Latest Water Vapor Results from GPS Radio Occultation (RO) + Next Gen E. R. Kursinski A.Kursinski 1, T. Gebhardt 2, C. Ao 3, D. Ward 4, A. Otarola 5, H

Latest Water Vapor Results from GPS Radio Occultation (RO) + Next Gen

E. R. Kursinski

A.Kursinski1, T. Gebhardt2, C. Ao3, D. Ward4, A. Otarola5, H. Reed2, D. Erickson2, and J. McGhee1

Special thanks to Mark Ringer (Met Office)1 MOOG Advanced Missions & Science, Golden, Colorado, USA

2 University of Colorado, Boulder, Colorado, USA3 Jet Propulsion Laboratory, Pasadena, California, USA

4 University of Arizona, Tucson, Arizona, USA5 Thirty Meter Telescope, Pasadena, California, USA

GVAP Oct 1, 2013 CIRA-CSU

Outline

• Quick Background on GPS RO• GPS Water Vapor Histograms1

– Comparisons with AIRS, analyses, GCMs• Cluster analysis of GPS WV1

• Next Gen RO: ATOMMS Overview2

1Funded by NOAA & NASA2Funded by NSF and Moog Broad Reach

Kursinski et al., 2 GVAP MOOG Oct 1, 2013

JCSDAAugust 19, 2009

• Delay(t)=> bending angle(a) => refractivity(z) where a=nr– Dry conditions: => dry density(z) => P(z) => T(z) via hydrostatic eqn– Wet conditions: refractivity + T,p,q (analysis) => better T,p,q

or refractivity + T (analysis) => water vapor(z)

GPS Occultation Summary

occultation geometry

An occultation occurs when the orbital motion of a GPS SV and a Low Earth Orbiter (LEO) causes the LEO ‘sees’ the GPS rise or set across the limb

This causes the signal path between the GPS and the LEO to slice through the atmosphere

Atmosphere acts as a lens bending the signal path

1D forward relation 1D inverse relation

JCSDA

Information vs. Altitude from GPS RO

Density, pressure & temperature vs. alt.

May 4, 2010

Ionospheric refractivity & Free electron density

H2O vapor

Upper altitude depends on solar & diurnal

cycles

Atmospheric Refractivity

N a1

P

T a2

Pw

T 2 40.3106 ne

f 2

200 m vertical resolution

All-weather

Density, pressure & temperature vs. alt.

MOOG Oct 1, 2013

GPS RO Features Summary • Least biased data set available?

– Global coverage – Diurnal coverage with > 6 satellite constellation like COSMIC– Works in clear and cloudy conditions ( l ~20 cm)– Works over land and water– Unique relation between bending angle & refractivity (except super-N)

insensitive to initial guess• Resolution

– Vertical resolution ~200 m• Capable of seeing stability related effects invisible to other sounders

– Horizontal resolution• Along track horizontal resolution ~ 100-250 km• Cross track ~ 1.5 km (limited by horizontal motion of raypath)• Inherent averaging good for climate (better horiz. res. desired for NWP)

• Vertical range– Useful to ~240 K level in troposphere (~9 km alt. in tropics)– Extends down very close to surface in extratropics– If we can deal with super-refraction, lower altitude can be the

surface in the tropics

Kursinski et al., 5 GVAPMOOG Oct 1, 2013

Zonal Mean Relative Humidity GPS-MET Jun 21-Jul 4 1995

• Zonal mean relative humidity from GPS/MET July 1995

Kursinski & Hajj, 2001

Winter Summer

ITCZ subtropicssubtropics

~2,000 profiles

GPS RO Missions Mission Period Occ/day• GPS-MET 1995-1997• CHAMP 2001-2010 250• COSMIC 2006- 1500-3000• METOP-A 2010- 700• Megha-Tropiques 2011- 500?• METOP-B 2012- 700• KOMPSAT-5 2013- 500?


Present & Future GNSS RO Missions• From www.wmo-sat.info/oscar/


Kursinski et al., 9 GVAP

400 GNSS Occulting Satellites over 3 HOURS, World

• Recently begun study at Moog AMS

MOOG Oct 1, 2013

Low Latitude Moisture Study• Convection creates wet & dry extremes => stretches water vapor distribution• Mixing and diffusion work to homogenize the distribution toward the center• Specific humidity is conserved in the absence of sources and sinks => tracer• Relative humidity important for conversion between vapor & condensed

phases => clouds & precipitation• Reducing forecast uncertainty => reducing uncertainty about processes =>

precise, hi-res all-weather obs that capture variability & constrain processes3000 km

