DYNAMO

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DYNAMO (Dynamics of the MJO) The US
Participation in CINDY2011 (Cooperative Indian
Ocean Experiment on Intraseasonal Variability in
Year 2011) US CLIVAR Summit 2009
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Outline

• Importance and problems of the MJO
• perspectives from operational forecast
• perspectives from research
• 2. Rationale, objectives, and hypotheses
• 3. Program structure
• (a) Field Campaign
• (b) Modeling
• 4. Remaining Issues
• 5. Experimental Design (breakout session)

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Operational MJO Prediction -- Perspective

• Our lack of understanding of the MJO initiation
  process has made operational prediction
  challenging
• Statistical forecast techniques and MJO
  composites perform well during established MJO
  events but not during MJO transitions
• Dynamical model predictions are improving (and
  are the way forward) but currently offer limited
  skill
• Daily monitoring remains our most important and
  reliable method for anticipation of MJO
  development/demise
• Operational MJO prediction is often forced to be
  reactive to the development or demise of the MJO
  after the fact

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How can DYNAMO help Operational MJO prediction?

• Current MJO monitoring / prediction is mainly
  based on the Wheeler-Hendon (2004) MJO index,
  which is formed using zonal winds and OLR. It is
  an excellent index to track the propagation of
  the MJO, but does not provide sufficient
  information about MJO initiation. 
• Targeted DYNAMO field obs may contribute to
  improved documentation of the MJO initiation
  process.
• Thus DYNAMO can have an immediate impact on
  operational MJO prediction as important
  precursors for the development of the large-scale
  MJO signal will be identified more clearly than
  in the past
• (see example)

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Operational MJO Prediction -- Example
(b)
(a)
GEFS MJO Index Fcst ? Developing MJO
Official CPC MJO Forecast
(d)
(c)
East Asian Cold Surge
Obs
Information included in the February monthly
outlook
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Operational MJO Prediction Forecast Models
NCEP
CMC
UK Met
(d)

• Dynamical models becoming an important component
  of the MJO forecast process.
• Model prediction framework is in place and can
  benefit from model improvements.
• Operational model prediction likely to benefit
  from DYNAMO CPT (i.e., better model physics)

ABOM
ECMWF
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Operational MJO Prediction -- Applications
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Limited intraseasonal prediction skill (lt 15
days) particularly low during the initiation of
the MJO in the Indian Ocean and during the
passage of the MJO over the Maritime Continent.
Correlation between predicted (by CFS) and
observed MJO indices (Courtesy of Jon Gottschalck
and Qin Zhang)
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CPC Operational Support for DYNAMO Field Campaign
1. Setup DYNAMO briefing web page(s) ? Build
upon existing CPC briefing pages, NAME experience
2. Participate in tropical weather briefings
and provide expert assessment on MJO status and
forecast evolution 3. Realtime experience using
Gotowebinar briefings 4. Maintain, provide
assessment of CLIVAR MJO index forecast tools
Example Briefing Page --Multiple Categories
Observational Data Forecast Tools
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Background II Importance of the MJO Science
Perspective

• monsoons, ENSO
• extreme events (flood, tropical storm/cyclones)
• Indian Ocean Dipole and Indonesian Throughflow
• teleconnections, extratropical circulation/weather
  
• North Atlantic Oscillation, Arctic Oscillation,
  Antarctic Oscillation
• atmospheric and oceanic chemistry and biosystem
  (ozone, CO2, aerosols, chlorophyll)
• global angular momentum, Earths rotation rate,
  length of the day

Maloney and Hartmann 2000
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• Challenges presented by the MJO
• inability to consistently/knowingly reproduce
  the MJO in global weather and climate models

Lin et al 2006
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Weaker MJO signals in the Indian Ocean than the
western Pacific in GCMs that reproduce the MJO to
a certain extent. MJO variance in 850 hPa
zonal wind (contours)
(Zhang et al 2006)
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U850
precipitation
2001
2000
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Scenarios of MJO Initiation

• A Internal Initiation The MJO is initialized
  over the tropical Indian Ocean through local
  interaction between the large-scale circulation
  and convective activity that self-organizes into
  large-scale patterns through atmospheric energy
  buildup, multi-scale interaction, air-sea
  interaction, or other processes.
• B External Initiation Perturbations from either
  the extratropics or upstream (west) lead to
  changes in the large-scale circulation and/or
  thermodynamics over the tropical Indian Ocean.
  Deep convection subsequently organizes into
  large-scale patterns that feed back to the
  large-scale circulation, giving rise to the MJO.

