Progress Towards the Assimilation of Cloud-Affected Radiances at the GMAO

Will McCarty1 and Jianjun Jin2

1 NASA Goddard Space Flight Center2 Goddard Earth Sciences Technology and Research/USRA

Global Modeling and Assimilation OfficeJCSDA Workshop

June 5, 2013

Cloud Efforts at the GMAO

• Microwave (J. Jin)– Preparations to implement all-sky microwave

assimilation in a method consistent with:• NCEP-developed methodology• GMAO systems (namely, GEOS-5 model background fields

and physics)

• Infrared (McCarty)– Focusing on expansion of GSI towards assimilation

of cloud-affected infrared radiance measurements using a graybody assumption

Cloudy Radiance Assimilation at the GMAO

Microwave• In an effort to prepare for the launch of the Global

Precipitation Mission (GPM), efforts are underway to investigate the assimilation of microwave imagery

• Efforts to assimilate TRMM / Microwave Imager (TMI) brightness temperatures (product 1B11) are underway• Clear-sky assimilation with GSI/GEOS-5 successful

• Efforts underway to expand TMI towards all-sky assimilation• Advance efforts towards all-sky microwave radiance assimilation

• Expand NCEP methodology (M.-J. Kim et al.) that has focused on microwave sounding towards microwave imagery

• Include modifications to utilize fields consistent with the GEOS-5 backgrounds in the state and control vectors (e.g., separate cloud liquid and ice fields)

TMI Clear-Sky Assimilation• Initially, clear-sky observations were

assimilated– Not much was performed other than

consistency in O-F calculation & data counts (left, 19.35 GHz Vertical Polarization

• Not much further investigation as observations were expected to have minimal impact– Recent reanalysis sensitivity studies

indicate a system-wide sensitivity to SSMI

– Studies have shown that the upcoming MERRA2 system has a precipitation signal correlated to SSMI observations

– Possibly related to GMAO implementation of qoption=2 & GEOS-5 sensitivity to q increments ~850 hPa

16 – 21 Mar 2012

Infrared Assimilation• In GMAO forward processing, infrared radiances are

assimilated from IASI, AIRS, and HIRS• Heritage “multi”-spectral sounders like HIRS (~ 18

channels) and the GOES Sounder are being phased out– The US HIRS instruments replaced by CrIS from NPP

onward (hyperspectral – 1297 ch total, 399 for DA)– The final European HIRS launched on MetOp-B. MetOp-

C will only fly IASI (hyperspectral – 8461 ch, 616 for DA) – No Sounder in US GEO beginning w/ GOES-R– Hyperspectral sounding potentially in GEO in a number

of future longitudes

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Number of observations considered for assimilation

Number of observations used for assimilation

Observation volumeJanuary 1977 to present

Reduction of observations heavily due to presence of clouds in observations

Observationsprocessed per 6h

1979 − 2011

Observationsused per 6h 1979 − 2011 AIRS

IASI

AIRS

IASI

After thinning, QC

Before thinning, QC

How are Clouds Handled in GSI

• Cloud screening is a two-step process1. Retrieve a cloud height

• This is done via a minimum residual method (Eyre and Menzel 1989)

2. Compare cloud height against transmittance profile

• If layer-to-top of atmosphere transmittance of a channel at the retrieved cloud height is greater than 2% reject the channel

• For channels most-sensitive to the surface, this rejects ~80% of these data.

