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Maintenance of the stable atmospheric boundary layer in heterogeneous terrain

Ph.D. ProspectusLuiz E. Medeiros

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Outline

• 1. Introduction – Background, relevance, and objectives of the proposed work

• 2. Experimental data

• 3. Plan of research

• 4. Preliminary results

• 5. Expected outcome

• 6. Timeline

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The Richardson number

Bulk Richardson number Rib

From: Pardyjak et al. 2002.( )) ( )⎥⎦

⎤⎢⎣⎡

ΔΔ⎜

⎝⎛ +ΔΔ⋅

ΔΔ⋅

=22

zV

zU

zgRi

ref

b

θ

θBulk Richardson number Rib

Kinematic Heat Flux

BD

SP

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The ‘conventional’ stable boundary layer

Heat Flux Momentum Flux

From: Garratt, J. R., 1992: The atmospheric boundary layer, Cambridge, 316pp.

From: Stull, R. B. 1988: An Introduction to Boundary Layer Meteorology, Kluwer Academic Publishers, 666pp.

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Wind tunnel study of bursting turbulence beneath a low level jet (Ohya et al., 2008)

w’

Ri

• Under stable conditions, turbulence can be intermittent and/or spatially localized if the terrain is not homogeneous.

Ricr = 0.25

Taylor transformation x = Ut

Re = 2800

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Motivation to better understand the SBL

• Numerical weather prediction (NWP) models have difficulties to estimate surface minimum temperatures, height of the turbulent layer, and lifetime of low-pressure systems (low surface drag) [Viterbo et al. 1999; McCabe and Brown 2007].

• Models adopt the classical text book concept of continuous and horizontally homogeneous turbulence. Low level jet (LLJ) at the top of SBL, from where turbulence extracts energy. K-Theory - (Km and Kh) - stability functions in terms of bulk Richardson number Rib.

• Different types of stability functions to adjust the amount of mixing: no mixing when Rib > 1/4 – Short tails; artificial mixing when Rib > 1/4 – Long tails.

• Long tails better minimum temperature and more surface drag.

• However, a too thick turbulent layer, LLJ too far from the ground and with weakened strength, weakened potential temperature inversion.

• Spatial heterogeneity can be one of the reasons to justify unphysical extra mixing to occur. – Localized pockets of mixing attenuated by averaging process.

( )z

RifKw b ∂

∂−=

χχ χ )(''

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Field observation of the SBL and influences of the landscape

•During nights with clear skies and weak pressure gradient over complex terrain, turbulence can be patchy and intermittent [Acevedo and Fitzjarrald 2003; Salmond and McKendry 2005].

St. 2

St. 4

St. 6

St. 8

St. 10St. 9

St. 7

St. 5

St. 3

St.1

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Field observation of the SBL and influences of the landscape

• Intermittent turbulence - Breaking gravity waves, Kelvin-Helmholtz (K-H) shear instabilities, and density currents → flow instabilities → turbulence (Finnigan et al. 1984, Einaudi and Finnigan 1993; CASES -99 - Sun et al. 2004; Poulos et al. 2002.). LLJ is another possibility (Description adopted in textbooks- Garratt, J. R. 1992).

• Land cover / land use, and topography are responsible for the positioning of “turbulent” and “stagnant” air layers. Representativeness of local measurements of wind, temperature, and other scalars are tied to these air layers.

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Landscape and mixing

bRiFr

12 ≈

•LeMone et. al, 2003 - Rib >> 0.1 the flow close to the ground (~2m) can stay disconnected from the flow above;

Rib ~ 0.1 the flow close to the ground can stay connected to the flow aloft.- Froude Fr number and Rib,

From Acevedo and Fitzjarrald 2003.

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Goals• The goal of the proposed research is to

establish a broader linkage between the observed flow with terrain surface characteristics and background flow under stable conditions.

• We seek to understand better how the SBL develops over complex terrain. To do this we will map out the turbulence activity over a region with complex terrain and as well as to determine the horizontal and vertical extensions of air layers that compose the regional SBL.

AF2003

FOG -82

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1.1 Scientific Questions• Main question - How does the stable boundary layer form over complex

terrain?

• Follow–up questions

(a) What is the role of topography and landscape structure in determining the representativeness of surface weather station measurements?

(b) What is the relationship between nocturnal intermittent mixing events, and the mean thermodynamic, trace gas and dynamical structure of the SBL?

(c) What method can be used to extrapolate surface fluxes that apply to a mesoscale area from a network of point measurements?