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• Recent Advancement in the MJO Study
• Moisture pre-conditioning for deep convection
  by shallow convective moistening (Kemball-Cook
  and Weare 2001 Bretherton et al, 2004
  Derbyshire et al. 2004 Holloway and Neelin 2009)
• Shallow diabatic heating sensitivity of
  low-level moisture convergence and surface wind
  (Wu 2000 Zhang and Hagos 2009)
• Multiscale interaction upscale momentum
  transport (Biello et al. 2007 Maloney 2009)
• Air-sea coupling (Fu et al. 2003)
• All are sore points of cumulus
  parameterization.

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Current Thinking on Key Processes of MJO
Initiation
Lower Troposphere
Moisture Profile
Deep
Shallow
Convective Types
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• DYNAMO Hypotheses Criteria and Guidance
• Testable using DYNAMO field observations and
  models
• Testing leading to specific information helping
  model improvement
• Two MJO initiation scenarios internal vs.
  external initiation
• Different stages of MJO initiation convective
  suppression, transition, persistence and
  termination
• Based on recent modeling, theoretical, and
  observational results
• role of moisture
• role of diabatic heating profile
• role of multi-scale interaction
• role of the upper ocean

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DYNAMO Hypotheses (abridged, under
development) Scenario A  internal
initiation Hypothesis I Limited moisture supply
inefficient moistening by shallow convection gt
prolonged convective suppression prior to MJO
initiation Hypothesis II Two-stage
transition shallow convection with low
precipitation efficiency gt lower-tropospheric
moistening slow shallow convection with high
precipitation efficiency gt surface and low-level
moisture convergence fast Hypothesis III A
balance between shallow, deep, and stratiform
precipitation gt convection sustained on the MJO
scales dominance of stratiform precipitation gt
convective termination Hypothesis IV Through
large variability in mixing and associated
entrainment cooling, the SCTR and Wyrtki Jets
provide interactive feedback and independent
background for MJO initiation
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DYNAMO Program Structure
Science Steering Committee
Program Supporting Office Data M/A Field Operation
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DYNAMO Modeling Activities General Strategy

• A CPT will be proposed (LOI submitted, Eric
  Maloney, PI), including five modeling centers
  NCAR, NCEP/EMC, NASA/GMAO, NASA/GISS (MAP), GFDL
  (CPPA) NRL/MTR pending
• DYNAMO will contribute to the field observation
  component of the CPT
• Connections between DYNAMO and the modeling
  centers will be strengthened through the CPT
• A DYNAMO Modeling Working Group (members overlap
  with the CPT) will conduct additional activities
  to support DYNAMO (experimental design,
  hypothesis testing, etc.)
• Leverage with existing modeling activities
  relevant to DYNAMO CAPT, WCRP/TFSP, etc.

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DYNAMO Modeling Activities Preliminary Ideas

• CPT
• (i) diagnostic metrics for processes of
  convection-circulation coupling (e.g., MJO) and
  its applications to selected AR4 and AR5 models
• (ii) common parameterization sensitivity
  experiments
• (iii) general procedures for identifying
  misrepresentations of processes connecting
  convection (MJO) and the mean state in models
• DYNAMO modeling working group
• experimental design
• hypothesis testing
• YOTC -gt YOTC2
• reforecast (CFS)

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DYNAMO/CINDY2011 Field Campaign

• sounding-radar array
• ship-based measurement of air-sea flux, aerosol,
  and upper-ocean mixing
• addition mooring of surface meteorology and
  upper ocean measurement
• enhanced soundings at operational sites

