Forecast Pressure
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Forecast Pressure
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Pressure Observations
• ASOS is the best…the gold standard
• Ships generally the worst
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ASOS Pressure Sensor
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High-Resolution Can Greatly Improve Pressure Forecasts Near
Terrain
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He S
12-km
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Major Problem is Pressure Reduction: For BOTH Analyses
and Forecasts
• Model pressure fields at sea level and geopotential heights at lower levels (e.g., 925 hPa) and based on assuming a 6.5 K per km lapse rate through the ground (also called the Shuell method)
• Can give deceiving or WACKY results
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Mesinger Method is alternative that uses free our temp. structures
on the side
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Even for WRF! H over Mts, L over lowlands
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Can also get lows over mountains, particularly under
stable conditions
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Although model improvements have occurred, major pressure
errors sometimes occur
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An example of a short-term forecast error
Eta 24-h
03 March 00UT 1999
Eta 48-h
03 March 00UT 1999
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48-hr Forecasts Valid 00 UTC 8 February 2002
AVN
UKMO
ETA
NOGAPS
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24-Hr Forecasts Valid 00 UTC 8 February 2002
AVN
UKMO
ETA
NOGAPS
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Extended Verification of Surface Pressure
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Station Locations
Tatoosh Is.
Cape Arago
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24 h Coastal Errors
Large Errors
Inter-annual variability
TTI, WA
Cape Arago, OR
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48-h Errors
48h errors much larger and frequent than 24-h errors
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GFS vs. NAM
24-h errors
NCEP GFS better than NAM on average
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48-h errors
GFS over forecastsEta under forecasts
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• The NCEP GFS has more skillful cyclone intensity and position forecasts than the NAM over the continental United States and adjacent oceans, especially over the eastern Pacific, where the NAM has a large positive (underdeepening) bias in cyclone central pressure.
• For the short-term (0–60 h) forecasts, the GFS and NAM cyclone errors over the eastern Pacific are larger than the other regions to the east.
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SLP analysis (a)MAEand (b)MEfor the stations from west to east in Fig. 1 for the
GFS (solid black), NAM(dashed), and NARR (gray).
The numbers of cyclones verified between 2002 and
2007 are shown in the parentheses. The dashed
horizontal lines represent the average error during the
period and the 90% confidence intervals are
shown using the vertical bar on the right.
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ECMWF has smaller errors than other models for most months. NAM has larger errors than other models for most months and forecast hrs.
Coastal Mean Absolute Error for all models, forecast hours and both coasts
NAM upgrade to WRF-NMM
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East Coast errors smaller than West Coast errors for all months and forecast hours
Coastal Mean Absolute Error for all models, forecast hours and both coasts
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Histograms of f48 Errors
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Frequency of Large Errors
>3hPa
>5hPa
>7hPa
Same conclusions apply: ECMWF smallest frequency and NAM the largest frequency of large errors, East Coast smaller frequency of large errors than West Coast for all models and forecast hours.
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Summary
• Large variations in quality of pressure observations (ASOS the best)
• Large semi-diurnal signal
• Difficult parameter for human intervention…need to pick best model.
• Resolution helps considerable in terrain.
• Major pressure errors still exist.
• Pressure reduction is a major problem, BOTH for analyses AND forecasts.
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2013 Update
• Some smartphones are measuring pressure information and one company is starting to collect it.
• Experimentation with more effective use of pressure information for model data assimilation.
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