satellite wind products

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010

Image:

MODIS Land Group,

NASA GSFC

March 2000

Satellite Wind ProductsSatellite Wind Products

Presented by

Jaime DanielsPresented by

Jaime Daniels

Center for Satellite Applications and Research (STAR) Review09 – 11 March 2010


Requirement, Science, and BenefitRequirement, Science, and Benefit

Requirement/Objective• Mission Goal: Weather and Water

– Research Area: Improve weather forecast and warning accuracy and amount of lead time • Mission Goal: Technology and the Mission Support

– Research Area: Advancing space-based data collection capabilities and associatedplatforms and systems

Science • How can we use polar imagers to provide wind information in the polar

regions where conventional wind observations are scarce?• How can we improve the quality of satellite-derived winds and improve

their utility and impact in Numerical Weather Prediction (NWP)?

Benefit• Satellite derived wind products:

– Provide vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans, polar regions, and Southern Hemisphere land masses.

– Provide vital tropospheric wind information over low latitudes and on scales in higher latitudes where the geostrophic relationship is invalid

– Provide key wind observations to operational NWP data assimilation systems where their use has been demonstrated to improved numerical weather prediction forecasts including tropical cyclones

– Provide improved guidance for NWS field forecasters



Challenges and Path ForwardChallenges and Path Forward

• Science challenges– Satellite wind height assignment for optically thin clouds– Assignment of a height uncertainty with each satellite wind for the NWP community

• Next steps– Development of a NPP VIIRS polar winds products (funded FY10 PSDI effort) – Complete development and validation of GOES-R satellite wind algorithm that includes new tracking

algorithm approach (GOES-R AWG funded effort)– Apply GOES-R satellite wind algorithm approach for current operational GOES and polar instruments,

but starting with the GOES instruments (funded FY10 PSDI effort)– Work with JCSDA and other NWP centers to assess impact of winds derived with new tracking

algorithm on NWP forecast accuracy– Continued development of improved satellite winds validation tools that leverage use of new data

sources (CALIPSO/CLOUDSAT, LIDAR winds) that will enable improved characterization of the accuracy and uncertainties associated with satellite derived winds

• Transition Path– The GOES-R derived motion winds algorithm is scheduled to be delivered to the GOES-R system

integrator by September 2010– Work to apply the GOES-R derived motion winds algorithm to the current GOES series of

satellites/instruments is scheduled to begin June 2010 (a PSDI funded effort). End goal of effort is to replace the current operational derived motion winds algorithm by March 2012

– Transition of VIIRS polar wind products to operations to begin late 2011 (a PSDI funded effort)



Basics of Satellite Winds DerivationBasics of Satellite Winds Derivation

• Atmospheric motion is determined through the tracking of features (clouds or moisture gradients) in time

– The choice of spectral band determines the intended target and location (low, mid, upper troposphere) in the atmosphere

• Use a pattern matching algorithm for estimating motion of features

– Sum-of-Squared Differences (SSD)

• Use multi-spectral height assignment algorithms to assign heights to features being tracked

– Multi-spectral approaches: CO2 slicing, H2O-intercept, Histogram algorithms

– Clear-sky radiances per a forward Radiative Transfer Model (RTM)

– Atmospheric state per NWP forecasts

• Apply quality control– NWP forecast to flag outliers– Internal consistency checks

• Compute and assign product quality indicators

– QI approach– Error Estimation (EE) approach

Visible (0.64um)

SWIR (3.9um)

Mid-IR (6.7um)

LWIR (11um)

Visible (0.64um)

SWIR (3.9um)

Mid-IR (6.7um)

LWIR (11um)

Visible Cloud-drift Winds

- Daytime

- Lower troposphere

Short-wave IR Cloud-drift Winds

- Night-time

- Lower troposphere

Water Vapor Winds

- Cloud-top

- Clear-sky

- Mid to Upper troposphere

Long-wave IR Cloud-drift Winds

- Day and night

- Lower, mid, and upper troposphere



Satellite Winds ResearchSatellite Winds Research

• Development of polar wind products – Motivation: Provide satellite wind observations over polar regions where conventional in-situ

wind observations are lacking

• Improving the refresh rate of geostationary wind products – Motivation: Provide more frequent satellite winds for use in emerging operational 4D-VAR

data assimilation systems at NWP centers

• Development of a new and novel tracking algorithm– Motivation: Address and minimize the long standing problem of the observed slow speed

bias associated with mid and upper-level satellite-derived winds; a significant concern of NWP community

• Development of new approaches and tools to validate satellite wind height assignments

– Motivation: Quantify the height uncertainty of satellite winds, improve their accuracy, and improve their use in NWP



Polar Wind Product InnovationsPolar Wind Product Innovations

Single Satellite (Aqua or Terra)

