1. Meteorological Impacts and Benefits of AMDAR Data Lee Cronce Ralph Petersen Cooperative Institute for Meteorological Satellite Studies (CIMSS) Space Science and Engineering Center (SSEC) University of Wisconsin - Madison AMDAR Regional Science and Technology Workshop Mexico City, Mexico 9 November 2011

2. Atmospheric Data Realities Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system The details within these profiles are especially important for recognizing and predicting hazardous weather events

3. Atmospheric Data Realities Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system The details within these profiles are especially important for recognizing and predicting hazardous weather events The locations of radiosonde sites are sparse, and the number of radiosonde reports is decreasing worldwide

4. Atmospheric Data Realities Vertical variation of temperature, moisture and wind within the atmosphere is what drives Atmospheric Science In situ profiles of these data (i.e., radiosondes) remain the backbone of any NWP analysis system The details within these profiles are especially important for recognizing and predicting hazardous weather events The locations of radiosonde sites are sparse, and the number of radiosonde reports is decreasing worldwide Satellites provide global coverage; however, not at detail necessary (especially near the surface) AMDAR fills this void!

5. AMDAR in a Nutshell Temperature and Wind Observations from Commercial Aircraft – High quality, high resolution data – Available at: Flight Level In Ascent / Descent – Instruments already on aircraft – Economical (~100 times less expensive than radiosondes) – Asynoptic (not only available at 00 and 12UTC, not a problem for NWP) – Looks, feels, tastes like radiosonde data – Retrieved through ACARS and MDCRS

6. Usage of AMDAR Data WHO WOULD BENEFIT FROM THIS DATA SOURCE?

7. How does NCEP use AMDAR data? In all of its atmospheric models NCEP runs a suite of Atmospheric and Oceanic models to meet a variety of user needs. AMDAR data are used in: – Climate (Coupling Atmosphere and Ocean) – Global (Medium Range Forecasts) 4/day - Deterministic and Probabilistic – Mesoscale (Higher-Resolution Weather Forecasts) 4/day - Deterministic and Probabilistic Rapid Update Cycle (RUC) [soon to be Rapid Refresh Model] – Hourly - Aviation and Hazardous Weather » Mexico included in coverage, so immediate use available

8. Vertical profiles of wind, temperature and humidity are the foundation of every NWP system NCEP has been using AMDAR data in its NWP models for over 10 years Over 300,000 reports arrive daily – Data delivered in real-time 24 hours daily – Most contain wind and temperature only Increasing numbers include humidity – The data arrive in BUFR format The program is a cooperative venture between data providers and users – Everyone benefits from the results How does NCEP use AMDAR data? In all of its atmospheric models

9. “Rule of Thumb” In Numerical Weather Prediction (NWP), one bad observation does more damage than the benefit that comes from 100 good observations! AMDAR data are extremely accurate and reliable, but – Good Quality Control of all observations is essential Requires multiple observations How does NCEP use AMDAR data? In all of its atmospheric models

10. A major advantage of AMDAR data – multiple observations corroborate each other Weekly Data counts by Cycle

11. Data Volume/Coverage by Layers 700-300mb 300-100mb 1000-700mb Six hours of data Note locations Of Ascent/descent Reports ←←

12. FORECAST IMPACTS OF AMDAR DATA

13. Determining Forecast Improvement from increased AMDAR volume – Use Wind forecasts as a measure of impact During weekday, when more AMDAR reports are available, short range forecasts are consistently better 0000-1200 UTC (overnight) AMDAR volume average Tu-Sa  >70,000 reports Su-Mo  only ~25,000 reports Difference is primarily due to lack of parcel delivery flights General Observation

14. Quantifying these Observations using the Rapid Update Cycle - RUC RUC is designed to produce hourly analyses and updates to very short range forecasts (0-12 hrs)  Real-time 1-hourly analysis/forecast cycle  Analyses intended to fit data very closely  Forecasts only from 3 to 12 hours into future  In general, 3 hr RUC wind forecasts are more accurate than 12 hr forecasts  Examination of verification against  Radiosonde observations

15. Weekend minus Weekday 3 hr Wind Forecast Errors for Jan-Oct RUC Wind forecasts -Verification against raob data 0.35 m/s / ~5.0 m/s = 7% better forecasts during weekdays due to more AMDAR reports at 200 hPa Off-time data on weekends produces less impact, especially after reduced overnight package carriers reports

16. Hourly AMDAR Volume Received at FSL (ESRL) 2-15 Sept 01 (starting 00z 2 Sept) Su Mo Tu We Th Fr Sa 2-8 Sept 01 9-16 Sept 01 Su Mo Tu We Th Fr Sa Notable reductions of aircraft data available to RUC at FSL on weekends and immediately after Sept. 11, 2001

17. Improvement in 3 hr over 12 hr wind forecasts during September 2001 RUC 250mb wind forecasts verified against raob data Period of data outage 11-13 Sept 2001 Forecasts from operational RUC run at NCEP 11-13 September 2011 No AMDAR data 20% loss of 3hr RUC wind forecast skill at 250mb 3 hr fcst skill ≅ 12hr skill No skill added by other off-time reports!!!

18. Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts Test performed using operational 20 km RUC – Ran data assimilation / forecast system for 3 weeks in June 2002 using two configurations: 1.Including all data 2.Eliminated aircraft data below 350 hPa – Kept High-level En-route Data – Ignored Ascent /Descent Data – Compared analyses and all forecasts (3, 6, 9, 12 Hr) against radiosonde at 00 and 12 UTC over CONUS – Results expressed in improvement due to Ascent/Descent Data EMC OSE by Ralph Petersen, Geoff Manikin and Dennis Keyser

19. Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts Question 1 – What was the effect of the addition of ascent/descent data on the data assimilation system and resulting 00 and 12 UTC analyses?

