1. 1 Rolf H. Langland (NRL-Monterey) Overview of Adaptive Observing JCSDA Summer Colloquium on Data Assimilation Santa Fe, N.M., 2 August 2012 Dr. Rolf H. Langland NRL-Monterey

2. 2 1. What is adaptive (“targeted”) observing? 2.Targeting methodologies 3.Targeting field programs (1997-2012) 4.Challenges for adaptive observing Outline of Presentation Note: this talk describes adaptive observing for atmospheric applications – ocean adaptive observing techniques have also been developed

3. 3 If one has the capability to add ~10-10,000 special atmospheric observations that can be assimilated to improve the forecast of a particular weather event, can the locations be determined using objective (e.g., model-based) methods? Optimization problem with two constraints… 1. The probability of making an analysis error at a particular location 2. The intrinsic instability of the flow in that location … sensitivity What is Targeted Observing ? Can the data assimilation method accurately incorporate the special observations? [MORE IMPORTANT THAN INITIALLY BELIEVED !!]

4. 4 Targeted Observing Objectives Improve 0 to 7 day forecasts of high-impact weather: Tropical cyclone track Extra-tropical winter storms [wind, precipitation] Forecast systems: Global and regional deterministic models of operational forecast centers Ensemble forecast systems [goal: reduce forecast uncertainty]

5. 5 Targeted Observing Resources Dropsondes from reconnaissance aircraft On-demand upper-air soundings from land- and ship-based radiosondes Land and ship surface observations Airborne lidar Un-manned aerial vehicles On-demand satellite rapid-scan wind observations

6. 6 6 Objective vs. subjective “targeting” Objective targeting involves use of an adjoint model or ensemble system to identify observation target areas through estimates of analysis uncertainty and potential forecast error growth Subjective targeting is based on inspection of map features or forecaster intuition  Jet streaks  PV anomalies  Hurricane structure Goal of objective targeting is not to correct the largest analysis error, but the analysis error that leads to the largest forecast error

7. 7 ms -1 Uncertainty in Atmospheric Analyses Root-Mean Square of Analysis Differences: 300mb u-wind GFS | ECMWF Jan-Dec 2010

8. 8 Forecast error growth is controlled by a few rapidly-growing perturbation structures 8 Singular Vectors in 72-hr East Coast Storm Forecast Initial Time Total Energy 12Z 22 Jan 2000 Final Time Sfc Pressure 12Z 25 Jan 2000 SV 1 SV 2 SV 3

9. 9 Large error growth in a 5-day forecast Remnant of tropical storm Kiko Error starts in mid-Pacific Forecast hour Forecast Error NOGAPS Potential Target Region Global total energy error norm (J kg -1 )

10. 10 In 1956, the US Weather Bureau, reacting to the devastation of three consecutive hurricane strikes along the east coast of the United States, received Congressional funding to establish the National Hurricane Research Project to conduct research on tropical cyclones that would advance our scientific understanding of these storms and provide the means to improve the accuracy of future hurricane forecasts. During this process, the NHRP began to ponder the horizontal thermal and vertical wind structures in the upper Troposphere / lower Stratosphere region of tropical cyclones. At the time, there were no high-altitude aircraft adequate to probe these upper regions over the tops of tropical cyclones, except the U-2. Beginning in early 1960, the AFCRL made available a U-2 to the Weather Bureau's hurricane research project (NHRP) and its component Project Stormfury (an experimental hurricane modification project) providing high- altitude photographs and gathering meteorological data in the Troposphere region over the hurricanes. Storms flown by the AFCRL U-2 included Hurricanes Donna (60), Carla (61), Esther (61), Flora (63), Beulah (63), Ginny (64), Isbell (64), Betsy (65) and Beulah (67) to name just a few. U-2 Hurricane Flights (1960-1968) Before the days of “objective targeting”