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2. Data and model output

• 2.1. Hudson Valley Ambient Meteorology Study (HVAMS)

• 2.2. Harvard Forest Environmental Site (HF- EMS)

• 2.3. Large-scale Biosphere-Atmosphere experiment in Amazonia (LBA)

• 2.4. Mesoscale model simulations (UIB)

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2.1. HVAMS

• A long-term ASRC JRG observational program designed to understand the planetary boundary layer in the Hudson valley between the cities of Albany and Poughkeepsie.

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HVAMS – Long Term Observational Period (LTOP) – From 11/02/03 to present time

ASOS st. (ALB, POU)

10_Anchor st.

HOBO weather st.4 → H1

S → H3

6 → H4

From Anchor st. we can build ensembles turbulence mixing events ~ 5 years of data.

From all HOBOs too because we have ~ 4.5 years of data.

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2.1. Land use and land cover map

Source: USGS Global Land Information Systemhttp://gisdata.usgs.net/website/mrlc/viewr.html

Resolution = 30m x 30m

Same resolution topographic data.

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2.2. Harvard Forest Environmental Measurement site (HF-EMS)

• 45 % red oak • 27 % red maple• 20 % hemlock• 6 % white ash(From: Staebler, 2003)

This experiment started in 1989 and still running until the present time. It certainly has enough data to build ensembles about turbulence breakdowns.

30m – Micromet Tower

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2.3

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2.4 Mesoscale model Meso-NH applied to HVAMS

• Modeled output data for the IOP, obtained from the mesoscale model Meso-NH. Grid cell size 2km x 2km x 3m and time step 2s.

• The simulations were performed by the meteorology research center of the Universitat de les Illes Balears (UIB), in Mallorca, Spain by Drs. Joan Cuxart and Maria Antonia Jiménez. Simulations are available for the nights of 06 – 07 and 07 – 08 of October, 2003 for comparison with surface observations and aircraft flight tracks.

• Such data will serve us to investigate the formation of the cold air pool in the bottom of the Hudson valley, to estimate of radiative flux divergence across the SBL, and to understand channeling.

[From: http://mesonh.aero.obs-mip.fr/mesonh/. A general description of the model operation is given by Lafore et al., (1998)]

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Conceptual model• The valley topography channels the flow to be along-valley (southerly

or northerly) inside the valley.

• Turbulence breakdowns are likely to bring the wind direction at a particular station close to channeled direction (N or S). Whenever these events occur, they will be followed by downward momentum and heat fluxes.

• We hypothesize that when there is a low level along-valley jet, and this jet is a main driver for breakdowns of turbulent intermittent events but is not the only mechanism for turbulence breakdowns.

• As the night evolves, the valley floor fills up with cold air. This layer can slosh back and forth, introducing cross-valley motions beneath the jet, as cold air parcels from drainage flows try to adjust underneath it. Depending on constancy of the jet, and pooling of cold air, station can disconnect or connect .

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Simulated cross-section at mid-valley by Meso-NH

0900 UTC (0600 LT) October 8, 2003

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Along valley flow felt at the surface (Wind roses obtained for the daytime period from September 19, 2003

until November 1, 2003.)

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Along valley flow felt at the surface (Wind roses obtained for the nocturnal period from September 19, 2003 until

November 1, 2003.)

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Along valley flow above the surface on the night of 10/07/2003 – 10/08/2003

UTC UTC

Aircraft and weather soundings

SBL channeling CBL channeling

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Schematic description of SBL interactions

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To address the scientific questions we start from the premise that the flow dynamics over a region is controlled by these external parameters:

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North

South

WestEast

Station 7

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Landscape: Agricultural clearance in a concave surface.

Wind rose and

TF

Local landscape/Sheltering

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Station 5Landscape: Cherry orchard in convex surface.

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Sheltering Transmission Factors (TF)

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3. Plan of research

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3.1. Turbulence activity

• To answer our scientific questions we first will separate periods in time series of wind and temperature by categories of standard deviation of the wind, or temperature or both combined (the flux product w’T’).

• Standard deviation, , where x represents w’, T’ and w’T’.

Guiding questions:

1 - Is the flow at the station connected or not the flow aloft (~ 100 to 150m above ground)? If not is it connected to other shallow flow like drainage?

2 – What is an intermittent event ?