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Current Thinking on Key Processes of MJO
Initiation
Lower Troposphere
Moisture Profile
Deep
Shallow
Convective Types
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DYNAMO/CINDY2011 Observation Strategy
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Program Synergy
CINDY2011/DYNAMO (September 2011 January 2012)
atmospheric heating and moistening profiles,
cloud and precipitation, upper-ocean mixing and
turbulence, aerosol AMIE (late 2011 early
2012) radiation, cloud, atmospheric profiles
(pairing with DYNAMO SMART-RAMF2) HARIMAU (2004
- ) cloud, atmospheric boundary
layer PAC3E-SA/7SEAS (2011) aerosol,
convection ONR Air-Sea (late 2011) meso-scale
air-sea-wave interaction
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Composite of TRMM Precipitation Anomalies Based
on MJO Phases
Phase 1
Phase 2
Phase 3
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MJO Probability in the Indian Ocean (1980-2008)
Season and ENSO
(Courtesy of Kunio Yoneyama)
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After TOGA COARE, Why DYNAMO?

• Unique prediction challenge (low skill vs. very
  low skill)
• Unique climate modeling challenge (in some GCMs
  moderate MJO vs. weak or no MJO)
• Unique MJO life stage (mature, propagation vs.
  initiation)
• Unique large-scale background (warm pool vs.
  South Asian monsoon, Wyrtki Jets,
  Seychelles-Chagos thermocline ridge)
• New observing technology (RAMA, Radar)

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Expected Outcome of DYNAMO

• a unique in situ data set available to the
  broader research and operations communities,
  whose utility will match GATE and TOGA COARE
  data
• advancement in understanding of the MJO dynamics
  and initiation processes
• identification of misrepresentations of processes
  key to MJO initiation that are common in models
  and must be corrected to improve MJO simulations
  and predictions
• provision of baseline information to develop new
  physical parameterizations and quantify MJO
  prediction model improvements, and
• enhanced MJO monitoring and prediction capacities
  that deliver climate prediction and assessment
  products on intraseasonal timescales for risk
  management and decision making.

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DYNAMO Climate Implications

• Field data to be collected will be available to
  all climate modeling centers
• Research results from DYNAMO and the CPT will
  provide targeted information for model
  improvement (entrainment/detrainment rates,
  precipitation efficiencies, heating profiles,
  etc.)
• DYNAMO will improve climate prediction and
  assessment products on intraseasonal timescales
  for risk management and decision making
• Improved MJO capability in climate models may
  help dynamical ENSO prediction
• Improved MJO capability in climate models will
  increase our confidence in their credibility in
  climate simulations and projection.
• First comprehensive air-sea interaction field
  campaign in the equatorial Indian Ocean
  landmark for future climate process studies

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Major Issues to be Resolved

• Ship time Ron Brown vs. Revelle (need to install
  TOGA radar)
• Sounding operation at Diego Garcia
• DYNAMO CPT connection
• DYNAMO ONR IO Exp coordination
• DYNAMO IAG coordination

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Thank you!

• Comments, Questions and Suggestions
• are Welcome!

DYNAMO website http//www.eol.ucar.edu/projects/dy
namo/
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Initial Field Observation Cost Estimates(excludin
g ship time)

• Soundings (one ship, two island) 2M (NSF
  deployment)
• S-PolKa 2M (NSF deployment)
• SMART-R 0.3M (NSF ATM, JAMSTEC)
• TOGA Radar 0.4M (NSF ATM)
• AMF2 0 (DOE/ARM)
• Surface flux, aerosol, drifters, moorings 1M
  (NOAA)
• Ocean 4M (NSF OCE, ONR)
• Total 10M

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DYNAMO Science Steering Committee

• Simon Chang (NRL/MRY)
• Chris Fairall (NOAA/ESRL)
• Wayne Higgins (NOAA/NCEP/CPC)
• Richard Johnson (CSU)
• Chuck Long (PNNL)
• Steve Lord (NOAA/NCEP/EMC)
• Mike MaPhaden (NOAA/PMEL)
• Eric Maloney (CSU)
• Mitch Moncrieff (NCAR)
• Jim Moum (OSU)
• Steve Rutledge (CSU)
• Augustin Vintzileos (NOAA/NCEP/EMC)
• Duane Waliser (CalTech/JPL)
• Chidong Zhang (UM)

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DYNAMO Data Policy and Management

• Follow the standard policy of recent field
  campaign (NAME, VOCAL)
• real-time data stream to operations center
  through GTS
• fixed time frame for data release
• International data exchange within CINDY2011
• The CINDY2011 data center will be established
  at JAMSTEC
• DYNAMO data center will be at EOL
• Two data centers will be mirrored at each site
  with links, so data access to both data will be
  transparent to users
• Similar approach will be pursued between
  DYNAMO/CINDY2011 and other programs (HARIMAU,
  AMIE, ONR Air-Sea, 7SEAS).
• If there will be YOTC 2 for 2011-12, then its
  data infrastructure will be linked to the
  DYNAMO/CINDY2011 data center.

