RO Winds, Reanalysis, PPE
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Transcript of RO Winds, Reanalysis, PPE
RO Winds, Reanalysis, PPE
Stephen Leroy1, Chi Ao2, Olga Verkhoglyadova2
CLARREO SDT Meeting, April 16-18, 2013NASA Langley Research Center
1Harvard School of Engineering and Applied Sciences2Jet Propulsion Laboratory, California Institute of Technology
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On-going activity• RO Winds: Balance winds?• Anchoring reanalysis: BAMS reviews• Developing a perturbed physics ensemble with
climateprediction.net
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Geostrophic Winds
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Leroy and Anderson, 2007: Geophys. Res. Lett., 34, doi:10.1029/2006GL028263.
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COSMIC Geostrophic Winds
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At dry pressure 100, 125, 150, 175, 200, 225, 250, 275, 300 hPa.
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Balance winds
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First order correction to geostrophic winds. In order to follow path of geostrophic winds, air parcels must accelerate. Include cyclostrophic acceleration terms.
Randel, W.J., 1987: The evaluation of winds from geopotential height data in the stratosphere. J. Atmos. Sci., 44, 3097-3120.
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Improvement with balance winds…
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Error, geostrophic winds from gridded ERA Interim
Error, balance winds from gridded ERA Interim
200 hPa
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Improvement with balance winds?
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Error, geostrophic winds after Bayesian mapping
Error, balance winds after Bayesian mapping
200 hPa
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Actual windGeostrophic wind
Jet Stream
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Jet stream location and strength, January 2007
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Actual windGeostrophic wind
Jet Stream Strength
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RO Winds: Conclusions• No benefit at this point in computing balance winds
rather than geostrophic winds– Does this change with denser RO sampling?– Is data assimilation absolutely necessary? What impact does RO
provide on winds in assimilation?
• Jet stream position seems well determined by geostrophic winds but strength is overestimated by ~10%. – Is accuracy in jet stream position sufficient for monitoring?– What influence does it have on North American weather?
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Data Assimilation Diagnostics
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+ AnalysisIncrement
NextAnalysis
= ‘Evolution’
Process order in each timestep
(e.g
.) Te
mpe
ratu
re
Dynamics
+ Radiation+ Verticaldiffusion(&GWD)
+ Convection
+ L.S.Precip
+ Other *numerics etc
‘First guess’
Analysis
* Deduced as a residual
Observation
+ Bias adj.
Departure
+ AnalysisIncrement
NextAnalysis
= ‘Evolution’
Dynamics
+ Radiation
+ Verticaldiffusion(&GWD)
+ Convection
+ L.S.Precip
+ Other *numerics etc
‘First guess’
Analysis
* Deduced as a residual
Observation
+ Bias adj.
Departure+ AnalysisIncrement
NextAnalysis
= ‘Evolution’
Process order in each timestep
(e.g
.) Te
mpe
ratu
re
Dynamics
+ Radiation+ Verticaldiffusion(&GWD)
+ Convection
+ L.S.Precip
+ Other *numerics etc
‘First guess’
Analysis
* Deduced as a residual
Observation
+ Bias adj.
Departure+ AnalysisIncrement
NextAnalysis
= ‘Evolution’
Dynamics
+ Radiation + Verticaldiffusion(&GWD)
+ Convection
+ L.S.Precip
+ Other *numerics etc
‘First guess’
Analysis
* Deduced as a residual
Observation
+ Bias adj.
Departure
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Numerical experiments at ECMWF• Investigate upper tropospheric (specific) humidity• Four runs, 4 April – 31 May 2011, 37r2 T511, 91 levels,
15min– Control– Perturb HIRS channel 12 radiative transfer (q @ 300 hPa)– Perturb AIRS channel 1783 & IASI channel 3645 radiative transfer (q @
350 hPa)– Perturb vertical diffusion
• Monitor multiple data types– Conventional in situ data: radiosondes T, q, u, v; aircraft T, u, v; – Satellite water vapor: AIRS and IASI humidity channels (1556 cm-1),
AMSU-B channel 3, HIRS channel 12– Other satellite data: AMSU-A, HIRS channel 11, scatterometer winds,
atmospheric motion winds, radio occultation bending angles, SSM/I channel 14
• Mistakes– AIRS and IASI passively assimilated– Bias correction remained dynamic
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Reanalysis
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Perturbed HIRS channel 12Perturbed vertical diffusion physics
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BAMS Review
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“In general the paper was well received by the reviewers, but …”
• Improve “crispness” of the ideas in the introduction;• Strengthen the claim that RO is anchoring the bias correction.
Perform two new runs:1) Control run without GPS RO2) Perturbed diffusion without GPS RO
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Bayesian Information on Data Types
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Form joint PDF P(x,y) using an ensemble of climate models. For each model, need (1) observation kernel to simulate data x from hindcast run, and (2) emissions scenario run to generate prediction variables y.
Internal variability in x and y and uncertain physics will both be accounted for.
With data d, set x = d and P(y|x=d ) is the projection PDF with data incorporated. P(x) is a normalization constant that guarantees a unit integral of P(y|x) over y.
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Ranking Data Types
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Satellite Measurements
In Situ Measurements
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Toward a Climate OSSE• Use a perturbed physics ensemble (PPE) with a radiance
and refractivity simulator– Consider atmospheric variable retrieval– Consider inference of radiative feedbacks and forcing
• Take advantage of accumulated expertise– Knowledge base of model sensitivity to changing parameters– Knowledge base of calibration of ensemble– Gain access to massive computing
• Collaboration with climateprediction.net– Based on HadAM3, Unified Model 4.5 of Met. Office– Legal agreement is in place– Embed PCRTM, specify sampling frequency– Specify initialization, boundary conditions
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GPS RO Processing (1)• Tool developed by Gorbunov (NOAA, CLARREO SDT)
– Use 2-parameter ionospheric fitting– Initialization at 100 km– Canonical transform type 2– Truncate lower troposphere when canonical transform signal drops below 50%
• Build on Harvard FAS cluster “odyssey”– 8.6 core-seconds per level1b calibration– 83% of CHAMP passes quality control
• Begin research– Systematic error from precise orbit determination (?)– Detectible climate signals in UTLS, stratosphere
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GPS RO Processing (2)
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7,902,055 total occultations, ~83% of which pass quality control (CHAMP).