jet.- 12.6 Buoyancy (gravity) waves.- 12.7 Terrain slope and drainage winds.- 12.8 References.- 12.9 Exercises.- 13 Boundary Layer Clouds.- 13.1 Thermodynamics.- 13.2 Radiation.- 13.3 Cloud entrainment mechanisms.- 13.4 Fair-weather cumulus.- 13.5 Stratocumulus.- 13.6 Fog.- 13.7 References.- 13.8 Exercises.- 14 Geographic Effects.- 14.1 Geographically generated local winds.- 14.2 Geographically modified flow.- 14.3 Urban heat island.- 14.4 References.- 14.5 Exercises.- Appendices.- A. Scaling variables and dimensionless groups.- B. Notation.- C. Useful constants parameters and conversion factors.- D. Derivation of virtual potential temperature.- Errata section.

9,111 citations


"Comparison of mixing layer heights ..." refers background in this paper

  • ...…into the boundary layer (BL) (Pruppacher and Klett 1978; Carlson 1979; Blanchard CONTACT R. Vishnu vishnu@narl.gov.in National Atmospheric Research Laboratory, Tirupati, Andhra Pradesh 517112, India © 2017 Informa UK Limited, trading as Taylor & Francis Group and Woodcock, 1980; Stull 1988)....

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DOI
01 Jan 2008
TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
Abstract: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication. Reports in this series are issued by the NCAR Scientific Divisions ; copies may be obtained on request from the Publications Office of NCAR. Designation symbols for the series include: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.

9,022 citations

Journal ArticleDOI
TL;DR: A rapid and accurate radiative transfer model (RRTM) for climate applications has been developed and the results extensively evaluated as discussed by the authors, which is performed using the correlated-k method: the k distributions are attained directly from the LBLRTM line-byline model, which connects the absorption coefficients used by RRTM to high-resolution radiance validations done with observations.
Abstract: A rapid and accurate radiative transfer model (RRTM) for climate applications has been developed and the results extensively evaluated. The current version of RRTM calculates fluxes and cooling rates for the longwave spectral region (10–3000 cm−1) for an arbitrary clear atmosphere. The molecular species treated in the model are water vapor, carbon dioxide, ozone, methane, nitrous oxide, and the common halocarbons. The radiative transfer in RRTM is performed using the correlated-k method: the k distributions are attained directly from the LBLRTM line-by-line model, which connects the absorption coefficients used by RRTM to high-resolution radiance validations done with observations. Refined methods have been developed for treating bands containing gases with overlapping absorption, for the determination of values of the Planck function appropriate for use in the correlated-k approach, and for the inclusion of minor absorbing species in a band. The flux and cooling rate results of RRTM are linked to measurement through the use of LBLRTM, which has been substantially validated with observations. Validations of RRTM using LBLRTM have been performed for the midlatitude summer, tropical, midlatitude winter, subarctic winter, and four atmospheres from the Spectral Radiance Experiment campaign. On the basis of these validations the longwave accuracy of RRTM for any atmosphere is as follows: 0.6 W m−2 (relative to LBLRTM) for net flux in each band at all altitudes, with a total (10–3000 cm−1) error of less than 1.0 W m−2 at any altitude; 0.07 K d−1 for total cooling rate error in the troposphere and lower stratosphere, and 0.75 K d−1 in the upper stratosphere and above. Other comparisons have been performed on RRTM using LBLRTM to gauge its sensitivity to changes in the abundance of specific species, including the halocarbons and carbon dioxide. The radiative forcing due to doubling the concentration of carbon dioxide is attained with an accuracy of 0.24 W m−2, an error of less than 5%. The speed of execution of RRTM compares favorably with that of other rapid radiation models, indicating that the model is suitable for use in general circulation models.

6,861 citations


"Comparison of mixing layer heights ..." refers methods in this paper

  • ...The Rapid radiative transfer model (Mlawer et al. 1997) and Dudhia scheme (Dudhia 1989) were used for longwave and shortwave radiation, respectively....

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