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Forecasting Pressure

Enhance your understanding of pressure forecasting by exploring advanced models and data sources, including smartphone pressure sensors. Learn strategies to improve synoptic and high-resolution pressure predictions for areas with complex terrain.

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Forecasting Pressure

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  1. Forecasting Pressure ATMS 452 Spring 2019

  2. Pressure Observations • ASOS is the best…the gold standard • Ships generally the worst • Huge number of pressures from smartphones are becoming available

  3. ASOS Pressure Sensor

  4. High-Resolution Models Can Greatly Improve Pressure Forecasts Near Terrain

  5. He S 12-km

  6. Effects of Terrain: Often a pressure ridge on windward side, pressure trough on lee side

  7. Pressure Reduction to Sea Level A Major Problem

  8. Major Problem in Pressure Reduction: For BOTH Analyses and Forecasts • Model pressure fields at sea level and geopotential heights at lower levels below terrain (e.g., 925 hPa) are 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

  9. At Night During Warm Season: phony troughs under terrain during night

  10. Why? • During night the atmosphere often becomes more stable than U.S. Standard Atmosphere at low levels. • Thus, starting with the same temperature at crest level, the low-level air is assumed warmer under the mountains, where reduction is occurring, than reality, producing lower sea level pressure under the mountains, compared to open areas.

  11. During a warm day, the opposite can happen, higher pressure than reasonable under the mountains

  12. Why? • During day, the atmosphere over the central valley is near dry adiabatic (9.8 C per km), while over the mountains we assume U.S. Standard atmosphere valley (6.5C per km). • Becomes cooler at low levels inside the mountains…thus higher pressure.

  13. During the day, phony trough inland Fig. 5. Composites of sea level pressure (solid lines, hPa) and 1000-hPa temperature (color shading, °C) using the (left) Shuell and (right) Mesinger methods for JJA at 0000 UTC.

  14. These problems are still evident in WRF

  15. Although model improvements have occurred, major pressure errors sometimes occur

  16. Eta 24-h 03 March 00UT 1999 Eta 48-h 03 March 00UT 1999 An example of a short-term forecast error (rarely this bad today

  17. Large Errors Inter-annual variability 24 h Coastal Errors TTI, WA Cape Arago, OR

  18. 48-h Errors 48h errors much larger and frequent than 24-h errors

  19. GFSvs. NAM 24-h errors NCEP GFSbetter than NAM on average

  20. 48-h errors GFS over forecasts Eta under forecasts

  21. More error in the winter

  22. 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 center pressure errors over western North America are larger than the other regions to the east.

  23. 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.

  24. Global Models: ECMWF is best. UKMET is second

  25. Smartphone Pressures • Some smartphones measure pressure information • Encouraging data assimilation experiments using this data source.

  26. Some of the smartphones/pads with pressure sensors • Iphone 6 • Samsung Galaxy III, IV, V, VI • Nexus 4 and 10 • Some Sony, Nokia, and Chinese phones

  27. By the end of 2018: 2 billion smartphones with pressure sensors

  28. Initial test: 114K Pressures in One Hour over US

  29. Conventional (Left), Smartphone (right)

  30. UW 36-4 km Testbed • DART EnKF System • WRF Model • Run in real-time operationally and for test periods. 4-km 36-km

  31. Convergence Zone Case • A “poorly-forecast” convergence zone forms around 1400 UTC (6AM PDT) and moves south across north Seattle during the morning commute

  32. Convergence Zone Case 3-hour forecasts from fully cycled EnKF of simulated composite reflectivity valid at 1500 UTC, Oct. 24, 2011

  33. Smartphone Pressures • Currently getting 60 million a day from IBM/weather.com • Excellent calibration using machine learning. • There may be a new source with much more data.

  34. Forecasting Strategy for Pressure • You are dependent on the models • Get task is to pick the best one for your area. • ECMWF/UKMET generally the best synoptic pressures. • High-resolution models needed to get pressures right in areas of complex terrain. • Learn about model pressure biases for locations of interest.

  35. When models are going bad • The use of pressure change can help identify where low centers are heading if NWP has problems. • An isallobaric analysis can help.

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