Further Exploiting IR Data

• To further exploit IR data, the next step is to include some characterization of clouds in the analysis

Clear IR Measurement = Surface + (Atmospheric Layers)

Cloudy IR Measurement = Cloud Top + (Atmospheric Layers above cloud)

Retrieved Cloud Height

• In the cloud height retrieval, a cloud fraction, N, is also solved

• Under the graybody assumption, the partially cloudy observation can then be considered for a single, fractional cloud as:

• In the GSI, we can then restructure the H operator to include the Cloud Height and Cloud fraction to allow for a partially cloudy forward operator (and also partially cloudy Jacobians)– In CRTM, cloud structure without scattering has the potential to

provide needed cloud information

Partially Cloudy IR Measurement = N * Cloudy IR Measurement +

(1 – N) * Clear IR Measurement

Clouds in the Infrared

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Obs minus Forecast (clear) Obs minus Forecast (cloudy)

• Considering the O-Fs versus cloud fraction, it is seen that the O-Fs are closer, but the cold bias is, as expected, amplified for higher (colder) clouds

• The accuracy of the calculated cloudy radiance is fundamentally dependent on the accurate retrieval of cloud height and fraction

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Cloudy Infrared Radiance Assimilation within the GSI

• Jacobians are adjusted to move sensitivity from below cloud to cloud surface

• Single footprint assimilation shows that the system is drawing to the retrieved cloud top• Magnitude is inflated due to

low observation errors.• Error in CTP will result in an

erroneous O-F, which then can negatively impact the analysis

• To compensate, CTP is allowed to vary in the minimization as a control variable

UncontaminatedIncluding Cloud

Cloud Top

Observation-Centered Control Variables

• Current GSI implementations consider control variable only in terms of grids (2D & 3D) and channel-by-channel bias predictors

• Bias prediction coefficients are of the dimension [5,number of channels]– each satellite channel on each instrument has its own set of

predictors (i.e. MetOp_AMSU-A channel 8 will have the same set of five coefficients across every footprint globally

• Observation-Centered control variables – consider a control variable at a footprint location over all

channels measured at that point – Dimension dynamic -> any number of observation-centered

control variables can be appended to the control vector 13

Observation-Centered Control Variables

• Once developed, the functionality was expanded to CTP– Cloud Fraction still considered constant and set as the

retrieved value• Jacobians

– In addition to modified TB/T(p), TB/qv(p), etc., the minimization now incorporates the CTP Jacobian, TB/pcld.

– TB/pcld can be directly differentiated from the radiative transfer equation (i.e. the appendix of Li et al. 2001)

• Background error for CTP

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Background error for CTP

• Background error for CTP (BCTP) was

considered first in a single-footprint case:– Initial CTP – 624 hPa– Initial N – 0.968

• Consider behavior of two values of BCTP

compared to clear-sky observations only and a static CTP (no variational CTP)– B

CTP = 50 hPa and 5 hPa

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Background error for CTP

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Clear

Cloudy Static CTP

Cloudy varCTP

Background error for CTP

• Variational CTP acts as a “sink”, as a function of BCTP

– As the bkg error is increases, the cloud signal is absorbed into the CTP variable

– the solution approaches clear-only result– As bkg error is decreased, result approaches static CTP

• Expected as CTP is tightly constrained to retrieved guess

• This is only for a single footprint. How does the analysis respond to a full suite of observations– Since only CTP is varying, only consider cloudy IR if 1.0 >

N > 0.9 -> higher confidence in cloud height for opaque clouds

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CTP IncrementsB = 50 hPa B = 5 hPa

• On a full analysis, large CTP background error values had a negative impact on the convergence of the minimization– Consistent with Tony McNally’s effort @ ECMWF

• One potential issue involved with this is the use of a single B value– In this study, the real sensitivity we are adding is the temperature of the

cloud top– The T/pc @ 250 hPa very different than T/pcld at 850 hPa

CTP IncrementsB = 50 hPa B = 5 hPa

B = f(CTP) Error Model

Observation Selection Criteria

• Cloud-affected AIRS observations are read as a separate data stream– Instead of type ‘airs’, type ‘airscld’– Bias correction is consistent between clear and cloudy– Data counts “doubled” in that clear are selected by

standard criteria, and cloudy are selected as coldest window channel in thinning box

• Not ideal, as this method would be biased towards multilayer clouds, but simple

– Above clouds, observations unaffected• double the number of observations in stratosphere

– Observation errors of airscld obs will be relatively deweighted in thinning box to clear obs

• Figure shows the assimilated observation counts for AIRS Ch. 123 (12 m)

