GOESR AWG Soundings Team: Legacy Vertical Moisture Profile, Legacy Vertical Temperature Profile, Der

1
GOES-R AWG Soundings Team Legacy Vertical
Moisture Profile, Legacy Vertical Temperature
Profile, Derived Stability Indexes, and Total
Precipitable WaterJuly 21, 2009
Presented By Tim Schmit1 and Jun Li2 1
NOAA/NESDIS/STAR 2 Cooperative Institute for
Meteorological Satellite Studies, UW

• Thanks to Zhaohui Cheng4, Hua Xie3, Walter
  Wolf1, Lihang Zhou4, and Shanna Sampson3
• 3 IMSG, 4 PSGS

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Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

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Executive Summary

• The purpose of the sounding products is to
  monitor the changes in the atmosphere related to
  water vapor and temperature (and derived
  products, such as total precipitable water).
• Using ABI data, products will be produced to
  continue the legacy products that currently are
  being done with the GOES Sounder.
• The team has met the schedules (ATBD, CDR, TRR).
  Both SEVIRI and simulated ABI data have been
  used.
• The retrieved products are within spec, with the
  possible exception of low-level temperature
  (depends on the status of the pending spec
  changes and definition of boundary layer).

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AWG ABI Legacy Atmospheric Profile (LAP)
Algorithm Status and Accomplishments

• Algorithm Development Status
• Version 3 codes delivered in April 2009
• 80 ATBD delivered in May 2009
• Proxy simulated products tested SEVIRI
  measurements, simulated ABI
• Version 3 has been integrated into AIT frame
  work
• Successful TRR in May 2009 (used simulated ABI
  data)
• LAP Algorithm for both SEVIRI and simulated ABI
  is integrated into the CIMSS GEOCAT
• Accomplishments so far
• Code delivery milestone met
• ATBD delivery milestone met
• Proxy simulated products validated

LAP Algorithm Developers Jun Li, Tim Schmit, Xin
Jin, Jinlong Li, Zhenglong Li, Elisabeth Weisz
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Test Readiness Review Legacy Atmospheric
Profile Unit Software Verification
Difference of (AIT-Geocat)
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Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

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Introduction Contents

• Project Objectives
• Stakeholders
• Teams
• Project Plan

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Project Objectives (1)

• Develop algorithms and deliver Algorithm
  Theoretical Basis Documents (ATBDs) to the GOES-R
  Ground Segment Project Office (GSPO)
• Legacy Vertical Moisture Profile
• 3 FPS Requirements
• Legacy Vertical Temperature Profile
• 3 FPS Requirements
• Derived Stability Indices
• 3 FPS Requirement
• Total Precipitable Water
• 3 FPS Requirements

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Project Objectives (2)

• Deliver Algorithm Package to the GOES-R GSPO for
  the Four Soundings Products (Legacy Vertical
  Moisture Profile, Legacy Vertical Temperature
  Profile, Derived Stability Indexes, and Total
  Precipitable Water)
• ATBD
• Test Data
• Proxy and Simulated Input/Output Test Data Sets
• Associated Coefficient Data Sets
• Software Toolkits Required
• Testing Information
• Programs
• Descriptions
• Plans and Procedures
• Performance Testing Results

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Project Stakeholders

• NOAA National Weather Service
• Weather Forecast Offices
• local forecasters
• National Center for Environmental Predictions
• SPC, MPC, TPC, AWC, EMC
• OSDPD
• Atmospheric research community
• OAR, STAR
• DoD
• Climate
• International partners
• South American collaborators
• EUMETSAT

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The AWG Sounding Team Members

• Product Development Team
• STAR
• Shanna Sampson
• Hua Xie
• Lihang Zhou
• Walter Wolf

• Co-Chairs
• Timothy J. Schmit
• Christopher D. Barnet
• AWG Soundings Team
• Jun Li (CIMSS)
• Xin Jin (CIMSS)
• Jinlong Li
• Zhenglong Li (CIMSS)
• William L. Smith (HU)
• Dave Tobin (CIMSS)
• Yong Han (STAR)
• Nick Nalli (Perot Systems Government Services,
  Inc.)
• Dan Zhou (NASA)

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Project Plan Schedule Milestones for All 4
Products

• Soundings Kickoff Meeting 11/16/05
• Proposal for Soundings Product Team 06/30/06
• Algorithm Design Review - 05/31/07
• Critical Design Review - 06/18/08
• Test Readiness Review 04/15/09 (03/10/09)
• Covers the software for the 80 delivery
• Code Unit Test Review 07/21/09
• System Readiness Review 03/08/10
• Covers the software for the 100 delivery

