Also known as CMIS

R. A. Brown 2005 LIDAR Sedona

R. A. Brown 2005 LIDAR Sedona

Passive Radars

R. A. Brown 2003 U. ConcepciÓn

Same principal as Scatterometer but signal is much weaker

Hence: speed only from SMMR, SSMI,…..

Solar reflectance

Brightness Temperature

Two looks at the same spot

R. A. Brown 2004

What is Ocean Observer?• Operational data for Navy and NOAA

• Science data for NASA and NOAA

• R&D sensor proof of concept for NASA

• Operational transition for NASA and NOAA

• Team approach to solving mutual problems at for OMB

• Oceans mainly

reduced agency cost

NPOESS

WindSat becomes CMIS

Atmospheric Vertical Moisture Profile Downward Longwave Radiance (Sfc) Precipitable WaterAtmospheric Vertical Temperature Profile Electric Fields Precipitation Type/RateImagery EDPs/Ionospheric Specification Pressure (Surface/Profile)Sea Surface Temperature Fresh Water Ice Radiation Belt/Low Energy ParticlesSea Surface Winds Geomagnetic Field Sea Ice Age and Edge MotionSoil Moisture Ice Surface Temperature Sea Surface Height/TopographyAerosol Optical Thickness In-situ Ion Drift Velocity Snow Cover/DepthAerosol Particle Size In-situ Plasma Density Solar EUV FluxAlbedo (Surface) In-situ Plasma Fluctuations Solar IrradianceAuroral Boundary In-situ Plasma Temperature Solar/Galactic Cosmic Ray ParticlesAuroral Imagery Insolation Supra-Thermal - Auroral ParticlesCloud Base Height Ionospheric Scintillation Surface Wind StressCloud Cover/Layers Land Surface Temperature Suspended MatterCloud Effective Particle Size Littoral Sediment Transport Total Auroral Energy DepositionCloud Ice Water Path Net Heat Flux Total Longwave Radiance (TOA)Cloud Liquid Water Net Short Wave Radiance (TOA) Total Water ContentCloud Optical Depth/Transmittance NDPs/Neutral Atm Specification TurbidityCloud Top Height Normalized Difference Vegetation Index Upper Atmospheric AirglowCloud Top Pressure Ocean Color/Chlorophyll Vegetation Index/Surface TypeCloud Top Temperature Ocean Wave CharacteristicsCurrents (Ocean) Ozone - Total Column/Profile

VIIRS CMIS CrIS/ATMS OMPS SESS GPSOS ERBS TSIS ALT

Primary Contributions to EDRs by Sensor

Environmental Data Records (EDRs) with Key Performance Parameters

Joint IPO/DoD/NASA Risk Reduction Demo

WindSat/CoriolisDescription: Measures Ocean Surface Wind Speed, Wind Direction,

Using Polarimetric Radiometer on a Modified Satellite Bus, Launched Into a 830 km 98.7° Orbit by the Titan II Launch Vehicle. 3 Year Design Lifetime.

Capability/ImprovementsCapability/Improvements

• Measure Ocean Surface Wind Direction (Non- Measure Ocean Surface Wind Direction (Non- Precipitating Conditions). Two looks at same spot.Precipitating Conditions). Two looks at same spot.

• 25km spatial resolution25km spatial resolution

• Secondary MeasurementsSecondary Measurements• Sea Surface Temperature, Soil Moisture, Rain Sea Surface Temperature, Soil Moisture, Rain

Rate, Ice, and Snow Characteristics, Water Rate, Ice, and Snow Characteristics, Water VaporVapor

Launched: January 2003

?