11. 11 Field Programs for Targeted Observing Programs for winter storm targeting: [Operational*] North Atlantic (FASTEX-1997, ATREC-2003) Eastern North Pacific (NORPEX-1998, WSRP-1999-2012) Entire North Pacific (Winter T-PARC 2009) Programs for hurricane / tropical cyclone targeting: North Atlantic (NOAA-HRD, 2000-2012) Western Pacific (DOTSTAR, 2003-2012) T-PARC (TCS-08) 2008 Antarctica: CONCORDIASI (2010-2011) Participants: Meteo France, ECMWF, UKMO, NRL, NCEP, NCAR, NOAA-AOC, NOAA-HRD, USAF Hurricane Hunters, NASA, CIMSS, MIT, Univ. of Miami, others

12. 12 FASTEX – first objective targeted observing program January – February 1997 NOAA G-IV flights St. John’s Goose Bay Shannon Learjet flights 8 targeted dropsonde missions tasked by Meteo France & NRL Adjoint-based targeting 12 targeted dropsonde missions tasked by NCEP and NCAR Ensemble-based targeting

13. 13 Adjoint-based target guidance to improve 42-hour forecast of intense cyclone over Ireland and Great Britain Sensitivity to 700mb Temperature Forecast Verification Region J = Vorticity (measure of cyclone intensity) FASTEX targeting example (IOP-17) Interpretation: the 42-hr forecast error over UK is most sensitive to the structure of this trough COLDER WARMER

14. 14 The observation “target region” A region in which initial condition error is expected to cause significant forecast error or uncertainty at the forecast verification time Occur in dynamically significant regions (baroclinic zones, strong advection, jet entrance / exit) The key initial “error” may involve relatively small changes to temperature and wind structure Does not necessarily correspond to most prominent synoptic features (surface low, PV max)

15. 15 FASTEX IOP-17 E-W Cross-section of Singular Vector target

16. 16 Dropsonde Aircraft in FASTEX (1997) Manned Reconnaissance Aircraft NOAA – GIV GPS Dropsonde – Deployed from 24,000 – 40,000 ft. Dropsondes, which are radiosondes deployed from aircraft, measure temperature, humidity, pressure, and wind speed/direction, as they fall through the atmosphere Temperature Accuracy +/- 0.2% at –40C

17. 17 How do we choose the optimal deployment of observations to improve a forecast between times t a and t v ? titititi tdtdtdtd tatatata tvtvtvtv Decision time Adaptive sampling (analysis) time Verification time t Current time Targeting Calculations Observations Target Planning Time-Line It has proven feasible to prepare targeting guidance ahead of time, and deploy in-situ observational resources (e.g., dropsondes) into identified target regions

18. 18 Vertical cross-section of sensitivity information from NOGAPS adjoint model Jet Stream Level Surface Level Dropsondes provide vertical profiles of temperature, wind, and humidity in region of maximum dynamic sensitivity (error source region) Dropsonde profiles

19. 19 NORPEX Targeting Mission (1998) SV Target Area NRL NCEP Forecast Verification + 2 days

20. 20 With targeted dropsondes: 48hr fcst error* reduced by 35% in verification region 300mb wind Sea Lev Pres NORPEX-98 Targeting Example (48-hr impact) *Fcst error is E- weighted error of T, u, v, ps from surface to 250mb NOGAPS SV Target Area

21. 21 Impact of 30 dropsondes on a 96-hr NOGAPS Forecast during NORPEX (Feb 1998) Significant Enhancement of Precipitation in Storms over California and Florida Control ForecastForecast with Targeted Data Target Region for special dropsonde observations NORPEX-98 Targeting Example (96-hr impact)

22. 22 Impact of NORPEX targeted dropsondes 16 January – 27 February 1998 (NRL-NCEP) RMSE 500mb ht of 2-day forecasts error with targeted dropsondes (m) In 45 forecast cases, ~ 10% mean error reduction over western North America, using NOGAPS forecast model Approx 700 dropsondes 45 forecast cases IMPROVED FORECASTS (n=35) DEGRADED FORECASTS (n=10) Langland et al. 1999 (BAMS) 10% mean error reduction (45 cases)