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IMS Delivery Schedules Sounding Products
ATBD draft 09/30/08
ATBD 80 09/30/09
ATBD 100 09/30/10
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Project Timeline Sounding Algorithm Development
Phase (toward 80 maturity)
ADR 05/31/07
1st Code Delivery to AIT 09/28/07
Sounding Algorithm Development Phase In Progress
CDR 06/18/08
ATBD 80 to AIT 05/29/09
ATBD draft to AIT 06/30/08
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Project Timeline Sounding Algorithm Development
Phase (toward 100 maturity)
ATBD 80 to AIT 05/26/09
4thCode Delivery to AIT 06/23/09
UTR 07/21/09
ATBD 100 to AIT 05/31/10
SRR 03/08/10
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Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

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• Requirements

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Requirements Legacy Vertical Moisture Profile
C CONUS FD Full Disk M - Mesoscale
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Legacy Vertical Moisture ProfileProduct
Qualifiers
Pending approval, Legacy Vertical Moisture
Profile - Remove 'Clear and above cloud
regions' from Geographic Coverage parameter.
C CONUS FD Full Disk M - Mesoscale
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Requirements Legacy Vertical Temperature
Profile
Pending approval, Legacy Vertical Temperature
Profile - Change accuracy from '0.1 K
improvement over numerical weather prediction
model analysis' to '1 K below 400 hPa and above
boundary layer' Change the Precision from '0.1
K improvement over numerical weather prediction
model analysis (TBR)' to '2 K below 400 hPa and
above boundary layer'.
C CONUS FD Full Disk M - Mesoscale
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Legacy Vertical Temperature Profile Product
Qualifiers
Pending approval, Legacy Vertical Temperature
Profile - Remove 'Clear and above cloud
regions' from Geographic Coverage parameter.
C CONUS FD Full Disk M - Mesoscale
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Requirements Derived Stability Indices
Pending approval, Derived Stability Indices -
Remove the qualifier before accuracy value -
Change Horizontal Resolution from 4 km to 10 km.
- Remove the '/-' before Precision values.
C CONUS FD Full Disk M - Mesoscale
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Derived Stability Indices Products Qualifiers
C CONUS FD Full Disk M - Mesoscale
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Requirements Total Precipitable Water
Pending approval, Total Precipitable Water -
Change accuracy from '10 compared to ground
truth' to 1 mm.
C CONUS FD Full Disk M - Mesoscale
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Total Precipitable Water Products Qualifiers
Pending approval, Total Precipitable Water -
Remove 'Clear and above cloud regions' from
Geographic Coverage parameter.
C CONUS FD Full Disk M - Mesoscale
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Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

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• Algorithm Theoretical Basis for Legacy
  Atmosopheric Profile (LAP)
• Legacy Vertical Moisture Profile, Legacy Vertical
  Temperature Profile, Derived Stability Indexes,
  and Total Precipitable Water
• Presented byJun Li and Tim Schmit

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Algorithm Theoretical Basis

• The purpose
• Provide a scientific and mathematical
  description of the GOES-R ABI legacy atmospheric
  profile retrieval algorithm for sounding and
  derived product developers, reviewers and users.
• Will be documented in the GOES-R ABI legacy
  atmospheric profile Algorithm Theoretical Basis
  Document (ATBD)

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Algorithm Theoretical Basis Outline

• Introduction
• Observing System Overview
• Product Generated
• Instrument Characteristics
• Algorithm Description
• Algorithm Overview
• Processing Outline
• Algorithm Input
• Theoretical Description
• Test Data Sets and Outputs
• Simulated/Proxy Input Data Sets
• Output from Simulated/Proxy Data Sets
• Error Budget

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Algorithm Theoretical Basis Outline

• Practical Considerations
• Numerical Computation Considerations
• Programming and Procedural Considerations
• Quality Assessment and Diagnosis
• Exception Handling
• Algorithm Validation
• Assumptions and Limitations
• Performance
• Assumed Sensor Performance
• Pre-Planned Improvements
• References

