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

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

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

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

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

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

ms-1

Uncertainty in Atmospheric Analyses

Root-Mean Square of Analysis Differences: 300mb u-wind

GFS | ECMWF Jan-Dec 2010

8

Forecast error growth is controlled by a few rapidly-growing perturbation structures

8

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

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

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

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

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

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)

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

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

1515

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

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

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 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

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 Forecast Forecast with Targeted Data

Target Region for special dropsonde observations

NORPEX-98 Targeting Example (96-hr impact)

22

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)

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

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

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

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)

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 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

28

29

FORECAST ERROR REDUCTION

FORECAST ERROR INCREASE FORECAST ERROR INCREASE

FORECAST ERROR REDUCTION

30

Probability of forecast degradation from assimilation of new observations

(percent)

Number of additional data assimilated

50

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

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

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

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

34 34 34

observations,

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

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

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

38

RAPID-SCAN WINDS IMPROVE SUPER-TYPHOON SINLAKU FORECASTS

39

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

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 PROFILES TARGETED SPECIAL AMDAR DATA

41

CONCORDIASI Field Program Sep-Dec 2009

42

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

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

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

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

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

For additional information:Dr. Rolf H. LanglandData Assimilation SectionNaval Research LaboratoryMonterey, CA 93940Rolf.langland@nrlmry.navy.mil