23. 23 FASTEX, NORPEX, WSRP, ATREC (mid-latitude programs), and tropical cyclone targeted with dropsondes – primarily involve small sets of in-situ observations deployed intermittently Average 10% reduction in 1-3 day forecast error over regional verification areas – maximum reductions as large as 50% in certain cases Approx. 60-70% of forecast cases improved Target areas incompletely surveyed in vast majority of field program cases … Impact per-observation of targeted data is about 3x larger than observations placed randomly, but total impact is generally limited by the relatively small number of targeted data Based on studies at ECMWF, Meteo France, NOAA, NRL and other centers Results of Early Targeting Field Programs (1997- 2003)

24. 24 Increasing the impact of targeted observing Goal 1: Increase the average beneficial impact of targeted data in deterministic and ensemble forecasts – Goal 2: Increase the percentage of forecasts that are improved by targeted data – Assimilate larger amounts of satellite, remote-sensed, and in-situ observations in target regions - do not rely on intermittent small sets of observations

25. 25 Why does assimilation of “good observations” make some forecasts worse ? Why doesn’t the assimilation of 10-50 dropsondes produce larger impacts on forecast skill? Examine the data assimilation procedure  Targeting and Observation Impact Questions

26. 26 Impact of Observations on Forecast Error The forecast error difference,, is due to the assimilation of observations at 00UTC OBSERVATIONS ASSIMILATED +24h XbXb XaXa 00UTC Langland and Baker (Tellus 2004)

27. 27 1 Jan – 28 Feb 2006 00UTC Analysis NOAA-WSRP 191 Profiles Beneficial (-0.01 to -0.1 J kg -1 ) Non-beneficial (0.01 to 0.1 J kg -1 ) Small impact (-0.01 to 0.01 J kg -1 ) Date: Jan-Feb 2006 Result: Average targeted dropsonde profile impact is beneficial – placement in sensitive regions provides 2-3x larger impact than average radiosonde profile USING ADJOINT-BASED OBSERVATION IMPACT TO EVALUATE IMPACT OF TARGETED DATA [WSRP DROPSONSDES] Observation impact is determined by size of innovation, assumed observation and background errors, distribution of other nearby observations and intrinsic error growth properties of the atmosphere in that location

28. 28

29. 29 FORECAST ERROR REDUCTION FORECAST ERROR INCREASE FORECAST ERROR REDUCTION

30. 30 Probability of forecast degradation from assimilation of new observations (percent) Number of additional data assimilated 50 0 1 10,000 1,000 100 10 Forecast degradations occur because the observation and background errors are not precisely known – however, our estimates of these errors work well if the number of assimilated observations is very large …. Few observations – significant chance of forecast degradation Many observations – small chance of forecast degradation One observation CAN change a forecast But adding small sets of observations is not a reliable method for targeted observing Probability of improving or degrading a forecast

31. 31 Amount and quality of data added is more important than small inaccuracies in target area guidance [better to cover entire target area than to place a few observations in location of maximum sensitivity] Continuous addition of targeted data for several days leading up to the critical analysis time is essential for larger forecast impact [conditioning of background] Advanced Targeting Hypotheses

32. 32 Satellite observations = targeting resource Observation selection is a form of “targeting” Radiances from infrared and microwave sounders on polar orbiters Cloud and water vapor motion vectors from geostationary platforms Surface winds from space-based scatterometers Satellite channel-selection Regional variations in satellite observation data-thinning Addition of rapid-scan AMVs LESS THAN 2% OF ATMOSPHERIC OBSERVATIONS ARE ACTUALLY ASSIMILATED FOR OPERATIONAL FORECASTING

33. 33 AMVs at 08GMT, 7th August 2007, during TCs Wutip and Pabuk Source: JMA, CIMSS Regular: 30min scanRAPID-SCAN: 3min sampling Improved data quantity and quality Rapid-Scan Satellite winds for Targeted Observing

34. 34 observations,

35. 35 Medium-RangeTargeted Observing Example Target Regions for 72h Forecast of Hurricane Floyd Target: 00UTC 13 Sep 1999 Forecast Verifies: 00UTC 16 Sep 1999 NOGAPS Adjoint Sensitivity Gradient HURRICANE VORTEX at targeting time UPSTREAM MID- LATITUDE SHORT-WAVE at targeting time VERIFICATION AREA