16 km

Zonal Mean Relative Humidity GPS-MET Jun 21-Jul 4 1995

• Zonal mean relative humidity from GPS/MET July 1995

Kursinski & Hajj, 2001

Winter Summer

ITCZ subtropicssubtropics

~2,000 profiles

SR

Free Tropospheric PW fromCOSMIC

Oct 06

Jan 07

Apr 07

Jul 07

Kursinski et al., 12 AGU Dec 17, 2010

Moisture Histograms• Low order moments like mean and variance

provide limited insight into water vapor distribution and the hydrological cycle

• Histograms of moisture on individual pressure levels yield much better indication of full range of behavior

• Plus insight into processes at work and adequacy of their representation in models


Two Methods for Extracting Water Vapor from GPS RO Refractivity Profiles

• Direct Method: Nwet = Ntot – Ndry

– Determine dry refractivity (Ndry) from analysis temperature profile and hydrostatic equation

– Scale Nwet to get water vapor

• (1D) Variational Method– Combine GPS refractivity with temperature & water vapor

profiles and surface pressure from analysis and error covariance estimates

– Overdetermined, least squares solution

• Advantage of Direct Method: Not affected by biases in background water vapor forecast/analysis


Negative q and Error Deconvolution

Direct method can and does produce negative q estimates

=> Produces an unphysical, negative tail in the q histograms

• This can be fixed by deconvolving the error distribution from histograms– Linearize error model: qmeasured = qtrue + eq

– Measured histogram (PDF) is then the convolution of the true PDF and the error PDF

PDFqmeas = PDFqtrue PDFe

• IF we understand the error PDF, we can then deconvolve it from the measured PDF to recover the true PDF

– Negative tail tells us shape of the error distribution



Automated, Error Deconvolution Low Latitude

-0.5 -0.25 0 0.25 0.5 0.75 1 1.25 1.5 1.75 20

0.05

0.1

0.15

0.2

0.25

0.3

Water vapor (g/kg)

Pro

ba

bilit

y

Histogram of water vapor levels from346mb.xlsx with25000 iterations

raw measurementsinitial truth guessupdated truth guesserror pdf w/ std dev0.14242

346 hPa

Error PDF

Deconvolved distribution

Measured distribution

• Adjust (1) (symmetric) Error PDF & (2) “true” q distribution PDF, • Convolve them to generate estimate of “measured” PDF,• Iterate adjustment until best fit to the measured PDF is achieved

Full Annual Cycle (2007)

Estimating the Accuracy of GPS-derived Water Vapor

Analogously, the error in relative humidity, U, is

where L is the latent heat and Bs = a1TP / a2es.

sq ~ 0.2 g/kg in mid & upper troposphere.

sq ~ 0.5 g/kg in lower troposphere

2/1

2

22

2

22

2

22 )2(

PB

TTR

LUB

NUB P

sT

vs

NsU

• Kursinski et al. 1995: Initial estimate of GPS water profile accuracy • Kursinski & Hajj, 2001: Error in specific humidity, q, due to errors in refractivity, N,

temperature, T, and pressure, P, from GPS

where C = a1Tmw/a2md ~ 35 g/kg

𝜎 𝑞=((𝐶+𝑞)2(𝜎𝑁

𝑁 )2

+(𝐶+2𝑞 )2(𝜎𝑇

𝑇 )2

+(𝐶+𝑞)2(𝜎 𝑃 𝑠

𝑃𝑠)

2

)1 /2

Kursinski et al., 17 GVAP Oct 1, 2013

Separating the Errors• Estimate water vapor error from negative tail of distribution• Resulting errors somewhat smaller than predictions of Kursinski &

Hajj, 2001• Presumably because analysis temperature errors are smaller

Specific

Humidity Error (g/kg)

Fractional Refractivity Error

(%)Temperature

Error (K)Reference

Pressure Error (%)

Pressure level (hPa) KH01 Error

deconv KH01 Error deconv KH01 Error

deconv KH01 Error deconv

346 0.24 0.145 0.2 0.2 1.5K 0.9K 0.3% 0.15%

547 0.31 0.25 0.5 0.6 1.5K 0.9K 0.3% 0.15%

725 0.47 0.39 0.9 1 1.5K 0.9K 0.3% 0.15%

• Dessler & Minschwaner 2007 compared advection-saturation model results & AIRS• PROBLEM: Model matches AIRS but neither looks like GPS. What’s going on?