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ABI Visible/Near-IR Bands
Schmit et al, 2005
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ABI IR Bands
Schmit et al, 2005
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Figure courtesy of J. Li, CIMSS
Concept of flex mode scanning animation
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The relative vertical information is shown for
radiosondes, a high-spectral infrared sounder,
the current broad-band GOES Sounder and the ABI.
The high-spectral sounder is much improved over
the current sounder. This information content
analysis does not account for any spatial or
temporal differences.
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While there are differences, there are also many
similarities for the spectral bands on MET-8 and
the Advanced Baseline Imager (ABI). Both the
MET-8 and ABI have many more bands than the
current operational GOES imagers.
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GOES-R ABI Weighting Functions
ABI has 1 CO2 band, so upper-level temperature
will be degraded compared to the current sounder
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GOES-13 Sounder WFs
The GOES-N sounder has 5 CO2 bands, more
Shortwave bands than ABI
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Legacy Atmospheric Profile (LAP) ADR Algorithm

• At ADR, the two-step algorithm was selected
• Regress for first guess followed by
• 1D variational physical algorithm, which is
  similar to the current GOES Sounder physical
  retrieval algorithm
• Further studies have been performed since ADR to
  optimized the above approaches

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LAP ADR Algorithm (2)

• Features of the Selected Legacy Atmospheric
  Profile Algorithm
• 1D variational approach
• Regression as first guess,
• Regression from combined IR radiances and
  forecast information
• Emissivity information from database
• Surface pressure from forecast
• EOF representation of profile
• Forecast error covariance matrix
• Radiance bias adjustment
• Excellent historical heritage (e.g. current GOES
  and MODIS)
• Advantages
• Low risks
• Efficient
• Algorithm is mature
• Disadvantages
• Request surface IR emissivity information
• Need radiance bias adjustment
• Time continuity is not included

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LAP ADR Algorithm (3) Key upgrades from the
current GOES sounding algorithm for ABI

• Temperature and moisture profiles as well as the
  surface skin temperature from a regression is
  used as the first guess in physical retrieval
• Surface IR emissivity from UW baseline fit
  database (Seemann et al. 2008)
• Use forecast error covariance matrix derived from
  matchup file in the physical retrieval
• Use model accompanying Jacobian or K-matrix

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LAP CDR Algorithm (4)Upgrades

• Overall meet the requirements
• Modified Current GOES Sounding Algorithm for ABI
• Regression is usually better than forecast
• Minimize emissivity impact on legacy sounding
  product
• Computational efficient
• Good historical heritage
• EOFs for profile representation
• From hemispheric training dataset (SeeBor
  training developed at CIMSS)
• Bias adjustment
• From comparisons between radiosondes/radiances
  matchup (post launch implementation)
• Handling surface emissivity use selection
• Database from baseline fit
• Emissivity spectra from LEO hyperspectral
  radiances
• Error covariance matrix
• From comparisons between radiosondes and forecasts

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LAP Algorithm Objectives

• Provides state-of-the-art LAP over the GOES-R
  observation domain, in all ABI scanning modes
• Single time step
• Meets the GOES-R mission requirement specified
  for the LAP products
• Algorithm maturity and LAP heritage
• Computational efficiency
• Improvement potential

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Processing Overview

• Use 2 km cloud mask product
• Average clear radiances within each N by N FOVs
• Take collocated forecast temperature and moisture
  profiles
• Take surface IR emissivities from database
• Perform regression
• Perform physical retrieval for temperature and
  moisture profiles
• Perform product generation for other derived
  products (TPW, Layer PW, LI, K-index, TOTO, CAPE,
  SI)
• Output product and QC flags

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Processing Outline Regression Flowchart
ABI IR Radiances Calibrated, Navigated
Bias adjusted, Clear ABI Radiances
Radiance averaging Bias adjustment Cloud masking
Forecast T/Q profiles
Apply regression coefficient
First guess (T, Q, O, Ts)
Go to physical Module
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Processing Outline - Physical Retrieval Flowchart
Error matrix, EOF file for T(p) and q(p), emiss
Start
Forward model calculation
Output
Input BT, forecast Surface analysis parameters
Jacobian calculation
Calculate derived products
Next iteration
Regression (T,W,O,Ts)
Return updated T,W,O,Ts
yes
No
Decrease gamma
Increase gamma
Inversion calculation Update profiles
(To physical module)
(Exit physical module)
Fail lt fm Check Iteration lt Im dR gt dRc
yes
no
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LAP Algorithm InputSensor Input
Current Input
Will be added
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Bands Used on Different Instruments
Bands used in regression procedure for different
instruments
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Bands Used on Different Instruments (2)
Bands used in physical procedure for different
instruments
Will be used
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LAP Algorithm InputSensor Input Details