Mixed Satellite (Aqua and Terra)

MODIS Winds

NOAA-AVHRR GAC Winds

METOP-AVHRR Winds

AVHRR Winds

Terra only or Aqua only

Aqua, Terra, Aqua

Benefits• Provide unprecedented

coverage of the polar wind field that improves polar wind analyses

• Continuity: Recent use of AVHRR for polar wind estimation prepares us for a future without MODIS

• Demonstrated positive forecast impacts– Medium range weather

forecasts, not just over the polar regions, but globally

– Reduction in the frequency of forecast busts

– Reduction in tropical storm track forecast errors



Innovation: Improving the Refresh Rateof Geostationary Wind Products

Innovation: Improving the Refresh Rateof Geostationary Wind Products

Benefits• Improve refresh rate of GOES-

E/W wind products from 3-hourly to hourly

• Provide a more continuous (in time) source of satellite wind observations for emerging operational 4D-VAR NWP data assimilation systems

• Potential for significant and positive impacts on NWP forecast accuracy

GOES-12 Hourly Cloud-drift Winds

GOES-11 Hourly Cloud-drift Winds



Feature Tracking Algorithm InnovationsFeature Tracking Algorithm Innovations

New Nested Tracking Algorithm

• Developed for future GOES-R ABI

• Aims to minimize observed slow speed bias of satellite winds; a significant concern for NWP

• Computes local motions (nested) within a larger target scene, together with a clustering algorithm, to arrive at a superior motion solution

• Potential for determination of motion at different levels and/or different scales

105

0-5

m/s

Date

Speed Bias

Sat vs. Rawinsonde

1–2 m/s slow biasMean Vector Difference

(100-400 hPa)

Motion of entire box

SPD: 22.3 m/s

Average of largest cluster

SPD: 27.6 m/s

After clusteringBefore clustering15 Elements

15

Lin

es 5 Elements

5 Lines

Nested Tracking



Feature Tracking Algorithm InnovationsFeature Tracking Algorithm Innovations

Benefits

• Improved wind estimates

• Near elimination of slow speed bias

• Reduction of vector RMS error

• Potential for significant and positive impacts on NWP forecast accuracy– Impact studies with JCSDA

planned

Control Winds Test Winds

RMSE 7.53 6.63

Avg Vector Difference 5.95 5.28

Speed Bias -1.97 0.06

Speed 17.46 17.71

Sample 14548 14553

Winds generated using Meteosat-8 10.8 μm imagery (15-minute loop interval) for the period Feb 1 - 28, 2008.

Test winds are better fit to radiosonde winds

RAOB Speed (m/s)

AM

V S

pee

d (

m/s

)

Black - control

Light Blue -test

Comparisons to Rawinsondes



Innovations in Satellite Wind Height Assignment Validation

Innovations in Satellite Wind Height Assignment Validation

Benefits• Leverages unprecedented cloud

information offered by CALIPSO and CloudSat measurements

• Enables improved error characterization of satellite wind height assignments

• Enables feedback for potential improvements to satellite wind height assignments

• Improvements to overall accuracy of satellite-derived winds

Using CALIPSO/CloudSat Data to Validate Satellite Wind Height Assignments

GOES-12 Cloud-drift Wind Heights Overlaid on CALIPSO total attenuated backscatter image at 532nm

CALIPSO Cloud Height

Satellite Wind Height



Challenges and Path ForwardChallenges and Path Forward

• Science challenges– Satellite wind height assignment for optically thin clouds– Assignment of a height uncertainty with each satellite wind for the NWP community

• Next steps– Development of a NPP VIIRS polar winds products (funded FY10 PSDI effort) – Complete development and validation of GOES-R satellite wind algorithm that includes new tracking

algorithm approach (GOES-R AWG funded effort)– Apply GOES-R satellite wind algorithm approach for current operational GOES and polar instruments,

but starting with the GOES instruments (funded FY10 PSDI effort)– Work with JCSDA and other NWP centers to assess impact of winds derived with new tracking

algorithm on NWP forecast accuracy– Continued development of improved satellite winds validation tools that leverage use of new data

sources (CALIPSO/CLOUDSAT, LIDAR winds) that will enable improved characterization of the accuracy and uncertainties associated with satellite derived winds

• Transition Path– The GOES-R derived motion winds algorithm is scheduled to be delivered to the GOES-R system

integrator by September 2010– Work to apply the GOES-R derived motion winds algorithm to the current GOES series of

satellites/instruments is scheduled to begin June 2010 (a PSDI funded effort). End goal of effort is to replace the current operational derived motion winds algorithm by March 2012

– Transition of VIIRS polar wind products to operations to begin late 2011 (a PSDI funded effort)

satellite wind products

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