Climatology of Planetary Boundary Layer Height-Controlling Meteorological Parameters Over the Korean Peninsula
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Improving Representations of Boundary Layer Processes
1 2 3 4 5 6 Regional climate modeling over the Maritime Continent: Improving 7 representations of boundary layer processes 8 9 Rebecca L. Gianotti* and Elfatih A. B. Eltahir 10 11 Ralph M. Parsons Laboratory, Massachusetts Institute of Technology, 12 15 Vassar St, Cambridge MA 02139, USA 13 14 15 16 17 18 19 20 ELTAHIR Research Group Report #3, 21 March, 2014 1 Abstract 2 This paper describes work to improve the representation of boundary layer processes 3 within a regional climate model (Regional Climate Model Version 3 (RegCM3) coupled to 4 the Integrated Biosphere Simulator (IBIS)) applied over the Maritime Continent. In 5 particular, modifications were made to improve model representations of the mixed 6 boundary layer height and non-convective cloud cover within the mixed boundary layer. 7 Model output is compared to a variety of ground-based and satellite-derived observational 8 data, including a new dataset obtained from radiosonde measurements taken at Changi 9 airport, Singapore, four times per day. These data were commissioned specifically for this 10 project and were not part of the airport’s routine data collection. It is shown that the 11 modifications made to RegCM3-IBIS significantly improve representations of the mixed 12 boundary layer height and low-level cloud cover over the Maritime Continent region by 13 lowering the simulated nocturnal boundary layer height and removing erroneous cloud 14 within the mixed boundary layer over land. The results also show some improvement with 15 respect to simulated radiation and rainfall, compared to the default version of the model. -
Moist Static Energy Budget of MJO-Like Disturbances in The
1 Moist Static Energy Budget of MJO-like 2 disturbances in the atmosphere of a zonally 3 symmetric aquaplanet 4 5 6 Joseph Allan Andersen1 7 Department of Physics, Harvard University, Cambridge, Massachusetts 8 9 10 Zhiming Kuang 11 Department of Earth and Planetary Science and School of Engineering 12 and Applied Sciences, Harvard University, Cambridge, Massachusetts 13 14 15 16 17 18 19 20 21 22 1 Corresponding author address: Joseph Andersen, Department of Physics, Harvard University, Cambridge, MA 02138. E-mail: [email protected] 1 1 2 Abstract 3 A Madden-Julian Oscillation (MJO)-like spectral feature is observed in the time-space 4 spectra of precipitation and column integrated Moist Static Energy (MSE) for a zonally 5 symmetric aquaplanet simulated with Super-Parameterized Community Atmospheric 6 Model (SP-CAM). This disturbance possesses the basic structural and propagation 7 features of the observed MJO. 8 To explore the processes involved in propagation and maintenance of this disturbance, 9 we analyze the MSE budget of the disturbance. We observe that the disturbances 10 propagate both eastwards and polewards. The column integrated long-wave heating is the 11 only significant source of column integrated MSE acting to maintain the MJO-like 12 anomaly balanced against the combination of column integrated horizontal and vertical 13 advection of MSE and Latent Heat Flux. Eastward propagation of the MJO-like 14 disturbance is associated with MSE generated by both column integrated horizontal and 15 vertical advection of MSE, with the column long-wave heating generating MSE that 16 retards the propagation. 17 The contribution to the eastward propagation by the column integrated horizontal 18 advection of MSE is dominated by synoptic eddies. -
A New Method to Retrieve the Diurnal Variability of Planetary Boundary Layer Height from Lidar Under Different Thermodynamic
Remote Sensing of Environment 237 (2020) 111519 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse A new method to retrieve the diurnal variability of planetary boundary layer T height from lidar under diferent thermodynamic stability conditions ∗ Tianning Sua, Zhanqing Lia, , Ralph Kahnb a Department of Atmospheric and Oceanic Science & ESSIC, University of Maryland, College Park, MD, 20740, USA b Climate and Radiation Laboratory, Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA ARTICLE INFO ABSTRACT Keywords: The planetary boundary layer height (PBLH) is an important parameter for understanding the accumulation of Planetary boundary layer height pollutants and the dynamics of the lower atmosphere. Lidar has been used for tracking the evolution of PBLH by Thermodynamic stability using aerosol backscatter as a tracer, assuming aerosol is generally well-mixed in the PBL; however, the validity Lidar of this assumption actually varies with atmospheric stability. This is demonstrated here for stable boundary Aerosols layers (SBL), neutral boundary layers (NBL), and convective boundary layers (CBL) using an 8-year dataset of micropulse lidar (MPL) and radiosonde (RS) measurements at the ARM Southern Great Plains, and MPL at the GSFC site. Due to weak thermal convection and complex aerosol stratifcation, traditional gradient and wavelet methods can have difculty capturing the diurnal PBLH variations in the morning and forenoon, as well as under stable conditions generally. A new method is developed that combines lidar-measured aerosol backscatter with a stability dependent model of PBLH temporal variation (DTDS). The latter helps “recalibrate” the PBLH in the presence of a residual aerosol layer that does not change in harmony with PBL diurnal variation. -