∑−

=

=1

0

2)(1 n

ix ix

Nσ

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3.1.1. Technique to identify intermittent events

• Howell’s algorithm (Howell, 1994).• Originally the algorithm was designed to identify periods of abrupt

changes in means of time series of any meteorological variables. • It was slightly changed to identify periods of abrupt changes in

standard deviation.

Time window = 60 min. Time window = 45 min.

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Another tool - WaveletsOctober 13-14 2003 HVAMS flux product for station 1))(( TTww −−

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2 - What is an intermittent event ? • Preliminary answer Due to the difficulties mentioned above to define intermittent

events, will include the following conditions:

Wind speed, turbulence intensity (sigma-w), and potential temperature have to increase.

Wind direction has to align with the flow aloft (if turbulence is generated by shear instabilities caused be the LLJ). However, this condition has to be abandoned if the turbulence is generated by other mechanism.

All followed by downward momentum and heat fluxes.

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Using simple weather stations to capture intermittent events and estimate fluxes

• The idea of using HOBO weather stations to identify breakdowns of turbulent intermittent events consist on seeing if mutual increase in wind speed perturbation and temperature perturbation obtained 1min./data are related to increase in fluxes of momentum and heat. This idea will be tested (using flux stations during the IOP) by comparing minute averages of 1Hz or 10 Hz sonic data with direct flux measurements (Obtained from eddy-correlation technique).

[ ] [ ])()1()()('' ' iTiTiuiuTw gustTu −+⋅−∝∝ ∑σσ

Heat flux (w/m^2)

Ρcpσ

u’σ T

The following step will be to estimate the fluxes using this 1min./data through empirical relations like

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3.2.1. Site representativeness – Importance of landscape features on the

representativeness of local measurements

• Guiding question: 1- What are the landscape characteristics of the sites that have more

intense turbulent activity and as well the ones that have less?

stable boundary layer:-low level jet;-size comparable to the; network size.

stable surface layer:-local flows or low level jet;-when disconnected has smaller size than network size and when connect SBL size.

• Observed wind, temperature, moisture, and other scalars are representative of certain volume of the atmosphere (air layer), but each time a mixing event happens it can bring “information” about these quantities from higher levels of the atmosphere.

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3.2.2. Determining Land use / land cover and topography influences in the SBL dynamics in the

Hudson valley

• From the digital topographic map we aim to calculate the terrain slope and curvature in our study areas and compare it with wind directions associated to abrupt temperature changes.

• The area of 3 km x 3 km (used for LU/LC map) are going to be the first attempt to correlate slope direction and wind direction, but if such relationships do not emerge, we will try larger and smaller areas.

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3.2.2. Determining Land use / land cover and topography influences in the SBL dynamics in the

Hudson valley

• Through land use /land cover map we aim to establish a relationship

with turbulent activity and minimum temperature.

(Preliminary work completed.)

Hudson valley land cover / land use

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• A plan to answer to question – 1 The fluxes calculations and counting of intermittent turbulent events will be

done for all the 10 stations, and by doing so we will be able to find how the turbulence activity is distributed among the sites (i. e. which ones are more turbulent and less turbulent, and as well as more intermittent and less). In addition, if we have information about topography, land use / land cover, transmission factor, we can find what are the site characteristics in determining site exposure and turbulent activity.

Correlations like: TF Vs. surface type (if we know the height and distance of the obstacles

around a station we can determine its TF = exp(-kobs) [Fujita and Wakimoto,1982].

TF Vs. turbulent activity.

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3.3. Mesoscale influences

• Guiding question1- What is the relationship between turbulence activity and the external forcing

(synoptic and/or mesoscale pressure gradient and cloud cover)?

A plan to answer to question -1

We will correlate along-valley surface pressure gradient and cloud cover fraction to turbulence activity over a site and over the study region as well.

Pressure data from the PAMs that had microbarometers during the IOP, and pressure data from the ASOS at Poughkeepsie and Albany during the LTOP will be used to calculate the surface pressure gradient. ASOS data at Albany and Poughkeepsie will provide the cloud cover fraction.

