1. 1 A Pacific Predictability Experiment - Targeted Observing Issues and Strategies Rolf Langland Pacific Predictability Meeting Seattle, WA June 6, 2005

2. 2 FASTEX Targeting Flight – Meteo France / NCAR / NRL / NOAA Goose Bay, Canada – 22 Feb 1997 IOP-18 Eight years since FASTEX - first targeting field program

3. 3 Previous Targeting Field Programs Winter storm targeting North Atlantic (FASTEX-1997, NA-TREC-2003) North Pacific (NORPEX-1998, WSR-1999-2005) Hurricane / tropical cyclone targeting North Atlantic (NOAA-HRD, 2000-2005) Western Pacific (DOTSTAR, 2003-2005) Participants: Meteo France, ECMWF, UKMO, NRL, NCEP, NCAR, NOAA-AOC, NOAA-HRD, USAF Hurricane Hunters, NASA, CIMSS, MIT, Univ. of Miami, Penn State Univ., others

4. 4 Forecast Impact of Targeted Data – (adding 10-50 dropsondes at single assimilation times) Targeted data improves the average skill of short-range forecasts*, by ~ 10–20% over localized verification regions – maximum improvements up to 50% forecast error reduction in localized areas In all analysis / forecast systems*, and for all targeting methodologies, it is found that ~ 20-30% of forecast cases are neutral or degraded by the addition of targeted data Impact “per-observation” of targeted dropsonde data is large, but total impact is generally limited by the relatively small amount of targeted data Targeting Results * Results based on published forecast impact studies performed at NCEP, ECMWF, Meteo France, UKMO, NRL

5. 5 Targeting Impact on Forecast Error ( regional verification area ) Average reduction in 2-day forecast error (percent) Total number of satellite or in-situ data assimilated per forecast case NOAA-WSR-04 NORPEX -98 NA-TReC -03 UPPER LIMIT SUGGESTED BY PREDICTABILITY STUDIES

6. 6 How to increase the beneficial impact of Targeted Observing? ECMWF – need to observe much larger part of the SV-targeting subspace NRL - use higher-density of satellite data in target regions, observe more frequently, observe larger region (requires satellite data targeting) NCEP – ?? UKMO – ??

7. 7 SENSITIVITY OF 72H FORECAST ERROR TO 300mb U-WIND FORECAST VERIFICATION AREA OBSERVATION TARGETS Targeting a major winter storm forecast failure Langland et al. (MWR, 2002)

8. 8 Pacific origins of the 2000 E. Coast blizzard 21 Jan 00 22 Jan 00 23 Jan 00 24 Jan 00 25 Jan 00 26 Jan 00 Figure by Mel Shapiro 250mb Daily-Mean Geopotential Height

9. 9 Objectives for future targeting programs 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 – More data in target sub-space (fully observe the sub-space and provide near-continuous observations) Improve targeting techniques Improve data assimilation procedures

10. 10 Pacific predictability questions -- 1.Are the analyses over the Pacific getting better ? 2.How much of the uncertainty or error that exists in current analyses over the Pacific will reduced by anticipated hyper- spectral (and other) satellite observations that will be provided over the next five to ten years? How to extract maximum benefit for NWP from this vast amount of satellite data? - Vertical resolution of satellite data vs. that of model background - Bias correction ? - Observations in sensitive cloudy regions ?

11. 11 NAVDAS Observation Count – 12 May 2005 Includes AMSU-A, scatterometer, MODIS, geosat winds, SSMI, raobs, land, ship, aircraft data Does not includes HIRS, AIRS, GPS, or ozone Number of obs within 5 o x 5 o lat-lon boxes All observation types - 00, 06, 12, 18 UTC MAX SENSITIVITY

12. 12 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 - Increase observations from commercial aircraft - On-request radiosondes at non-standard times Targeting Strategies –

13. 13 What major scientific and technical objectives can be addressed by a Pacific predictability experiment? 1. Use field program data set to improve impact of satellite data for NWP (mid-latitude and tropical) observation and background error bias correction – calibration and validation data thinning – channel selection on-request targeted satellite data 2. Test viability of new in-situ observing systems for targeting – driftsonde, aerosonde, rocketsonde, smart balloon, etc.