Chapter 3 Moist Thermodynamics
Chapter 3 Moist thermodynamics In order to understand atmospheric convection, we need a deep understand- ing of the thermodynamics of mixtures of gases and of phase transitions. We begin with a review of some of the fundamental ideas of statistical mechanics as it applies to the atmosphere. We then derive the entropy and chemical potential of an ideal gas and a condensate. We use these results to calcu- late the saturation vapor pressure as a function of temperature. Next we derive a consistent expression for the entropy of a mixture of dry air, wa- ter vapor, and either liquid water or ice. The equation of state of a moist atmosphere is then considered, resulting in an expression for the density as a function of temperature and pressure. Finally the governing thermody- namic equations are derived and various alternative simplifications of the thermodynamic variables are presented. 3.1 Review of fundamentals In statistical mechanics, the entropy of a system is proportional to the loga- rithm of the number of available states: S(E; M) = kB ln(δN ); (3.1) where δN is the number of states available in the internal energy range [E; E + δE]. The quantity M = mN=NA is the mass of the system, which we relate to the number of molecules in the system N, the molecular weight of these molecules m, and Avogadro’s number NA. The quantity kB is Boltz- mann’s constant. 35 CHAPTER 3. MOIST THERMODYNAMICS 36 Consider two systems in thermal contact, so that they can exchange en- ergy. The total energy of the system E = E1 + E2 is fixed, so that if the energy of system 1 increases, the energy of system 2 decreases correspond- ingly. -
The Seasonal Cycle of Planetary Boundary Layer Depth
1 The seasonal cycle of planetary boundary layer depth 2 determined using COSMIC radio occultation data 3 4 Ka Man Chan and Robert Wood 5 Department of Atmospheric Science, University of Washington, Seattle WA 6 7 Abstract. 8 The seasonal cycle of planetary boundary layer (PBL) depth is examined globally using 9 observations from the Constellation Observing System for the Meteorology, Ionosphere, and 10 Climate (COSMIC) satellite mission. COSMIC uses GPS Radio Occultation (GPS-RO) to derive 11 the vertical profile of refractivity at high vertical resolution (~100 m). Here, we describe an 12 algorithm to determine PBL top height and thus PBL depth from the maximum vertical gradient 13 of refractivity. PBL top detection is sensitive to hydrolapses at non-polar latitudes but to both 14 hydrolapses and temperature jumps in polar regions. The PBL depths and their seasonal cycles 15 compare favorably with selected radiosonde-derived estimates at Tropical, midlatitude and 16 Antarctic sites, adding confidence that COSMIC can effectively provide estimates of seasonal 17 cycles globally. PBL depth over extratropical land regions peaks during summer consistent with 18 weak static stability and strong surface sensible heating. The subtropics and Tropics exhibit a 19 markedly different cycle that largely follows the seasonal march of the intertropical convergence 20 zone (ITCZ) with the deepest PBLs associated with dry phases, again suggestive that surface 21 sensible heating deepens the PBL and that wet periods exhibit shallower PBLs. 22 Marine PBL depth has a somewhat similar seasonal march to that over continents but is 23 weaker in amplitude and is shifted poleward. -
Chapter 2. Turbulence and the Planetary Boundary Layer
Chapter 2. Turbulence and the Planetary Boundary Layer In the chapter we will first have a qualitative overview of the PBL then learn the concept of Reynolds averaging and derive the Reynolds averaged equations. Making use of the equations, we will discuss several applications of the boundary layer theories, including the development of mixed layer as a pre-conditioner of server convection, the development of Ekman spiral wind profile and the Ekman pumping effect, low-level jet and dryline phenomena. The emphasis is on the applications. What is turbulence, really? Typically a flow is said to be turbulent when it exhibits highly irregular or chaotic, quasi-random motion spanning a continuous spectrum of time and space scales. The definition of turbulence can be, however, application dependent. E.g., cumulus convection can be organized at the relatively small scales, but may appear turbulent in the context of global circulations. Main references: Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp. Chapter 5 of Holton, J. R., 1992: An Introduction to Dynamic Meteorology. Academic Press, New York, 511 pp. 2.1. Planetary boundary layer and its structure The planetary boundary layer (PBL) is defined as the part of the atmosphere that is strongly influenced directly by the presence of the surface of the earth, and responds to surface forcings with a timescale of about an hour or less. 1 PBL is special because: • we live in it • it is where and how most of the solar heating gets into the atmosphere • it is complicated due to the processes of the ground (boundary) • boundary layer is very turbulent • others … (read Stull handout). -
