Forecast Pressure

Pressure Observations

• ASOS is the best…the gold standard

• Ships generally the worst

ASOS Pressure Sensor

High-Resolution Can Greatly Improve Pressure Forecasts Near

Terrain

He S

12-km

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

Mesinger Method is alternative that uses free our temp. structures

on the side

Even for WRF! H over Mts, L over lowlands

Can also get lows over mountains, particularly under

stable conditions

Although model improvements have occurred, major pressure

errors sometimes occur

An example of a short-term forecast error

Eta 24-h

03 March 00UT 1999

Eta 48-h

03 March 00UT 1999

48-hr Forecasts Valid 00 UTC 8 February 2002

AVN

UKMO

ETA

NOGAPS

24-Hr Forecasts Valid 00 UTC 8 February 2002

AVN

UKMO

ETA

NOGAPS

Extended Verification of Surface Pressure

Station Locations

Tatoosh Is.

Cape Arago

24 h Coastal Errors

Large Errors

Inter-annual variability

TTI, WA

Cape Arago, OR

48-h Errors

48h errors much larger and frequent than 24-h errors

GFS vs. NAM

24-h errors

NCEP GFS better than NAM on average

48-h errors

GFS over forecastsEta under forecasts

• 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.

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.

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

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

Histograms of f48 Errors

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.

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.

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.