• For each field of regard (FOR 5 by 5
  fields-of-view), within each ABI scanning mode
• Averaged clear sky ABI brightness temperatures
  (with more than 10 clear FOVs)
• Satellite-view geometry (averaged local zenith
  angle from FOR)
• ABI sensor quality flags
• ABI NeDT

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LAP Algorithm InputAncillary Data

• Three types of ancillary data needed
• ABI Data Clear sky mask
• Non-ABI Dynamic Data 6-18 hour forecast
  temperature and moisture profiles, forecast error
  covariance matrix (assume no correlation between
  temperature and moisture), surface pressure
• Non-ABI Static Data Surface IR emissivity,
  Temperature and moisture EOFs, Regression
  coefficient and QC on ABI IR radiances

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LAP Algorithm InputAncillary Input Details

• ABI Product Data Clear Mask

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LAP Algorithm InputAncillary Input Details

• ABI Channel Use Index

3.9 µm will be used at night in physical
iteration
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LAP Algorithm InputAncillary Input Details (2)

• Non-ABI Dynamic Data 6-18 hour forecast
  temperature and moisture profiles, forecast error
  covariance matrix, surface pressure, IR surface
  emissivity

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LAP Algorithm InputAncillary Input Details (3)

• Non-ABI Static Data EOFs and Coefficients

q water vapor mixing ratio (g/kg)
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LAP Algorithm Output (1)

• Metadata in header of the file
• Processing date stamp
• Others

• Scientific data

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LAP Algorithm Output (2)

• Metadata in header of the file
• Processing date stamp
• Others

• Scientific data

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LAP Algorithm Output (3)

• Metadata in header of the file
• Processing date stamp
• Others

• Scientific data

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LAP Retrieval Strategy

• LAP retrieval will be performed
• Within each scanning mode
• Average clear pixels within FOR (up to 5 by 5
  FOVs, M by M FOVs) only
• For day and night
• Cloud detection method
• Using ABI clear/cloud mask
• Combining short range forecast information and
  ABI radiances for first guess
• Use all IR spectral band radiances except 3.9 um
  band, 3.9 um will be used during night
• Forecast information from 100 hPa to surface are
  used
• Strategy for handling surface IR emissivity in
  LAP retrieval
• Using baseline fit (BF) emissivity database
  (Seemann et al. 2008)
• Variational approach
• EOF for profile representation (for computation
  efficiency and inverse stability)
• Using forecast error covariance matrix and
  observational errors
• Using Newtonian iteration

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LAP Physical Description (1)
Regression All ABI IR spectral bands are used
except 3.9 µm band
Not used in REG
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LAP Physical Description (2)
Physical All ABI IR spectral bands are used
except 9.7 um band. Note that 3.9 um is used at
nighttime only.
Not used
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LAP Physical Description Fast RT
Given an atmospheric state X (T,q,Ts, ) a fast
RT model F allows one to compute the top of
atmosphere radiance for a radiometer channel
within a few msecs. This allows Observed minus
Calculated radiance values to be computed on the
fly Biases are possible in the forward model
calculations
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Radiative Transfer Process
LAP Physical Description fast RT (2)
Is(n) contributed from surface
emission Is(n)h contributed from upwelling
radiance Is(n)i contributed from reflected
downwelling radiance
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LAP Physical Description Fast RT (3)
All the LAP algorithm derivations are based on
the radiative transfer equation (fast and
accurate radiative transfer model is needed
surface emission
upwelling radiance
n sensing frequency e surface emissivity t
atmos. Transmittance Ts surface temperature B
Planck function T(p) air temperature at
atmospheric pressure p
reflected downwelling radiance
Radiance is converted to Brightness Temperature
(Tb) before retrieval process !
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LAP Physical Description RT - Linearization
In addition for retrievals the gradient of the RT
model with respect to the atmospheric state
variables is also required. This is called the
Jacobian matrix, or K-Matrix. Biases are
possible in the Jacobian calculations
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LAP Mathematical Description RT Linearization
(2)
Linearization of the Radiative Transfer Equation
using RTM accompanying linear model or K-matrix.
RTTOV and cRTM have K-matrix calculations.
Surface skin temperature Jacobian
Temperature profile Jacobian
WV mixing ratio Jacobian
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LAP Mathematical Description Profile
Information from Radiance Measurements
Baselined on the RT linearization, we have
For example,
With background profiles and delta TB, the
unknown profiles can be solve by the left
equation (N number of ABI IR channels, L number
of vertical pressure levels)
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LAP Mathematical DescriptionTerminology
Where Y is vector of IR radiance channels, ABI
is 10 X is state vector Profile T(p) and
q(p) on L vertical pressure levels plus surface
skin temperature F is fast radiative transfer
model (operator) for radiance measurements
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LAP Mathematical DescriptionTerminology (2)

• Operators to compute gradient of model YF(X)
  about atmospheric state X. The full Jacobian
  matrix F is
• Y has dimension of number of channels and X the
  number of state vector variables
• F can be a large matrix if more than 1 profile
  at a time is operated on (hence the TL/AD
  operators) but for 1 profile it is chans x
  (levels x ngases surface) so is used in 1DVar
  applications.