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Moisture Profiles Observations
(Kiladis et al. 2005)
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NCAR CAM3
(Zhang and Song 2009)
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Circulation Sensitivity to Heating Profiles
Stratiform heating
Deep heating
Shallow heating
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Seychelles-Chagos Thermocline Ridge (SCTR)

• shallow thermocline driven by wind pattern S of
  equator (Ekman divergence)
• heating intensified in thin ML cool water
  available in close proximity to sea surface
• role of sub-surface mixing?
• changes in surface fluxes, SST linked to MJO
  (Vialiard et al, 2008)
• potential feedback?

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Wyrtki Jets

• vigorous eastward surface currents in boreal
  spring/fall
• dominant currents / dominate transport
• current structure is unique from equatorial
  Pacific/Atlantic
• to date, current structure is under-resolved,
  mixing not measured
• anticipate high vertical shear, strong mixing
  when jets present

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Indian Ocean Dipole (IOD)
http//www.jamstec.go.jp/frsgc/research/d1/iod/
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Stratiform Rain Fraction
(19982000 TRMM PR rain 0.6 m yr-1. Schumacher
and Houze 2003)
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NCAR S-PolKa Radar

• S-band (10 cm) convection spectrum,
  organization, and evolution are needed to
  determine MJO phase and convective/stratiform
  partition
• Single Doppler convective storm circulations
  (updrafts)
• Ka-band (8 mm) 3D structure of shallow cumulus
  clouds
• Dual wavelength (Ka- and S-band) vertical
  profile of boundary layer specific humidity
• Dual polarimetric microphysics of oceanic
  tropical convection
• Need upgrade

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Proposed Sounding-Radar Array
Hanimaadhoo
Gan
Gan
Diego Garcia
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DYNAMO Field Observations

• Sounding array vertical profiles of diabatic
  heat and moisture budgets (Q1, Q2), divergence,
  wind shear
• Radar array cloud population statistics, cloud
  and precipitation structure and evolution, cloud
  microphysics, boundary-layer humidity

• Ship-based surface fluxes and rain rate
  profiles of temperature, salinity, current, and
  turbulence aerosol
• Mooring-based surface fluxes and rain rate
  subsurface temperature, salinity, current, flux,
  mixing, and wave propagation

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• DYNAMO/CINDY2011 EOP
• SMART C-band radar AMF2
• surface met and upper ocean moorings
• drifters

AMIE
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Long-Term Monitoring in the Indian Ocean
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RAMA
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HARIMAU
Yamanaka et al. 2008
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PAC3E-SA
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• ?pod
• moored subsurface flux measurement
• analogous to a subsurface flux tower

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subsurface heat fluxes at 0 140W
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shipboard profiling flux measurements
multiple high-res modern ADCPs sampled rapidly
Hull 300 kHz 75 kHz Over-the-side 150
kHz
Chameleon turbulence profiler
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DYNAMO Hypotheses Hypothesis I Convective
suppression prior to MJO initiation is prolonged
because, in the absence of external influences,
moisture source through surface evaporation over
warm sea surface with weak to moderate surface
wind is sufficient to support only isolated
precipitating systems. The timescale of the
suppressed phase prior to MJO initiation is
determined by the low efficiency of
lower-troposphere moistening by shallow
convection. Hypothesis II Population of
shallow convection plays a two-stage role in the
transition period of MJO initiation when the
precipitation efficiency is low, the moistening
effect in the lower troposphere dominates, which
slowly creates a favorable condition for deep
convection when the precipitation efficiency is
high, the low-level heating effect dominates,
which induces surface and low-level moisture
convergence as an energy source for deep
convection and accelerates the initiation
process.
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DYNAMO Hypotheses Hypothesis III A delicate
balance between precipitating shallow convection
and deep, stratiform precipitation is needed to
sustain convective period over the MJO space
scale. In the absence of pre-existing large-scale
influences, the time scale of the active phase of
the MJO is determined by a graduate shift from
this balance to a dominance of stratiform heating
profiles on the large scale. Hypothesis IV
Upper-ocean processes contribute to MJO
initiation through maintaining high SST prior to
the initiation and rapid surface cooling during
the transition and early active periods.
Turbulent mixing plays a critical role in these
because of the shallow mixed layer associated
with the SCTR, the current shear associated with
the Wyrtki jets, and their intraseasonal
variability. Hypothesis V External
(extratropical and upstream) perturbations, with
their large-scale ascents and low-level
convergence, play an activating role to
accelerate MJO initiation primed by internal
processes.
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Hope Dynamical Models can have better skill than
Statistical models
model
statistical
persistence

