Forecast Pressure

1. Forecast Pressure

2. Pressure Observations • ASOS is the best…the gold standard • Ships generally the worst

5. ASOS Pressure Sensor

6. High-Resolution Can Greatly Improve Pressure Forecasts Near Terrain

11. He S 12-km

12. Effects of Terrain

13. Pressure Reduction

14. 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) 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

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

17. Why? • During night the atmosphere can become more stable than U.S. Standard Atmosphere at low levels. • Thus, starting with the same temperature at crest level, the low level air is warmer under the mountains, where reduction is occurring, producing lower pressure over the mountains.

18. During a warm day, the opposite can happen, with low pressure over the lowlands

19. 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.

20. 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.

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

22. Eta 24-h 03 March 00UT 1999 Eta 48-h 03 March 00UT 1999 An example of a short-term forecast error

23. ETA AVN UKMO NOGAPS 48-hr Forecasts Valid 00 UTC 8 February 2002

24. AVN ETA UKMO NOGAPS 24-Hr Forecasts Valid 00 UTC 8 February 2002

25. Station Locations Tatoosh Is. Cape Arago

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

27. 48-h Errors 48h errors much larger and frequent than 24-h errors

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

29. 48-h errors GFS over forecasts Eta under forecasts

30. 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 the eastern Pacific are larger than the other regions to the east.

31. 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.

36. 2016 Update • Some smartphones are measuring pressure information • Positive data assimilation experiments using this data source.

37. 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

38. By the end of 2016: 1 billion smartphones with pressure sensors

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

41. Conventional (Left), Smartphone (right)

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

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

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