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LAP Mathematical DescriptionVariational
Equation
Minimize the cost function for optimal solution
of X
The first term reflects optimal solution, while
the second term is added for stabilizing the
solution due to the ill-posed inverse problem
Where X state vector Ym observed brightness
temperature vector F radiative transfer model
(function of X) E Observation error covariance
matrix (diagonal) Xb Background state vector
from forecast B Background error covariance
matrix
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LAP Mathematical DescriptionIterative Form
Using Newtonian iteration to solve (by first
order of Taylor expansion f(x)f(x0)
f(x0)(x-x0)) the following equation
We can derive the iterative form
Where
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LAP Mathematical DescriptionIterative Form (2)
Iterative form
Where
X0 starting point in iterations, also called
first guess
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LAP Mathematical DescriptionIterative Form (3)
EOF Representation of Profile
Using EOFs for profile representation (2 for
T(p), 3 for lnq(p), and 1 for Ts), assume
where
and
The new iteration form
where
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LAP Mathematical DescriptionBackground Error
Covariance Matrix
L by L temperature/temperature Error covariance
values
L by L temperature/moisture Error covariance
values (all 0)
0 0 0..0
L number of vertical atmospheric pressure levels
L by L Moisture/temperature Error Covariance
values (All 0)
L by L Moisture/moisture Error Covariance values
0 0 0..0
10
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LAP Mathematical DescriptionNotes on Background
and First Guess

• Background Xb, usually from forecast, ideally Xb
  should be independent of satellite observations
• First guess X0, is the starting point in physical
  iteration. First guess is very important, for
  example, if the first guess contains structure
  similar to the real atmosphere, the final
  solution will be good. Usually two types of
  first guess can be used
• From background X0 Xb
• From regression X0 XReg (use in ABI LAP
  algorithm)

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Derived Products from LAP

• From the retrieved temperature and moisture
  profiles, the following products can be derived
• TPW layered PW (WV_low, WV_mid, WV_high)
• LI
• CAPE
• K-Index
• Total-Totals
• Showalter Index

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Derived ProductTotal Precipitable Water

• The total atmospheric water vapor, or the Total
  Precipitable Water (TPW), is the vertically
  integrated amount of vapor from the surface to
  the top of the atmosphere.
• Expressed in terms of the height to which that
  water substance would stand if completely
  condensed.

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Derived ProductLayered Precipitable Water

• Layered Precipitable Water, is the vertically
  integrated amount of vapor from given two
  specific atmospheric pressure levels.
• WV_low Integrated PW from 900 hPa - SFC
• WV_mid Integrated PW from 700 900 hPa
• WV_high Integrated PW from 300 700 hPa

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Derived ProductLifted Index (LI)

• Lifted Index (LI) (Degrees Celsius) The Lifted
  Index is calculated by lifting a parcel of air
  dry adiabatically while conserving moisture until
  it reaches saturation. At that point the parcel
  is lifted moist adiabatically up to 500 hPa. The
  Lifted Index is the ambient air temperature minus
  the lifted parcel temperature at 500 hPa.
• More negative values denote more unstable
  profiles.

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Mathematical DescriptionLifted Index (LI)

• LI T500 Taa
• 0lt LI ? stable
• -3lt LI lt0 ? marginally unstable
• -6lt LI lt-3 ? moderately unstable
• -9lt LI lt-6 ? very unstable
• LI lt-9 ? extreme instability

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Derived ProductConvective Available Potential
Energy (CAPE)

• Convective Available Potential Energy (CAPE,
  Joules/kg) Convective Available Potential
  Energy, a measure of the cumulative buoyancy of a
  parcel as it rises, in units of Joules per
  kilogram.
• CAPE values larger than 1000 J/kg represent
  moderate amounts of atmospheric potential energy.
  Values exceeding 3000 J/kg are indicative of very
  large amounts of potential energy, and are often
  associated with strong/severe weather. 

