national blend of models v3.2: das - national weather service saws8_ nbm v3.2 das.pdfnational blend...
TRANSCRIPT
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National Blend of Models v3.2: DAS
Jeff Craven, David Rudack, and Robert JamesNOAA/NWS/OSTI/MDL/SMB
SAWS 8 Jun 7, 2019
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and Verification• Example products• Potential future probabilistic capabilities
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and verification• Example products• Potential future probabilistic capabilities
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Suite of 65 NBM products produced
31 different NWP and MOS inputs from 5 different centers {NCEP, CMC (Canada), FNMOC (Navy), ECMWF, BoM Australia}
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NBM runs every hour
• Just like LAMP and HRRR/RAP• Time of Day notation: 12z NBM does not
have any 12z guidance in it• 12z NBM (example) has 11z GLMP, 10z
HRRR and RAP, 06z NAMNest, etc
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NBM v3.2 Aviation core elements
• 10-m wind speed and direction• 10-m wind gust (top of hour)• Sky Cover• Ceiling• Visibility• Lowest Cloud Base (Cloud Base Primary)
NOTE: All at 2.5km resolution in CONUS
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NBM v3.2 Aviation - others
• LLWS Speed and Direction• LLWS Height• 30-m and 80-m wind speed• Echo Tops• Max Hourly Reflectivity• VIL• Elrod Index Turbulence (300-400 mb)• Low Level Mountain Wave Turbulence• Prob Thunder 1, 3, and 12 hour
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NBM v3.2 Aviation - even more
• SBCAPE (Surface based)• Snow Level (0.5C Wet Bulb height AGL)• SnowAmt01• IceAmt01• QPF01• Prob Snow, Freezing Rain, Sleet• Predominant Weather
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and verification• Example products• Potential future probabilistic capabilities
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Ceiling/Visibility/Lowest Cloud Base
NWP components and expert weights
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Ceiling CONUS
9 total inputs: 6 DMO, 3 MOS
NWP Weight (%) in forecast hour
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Lowest Cloud Base CONUS(AKA Cloud Base Primary)
16 total inputs: 6 DMO, 10 RH blend
NWP Weight (%) in forecast hour
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NWP Weight (%) in forecast hour
RH Blend method components (per WFO GGW and CRGMAT)
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Visibility CONUS
10 total inputs: 7 DMO, 3 MOS
NWP Weight (%) in forecast hour
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Probability of Thunderstorms: 1 hour, 3 hour, and 12 hour
NWP components and expert weights
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25% 15 min
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Probability of Thunderstorm• Expert Weights Prob Thunder 3, 12Short-range through 84 hours:
50% SREF (67% currently) - developed by SPC
20% ECMWF MOS
15% GFS MOS (33% currently)
15% NAM MOS
Extended-range after 84 hours:
55% ECMWF MOS
45% GFS MOS (100% currently)
• Expert Weights Prob Thunder 11-36 hour:
50% GLMP
50% SREF (100% currently) - developed by SPC16
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and verification• Example products• Potential future probabilistic capabilities
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Techniques and verification issues
• LLWS technique• Wind speed and gust (URMA vs METAR
issue) verification issues
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NBM Algorithm for Calculating Low Level Wind Shear (LLWS) Speed, Height, and Direction
1. Leverage the individual model inputs of the U and V components at:Surface, 500 ft (interpolated from Surface and 1000 ft), 1000 ft, 1500 ft(interpolated from Surface and 1000 ft), 2000 ft
2. Calculate the wind speed at each height and determine the wind speed differences between all possible layers: (a) [Surface-500 ft], (b) [Surface-1500 ft], (c) [Surface-2000 ft], (d) [500-1000 ft], (e) [500-1500 ft], (f) [500-2000 ft], (g) [1000-1500 ft], (h) [1000-2000 ft], (i) [1500-2000 ft]
3. The models for any particular layer whose wind speed differences are less than 30 knots are ignored in the calculations for (4), (5), and (6).
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NBM Algorithm for Calculating Low Level Wind Shear (LLWS) Speed, Height, and Direction
4. Normalize all wind speed shear values (for those models that indicate wind shear at any given level) so that the relative wind speed shear magnitudes between layers with different thicknesses can be compared.
5. Tabulate which model has the greatest wind speed shear for any given layer and assign the wind speed found at the top of that layer as the LLWS Speed. The LLWS height is simply assigned to the top of this layer.
6. Use that model’s U- and V- component (at the top of that layer that exhibits the greatest LLWS in (5)) to calculate LLWS direction.
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Pronounced bias of NBM versus METARs
● NBM tuned to URMA, not METARs
● URMA has a wind speed low bias versus METARs,
and so does NBM
● URMA has a wind gust high bias versus METARs,
and so does NBM
● Therefore, the NBM gust factors tend to be higher
than what you would normally expect (1.3 for
marine, 1.5 to 2.0 for land). NBM are frequently
2.0 to 3.0+ 21
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Case study of METAR vs URMA in Phoenix area 00z May 22, 2019
METAR at KPHX 20 knots, URMA 7 knots
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50% 10 min
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URMA URMA FG
BCDG URMA-URMA FG27
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URMA URMA FG
BCDG URMA-URMA FG28
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and verification• Example products• Potential future probabilistic capabilities
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NBM Text Products
1. NBH (NBM Hourly) to 24 hours (like LAMP)2. NBS (NBM Short Range) to 72 hours (like MET/MAV)3. NBE (NBM Extended Range) to 192 hours (like MEX)4. NBX (NBM Extra-Extended Range) 204-264 hours5. NBP (NBM Probability) to 228 hours
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NBHexample
from KAVX(CatalinaIsland CA)
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NBS examplefrom KSLC
(Salt Lake City) UT
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Ceiling SoCal 18z Sunday May 26, 2019
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GLMP NBM30 hour
URMA BCDG34
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GLMP NBM18 hour
URMA BCDG35
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GLMP NBM6 hour
URMA BCDG36
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Lowest Cloud Base: v3.1 vs v3.221z (18 hour forecast) Jun 5, 2019
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v3.1
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v3.2
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Visibility NrnCal 12z Sunday May 19, 2019
40
75% 5 min
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GLMP NBM30 hour
URMA BCDG41
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GLMP NBM18 hour
URMA BCDG42
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GLMP NBM6 hour
URMA BCDG43
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Echo Tops/Max Hourly Reflectivity12z Sunday May 19, 2019
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MRMS NBM30 hour45
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MRMS NBM18 hour46
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MRMS NBM6 hour47
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Wind 10-m, 30-m, 80-m wind speed 24 hour forecast
00z Wednesday May 22, 2019
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URMA 10-m
30-m 80-m 49
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Wind Gust00z Wednesday May 22, 2019
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GLMP NBM30 hour
URMA BCDG 51
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GLMP NBM18 hour
URMA BCDG 52
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GLMP NBM6 hour
URMA BCDG 53
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NBM Home Page• https://www.weather.gov/mdl/nbm_home•https://blend.mdl.nws.noaa.gov/
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Outline
• NBM Digital Aviation Services products• NWP components and expert weights• Techniques and verification• Example products• Potential future probabilistic capabilities
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Probabilistic Aviation Forecasts
● Probability of VLIFR, LIFR, IFR, MVFR:○ Ceiling○ Visibility○ Combined
● Probability of exceedance for wind speeds and wind gusts
● Probability of runway usage based on wind speed and direction
● Probability of LLWS, Icing, turbulence56