725

Motivation

You are here

547 hPa Specific Humidity Comparisons• c

Analyses don’t like really dry air

Analyses & AIRS underestimate very high humidity air

GPS & AIRS agree well on very dry end in mid-troposph

Overestimates of mid-humidity air

~5 km altitude


Comparison with CMIP3 & CMIP5 models• Noticeable improvement in some CMIP5 models:

NCAR and MPI in particular• Resolution contributing but definitely not the

whole answer (MIROC 5 has highest resolution)CMIP3 CMIP5

547 hPa 547 hPa


Comparison with Advection- Saturation Model

• Model from Dessler & Minschwaner, 2007– Start with moisture in air parcel observed by AIRS– Advect parcel according to NCEP wind analyses– Limit mixing ratio to the saturation mixing encountered along trajectory

• Produces more very dry and wet air than GPS RO observes– Model’s peak in wet end is not observed

• Lack of mixing in adv-sat model likely explains model’s higher extremes than observed

547 mb


GPS v. AIRS: 725 hPa Specific Humidity

AIRS underestimates low & high ends of distribution

IR can’t see thru clouds

AIRS doesn’t see the very dry air likely because IR vertical resolution can’t separate PBL from free troposphere

Very dry air (<5% RH) is reaching the PBL top


725• compare

You are here

CMIP5 GCMs vs. GPS RO vs. AIRS

Too much vertical diffusion in GCMsWV feedback likely too positive in GCMs

GCMs overestimating

% of wet air

AIRS-GPSRO Histogram Comparison Summary

• AIRS misses high end of moisture distribution– Presumably due to clouds

• Cloud clearing does not eliminate fundamental IR limitation• Extremely dry air

– AIRS & GPSRO show similar % of dry air in mid-troposphere, much more than in GCMs and analyses

– GPS RO sees far more just above PBL than AIRS, • Likely associated with limited AIRS vertical resolution.

• GPS’ cloud penetration & 200 m vert. res. more important than its 100-250 km horizontal resolution

• What is the horizontal resolution of GPS RO, 250 km, 100 km ?

• These differences are important for inferring processesKursinski et al., 25 GVAP MOOG Oct 1, 2013

Error Pattern of Analyses & GCMs• Too little of the very dry air in mid & lower tropos

Þ too much vertical diffusion in modelsÞ Suggests WV feedback too strongly positive in free tropos

• Not enough high q & RH distributionsÞ “trigger happy” convection parameterizations– IF GCM cloud & rainfall are reasonable => must be

(erroneous) compensating cloud and precipitation parameterizations

– May be related to why GCMs under-predict observed increases in heavy rainfall


GCM AssessmentGCMs improving• Resolution is contributing to model improvement

– BUT NCEP analysis res: 1o; MIROC 5 highest resolution GCM but definitely not the best GCM

• NCAR & MPI CMIP5 models agree best with GPS RO moisture distribution

Diffusion • Some diffusion necessary to reproduce what GPS RO

observes• Compressed moisture distribution indicates too much

mixing in analyses and (older) GCMs

Monthly means are inadequate for making this assessmentKursinski et al., 27 GVAP MOOG Oct 1, 2013

Causes of Limited NWP Moisture Impact?

1. Mismatch of GPS RO & NWP GCM moisture climatologies, particularly at wet and dry extremes– Moisture assimilation not utilizing observations well (particularly

mid-trop) (Analyses look like free running CMIP3 GCMs)– Sensitivity of moisture analyses to initial guess limits their utility

for assessing realism of GCM hydrological cycle IMPORTANT:– Dry part is important for suppressing convection– Assimilation of observed wet air => rain outÞ Impact cannot improve until GCMs better represent real

moisture distribution


Causes of Limited NWP Moisture Impact?

GOOD NEWS: GCMs are getting better• AR5 models: CESM 1.0 & MPI-ESM-LR look significantly better• Good for both climate and NWP

Þ Suggests assimilation experiments with GCMs with matching climatologiesÞ Good to have same NWP & climate GCMs like Met Office

2. Relatively sparse GPS RO coverage • Water varies rapidly on short temporal spatial scales (8 day average time)

• Higher low latitude density of COSMIC2 has potential for significant increase in impact

– Low lat. equivalent ~18K global occ/day


Relative Humidity Histogram 346 mb• Sharp fall off at 100% RH• Upper & lower edge asymmetry => T error = 0.8 K • Suggestion of small % of supersaturation• Apparently no air drier than 5% RH => 0.075 g/kg


Cluster AnalysisUsing cluster analysis • to extract patterns in the vertical structure of the

moisture profiles• and their horizontal distribution • to help infer underlying processesThis led to identification of an ENSO related pattern and related research


Deep dry boundary layers over North Africa

• Deep well mixed layers to 6 to 7 km altitude in July

• Note 200-300 m vertical resolution across BL tops


Free Troposphere Water Vapor-based ENSO Index

Centroids of 2 wettest Nov-Dec-Jan (NDJ) clusters from CHAMP & COSMIC track ENSO (SST) phase and intensity• Tied to deepest, wettest convection, tracks max SST• 3rd & 4th wettest clusters do NOT track the ENSO

• Signature NOT present in AIRS or analyses

• CHAMP centroids are noisier than COSMIC due to limited # of samples

• PWFT centroids are shifted west of SST centroids

– due to trade winds?

