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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
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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
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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 !!]
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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]
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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
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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
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ms-1
Uncertainty in Atmospheric Analyses
Root-Mean Square of Analysis Differences: 300mb u-wind
GFS | ECMWF Jan-Dec 2010
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Forecast error growth is controlled by a few rapidly-growing perturbation structures
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Singular Vectors in 72-hr East Coast Storm ForecastInitial Time Total
Energy 12Z 22 Jan 2000Final Time Sfc
Pressure 12Z 25 Jan 2000
SV 1
SV 2
SV 3
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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)
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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”
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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
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FASTEX – first objective targeted observing programJanuary – February 1997
NOAA G-IV flights
St. John’s 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 NCAREnsemble-based targeting
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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)
700
J
FASTEX targeting example (IOP-17)
Interpretation: the 42-hr forecast error over UK is most sensitive to the structure of this trough
COLDER
WARMER
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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)
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FASTEX IOP-17 E-W Cross-section of Singular Vector target
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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
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How do we choose the optimal deployment of observations to improve a forecast
between times ta and tv?
ti td ta tv
Decisiontime
Adaptive sampling(analysis) time
Verificationtime
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
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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
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NORPEX Targeting Mission (1998)
SV Target Area
NRL
NCEP
Forecast Verification +
2 days
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With targeted dropsondes:
48hr fcst error* reduced by 35% in verification region
300mb wind 300mb wind
Sea Lev PresSea 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
SV Target Area
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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 Forecast Forecast with Targeted Data
Target Region for special dropsonde observations
NORPEX-98 Targeting Example (96-hr impact)
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Impact of NORPEX targeted dropsondes16 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 dropsondes45 forecast cases IMPROVED
FORECASTS (n=35)
DEGRADED FORECASTS (n=10)
Langland et al. 1999 (BAMS)
10% mean error reduction(45 cases)
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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)
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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
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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
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Impact of Observations on Forecast Error
The forecast error difference, , is due to the assimilation of observations at 00UTC
OBSERVATIONS ASSIMILATED
+24h
Xb
Xa
3 02 4 3 0 2 4e e e
30e
24e
00UTC
Langland and Baker (Tellus 2004)
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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 2006Result: 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
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FORECAST ERROR REDUCTION
FORECAST ERROR INCREASE FORECAST ERROR INCREASE
FORECAST ERROR REDUCTION
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Probability of forecast degradation from assimilation of new observations
(percent)
Number of additional data assimilated
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01 10,0001,00010010
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
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• 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
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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
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AMVs at 08GMT, 7th August 2007, during TCs Wutip and Pabuk Source: JMA, CIMSS
Regular: 30min scan RAPID-SCAN: 3min sampling Improved data quantity and
quality
Rapid-Scan Satellite winds for Targeted Observing
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observations,
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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
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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]
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Rapid-Scan and Dropsonde targeted observing in super-typhoon Sinlaku 10-18 Sep 2008 [TCS-08]
Forecast Error Reduction Forecast Error Increase
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
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RAPID-SCAN WINDS IMPROVE SUPER-TYPHOON SINLAKU FORECASTS
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DOTSTAR Targeting Mission 00UTC 10Sep 2008
Super-Typhoon Sinlaku
Targeting guidance prepared
12th10th8th
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
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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 PROFILES TARGETED SPECIAL AMDAR DATA
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CONCORDIASI Field Program Sep-Dec 2009
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Ob Impact results for Concordiasi October 2010 (124 analyses and forecasts)
NOGAPS-NAVDAS_AR (4d-Var) operational forecast system
Observation Impact on 24hr forecast error norm (total moist energy) For observations in geographic domain south of 60°S
Observation Type Summed Impact Impact per-ob Number Obs J kg-1 x10-5 J kg-1 Radiosondes -2.8260 -3.2050 88,175 Dropsondes -1.0658 -2.8050 37,996 GeoSat Wind -0.0474 -0.7895 6,004 MODIS Wind -10.4172 -1.3243 786,618 AVHRR Wind -2.6195 -0.6724 389,562 LEO – GEO Wind - - - AIREP -0.0001 -0.0342 292 AMDAR -0.0793 -11.1690 710 LAND SFC -2.0729 -1.9161 108,185 SHIP SFC -0.2345 -4.6704 5,021 SSMI SFC WIND +0.0327 +0.1189 27,493 SCAT SFC WIND +0.0082 +0.6193 1,324 ASCAT SFC WIND -0.1490 -1.5351 9,706 WINDSAT SFC WIND -0.0403 -1.0172 3,962 SSMI TPW -0.0112 Profiles 11,902 WINDSAT TPW -0.0012 Profiles 633 GPS-RO -2.1002 -0.1394 1,507,041 AMSU-A -8.3132 -0.2088 3,981,468 IASI -3.8546 -0.0650 5,932,979 SSMIS -3.4714 -0.0909 3,820,906 AQUA -0.5572 -0.4474 124,553 Total -37.8201 -0.2329 16,844,530 Compiled by Rolf H. Langland NRL-Monterey
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
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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)
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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 …
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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
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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
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For additional information:Dr. Rolf H. LanglandData Assimilation SectionNaval Research LaboratoryMonterey, CA 93940Rolf.langland@nrlmry.navy.mil