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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, 19-21 May 2008
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Outline of Talk
1. Methodology
2. Observation impact examples
3. On-line observation monitoring system
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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
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NAVDAS: NRL Atmospheric 3d-Variational Data Assimilation System (0.5o 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:
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xb
xg
t= -6 hrs
eg
xt
xf
xa
t= 24 hrst=0
ef
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 (ef - eg)
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Forecast error norms and differences
e30
e24
Forecasts from 0600 and 1800 UTC have larger errors
e24 – e30 (nonlinear) e24 – e30 (adjoint)
Global forecast error total energy norm (J kg-1)
Forecast errors on background-trajectories
Forecast errors on analysis-trajectories
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Observation Impact Equation
b
a b
, f ggf
e ee
Ty Hx Kx x
• 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
T T 1
b b[ ]
K HP H R HP
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Observation impact interpretation
< 0.0 the observation is BENEFICIAL
> 0.0 the observation is NON-BENEFICIAL
3024e
3024e
For any observation / innovation … using this error measure
the effect of the observation is to make the error of
the forecast started from xa less than the error of the
forecast started from xb, e.g. forecast error decrease
e.g., forecast error increase
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Dry TE Norm (150mb-sfc)
Dry TE Norm (150mb-sfc)
Total impact by instrument type – Jan2007
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Impacts per-observation by instrument type
10e-5 J kg-1 10e-5 J kg-1
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Percent of observations that produce forecast error reduction (e24 – e30 < 0)
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Impact for AMSU-A channels - NAVDAS-NOGAPS
1 – 31 Jan 2007, 00,06,12,18 UTCUnits of impact = J kg-1
-30
-25
-20
-15
-10
-5
0
5 4_15
4_16
4_18
4_19
5_15
5_16
5_18
5_19
6_15
6_16
6_18
6_19
7_15
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Beneficial
5 6 7 89 10
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
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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
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Beneficial
Non-beneficial
Impact of AMSU-A radiance data
Observations assimilated at 0000 UTC 4 May 2008
Sum = - 0.906 J kg-1
86,308 observations
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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 …
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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.
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Restricting SSEC MTSAT Winds 500 mb Height Anomaly Correlation
Southern Hemisphere
Restricted Winds ControlFebruary 16 – March 27, 2006
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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
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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
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AMMA RAOB Temperature Ob Impacts May-Oct 2006
TAMANASET:60680 SUM= -0.2791 J kg-1
BANAKO:61291 SUM= -0.5755 J kg-1
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AMMA RAOB Summary Ob Impacts Aug 2006 SOP
Largest Fcst Error Reductions
Fcst Degradations
< -0.10 J kg-1
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Current Uncertainty in Analyzed 500mb Temperature – Operational Systems
RMSD
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Current Radiosonde Distribution
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• 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
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On-line observation Impact monitorwww.nrlmry.navy.mil/ob_sens/
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Time-series of observation impact www.nrlmry.navy.mil/ob_sens/
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Menu for upper-air satellite wind plotswww.nrlmry.navy.mil/ob_sens/
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MTSAT: 300-500 hPa wind obswww.nrlmry.navy.mil/ob_sens/
30-day cumulative impact 30-day mean innovation
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MTSAT: 300-500 hPa wind obswww.nrlmry.navy.mil/ob_sens/
30-day cumulative impact 30-day mean wind speed
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MDCRS Level-Flight: wind obswww.nrlmry.navy.mil/ob_sens/
30-day cumulative impact
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-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
-2.5 -1.5 -0.5 0 0.5 1.5 2.5
In-Situ Satellite
hours before hours afterAnalysis Time
NAVDAS-AR 8 Apr - 7 May 2008
00UTC observations
Impact per-observation (10-5 J kg-1)
4d-VAR
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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
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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
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• 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 $dtg
By data category, channel, variable type as appropriateFor seven hPa pressure levels: sfc-901, 900-801, 800-701, 700-501, 500-301, 300-101, 100-10
ObSens Pre-Processing
obsens_52.$dtg stats
gridsrd_obsens(C-code)
GrADSscript
AWKscript
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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, sensitivitygeo 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 pagehtml
Javascript
GrADS script
Display pagehtml
grids
Tcl cgi
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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
grids
GrADS script
Unzip andopen file
Zip
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Data Assimilation Equation
OBSERVATIONS Temperature
Moisture
Winds
Pressure
BACKGROUND (6h) FORECAST ANALYSIS
T T 1a b b b b[ ] ( ) x x P H HP H R y Hx
K
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Sensitivity to Observations:
Sensitivity to Background:
Adjoint of Assimilation Equation
Adjoint of forecast model produces sensitivity to
ax
T 1b b
a
[ ]J J
HP H R HPy x
T
b a
J J J
H
x x y
TK
Baker and Daley 2000 (QJRMS)
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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
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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