20. Significant improvement by including Ascent / Descent data Positive effects at all levels Greatest effect at 30,000’ and below Positive impact on Winds, Temp and RH. Normalize error: compares forecast differences with overall forecast error

21. Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts Question 2 – What was the effect of these analysis differences on the 12 hr forecasts?

22. Tropospheric Improvements up to twice those in changing RUC from 40 to 20 km Significant improvement by including Ascent / Descent data Positive effects at all levels on Winds, Temp and RH Above 25,000’, impact comparable to analysis differences Below 25,000’, impact still large - but slightly smaller than in analysis

23. Impact of AMDAR Ascent/Descent data in Rapid Update Cycle (RUC) forecasts The fundamental purpose of the RUC is to use ‘off-time’ data to make repeated corrections to traditional ‘on-time’ model guidance Question 3 – How did the continued data assimilation affect model performance?

24. After 9 hrs of continued use of ascent/descent data, tropospheric forecasts have improved by yet another 1-2% Tropospheric Improvements are 2-3 times greater than those in changing RUC from 40 to 20 km

25. 12 Hr Forecast Error – Red 3 Hr Forecast Error – Blue Both forecasts valid at same times Impact of AMDAR Ascent/Descent data in updating operational RUC forecasts 10–20% improvement at all levels from forecast updates Descent

26. Impact of AMDAR Ascent/Descent Data in Rapid Update Cycle (RUC) forecasts The fundamental purpose of the RUC is to use ‘off-time’ data to make repeated corrections to traditional ‘on-time’ model guidance Overall question now becomes: – How much of the impact was the result of including ‘off-time’ ascent / descent data?

27. Assimilation/forecasts with Ascent/Descent Data– Red Assimilation/forecasts without Ascent/Descent Data– Blue Difference between 12 hr operational RUC forecast and a later 3 hr forecast (valid at the same time but using additional asynoptic reports) from systems with & without ascent/descents Lack of ascent/descent data in assim./fcsts eliminates virtually all tropospheric benefits of off-time updates and degrades upper-levels Descent

28. LONG TERM FORECAST IMPACTS OF AMDAR DATA

29. Diff in RMS of Analyses: Exp-Control 300 hPa, 20010102-0131, Valid 12 UTC Exp: Denied ascending and descending aircraft, p>350 hPa Positive Values indicate ascent/descent data added value AMDAR Ascent/Descent Data Impact Study with ECMWF Erik Andersson, Carla Cardinali, Antonio Garcia-Mendez

30. Diff in RMS of 48 Hr Forecast Error: Exp-Control 500 hPa, 20010102-0131, Valid 12 UTC Exp: Denied ascending and descending aircraft, p>350 hPa Positive Values indicate ascent/descent data added value

31. Diff in RMS of 120 Hr Forecast Error: Exp-Control 500 hPa, 20010102-0131, Valid 12 UTC Exp: Denied ascending and descending aircraft, p>350 hPa Positive Values indicate ascent/descent data added value

32. LOCAL APPLICATIONS OF AMDAR DATA

33. Severe Weather – Capping Inversions – Convective Instability – Wind Shear Precipitation and Type – Timing, Location, Intensity Fog Onset/Dissipation – Trapping Inversion Development/Decay – Calm Winds Air Quality/Fire Weather – Wind, Stability, Mixing, Extended Coverage Local Applications

34. Low-Level Wind Shear Based on this observation, the aviation forecaster was able to update the TAF and begin the LLWS more than 3 hours earlier than the prior forecast. Green Bay, Wisconsin, 29 October 2005 LLWS was forecast to begin after 0600 UTC in the TAF Aircraft soundings near 0120 UTC already showed LLWS

35. Low Ceilings, Visibilities and Fog Detroit, Michigan, 4 February 2005 Soundings near 2230 UTC showed light boundary layer winds, near-surface moisture, dryness above Commonly favorable conditions for fog development Based on the observations, the TAFs for 09 and 12 UTC were amended, reducing visibilities to ½ mile. METARS showed that visibilities did decrease KDTW 0532z 00000kt 2sm br clr KDTW 0739z 17003kt 1 3/4sm br r04/ 1000v3500 KDTW 0936z 17004kt 1/4sm fg r04/ 0500v0600 KDTW 1154z 16004kt 1/4sm fg r04/ 2800v0600

36. Buffalo, New York, 15 December 2005 Forecasters initially were calling for larger snow accumulations AMDAR temperature profile shows a larger than expected warm layer aloft Precipitation Type With the existence of this deep warm layer aloft, forecasters amended the forecast calling for smaller snow accumulations and increased chances for sleet and freezing rain

37. Convective Storms Central Wisconsin, 6 July 2005 Linear mesoscale convective system expected to persist into Wisconsin Severe thunderstorm watch was issued at 1530 UTC for most of Central Wisconsin

38. Convective Storms Aircraft soundings from watch area at watch issuance and later showed strong capping inversion unlikely to break Forecasters lowered the chance for storms and the severe thunderstorm watch was cancelled

39. Very dry air could be seen on aircraft soundings earlier in the day when the Red Flag Warning was issued Later soundings showed there was sufficient dry air in other parts of the forecast area to expand the warning Temperature >75F, RH 25 mph Fire Weather Northern and Central Wisconsin, 15 June 2006 Aircraft data showed extremely dry conditions coupled with the potential for high winds due to mixing

40. In Summary AMDAR is a very important and necessary data set Fills spatial and temporal voids apparent in radiosonde and satellite data sets En-route data, but more so, ascent/descent data are vital to NWP skill Not just a NWP benefit, but an important local forecast area data set