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Mathematical DescriptionConvective Available
Potential Energy (CAPE)
Zf level of free convection Ze the equilibrium
level Tve environmental virtual temperature Tae
air parcel virtual temperature
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Derived ProductTotal Totals Index (TT)

• Total Totals Index (TT) The Total Totals Index
  is computed using discreet pressure level
  information and is indicative of severe weather
  potential.
• Total Totals Index is a sum of two separate
  indices
• Vertical Totals (measure of static
  instability)
• Cross Totals (measure of moist instability)
• Total Totals Vertical Totals Cross Totals
• TT VT CT (T850 - T500) (Td850 T500)
• Generally, TT values below 40-45 are indicators
  of little or no thunderstorm activity, while
  values exceeding 55 in the Eastern and Central
  United States or 65 in the Western United States
  are indicators of considerable severe weather.

83
Derived ProductShowalter Index (SI)

• Showalter Index (SI) The SI is a parcel-based
  index, calculated in the same manner as the
  Lifted Index, using a parcel at 850 hPa. That
  is, the 850 hPa parcel is lifted to saturation,
  then moist adiabatically to 500 hPa. The
  difference between the parcel and environment at
  500 hPa is the Showalter Index.

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Mathematical DescriptionShowalter Index (SI)
T500 air temperature 500 hPa T850 air
temperature at 850 hPa
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Derived ProductK Index (KI)

• K index (KI) The K-Index is a simple index
  using data from discreet pressure levels, instead
  of a lifted parcel. It is based on vertical
  temperature changes, moisture content of the
  lower atmosphere, and the vertical extent of the
  moist layer. The higher the K-Index the more
  conducive the atmosphere is to convection. The
  formula for KI is
• KI (T850 hPa-T500 hPa) (TD850 hPa - (T700
  hPa - TD700 hPa)
• where TTemperature, TD Dewpoint Temperature

86
LAP Algorithm Description Summary

• GOES sounding algorithm is used for ABI profiles
  and derived product.
• Variational approach with Newtonian iteration
• Forecast are used together with ABI IR radiances
  for first guess
• EOF representation of profile
• Using emissivity database from UW baseline fit
• Forecast error covariance matrix and ABI
  observation covariance matrix
• Fast and accurate radiative transfer model and
  K-matrix
• Use radiance bias adjustment (need to be
  estimated)

87
LAP Algorithm Description Summary (2)

• TPW will be derived from moisture profile
• Layer PW will be derived from moisture profile
• LI will be derived from temperature and moisture
  profiles
• CAPE, KI, TT, SI will be derived from temperature
  and moisture profiles

88
LAP Practical Considerations

• Numerical computational considerations
• EOFs for inverse stability (condition the
  ill-posed problem)
• Newton iteration for computation efficient (up to
  3 iterations)
• Programming and procedure considerations
• LAP is a purely FOR by FOR algorithm
• Functional blocks should be used for integration
  ease
• ABI clear/cloud mask must be available before the
  retrieval
• Mapping processes for forecast profiles, forecast
  error covariance matrix, and baseline fit
  emissivity can be processed offline since they
  are not instantaneous data

89
LAP Practical Considerations (2)

• Configuration of the retrieval
• The following data should be configurable for
  possible post-launch justification
• RTM coefficients
• Regression coefficient
• Temperature and moisture EOFs
• Emissivity dataset (possible resource change)
• Metadata setting
• Quality assessment and diagnostics
• Quality flags will be produced for
• Missing/No data
• Ocean, land
• Clear percentage
• Daytime, nighttime
• Large residual
• Non-convergence in iterations
• Large view angle pixel

90
LAP Practical Considerations (3)

• Exception handling
• Quality control flags will be checked and
  inherited from the sensor input data for handling
  these exceptions
• Bad sensor input data (depending on what input QC
  available)
• Missing sensor input data
• Regression will be skipped if any of the channels
  (Algorithm can not be run if any of the channels
  (8, 9, 10, 12, 13, 14, 15, and 16) data are bad
  or missing, forecast will be used as first guess
• Quality control flags will be checked and
  inherited from ABI clear/cloud mask for handling
  the following exceptions
• Missing clear radiance data
• Quality control flags will be generated for
  handling these exceptions
• Missing emissivity data

91
Bands Used on Different Instruments
Bands used in regression procedure for different
instruments
92
Bands Used on Different Instruments (2)
Bands used in physical procedure for different
instruments
Will be used
93
LAP Algorithm Description Handling Surface IR
Emissivity
Using emissivity database from UW baseline fit
Courtesy of Eva Borbas
94