• POAMA hindcasts 10 members from 1st of month for
  25 years.
• Correlation RMS for RMM1 and RMM2 (combined)

Courtesy M. Wheeler
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• Background I Importance of the MJO
• Societal benefit
• monsoons, ENSO
• teleconnections, extratropical
  circulation/weather
• extreme events (flood, tropical storm/cyclones)
• seamless weather-climate prediction (2-4 weeks)
• Current MJO prediction at NCEP/CPC
• Contributions from operations centers of the US,
  Canada, Brazil, Japan, Australia, UK, ECMWF
  (Taiwan, India in the near future)
• End users
•  emergency responding agencies (e.g., American
  Red Cross, International Federation of Red Cross
  and Red Crescent Societies)
•  US government (e.g., USAID, Forest Service,
  National Marine Fisheries Service, River Forecast
  Centers, NWS Regional HQs)
•  private industry (e.g., American Electric
  Power, Earth Satellite Corporation, Moore Capital
  Management, and many more)

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• DYNAMO Objectives
• Collect in situ observations from the equatorial
  Indian Ocean that are urgently needed to advance
  our understanding of the processes key to MJO
  initiation and to improve their representations
  in models
• (b) Identify critical deficiencies in models that
  are responsible for the low prediction skill and
  poor simulations of MJO initiation, and assist
  the broad community effort of improving model
  parameterization
• (c) Provide guiding information to enhance MJO
  monitoring and prediction capacities that deliver
  climate prediction and assessment products on
  intraseasonal timescales for risk management and
  decision making over the global tropics.

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MJO Probability in the Indian Ocean (1980-2008)
IOD
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• The Need of an MJO Process Study
• MJO initiation in the Indian Ocean poses a unique
  challenge to prediction, simulation, and
  understanding of the MJO
• Recent progress has brought us to the dawn of a
  breakthrough in the MJO study
• Data needed to make the breakthrough are
  available only from field campaigns (not provided
  by previous field campaigns in the Indian Ocean,
  e.g, INDOEX, JASMINE, MISMO, Vasco-Cirene)
• The modeling community is experienced with using
  field observations to assist model improvement
  and development.

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DYNAMO Timeline
Daily Planning Process In-Field Data
Management. Operational Data Collection Facility
Coordination Status Operations Center User
Services In-field Catalog
Project Planning Phase
Initial Feasibility Cost Estimates (July 2009)
Facility Science Coordination Mtg (October 2010)
Site setup Initiate Data Collection Comm System
Test
Science Planning Meeting (April 13-14, 2009)
Proj Team selection Prepare Ops Plan Data Mgmt.
Plan Logistics
Routine Archive/ Access
Data Processing Quality Control
Field Phase
Initial Site Survey
Operations Planning Mtg. Finalize Ops
Plan Project Safety Review Finalize Data Mgmt Plan
1 yr
-1 yr
Data Analysis Workshop
Program Assessment
R.V Ron Brown Time Request (April, 2009)
Draft SPO EDO US Clivar Summit (July 16, 2009)
Long-Term Data Management Support Phase
OFAP FacilitiesDecisions (May 2010)
NSF Proposal Submissions (January 2010)
Facilities Request LOI (May 15, 2009)