Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study

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Fog and low cloud ceilings in the northeastern
US climatology and dedicated field study

• Robert Tardif
• National Center for Atmospheric Research
• Research Applications Laboratory

Workshop on Fog Remote Sensing and Modeling
(FRAM), June 14-15, Montréal, Canada
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Overview of project

• Objectives
• Improve short-term CV forecasts
• Increase understanding of physics of CV in
  complex environments
• Assess performance of NWP models and develop
  improved key parameterizations for CV
• Validate current develop improved CV
  translation algorithms
• Support development of statistical forecast
  models
• Activities
• Climatology ? scope out the extent and
  characteristics of the fog/low ceiling problem in
  the NE region (variability, type, main
  influences)
• Field study/data analysis ? gather specialized
  observations relevant to CV. More in-depth look
  through case study analyses
• Numerical modeling ? complement data analysis
  gain greater insights into physics of CV and
  model strengths/weaknesses

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Climatology of CV in northeastern US

• Characteristics of CV
• Fog 50 to 300 hours/year in 10 to 35
  events/year
• Low ceiling (lt 300m) 580 to 1100 hours/year in
  60 to 95 events/year

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Climatology of CV in northeastern US
Fog
Low ceiling
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Fog climatology

• Conditions at onset (wind direction)

• Evidence of onshore flow as fog enhancing factor
• NE flow

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Fog climatology

• Fog types gt is there a prevailing fog type in
  the region?

• Classification algorithm
• Precipitation If some type of precip. is
  observed at onset and/or 1hr before
• Radiation
• Cooling _at_ surface under calm or light winds
• No ceiling hour before onset, or ceiling height
  increasing or cloud cover decreasing just before
  onset
• Advection
• Significant wind speed
• Sudden decrease in visibility and ceiling height
• Cloud base lowering
• Low ceiling (below 1km) w/ height gradually
  decreasing within 6 hours leading to fog onset
• Morning evap. fog
• Within 1hr of sunrise
• Warming but larger increase in dew point

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Fog climatology

• Fog types - results

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Fog climatology

• Fog types temporal variability

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Fog climatology

• Summary
• Low ceilings much more frequent than fog
• Fog most common at coastal and inland locations
  (minimum in urban center)
• Overall fog problem in NE is multi-faceted
  (various fog regimes)
• Precipitation-induced fog most frequent across
  region
• Cloud base lowering fog is another important
  component
• Marine fog/stratus at coastal locations
• Radiation fog inland
• Distinct temporal variability according to fog
  types
• Fog onset distinct flow regimes, but with
  various synop wx patterns

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CV field study in northeastern US
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CV field program in Northeastern US

• Central facility
• 90-m tower surface-based instrumentation
• East-central Long Island (Brookhaven Natl Lab.)
• Various fog types (climo)

• Other available data
• ASOS network (1-min data)
• Twice-daily NWS soundings at Upton NY
• Buoys (hourly data)
• NEXRAD satellite prods

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Central facility - instrumentation

• 90-m tower
• 7 levels of T/Hum/Wind
• 3 levels of visibility present wx
• 2 levels of fast-response T,Hum,Wind (fluxes) and
  radiation (LW?? SW??)
• Fog spectrometer (32m)
• Surface instrumentation
• T/Hum/Pressure
• Rain gauge
• Soil T Moisture (6 levels)
• Remote sensing
• Ceilometer (30 sec. cloud backscatter)
• Profiling Microwave Radiometer (1 min. profiles
  of T/Hum/Cloud water)

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Central facility

• Complex environment _at_ various scales

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Highlights from data analysis

• Case studies
• Variability in microphysical structure of fog
  layers
• A look into translation algorithms
• (bext vs RH, bext vs LWC)

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Highlights from data analysis

• From Oct. 2003 to June 2005 ? 40 events of
  interest!
• 11 cloud base lowering fog
• 10 precipitation fog
• 6 radiation fog
• 2 advection fog 1 marine fog transforming into
  stratus during inland propagation
• 1 morning evaporation fog
• 7 low ceiling without dense fog
• 4 near radiation fog

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Highlights from data analysis

• Observations during an event (fog w/ precip)

Visibility
Biral/HSS visibility / present wx sensors
Precip.
Ceilometer
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Highlights from data analysis

• Observations during an event (fog w/ precip)

dense fog
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Highlights from data analysis

• Observations during an event (fog w/ precip)

dense fog
wind shear
turbulence intensity
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Highlights from data analysis

• Microphysical variability (over life cycle)

LWC
Vsettl
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Highlights from data analysis

• Microphysical variability (over life cycle)

Visibility
dense fog
Droplet spectra
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Highlights from data analysis

• Microphysics variability (w.r.t. fog type)

Drop size distribution
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Highlights from data analysis

• Translation algorithms (translating model
  parameters to visibility)

bext vs LWC others (in fog) bext vs RH
(pre-fog)
obs
obs bext vs LWC
- Limitation of instruments? - Importance of
interstitial haze particles?
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Highlights from data analysis

• Translation algorithms (translating model
  parameters to visibility)

bext vs others (in fog)
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Highlights from data analysis

• Translation algorithms (translating model
  parameters to visibility)

bext vs RH (pre-fog)
(LIFR)
(IFR)
(MVFR)
Huge variability!
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Highlights from data analysis

• Translation algorithms (translating model
  parameters to visibility)

bext vs RH (pre-fog)
Problem more complex than bext bext(RH)!
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Summary and perspectives

• Analysis of field data (specialized
  operational) ongoing
• Analysis provides some insights into complexity
  of physical processes involved in CV events in
  NE
• Significant variability in fog microstructure
• Better characterization and understanding of TA
  parameters needed (more observations)
• Whats next?
• In-depth look at physical processes associated to
  precip-induced fog
• Further analysis of microphysical data from fog
  spectrometer (variability parameterizations
  relationship to visibility (TA))

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Outstanding questions/challenges

• Roadmap toward better CV forecasts?
• Parameterizations of current NWP models adequate?
  develop improved model physics
• Observations required for assimilation?
• Identify sensitivity to physical
  processes/parameterizations
• Basis for probability forecasts from ensembles
  feasible?
• Predictability issues
• Statistical forecast models capturing the
  physics. Which predictors are required?

• Challenge gt comprehensive dataset required!
• Boundary layer structure (temperature, moisture,
  flow)
• Cloud/fog structure (depth, LWC distribution)
• Mesoscale structure of coastal atmosphere
• Aerosol characteristics gt variability in
  microphysical structure