• Legacy Atmospheric Profiles and Derived Products
• Test Plan

95
ABI LAP/DPs Test Plan Offline Validation Truth
Data Over Land

• Radiosondes (MSG spatial coverage) will be used
  for validating LAP and DPs over land
• ARM site radiosondes and microwave based TPW
  (frequent observations)

Radiosonde stations over land
ARM sites
96
ABI LAP/DPs Test Plan Offline Validation Truth
Data Over Ocean

• Radiosondes from AERosol and Ocean Science
  Expedition (AEROSE) (2006 2008)
• AMSR-E TPW observations

Radiosonde sites during 2006 AEROSE and SEVIRI
local zenith angle
97
ABI LAP/DPs Test Plan Offline Validation Truth
Data Over both Land and Ocean

• ECMWF analysis (6-hour global ECMWF analysis for
  temperature and moisture profiles, 0.25 degree by
  0.25 degree). One months data (August 2006)

98
ABI LAP/DPs Test Plan Offline Validation Test
Results (1)
Comparison of SEVIRI (with ABI LAP algorithm)
TPW with RAOBs over land
SEVIRI TPW using ABI LAP algorithm agree with
RAOB over land, one month (August 2006) matchup
(SEVIRI/RAOB) data are used
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ABI LAP/DPs Test Plan Offline Validation Test
Results (2)
Comparison of SEVIRI (with ABI LAP algorithm) LI
with RAOBs over land
SEVIRI Lifted Index using ABI LAP algorithm agree
with RAOB over land, one month (August 2006)
matchup (SEVIRI/RAOB) data are used
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ABI LAP/DPs Test Plan Offline Validation Test
Results (3)
Comparison of SEVIRI (with ABI LAP algorithm)
CAPE with RAOBs over land
SEVIRI CAPE using ABI LAP algorithm over land,
one month (August 2006) matchup (SEVIRI/RAOB)
data are used
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ABI LAP/DPs Test Plan Offline Validation Test
Results (4)
Comparison of SEVIRI (with ABI LAP algorithm)
profiles with RAOBs over land
ABI/SEVIRI improves the forecast moisture
profiles with ABI LAP algorithm
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ABI LAP/DPs Test Plan Offline Validation Test
Results (4)
Comparison of SEVIRI (w/ ABI LAP algorithm)
profiles with RAOBs over land
ABI/SEVIRI improves the forecast moisture
temperature with ABI LAP algorithm. Note the
issue of low-level temperature, this highlights
the importance of the (parallel work) regarding
surface emissivity.
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ABI LAP/DPs Test Plan Offline Validation Test
Results (5)
Comparison of SEVIRI (with ABI LAP algorithm)
TPW with AMSR-E product over ocean
SEVIRI TPW using ABI LAP algorithm agree with
AMSR-E over ocean, one month (August 2006)
matchup (SEVIRI/AMSR-E) data are used
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ABI LAP/DPs Test Plan Offline Validation Test
Results (6)
Comparison of SEVIRI (with ABI LAP algorithm)
TPW, WV1, WV2, and WV3 with ECMWF analysis over
both land and ocean
SEVIRI TPW, WV1, WV2, and WV3 validation with
ECMWF analysis in August 2006 (31044 samples
which is 1 of all samples). ABI LAP algorithm
is used for SEVIRI products. TPW reaches
approximately accuracy of 10 over land and
ocean.
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LAP Algorithm Description SEVIRI TPW
Demonstration
TPW retrieved from SEVIRI using ABI LAP algorithm
106
LAP Algorithm Description SEVIRI LI
Demonstration
LI retrieved from SEVIRI using ABI LAP algorithm
107
LAP Algorithm Description SEVIRI CAPE
Demonstration
CAPE retrieved from SEVIRI using ABI LAP algorithm
108
Legacy Soundings Unit Software Verification

• Test the software readiness ABI data, plus
  emissivity map and cloud mask for 2200 UTC 04
  June 2005 have been used to generate LAP on AWG
  developers Linux and the AITs Framework.
• The results were compared and confirmed on the
  pixel by pixel basis for the cloud free tested
  area.

