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Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study
Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study
Robert TardifNational Center for Atmospheric ResearchResearch Applications Laboratory
Robert TardifNational Center for Atmospheric ResearchResearch Applications Laboratory
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Overview of projectOverview of projectObjectivesObjectives:
Improve short-term C&V forecastsIncrease understanding of physics of C&V in complex environments
Assess performance of NWP models and develop improved key parameterizations for C&VValidate current & develop improved C&V translation algorithmsSupport development of statistical forecast models
ActivitiesActivities: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 C&V. More in-depth look through case study analyses Numerical modeling → complement data analysis & gain greater insights into physics of C&V and model strengths/weaknesses
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Climatology of C&V in northeastern USClimatology of C&V in northeastern USCharacteristics of C&VCharacteristics of C&V:
Fog: ~50 to 300 hours/year in ~10 to 35 events/year
Low ceiling (< 300m): ~580 to 1100 hours/year in ~60 to 95 events/year
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Fog climatologyFog climatologyConditions at onset (wind direction) :
Evidence of onshore flow as fog enhancing factorNE flow
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Fog climatologyFog climatologyFog types => is there a prevailing fog type in the region?
Classification algorithm: Precipitation: If some type of precip. is observed at onset and/or 1hr beforeRadiation
• Cooling @ surface under calm or light winds• No ceiling hour before onset, or ceiling height increasing or cloud
cover decreasing just before onsetAdvection
• 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 climatologyFog climatologySummary:
Low ceilings much more frequent than fogFog 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 componentMarine fog/stratus at coastal locationsRadiation fog inland
Distinct temporal variability according to fog typesFog onset: distinct flow regimes, but with various synop wx patterns
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C&V field program in Northeastern USC&V field program in Northeastern USCentral facility
90-m tower + surface-based instrumentationEast-central Long Island (Brookhaven Natl’ Lab.)Various fog types (climo)
Other available dataASOS network (1-min data)Twice-daily NWS soundings at Upton NYBuoys (hourly data)NEXRAD + satellite prod’s
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90-m tower 7 levels of T/Hum/Wind3 levels of visibility & present wx2 levels of fast-response T,Hum,Wind (fluxes) and radiation (LW↓↑ + SW↓↑)Fog spectrometer (32m)
Surface instrumentationT/Hum/PressureRain gaugeSoil T + Moisture (6 levels)
Remote sensingCeilometer (30 sec. cloud backscatter)Profiling Microwave Radiometer (1 min. profiles of T/Hum/Cloud water)
Central facility Central facility -- instrumentationinstrumentation
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Highlights from data analysisHighlights from data analysis
Case studies
Variability in microphysical structure of fog layers
A look into translation algorithms (βext vs RH, βext vs LWC)
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Highlights from data analysisHighlights from data analysis
From Oct. 2003 to June 2005 ► 40 events of interest!
11 “cloud base lowering” fog10 “precipitation” fog6 radiation fog2 advection fog + 1 marine fog transforming into stratus during inland propagation1 “morning evaporation” fog7+ low ceiling without dense fog4+ “near radiation fog”
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Highlights from data analysisHighlights from data analysisObservations during an event (fog w/ precip):
Visibility
Precip.
Biral/HSS visibility /present
wxsensors
Ceilometer
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Highlights from data analysisHighlights from data analysisObservations during an event (fog w/ precip):
dense fog
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Highlights from data analysisHighlights from data analysisObservations during an event (fog w/ precip):
dense fog
wind shear turbulence intensityw
hVσ
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Microphysical variability (over life cycle)Highlights from data analysisHighlights from data analysis
LWC
Vsettl
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Microphysical variability (over life cycle)Highlights from data analysisHighlights from data analysis
dense fog
Visibility
Droplet spectra
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Microphysics variability (w.r.t. fog type)Highlights from data analysisHighlights from data analysis
Drop size distribution
βext vs LWC
Kunkel (1984)Visi=1kmVisi=0.4km
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Highlights from data analysisHighlights from data analysisTranslation algorithms (translating model parameters to visibility)
βext vs LWC & others (in fog) + βext vs RH (pre-fog)
( )2
0
2ext ext
rQ r n r drπβ πλ
∞
= ∫obs
obs βext vs LWC
- Limitation of instruments?- Importance of interstitial haze particles?
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Highlights from data analysisTranslation algorithms (translating model parameters to visibility)
Highlights from data analysis
βext vs others (in fog)
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Highlights from data analysisTranslation algorithms (translating model parameters to visibility)
Highlights from data analysis
βext vs RH (pre-fog)
(MVFR)(IFR)
(LIFR)
Huge variability!
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Highlights from data analysisTranslation algorithms (translating model parameters to visibility)
Highlights from data analysis
βext vs RH (pre-fog)
0730z
2300z
0730z
2300z
Problem more complexthan βext = βext(RH)!
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Summary and perspectivesSummary and perspectivesAnalysis of field data (specialized & operational) ongoingAnalysis provides some insights into complexity of physical processes involved in C&V events in NESignificant variability in fog microstructureBetter characterization and understanding of TA parameters needed (more observations)
What’s next?In-depth look at physical processes associated to precip-induced fogFurther analysis of microphysical data from fog spectrometer (variability + parameterizations + relationship to visibility (TA))
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Outstanding questions/challengesRoadmap toward better C&V forecasts?
Parameterizations of current NWP models adequate? –develop improved model physicsObservations 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 => comprehensive dataset required!Boundary layer structure (temperature, moisture, flow)Cloud/fog structure (depth, LWC distribution)Mesoscale structure of coastal atmosphere Aerosol characteristics => variability in microphysical structure
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