1 Rolf H. Langland Naval Research Laboratory – Monterey, Ca. USA Gary G. Love, Nancy L. Baker Adjoint-based observation impact monitoring at NRL-Monterey Fourth Workshop on Observing System Impact in NWP WMO, Geneva, May 2008

2 Outline of Talk 1. Methodology 2. Observation impact examples 3. On-line observation monitoring system

3 Develop a method to estimate the impacts of all assimilated observations on a measure of short-range forecast error in an operational NWP system Must be computationally efficient – run in near- real-time for routine observation monitoring Goal for observation impact monitoring system

4 NAVDAS: NRL Atmospheric 3d-Variational Data Assimilation System (0.5 o lat-lon, 60 levels) Adjoint provides sensitivity to observations, including moisture data NOGAPS: Navy Operational Global Atmospheric Prediction System (T239L30) Adjoint run at T239L30, includes simplified vertical mixing, large-scale precipitation Forecast Model and Analysis Procedure Analysis procedure: Forecast model:

5 xbxb xgxg t= -6 hrs egeg xtxt xfxf xaxa t= 24 hrs t=0 efef 6 hr assimilation window Observation Impact Concept Langland and Baker (Tellus, 2004) Observations move the forecast from the background trajectory to the trajectory starting from the new analysis In this context, “OBSERVATION IMPACT” is the effect of observations on the difference in forecast error norms ( e f - e g )

6 Forecast error norms and differences e 30 e 24 Forecasts from 0600 and 1800 UTC have larger errors e 24 – e 30 (nonlinear) e 24 – e 30 (adjoint) Global forecast error total energy norm (J kg -1 ) Forecast errors on background-trajectories Forecast errors on analysis-trajectories

7 Observation Impact Equation Dry or moist total energy forecast error norm, f = 24h, g = 30hr Forecasts are made with NOGAPS-NAVDAS. Adjoint versions of NOGAPS and NAVDAS are used to calculate the observation impact The impact of observation subsets (separate channels, or separate satellites) can be easily quantified

8 Observation impact interpretation < 0.0 the observation is BENEFICIAL > 0.0 the observation is NON-BENEFICIAL For any observation / innovation … using this error measure the effect of the observation is to make the error of the forecast started from x a less than the error of the forecast started from x b, e.g. forecast error decrease e.g., forecast error increase

9 Dry TE Norm (150mb-sfc) Total impact by instrument type – Jan2007

10 Impacts per-observation by instrument type 10e -5 J kg -1

11 Percent of observations that produce forecast error reduction (e 24 – e 30 < 0)

12 Impact for AMSU-A channels - NAVDAS-NOGAPS 1 – 31 Jan 2007, 00,06,12,18 UTC Units of impact = J kg Beneficial Channel Ch. peak near 11: 20mb 10: 50mb 9: 90mb 8: 150mb 7: 250mb 6: 350mb 5: 600mb 4: surface NOAA 15 NOAA 16 NOAA 18

13 Why do some “good data” have non-beneficial impact ? Observation and background error statistics for data assimilation cannot be precisely specified This implies a statistical distribution of beneficial and non- beneficial observation impacts Assimilating the global set of observations improves the analysis and forecast, even though 40-50% of observation data are non-beneficial in any selected assimilation Information about the impact of individual observations and subsets of observations can be used to improve the data assimilation and observation selection procedures

14 Beneficial Non-beneficial Impact of AMSU-A radiance data Observations assimilated at 0000 UTC 4 May 2008 Sum = J kg -1 86,308 observations

15 Interpretation of observation impact Non-beneficial impacts: look for data QC issues, instrument accuracy, specification of observation and background errors, bias correction, or model (background) problems … Beneficial impacts: associated with heavily weighted observations in sensitive regions; “good”, but extreme impacts indicate need for greater observation density … Best strategy: many observations which produce small to moderate impacts, not few observations which produce large impacts …

16 Example 1: AMV impact problem Date: Jan-Feb 2006 Issue: Non-beneficial impact from MTSAT AMVs at edge of coverage area Action Taken: Data provider identified problem with wind processing algorithm.