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4. Preliminary results

Aircraft/TB vertical soundings illustrate along-valley flow

30-31 October 2003

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Night of October 30 - 31, 2003Aircraft Profiles at 1B1 – Columbia County

Aircraft Profiles at st. 4 - Green Acres

Tethered balloon flying at constant height at st. 10 from 1700 LST until 0300 LST on October 16 -17, 2003

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A close look at turbulent mixing on the case study night of

October 30 – 31, 2003during IOP

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October 30 – 31, 2003

Vertical velocity

St. 2

St. 4

St. 6

St. 8

St. 10St. 9

St. 7

St. 5

St. 3

St.1

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October 30 – 31, 2003

Wind speed

St. 2

St. 4

St. 6

St. 8

St. 10St. 9

St. 7

St. 5

St. 3

St.1

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October 30 - 31, 2003

Wind direction

St. 2

St. 4

St. 6

St. 8

St. 10St. 9

St. 7

St. 5

St. 3

St.1

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October 30 – 31, 2003

Momentum and heat fluxes

St. 2

St. 4

St. 6

St. 8

St. 10St. 9

St. 7

St. 5

St. 3

St.1

St. 3

St.1

St. 5

St. 3

St.1

St. 7

St. 5

St. 3

St.1St.1

St. 3

St.1

St. 5

St. 3

St.1

St. 7

St. 5

St. 3

St.1

St. 9

St. 7

St. 5

St. 3

St.1 St. 2

St. 9

St. 7

St. 5

St. 3

St.1

St. 4

St. 2

St. 9

St. 7

St. 5

St. 3

St.1

St. 6

St. 4

St. 2

St. 9

St. 7

St. 5

St. 3

St.1

St. 8

St. 6

St. 4

St. 2

St. 9

St. 7

St. 5

St. 3

St.1

St. 10

St. 8

St. 6

St. 4

St. 2

St. 9

St. 7

St. 5

St. 3

St.1

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Potential TemperatureOctober 30 – 31, 2003Surface

Thermal consequences of the intermittent mixing at the surface.

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October 24 - 25No alignment of the wind at st. 7 with the SLLF

W’

Wind speed

wind direction

Momentum and heat fluxes

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Total # of minutes (or data points of potential temperature) of the time series = 2956 or 4 nights

Station 1 2 3 4 5 6 7 8 9 10Percentage (%) of time that a station had the maximum temp. over the network

0 0 1.25 9.4 38.4 15.0 3.6 1.7 0 30.6

Percentage (%) of time that a station had the mimnimum temp. over the network

19.7 36.7 3.2 4.5 0 0 1.7 13.0 20.6 0.2

Going back to the question - Is the flow at the station connected or not the flow aloft (~ 100 to 150m above ground)? If not is to connected to other shallow flow like drainage ?

Preliminary answer If the wind direction at the station follows the wind direction of the flow aloft, the station is connect, if does not, is not. We will use the tethered balloon data to verify it during IOP and during LTOP Albany soundings.

Bulk network properties (I)

Used only 4 nights out of 45 (from IOP) + 4.5x365 (from LTOP) =1687 nights.

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Bulk network properties (II)

Scaled temperature envelope Vs. maximum network wind speed

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5. Expected outcomes (I)Mapping turbulent activity

• Analysis of five years of direct turbulent measurements at the HVAMS Anchor Station and HF forest. An analysis of surface observations of long duration from simpler HOBO stations.

Method: Build up statistics about turbulence breakdowns per observational site, and also techniques to calculate fluxes during stable conditions using data from IOP and LTOP of HVAMS.

• Through land use / land cover map quantify how representative are the surface characteristics found at site study- area along the whole area, in order to extrapolate fluxes measured at specific sites.

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Expected outcomes (II) Linking external parameters to flow regime

• Define a regional and local bulk Richardson number that can evaluate the average flow stability across the network and at individual stations as well in valley region.

s

P

∂∂

−ρ1

( ) ( )( )2

,U

hgregionallocalRi

ref

bΔ

⋅Δ⋅=

θ

θ

-Background flow (pressure gradient)-Surface characteristics-Cloud cover fraction

Observed flow regime (Turbulent activity)

ΔU ( TF, )

ΔӨ (Cloud cover, terrain curvature, altitude)

h (terrain curvature, altitude)

ΔU ( valley roughness length, )ΔӨ (Cloud cover, cold air pool)h (How do we define it? The height where there is turbulence, or the height where the along valley flow starts to turn in order to match the cross valley geostrophic flow, or … )

s

P

∂∂

−ρ1

Local (stable surface layer) Regional (stable boundary layer)

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Expected outcomes (III) Site representativeness

• The representativeness of local measurements of wind, temperature, and humidity made at single observational site will be tied to such local Rib.

[If Rib < Ricr , local flow is connected with the flow above. Local measurements can represent regional values (network size).]

• Investigation of the mean wind direction at site when the local flow is not connected to the flow aloft. Compare mean wind direction to direction of the steepest slope of the surrounding area (what size? – ties directly with representativeness of local measurements).