(A stealth mission)

R. A. Brown 2004

Data release: Sept. 2004

Neil Tyson’s address/campaignOn the Future of NASA Jan 20, 2005

“LEO (low earth orbits) are old hat and boring. NASA must do new stuff – space”

President’s commission --- “Vision” (thing)

Winners: Space Exploration Planetary Science Astrobiology Astrophysics Astronomy

Losers: Einstein prerogatives Earth Science

R. A. Brown 2005 LIDAR Sedona

WindSAT Cal/Val with SLP Retrievals

Ralph Foster, Applied Physics Laboratory, U. WA

Jerome Patoux, R.A. Brown, Atmospheric Sciences, U. WA

R. A. Brown 2005 LIDAR Sedona

Outline• Two questions:

– How well does WindSAT perform when it’s working at its best?

– Can Sea-Level Pressure (SLP) fields help improve model function and ambiguity selection?

• Physics of SLP(U10)• QuikSCAT example• Methodology• WindSAT results

– Comparison with ECMWF SLP Analyses & QuikSCAT wind distributions

– Ambiguity selection procedureR. A. Brown 2005 LIDAR Sedona

SLP from Surface Winds

• UW PBL similarity model

• Use “inverse” PBL model to estimate from satellite

• Use Least-Square optimization to find best fit SLP to swaths

• Extensive verification from ERS-1/2, NSCAT, QuikSCAT

(UGN )

(UGN )

R. A. Brown 2005 LIDAR Sedona

ECMWF analysis

QuikScat analysis

Surface Pressures

Surface Pressure as Surface Truth

• For good quality and consistent U10 input, SLP fields are a good match to ECMWF analyses

• SLP/Model-derived U10 is an “optimally smoothed” low-pass filtered comparison data set– Wind-sensor derived product only– Model U10 tend to agree with input U10 for good

swath input

• If SLP fields are wrong, pressure gradients and hence U10 are wrong.

R. A. Brown 2005 LIDAR Sedona

Dashed:ECMWF

Dashed:ECMWF

All four swaths for both WindSAT and QuikSCAT

Results

• WindSAT is biased high for U10 ~ < 8 m/s– Too few winds U10 < 5 m/s– Too many winds 5 < U10 < 8 m/s

• Implied grad(SLP) too high when U10 ~< 8 m/s– Implications for assimilation in NWP

• Too few WindSAT winds in 8 < U10 < 12 m/s• Comparable to QuikSCAT 12 < U10 < 15 m/s

– SLP agrees better in higher wind regime

• Too small sample to assess higher winds

R. A. Brown 2005 LIDAR Sedona

Use SLP to Assess Direction

• Winds derived from SLP are optimal smooth winds

• Arbitrary threshold of 35o from Model U10

used to distinguish potentially wrong ambiguity choice

• Look for a WindSAT ambiguity with closer direction to Model winds in these cases

R. A. Brown 2005 LIDAR Sedona

• Noisy directions• Front captured• Changed ambiguities away from clouds & low

winds: Why?

Conclusions

• There is a lot of wind vector information in the WindSAT swaths

• The agreement of the WindSAT-derived SLP fields with ECMWF is surprisingly good for a first-cut model function.

• Better in higher winds• An improved model function will produce better SLP

• SLP can be used to assess and improve the WindSAT wind data

R. A. Brown 2005 LIDAR Sedona

Conclusions (cont.)

• SLP fields demonstrate that the current WindSAT model function often produces a poor wind speed distribution– Wind speed distribution can be robustly

evaluated with SLP– Storm analyses will address high wind

distribution• Wind directions are noisy and there there is room for ambiguity selection improvement.

• SLP shows promise for this needR. A. Brown 2005 LIDAR Sedona

Next

• SLP adds the robust ECMWF & NCEP surface analyses and buoy pressure observations to the WindSAT Cal/Val data– We are developing methods to use buoy/analysis

pressures to identify & correct deficiencies in model function, e.g. Zeng and Brown (JAM 37 1998)

• Continue development of SLP ambiguity selection procedure

• Combining SLP with water vapor, clouds & SST will greatly improve storms and fronts research and analysis

R. A. Brown 2005 LIDAR Sedona