A Pacific Predictability Experiment -Targeted Observing Issues and Strategies Rolf LanglandPacific Predictability MeetingSeattle, WA June 6, 2005

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

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

Forecast Impact of Targeted Data (adding 10-50 dropsondes at single assimilation times) Targeted data improves the average skill of short-range forecasts*, by ~ 1020% 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

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 caseNOAA-WSR-04 NORPEX -98NA-TReC -03UPPER LIMIT SUGGESTED BY PREDICTABILITY STUDIES

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 ??

SENSITIVITY OF 72H FORECAST ERROR TO 300mb U-WINDFORECAST VERIFICATION AREAOBSERVATION TARGETSTargeting a major winter storm forecast failureLangland et al. (MWR, 2002)

Pacific origins of the 2000 E. Coast blizzard 21 Jan 0022 Jan 0023 Jan 0024 Jan 0025 Jan 0026 Jan 00Figure by Mel Shapiro250mb Daily-Mean Geopotential Height

Objectives for future targeting programsGoal 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

Pacific predictability questions -- Are the analyses over the Pacific getting better ?

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 ?

NAVDAS Observation Count 12 May 2005Includes AMSU-A, scatterometer, MODIS, geosat winds, SSMI, raobs, land, ship, aircraft data

Does not includes HIRS, AIRS, GPS, or ozoneNumber of obs within 5o x 5o lat-lon boxesAll observation types - 00, 06, 12, 18 UTCMAX SENSITIVITY

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

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

1. 2.3.4. 5.6.7.8.9.10.

Data AssimilationForecast ModelSatellite ObservationsData Selection & Thinning ProceduresIn-situ observationsRejected DataTargeting Guidance

Targeting Strategy

Forecast and Analysis ProcedureObservation(y)Data AssimilationSystemForecast ModelForecast(xf)Gradient ofCost FunctionJ: (J/ xf)Background(xb)Analysis(xa)Adjoint of theForecast Model Tangent PropagatorObservationSensitivity(J/ y)BackgroundSensitivity(J/ xb)AnalysisSensitivity(J/ xa)Observation Impact (J/ y)Adjoint of the Data AssimilationSystemWhat is the impact of the observations on measures of forecast error (J) ?Adjoint of Forecast and Analysis Procedure

New vs. Old Targeting Approach

Issue New TargetingOld TargetingNumber of obs in target region~ 10,000 or more obs in target area10-50 dropsonde profilesType of obsSatellite and some in-situMostly in-situFrequency of obsAt least every 6 hours or continuous Once at target timeSampling ApproachSample larger area of target subspaceDropsondes in localized regionForecast ImpactMore reliable and larger forecast impactsMixed impact, many null cases

Large Impact of Observations in Cloudy Regions

High Forecast Impact

High Forecast Impact

High Forecast Impact

High Forecast Impact

Med-Low Forecast Impact

Med-Low Forecast Impact

Med-Low Forecast Impact

Example of Driftsonde sounding coverage at one assimilation time after five days of deployment from launch sites along the Asian Pacific rimInitial Launch Time: 00 UTC 06 Feb 1999 13 launch sites

Launch Interval: 12hrDropsonde Interval: 6hrDrift Level: 100 mbCoverage at: 00UTC 11Feb 1999FIGURE IN EARLY VERSION OF THORPEX PLAN (April 2000)

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

PROPAGATION OF PACIFIC TARGETING SIGNAL KINETIC ENERGYFrom 00UTC 20 Jan 2005 (+ 7 days)FROM S. MAJUMDARU.S.CHINAEUROPE

Extended-duration targeting flow regime 1

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 applicationsResearch Tasks

- 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

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 impactNew and next-generation satellite data are the primary resource that can advance the impact of targetingIn-situ targeted observations provide value in certain situations where satellite observations are insufficient (including cloudy areas)

Interpretation of previous targeting results

1Nov-31Dec 2003 global domainObservation Impactduring THORPEX NA-TReC18UTCDoes not include moisture observations or rapid-scan satellite wind data

Observation Type

(J kg-1)

% of total

# obs

per ob

(10-5 J kg-1)

AMSU-Aa

-88.68

47.8%

4,461,709

-2.0

Geosat windsb

-32.44

17.4%

2,958,608

-1.1

Aircraftc

-29.24

15.8%

2,511,540

-1.2

Land-surfaced

-14.20

7.7%

696,140

-2.0

Ship-surfacee

-11.20

6.0%

214,143

-5.2

Rawinsondesf

-7.44

4.0%

362,489

-2.1

TC Synthg

-1.74

0.9%

11,152

-15.6

Dropsondesh

-0.67

0.4%

13,418

-5.0

Total

-185.61

100%

11,229,199

-1.7

Table 1: Cumulative observation impact EMBED Equation.DSMT4 (J kg-1) from observations assimilated in NAVDAS at 1800 UTC in the complete global domain from 1 November to 31 December 2003. Observation data are: (a) radiance assimilated as brightness temperature at 45 km resolution, (b) wind vectors, 475-775 hPa excluded, (c) wind vectors and temperature at single-level, and in ascent and descent profiles, (d) temperature, and surface pressure assimilated as height, (e) wind vectors, temperature, and sea-level pressure assimilated as height, (f) wind vectors and temperature on mandatory and significant levels, surface pressure assimilated as height, (g) synthetic wind vectors, and sea-level pressure assimilated as height, for tropical cyclone bogusing, (h) wind vectors and temperature in profiles from flight-level to the sea-surface.

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