14. 14 of THORPEX 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

15. 15 Data Assimilation Forecast Model Satellite Observations Data Selection & Thinning Procedures In-situ observations Rejected Data Targeting Guidance Targeting Strategy

16. 16 Forecast and Analysis Procedure Observation (y) Data Assimilation System Forecast Model Forecast (x f ) Gradient of Cost Function J: (  J/  x f ) Background (x b ) Analysis (x a ) Adjoint of the Forecast Model Tangent Propagator Observation Sensitivity (  J/  y) Background Sensitivity (  J/  x b ) Analysis Sensitivity (  J/  x a ) Observation Impact (  J/  y) Adjoint of the Data Assimilation System What is the impact of the observations on measures of forecast error (J) ? Adjoint of Forecast and Analysis Procedure

17. 17 IssueNew TargetingOld Targeting Number of obs in target region ~ 10,000 or more obs in target area 10-50 dropsonde profiles Type of obsSatellite and some in-situ Mostly in-situ Frequency of obsAt least every 6 hours or continuous Once – at target time Sampling Approach Sample larger area of target subspace Dropsondes in localized region Forecast ImpactMore reliable and larger forecast impacts Mixed impact, many null cases “New” vs. Old Targeting Approach

18. 18 Observation impact (average magnitude per observation, in J kg -1 ) as a function of model- diagnosed cloud-cover. The “impact” in this figure includes both improvements and degradations of 72h global forecast error. Based on results from 29 June – 28 July 2002. SATWIND RAOB ATOVS Large Impact of Observations in Cloudy Regions

19. 19

20. 20

21. 21 High Forecast Impact

22. 22 High Forecast Impact

23. 23 High Forecast Impact

24. 24 High Forecast Impact

25. 25 Med-Low Forecast Impact

26. 26 Med-Low Forecast Impact

27. 27 Med-Low Forecast Impact

28. 28

29. 29 Example of Driftsonde sounding coverage at one assimilation time after five days of deployment from launch sites along the Asian Pacific rim Initial Launch Time: 00 UTC 06 Feb 1999 13 launch sites Launch Interval: 12hr Dropsonde Interval: 6hr Drift Level: 100 mb Coverage at: 00UTC 11Feb 1999 FIGURE IN EARLY VERSION OF THORPEX PLAN (April 2000)

30. 30 Percent of 2-day forecasts improved Targeting Impact – Percent of Improved Forecasts NOAA-WSR-04 NORPEX -98 NA-TReC -03 Total number of satellite or in-situ data assimilated per forecast case

31. 31 PROPAGATION OF PACIFIC TARGETING – SIGNAL KINETIC ENERGY From 00UTC 20 Jan 2005 (+ 7 days) FROM S. MAJUMDAR U.S. CHINA EUROPE

32. 32 Extended-duration targeting – flow regime 1

33. 33 – OSEs (real data) test procedures for targeted satellite data thinning and channel selection – OSSEs (synthetic data) test impact of future satellite and in-situ observing systems – Evaluate impact of targeted feature-track geosat wind data and other targeted satellite data - Examine 3d-var, 4d-var deterministic, TIGGE, various metrics and various forecast verification areas – Perform operational tests of driftsonde, aerosonde, rocketsonde, smart balloon, etc. for potential field program applications Research Tasks

34. 34 - Where are the most critical analysis errors or uncertainties over the Pacific? How well are cloudy regions analyzed? - Is there a benefit from using higher horizontal or vertical resolution of satellite data in target areas? - What is the realistic upper-limit of forecast improvement that can be expected from targeted observing in various situations? - What is the potential benefit from observing larger sections of the targeting subspace, instead of attempting to survey the smaller-scale areas of maximum sensitivity, which have been the primary focus of previous field programs? How can this be accomplished? Predictability Questions

35. 35 Targeted observing has the potential for significant improvement to deterministic and ensemble forecasting Previous targeting field programs have achieved only a small fraction of this potential – intermittent small sets of data (10-50 dropsondes) have modest beneficial impact New and next-generation satellite data are the primary resource that can advance the impact of targeting In-situ targeted observations provide value in certain situations where satellite observations are insufficient (including cloudy areas) Interpretation of previous targeting results

36. 36 1Nov-31Dec 2003 – global domain Observation Impact during THORPEX NA-TReC 18UTC Does not include moisture observations or rapid-scan satellite wind data