36. 36 Katrina position at 00UTC 27 Aug 2005 RS-wind vectors: assimilated every 6-hr from 00UTC 20 Aug to 120UTC 29 Aug 2005 Katrina position at 00UTC 30 Aug 2005 Region around cyclone vortex is sampled by dropsondes Upstream trough affects medium-range forecasts Source: NOAA, CIMSS Rapid scan targeting for Hurricane Katrina Average 12% reduction in track forecast errors [24hr-120hr]

37. 37 Rapid-Scan and Dropsonde targeted observing in super-typhoon Sinlaku 10-18 Sep 2008 [TCS-08] Forecast Error Reduction Forecast Error Increase Dropsondes and Driftsondes Rapid-Scan Winds -1.95 J kg -1 + 0.10 J kg -1 Rapid-scan winds produce significant reduction in 24hr forecast error energy norm – new data in areas that cannot be sampled with dropsondes Dropsondes in this case do not reduce 24hr forecast error energy norm

38. 38 RAPID-SCAN WINDS IMPROVE SUPER-TYPHOON SINLAKU FORECASTS

39. 39 DOTSTAR Targeting Mission 00UTC 10Sep 2008 Super-Typhoon Sinlaku Targeting guidance prepared 12 th 10 th 8 th Targeted observations deployed Target Areas [much larger than area sampled with dropsondes (at left)] Forecast verification On average, small improvements in track forecasts from added dropsondes

40. 40 Winter T-PARC Field Program Feb 2009 Added about 18% to total 24hr global forecast error reduction from radiosonde profiles at 06UTC and 18UTC 29, 898 observations Added about 2% to total 24hr global forecast error reduction from global aircraft observations 24,423 observations TARGETED SPECIAL RAOB PROFILESTARGETED SPECIAL AMDAR DATA

41. 41 CONCORDIASI Field Program Sep-Dec 2009

42. 42 Targeted dropsondes equivalent to addition of three full-time raob stations Largest sensitivity to targeted observations on the periphery of Antarctica CONCORDIASI Field Program Sep-Dec 2009

43. 43 Successes of Targeted Observing The theory of targeted observing to improve short- and medium-range forecasts has been validated Targeted observing has the potential for significant improvement to deterministic and ensemble forecasts of high- impact weather Several targeting programs are in routine operations: [NOAA: WSRP, N.Atlantic Hurricanes, Taiwan:DOTSTAR] The targeted use of rapid-scan AMVs is now a routine part of operations at JMA and NESDIS (for land-falling tropical cyclones)

44. 44 Limitations and Challenges for Targeting Dropsonde targeting provides only partial surveys of target areas, relatively small amounts of data, and is thus not able to achieve the full potential of targeted observing Addition of rapid-scan winds provides more-complete surveys of target areas, and larger forecast error reductions, but cannot be used in all situations because of logistical limitations In-situ data (radiosondes, dropsondes, aircraft) appears necessary to supplement current satellite data, especially in cloudy areas, where targeted data are most needed …

45. 45 Research Questions for Targeted Observing How much benefit can we obtain by “tuning” the network of existing regular satellite and in-situ observations in a targeted sense? - Targeted satellite data thinning - Targeted satellite channel selection - On-request feature-track wind data for anticipated high-impact weather events - Increase observations from commercial aircraft in certain regions - Request radiosondes at non-standard times

46. 46 WMO Targeting Recommendations Focus on critical deficiencies that exist in the current global observing network – note that large-scale differences in analysis uncertainty has not been eliminated by huge amounts of radiance data Aircraft data should be maintained or expanded in regions where current in-situ observations are sparse [e.g., oceans, South America, Africa] Current radiosonde stations should be maintained with increases in off-time [06Z, 18Z] soundings in remote locations and regions with sparse aircraft data Where possible, rapid-scan and other satellite resources should be used in targeted mode to improve forecasts of high-impact weather events in regions with large analysis uncertainty

47. 47 For additional information: Dr. Rolf H. Langland Data Assimilation Section Naval Research Laboratory Monterey, CA 93940 Rolf.langland@nrlmry.navy.mil