• More clear-sky observation accepted in CLD experiment

• 85% of additional clear-sky observations correspond to less rejections due to cloud screening (black vs. orange)

• Additional 184% observations are assimilated (sum of red vs. orange)

• This is for a window channel, where overall rejection rates are large

Observation Counts

AIRS Clear Sky (CTL)AIRS Clear Sky (CLD)AIRS Cloudy Sky (CLD)

25 Mar - 16 Apr 2012

Observation Counts

AIRS Clear Sky (CTL)AIRS Clear Sky (CLD)AIRS Cloudy Sky (CLD)

• Though the coldest footprint is chosen, the distribution peaks at ~700 hPa

• IR-derived CTP distribution of the atmosphere is typically bimodal, w/ a lack of mid-tropospheric clouds (700-400 hPa)

• Distribution tendency towards high clouds largely affected by low fraction/transmissive cirrus

25 Mar - 16 Apr 2012

Variance of the Analysis Differences

• The analysis differences show the most variation where the additional cloudy observations are added– Shown by the distribution of assimilated Cloud-affected AIRS

observations on the left• Areas driven by common, high weight observations (i.e. sondes

over North America and Europe) show little variation1-16 Apr 2012

Std. Dev (T(CLD) – T(CTL)) @ 850 hPa Used Cloudy Obs for AIRS Ch. 123 (12 m)

Temperature (K)

Observation Characteristics

• Analysis of the clear-sky AIRS observations show that the BC is larger in CLD

• More Accurate low-levels? Or forcing O-F to be less negative -> less “cloudy” -> more observations accepted?

AIRS Clear Sky (CTL) Mean: 0.03 KAIRS Clear Sky (CLD) Mean: 0.25 K

AIRS Clear Sky (CTL)AIRS Clear Sky (CLD)

Mean Temperature Analysis DifferenceCLD - CTL

|T(CLD) – T(CTL)| @ 850 hPa |T(CLD) – T(CTL)| @ 700 hPa

1-16 Apr 2012

Temperature (K) Temperature (K)

Mean Temperature Analysis DifferenceCLD - CTL

|T(CLD) – T(CTL)| @ 500 hPa |T(CLD) – T(CTL)| @ 300 hPa

1-16 Apr 2012

Temperature (K) Temperature (K)

4-Day Forecast Verification

• Red -> CLD forecast is closer to verifying analysis than CTL (CLD is improved)• Blue -> CLD forecast is further from verifying analysis than CTL (CLD is degraded)• *VERY* limited sample

|T(CTL, t=96h) – T(CTL,t=0)| - |T(CTL, t=96h) – T(CTL,t=0)|

1-15 April 2012, 00Z only

850 hPa 600hPa

4-Day Forecast Verification

• Red -> CLD forecast is closer to verifying analysis than CTL (CLD is improved)• Blue -> CLD forecast is further from verifying analysis than CTL (CLD is degraded)• *VERY* limited sample

|Z(CTL, t=96h) – Z(CTL,t=0)| - |Z(CTL, t=96h) – Z(CTL,t=0)|

1-15 April 2012, 00Z only

500 hPa 300 hPa

Final Remarks• This effort is hampered by the initial guess of CTP

– Some effort has been in place to improve this, but the minres algorithm in its current form isn’t accurate enough

– Interpolation between layers is likely necessary, but initial implementation caused bias correction to run amok (could have been a bug)

– Inclusion of co-located imager data could likely be used to improve QC

• Sub-gridscale info can help constrain obs that don’t violate the graybody single-layer cloud assumption

• “Sink” control variables are nearly ready to be handed off (summer goal)– Effort to implement in a general manner so that it can be

readily expanded to other variables

Final Remarks

• Plans to expand beyond the graybody assumption to a more in-depth all-sky methodology are on the horizon– Particularly focusing on the analysis of thin cirrus– B. Kahn’s efforts @ JPL have shown some promise

in the retrieval of thin ice cloud properties