Blue La NinaBlack neutralRed El Nino

CHAMPCOSMIC

SST

PWFT-wettest


547 hPa Specific Humidity Comparisons• c

Analyses & AIRS underestimate very high humidity air

~5 km altitude


Relation to Precipitation• Water vapor-precipitation relation evident in overlaying very high

PWFT over TRMM rainfall over a month

• Next year: Direct observation of large hydrometeors via polarization (Spanish PAZ experiment)


NCARMarch 5, 2009

Driest air PDFs January 2007 minus 2008

El Nino

La Nina


GPS vs AIRS Fractional DRH vs. AltitudeDJF 06-07 minus DJF 07-08 in %

725

600

500

400

346

650

AIRS GPS

• Similar patterns• GPS DRH is larger by x2-3


Future cm & mm wavelength Occultation System:

Active Temperature, Ozone & Moisture Microwave

Spectrometer (ATOMMS)


ATOMMS Overview• Actively probes H2O 22 GHz & 183 GHz lines

– Profile both speed of light and absorption of light • Profile water vapor & temperature simultaneously which

GPS RO cannot do, to much higher altitudes• Works in clear air and clouds

• Also other constituents like O3, N2O, H218O, HDO

Þ Cross between GPS RO & MLSÞ LEO Constellation of ATOMMS

ATOMMS


Precision of Individual Water Vapor Profiles

0.01 0.1

0

20

40

60

80

lo-band: 8.0, 13.0, 17.5, 20.0, 22.21, 32.0+ hi-band: 179.0, 182.2, 183.0, 183.2, 183.3, 183.31+ high altitude pressure boundary condition

lo-band: 8.0, 13.0, 17.5, 20.0, 22.21, 32.0+ hi-band: 179.0, 182.2, 183.0, 183.2, 183.3, 183.31 without the high altitude pressure boundary condition

lo-band only: 8.0, 13.0, 17.5, 20.0, 22.21, 32.0

Fractional RMS water vapor error

alti

tud

e (

km)

183.31/179.0

183.30/179.0

10%

With turbulence

1%


Precision of Individual Temperature Profiles

0.01 0.1 1 100

20

40

60

80

SNRv0

(183GHz) = 1800

ionosphere max day (abs)0.005 mm/s RMS velocity error

local multipathabel integral initialization

hydrostatic integral initialization

Horizontal + tropical water vapor errorWater vapor error for 35S (June)

Water vapor error for 60S (June)no hydrostatic initialization

RMS temperature error (K)

Alti

tud

e (

km)

10.1


ATOMMS Instrument• Prototype instrument developed & making optical depth &

spectroscopy measurements on mountaintops• New instrument design to reduce mass & size for WB57

– Reflective rather than refractive optics, closer to LEO version

• Proposed to NASA IIP for aircraft to aircraft demonstration


Water Vapor Retrievals: Clear, Cloudy & Rain• Using mountaintop observations to demonstrate ability to

retrieve water vapor spectra in clouds and rain – Enabled by calibration tone at 198 GHz– Figures show spectrum of amplitude ratios relative to calibration

tone

Clear Rain


ATOMMS Constraints on Surface Fluxes?

• Vertical fluxes of sensible heat, FSH, & latent heat, FLH, can be represented as downgradient diffusion processes

ATOMMS can measure • Vertical gradients of q & q• Scintillations, related to turbulence & K• over regions of low topography like the oceans

FSH K HacP

z

FLH KWaLv

q

z


Near-Surface Precision with 3, 22 & 183 GHz tones

• a

subArctic WintersubArctic Winter

Temperature Error

Temperature Error

Water Vapor Fractional Error

Water Vapor Fractional Error

TropicsTropics


Thanks


Documents

Latest Water Vapor Results from GPS Radio Occultation (RO) + Next Gen E. R. Kursinski A.Kursinski 1, T. Gebhardt 2, C. Ao 3, D. Ward 4, A. Otarola 5, H