The Moist Static Energy Budget and Wave Dynamics Around the ITCZ in Idealized WRF Simulations
The moist static energy budget and wave dynamics around the ITCZ in idealized WRF simulations Scott W. Powell and David S. Nolan RSMAS/University of Miami; Miami, FL Eighth Conference on Coastal Atmospheric and Oceanic Prediction and Processes, 89th AMS Annual Meeting; Phoenix, AZ January 12, 2009 This research has been supported by a NSF/REU grant under proposal ATM-0354763. A shallow meridional circulation (SMC) in Eastern Pacific ● EPIC (2001) field program ● Zhang, et al. (2004) indicates that a meridional flow out of the ITCZ was present in observational data. a) Eight-flight mean b) Oct. 2, 2001 A shallow meridional circulation (SMC) in Eastern Pacific ● Nolan, et al. (2007) used a full physics model to show such a circulation in a tropical half-channel. ● Regional model by Wang, et al. (2005) and radiative cooling ● “Sea-breeze” circulation ● Newer simulations with linear and hyperbolic temperature profiles on a full SRF above channel. Boundary layer boundary layer inflow Linear temperature profile across equator Log10 RR(+1) (mm/hr), 03-21:00 max=1.71e+00 min=1.00e-02 int=4.00e-01 y(km) x (km) Moist static energy (MSE) budget Moist static energy: dry static energy plus the product of the latent heat of vaporization and water vapor mixing ratio: s = CpT + Ф + Lvr, Cp = specific heat of air at constant pressure T = absolute temperature Ф = geopotential Lv = latent heat of vaporization r = mixing ratio MSE Flux due to Advection: ΦMSE = ρ*v*s, ρ = density v = meridional wind velocity Calculation of the MSE budget: Advection ULO MLI SRF SMC Level MSE Flux*: 2S MSE Flux*: 2N Upper level return flow -2.26E+9 -2.05E+9 Mid-level inflow 7.50E+8 4.42E+8 *Fluxes in units J*m-1*s-1. -
The Planetary Boundary Layer
January 27, 2004 9:4 Elsevier/AID aid CHAPTER 5 The Planetary Boundary Layer The planetary boundary layer is that portion of the atmosphere in which the flow field is strongly influenced directly by interaction with the surface of the earth. Ultimately this interaction depends on molecular viscosity. It is, however, only within a few millimeters of the surface, where vertical shears are very intense, that molecular diffusion is comparable to other terms in the momentum equation. Outside this viscous sublayer molecular diffusion is not important in the boundary layer equations for the mean wind, although it is still important for small-scale tur- bulent eddies. However, viscosity still has an important indirect role; it causes the velocity to vanish at the surface. As a consequence of this no-slip boundary con- dition, even a fairly weak wind will cause a large-velocity shear near the surface, which continually leads to the development of turbulent eddies. These turbulent motions have spatial and temporal variations at scales much smaller than those resolved by the meteorological observing network. Such shear-induced eddies, together with convective eddies caused by surface heating, are very effective in transferring momentum to the surface and transferring heat (latent and sensible) away from the surface at rates many orders of magnitude faster than can be done by molecular processes. The depth of the planetary boundary layer produced by this turbulent transport may range from as little as 30 m in conditions of large 115 January 27, 2004 9:4 Elsevier/AID aid 116 5 the planetary boundary layer static stability to more than 3 km in highly convective conditions. -
1 Theory of Tropical Moist Convection
OUP UNCORRECTED PROOF – FIRST PROOF, 30/10/2019, SPi 1 Theory of tropical moist convection David M. Romps Department of Earth and Planetary Science, University of California, Berkeley, CA Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA Romps, D., Theory of tropical moist convection. In: Fundamental aspects of turbulent flows in climate dynamics. Edited by Freddy Bouchet, Tapio Schneider, Antoine Venaille, Christophe Salomon: Oxford University Press (2020). © Oxford University Press. DOI: 10.1093/oso/9780198855217.003.0001 1 Theory of Tropical Moist Convection David M. Romps Department of Earth and Planetary Science, University of California, Berkeley, CA Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 1.1 Introduction These lecture notes cover the theory of tropical moist convection. Many simplifications are made along the way, like neglecting rotation and treating the atmosphere as a two- dimensional fluid or even reducing the atmosphere to two columns. We can gain an immense amount of insight into the real atmosphere by studying these toy models, including answers to the following questions: what is the dominant energy balance in the tropical free troposphere; what sets the temperature structure of the tropical free troposphere; what happens to the pulse of heating deposited into the atmosphere by a rain cloud; why does the tropical atmosphere have the relative-humidity profile that it does; and what sets the amount of energy available to storms? These notes attempt to give the first-order answers to these questions in a format that is accessible to beginning graduate students. We begin with a discussion of atmospheric energy. -