17 Restricting SSEC MTSAT Winds 500 mb Height Anomaly Correlation Southern Hemisphere Restricted WindsControl February 16 – March 27, 2006

18 Example 2: Ship data problem Date: Jan-Feb 2006 Issue: Some ship data having non-beneficial impact Actions Taken: Ship ID blacklist implemented; increase wind observation error for ship data (previously was equal to radiosonde surface wind error) SEA ARCTICA – one of the “problem” ships

19 Example 3: AMSU-A over land surface Date: Jan-Feb 2006 Issue: Some AMSU-A channels over-land surfaces produce non- beneficial impact Action Taken: Investigate bias correction dependence on land surface temperature

20 AMMA RAOB Temperature Ob Impacts May-Oct 2006 TAMANASET:60680 SUM= J kg -1 BANAKO:61291 SUM= J kg -1

21 AMMA RAOB Summary Ob Impacts Aug 2006 SOP Largest Fcst Error Reductions Fcst Degradations < J kg -1

22 Current Uncertainty in Analyzed 500mb Temperature – Operational Systems RMSD

23 Current Radiosonde Distribution

24 Adjoint-based observation impact information is a valuable supplement to “conventional” data impact studies (OSEs, OSSEs) Provides quantitative information about every observation that is assimilated and spatial patterns in observation impact Identifies possible problems with NAVDAS (observation and background error, bias correction, etc.) Information is relevant to QC issues and daily monitoring of observations in operational data assimilation Applications of observation impact information

25 On-line observation Impact monitor

26 Time-series of observation impact

27 Menu for upper-air satellite wind plots

28 MTSAT: hPa wind obs 30-day cumulative impact30-day mean innovation

29 MTSAT: hPa wind obs 30-day cumulative impact30-day mean wind speed

30 MDCRS Level-Flight: wind obs 30-day cumulative impact

31 hours before hours after Analysis Time NAVDAS-AR 8 Apr - 7 May UTC observations Impact per- observation (10 -5 J kg -1 ) 4d-VAR

32 Summary An adjoint-based system has been developed for daily (currently for 00UTC) monitoring of all observations used in data assimilation (3d-VAR and 4d-VAR) at NRL-FNMOC Computational cost is slightly less than the regular data assimilation and (24h) nonlinear forecast Information can be used for observation quality-control and improvement of the data assimilation procedure – valuable supplement to data-denial or data-addition experiments

33 ObSens Monitor Design Pre-Processing –Bin statistics into 2.5 degree grid –Sort data combinations, totals and groups Web-Processing –Provide top-level overviews and time lines –Present comprehensive menus of choices –Render on-demand maps, charts and time lines Archiving –Zip 90-day old data, unzip as needed

34 Ingest obsens_52.$dtg Calculate stats for $dtg, and 30-day and 1-yr stats Calculate impact average and sum by category Create category bar chart and time bar chart Create 2.5 degree binned grids for $dtg By data category, channel, variable type as appropriate For seven hPa pressure levels: sfc-901, , , , , , ObSens Pre-Processing obsens_52.$dtg stats grids rd_obsens (C-code) GrADS script AWK script

35 ObSens Web-Processing Display top page with bar and time charts Show other bar and time charts on mouse roll-over Present menus for Observation Category For selected Observation Category, present: Combinations of platform, pressure, channel, variable, etc. parameter: counts, ob value, innovation, impact, sensitivity geo area: global, northern hemisphere, southern hemisphere Totals for All platforms, pressures, channels, variables, etc. Groups for classes satellite/aircraft types: All GOES, SSEC, Ascending, etc. On Demand –Calculate 30-day/1-year grid stats –Create map plots and time lines Menu page html Javascript GrADS script Display page html grids Tcl cgi

36 ObSens Archiving Compress data grids over 90-days old Sparse grids compress 50:1 Uncompress data on-demand: ~ 2 sec/grid Leave on-demand data uncompressed Assuming future interest in uncompressed data grids GrADS script Unzip and open file Zip

37 Data Assimilation Equation OBSERVATIONS Temperature Moisture Winds Pressure BACKGROUND (6h) FORECAST ANALYSIS

38 Sensitivity to Observations: Sensitivity to Background: Adjoint of Assimilation Equation Adjoint of forecast model produces sensitivity to Baker and Daley 2000 (QJRMS)

39 Non-beneficial observations Example 3: Isolated aircraft tracks Date: First noticed Jan 05, ongoing in several regions Issue: aircraft flies in jet max eastbound, outside of jet max westbound: observation error representativeness problem ? Action Taken: Possible change to observation error AMDAR Level Flight Hong Kong - LAX

40 Example 4: QC for land observing stations Date: Jan-Feb 2005 Issue: Land station observation problems linked to high elevation and cold surface temperatures (METAR), also problems with station elevation metadata (MIL, conventional) Actions Taken: Selected stations blacklisted, data flagged if stations above 740m, or above 300m and background temperature below -15°C Conventional Land Stations KQ-MIL Stations AK-METAR Stations