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Map out turbulent activity including the identification of turbulence breakdowns

Development of a local and regional bulk Richardson number.

Compare mean wind direction with the direction of the steepest slope when the flow is not connected to the flow aloft, in order to see if abrupt nocturnal temperature drops are associated with drainage flows .

Summary of Tasks

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Timeline

Year 2009 2010

Month MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR

Finalization of IOP data analysis

Complete flux calculations and site characterization

LTOP data analysis

Start of Anchor st. data analysis. Test the methods (described in the plans of research) for counting turbulence breakdowns and flux estimation using HOBO st.. (Development of codes to work with large amounts of data > year).

Anchor and HOBO station data analysis. Turbulence breakdowns identification and flux calculation for Anchor, and if possible HOBO stations.

End of LTOP data analysis, including Anchor st. and HOBO Stations data. Development of a local and a non-local Rib for

flow stability. The Rib

developed to use in AF 2003 should be a starting point for local Rib.

HF data analysis

Start of data analysis, including adaptation of numerical codes used to analyze Anchor data.

Counting of turbulence breakdowns and flux calculations

End of HF data analysis

Thesis composition

Start thesis composition. Composition of data section.

Description of the methods used to identify turbulence breakdowns

and fluxes calculations. It should start by methods used for HVAMS, and

then HF.

. Start writing results and conclusion s about HVAMS, and Introduction. Incorporate HF final results in the thesis, including update of methods section, results and conclusion.

Thesis presentation. After this refine chapters in order to submit one paper about turbulence activity over complex terrain under stable conditions and another about site representativeness

LBA data analysis

(Optional)

End of LBA data analysis. Start writing about results found for posteriori submission to a journal.

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Operational periods during IOP and LTOP

Timeline Intense Observational Period September/2003 | October/2003

15……...20….…..25…......30..……5……...10……....15……...20……...25…….30.....

Long Term Observational Period(After November 2, 2003)

PAMs 15……………………………………………………………………….......................31 Stopped running

TAOS 3 5.……..10 13.14 16..18 20 24.25 30.31 Stopped running

Schodack –IS 15……………………………………………………………………….......................31 Ran from 11/01 to 12/03/03

UWKA 3 6.……..11 13.14 16..18 24.25 28 30.31 Stopped running

G-Anchor 16....………………………31 Still running

HOBO1 18.……......……………………………………………………..………...........31 Ran from11/01 to 11/24/03, but was relocated to Green Acres on 11/24/03, and still running.

HOBO2 16…...…………………………………………………………………………..….31 Still running

HOBO3 18… ..……………………………………………………….............................31 Ran from11/01 to 11/20/03, but was relocated to Schodack-Is on 07/01/04, and stopped running in Sep/08

HOBO4 16..…………………………………….………………………………..………….31 Continued running from11/01 to 11/25/03, but was relocated to Van Orden on 11/25/03, and stopped running in Sep/08

HOBO5 15………………………………………..………………………………………..…...31 Continued running till Mar/08

MIPSProfilerSodar

Weather/st ceilometer

26………………………………………………....24 25 ………………………………….….………………………….31 25..……………………………………………….……………….31 26………………...........10

Stopped runningStoppedrunning Stoppedrunning Stopped running

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MESO-NH UIB HVAMS simulation details:

* from 1800UTC (14 LST) 07/10/2003 to 1800UTC (14LST) 08/10/2003

* 1 domain centered at (lat,lon) = (42.75,-74.00)

* resolution = 2 km x 2 km x 3 m (close to the ground but stretched above)

* gridpoints = 200 x 270 x 85

* time step = 2s

* turbulent + radiative + surface + condensation schemesOutputs * 3D fields every 30 min: Here there are plots of horizontal and vertical cross-sections every 3 hours

* time series at HVAMS surface stations from model output

* direct comparison of model and aircraft flight track meteorological parameters

http://turbulencia.uib.es/hudson_NEW/hudson_mesoscale.html

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Night of October 16 -17, 2003Aircraft Profiles at st. 1 – Alexander Farm

Aircraft Profiles at 1B1- Columbia County

TAOS flying at constant height at st. 10 from 1920 LST until 2300 LST on October 16, 2003

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October 16 – 17, 2003

Momentum and heat fluxes

w’

Wind speed

Wind direction

Momentum and heat fluxes

w’

Wind speed

Wind direction

St. 5

St. 7

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October 16 – 17, 2003

Surface

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From Acevedo and Fitzjarrald 2003