An Analysis of the Moisture and Moist Static Energy Budgets in AMIP Simulations
An Analysis of the Moisture and Moist Static Energy Budgets in AMIP Simulations by Kristine Adelaide Boykin Bachelor of Science Physics Southern Polytechnic State University 2004 A thesis submitted to the Department of Marine and Environmental Systems at Florida Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Meteorology Melbourne, Florida September, 2016 © Copyright 2016 Kristine Adelaide Boykin All Rights Reserved The author grants permission to make single copies_____________________________ We the undersigned committee hereby recommend that the attached document be accepted as fulfilling in part of the requirements for the degree of Master of Science of Meteorology. An Analysis of the Moisture and Moist Static Energy Budgets in AMIP Simulations by Kristine Adelaide Boykin _____________________________________________ P. K. Ray, Ph.D., Major Advisor Assistant Professor, Marine and Environmental Systems _____________________________________________ G. Maul, Ph.D. Professor of Oceanography _____________________________________________ M. Bush, Ph.D. Professor of Biological Sciences _____________________________________________ T. Waite, Ph.D., Department Head Marine and Environmental Systems Abstract An Analysis of the Moisture and Moist Static Energy Budgets in AMIP S by Kristine Adelaide Boykin Major Advisor: P. K. Ray, Ph.D. An analysis of the second phase of the Atmospheric Model Intercomparison Project (AMIP) simulations has been conducted to understand the physical processes that control precipitation in the tropics. This is achieved primarily through the analysis of the moisture and moist static energy budgets. Overall, there is broad agreement between the simulated and observed precipitation, although specific tropical regions such as the Maritime Continent poses challenges to these simulations. The models in general capture the latitudinal distribution of precipitation and key precipitating regions including the Inter- Tropical Convergence Zone (ITCZ) and the Asian Monsoon. -
Climatology of the Boundary Layer Height and of the Wind Field Over Greece
atmosphere Article Climatology of the Boundary Layer Height and of the Wind Field over Greece Nikolaos A. Bakas 1,* , Angeliki Fotiadi 2 and Sophia Kariofillidi 1,† 1 Laboratory of Meteorology and Climatology, Department of Physics, University of Ioannina, 45110 Ioannina, Greece; s.kariofi[email protected] 2 Department of Environmental Engineering, University of Patras, 30100 Agrinio, Greece; [email protected] * Correspondence: [email protected]; Tel.: +30-26510-08599 † Current address: Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece. Received: 17 July 2020; Accepted: 21 August 2020; Published: 27 August 2020 Abstract: In this study, a climatology of two key boundary layer features, the Planetary Boundary Layer Height (PBLH) and the wind field over Greece is derived. The climatology is based on daily soundings collected in Athens, Thessaloniki and Heraklion and spanning a 32-year period. The PBLH is estimated using a method based on the gradient of potential temperature and a method based on the bulk Richardson number. The wind field is analyzed by calculating the wind shear and the turning angle of the wind vector between the surface and the top of the boundary layer. The PBLH of the daytime boundary layer over Athens and Thessaloniki is found to exhibit seasonal variability with summer maxima and winter minima and has annual median values in the range of 1.4–1.7 km estimated using the gradient method. The PBLH over Heraklion is found to exhibit weak seasonal variability with a lower median value of 1.2 km. The nighttime boundary layer over all three sites is found to be much shallower with PBLH values in the range of 150–200 m with no seasonal variations. -
On the Estimation of Boundary Layer Heights: a Machine Learning Approach Raghavendra Krishnamurthy1, Rob K
On the estimation of boundary layer heights: A machine learning approach Raghavendra Krishnamurthy1, Rob K. Newsom1, Larry K. Berg1, Heng Xiao1, Po-Lun Ma1, David D. Turner2 5 1Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA 2 National Oceanic and Atmospheric Administration / Global Systems Laboratory, Boulder, 80305, USA Correspondence to: Raghavendra Krishnamurthy ([email protected]) Abstract. The planetary boundary-layer height (zi) is a key parameter used in atmospheric models for estimating the exchange 10 of heat, momentum and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as Random Forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a four-year dataset from 2016 to 2019 at the 15 Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018, and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates 20 were observed using the RF model, with a 50% improvement in mean absolute error and an R2 of greater than 85% compared to the Tucker method zi.