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

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1 Fog and low cloud ceilings in the northeastern US: climatology and dedicated field study Robert Tardif National Center for Atmospheric Research Research Applications Laboratory 1

2 Overview of project Objectives: Improve short-term C&V forecasts Increase understanding of physics of C&V in complex environments Assess performance of NWP models and develop improved key parameterizations for C&V Validate current & develop improved C&V 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 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 2

3 Climatology of C&V in northeastern US Characteristics 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 3

4 Climatology of C&V in northeastern US Fog Low ceiling 4

5 Fog climatology Conditions at onset (wind direction) : Evidence of onshore flow as fog enhancing factor NE flow 5

6 Fog climatology Fog 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 before Radiation 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 6

7 Fog climatology Fog climatology Fog types - results 7

8 Fog climatology Fog types temporal variability 8

9 Summary: Fog climatology 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 9

10 C&V field study in northeastern US 10

11 C&V field program in Northeastern US Central facility 90-m tower + surfacebased 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 prod s 11

12 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) 12

13 Central facility Complex various scales 13

14 Case studies Highlights from data analysis Variability in microphysical structure of fog layers A look into translation algorithms (β ext vs RH, β ext vs LWC) 14

15 Highlights from data analysis From Oct to June 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 15

16 Highlights from data analysis Observations during an event (fog w/ precip): Biral/HSS visibility / present wx sensors Visibility Precip. Ceilometer 16

17 Highlights from data analysis Observations during an event (fog w/ precip): dense fog 17

18 Highlights from data analysis Observations during an event (fog w/ precip): dense fog wind shear turbulence intensity σ w V h 18

19 Highlights from data analysis Microphysical variability (over life cycle) LWC V settl 19

20 Highlights from data analysis Microphysical variability (over life cycle) Visibility dense fog Droplet spectra 20

21 Highlights from data analysis Microphysics variability (w.r.t. fog type) Drop size distribution β ext vs LWC Visi=0.4km Visi=1km Kunkel (1984) 21

22 Highlights from data analysis Translation algorithms (translating model parameters to visibility) β ext vs LWC & others (in fog) + β ext vs RH (pre-fog) β ext = Q 0 ext 2π r λ π obs ( ) 2 r n r dr obs β ext vs LWC - Limitation of instruments? - Importance of interstitial haze particles? 22

23 Highlights from data analysis Translation algorithms (translating model parameters to visibility) β ext vs others (in fog) 23

24 Highlights from data analysis Translation algorithms (translating model parameters to visibility) β ext vs RH (pre-fog) (LIFR) (IFR) (MVFR) Huge variability! 24

25 Highlights from data analysis Translation algorithms (translating model parameters to visibility) β ext vs RH (pre-fog) 0730z 0730z 2300z 2300z Problem more complex than β ext = β ext (RH)! 25

26 Summary and perspectives Analysis of field data (specialized & operational) ongoing Analysis provides some insights into complexity of physical processes involved in C&V events in NE Significant variability in fog microstructure Better characterization and understanding of TA parameters needed (more observations) What s 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)) 26

27 Outstanding questions/challenges Roadmap toward better C&V 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 => 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 27

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