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Legacy Soundings Unit Software Verification
TPW/WV1/WV2/WV3 retrieved using Geocat version
soundings codes for ABI. Compared to framework
at TRR.
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Legacy Soundings Unit Software Verification
LI retrieved using Geocat version soundings codes
for ABI. Compared to framework at TRR.
111
ABI Scan Loop Product Demonstration

• Time is 22 24 UTC for 04 June 2005
• Total precipitable water (TPW) and Lifted Index
  (LI) are used in the demonstration
• Flex scan mode (3) are demonstrated
• ABI full disk CONUS Mesoscale
• Shows what products could be produced.

112
Lifted Index
Figure courtesy of J. Li, CIMSS
Overlay on 11 µm BT (black/white)
113
LAP Algorithm Description Validation of the
Rest Derived Products

• The other products will be validated using
  collocated SEVIRI and Radiosonde matchup data
• Temperature profile, K-index, TT, and SI will be
  validated using ECMWF analysis, and derived
  product image (DPI) from SEVIRI measurements

114
LAP Performance Estimates

• The developed LAP algorithm will meet the
  missions requirement
• Results from SEVIRI using ABI legacy algorithm
  show that SEVIRI/ABI improve moisture forecast
  between 300 700 hPa when compared with one
  months radiosondes
• TPW can reach the accuracy of approximate 9.5
  over ocean when compared with collocated one
  months AMSR-E data
• TPW can reach the accuracy of approximate 11.5
  over land when compared with radiosondes
• Overall TPW can reach accuracy of approximate 10
  when compared with ECMWF analysis
• LI has error of 2.05 K when compared with
  radiosondes over land
• ABI accuracy is expected to be better than SEVIRI
  (more water vapor information, etc.)

115
LAP Algorithm Validation Summary
Results from SEVIRI with ABI LAP algorithm

• ABI LAP algorithm is applied to process SEVIRI
  radiance measurements for LAP products
• Comparisons are performed against radiosondes,
  LEO satellite measurements, ECMWF analysis
• Water vapor relative humidity (RH) from SEVIRI
  with ABI LAP algorithm meets the requirement
  (18)
• TPW can reach 9.5 over ocean and 11.5 over land
• LI can reach 2.05 K over land
• Validation for other products will be carried out
• ABI is expected to be better for LAP products
  than SEVIRI

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Assumptions and Limitations

• Assumptions
• The single field-of-view ABI cloud mask is
  available before the LAP retrieval
• A high quality dynamic land surface emissivity
  product is available
• Forecast temperature, moisture profiles, as well
  as surface pressure are available
• NeDR for all IR bands are reasonably good
• A fast and accurate radiative transfer model
  (RTM) along with K-Matrix computation are
  available, RTM coefficients will be updated when
  the sensor spectral response functions are
  available
• Algorithm will be calibrated with intensive
  ground measurements
• Forecast error covariance matrix will be updated
  routinely from matchup file
• Retrieval is performed on FOR basis
• Limitations
• LAP is available over clear FORs only (more
  than 10 of clear FOVs within the FOR)
• Effect of emissivity temporal variation is not
  handled
• Surface roughness and skin temperature
  non-homogeneousness are not handled
• Since it is an iterative physical retrieval,
  computation is relative expensive
• Forecast temperature is hard to improve with ABI

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Error Budget
According to SEVIRI with ABI LAP algorithm

• RH is expected to be better than 18 when
  forecast is included
• TPW can reach 9.5 over ocean and 11.5 over
  land.
• LI can reach 2.05 K over land
• Error estimates for other derived products will
  be evaluated
• ABI products should be better than that of SEVIRI

118
Future Development Plan Post-launch Plan

• Inter-Comparison with LEO (NPOESS) hyperspectral
  IR sounding data and other instrument
  measurements
• Compare with radiosondes
• Ground-truth campaign
• Compare with ECMWF, etc. analysis
• Enhance Clear Detection
• Improve Handling Surface Emissivity
• Algorithm Improvement
• Time continuity incorporation

119
AWG Sounding Team LAP Algorithm Journal
Publications
Jin, X., J. Li, T. Schmit, M. Goldberg, and
Jinlong Li, 2008 Retrieving Clear Sky
Atmospheric Parameters from SEVIRI and ABI
infrared radiances, Journal of Geophysical
Research - Atmosphere. (in press) Li, J., W.
Wolf, W. P. Menzel, W. Zhang, H.-L. Huang, and T.
H. Achtor, 2000 Global soundings of the
atmosphere from ATOVS measurements The algorithm
and validation, J. Appl. Meteorol., 39 1248 -
1268. Li, J., and H.-L. Huang, 1999 Retrieval
of atmospheric profiles from satellite sounder
measurements by use of the discrepancy principle,
J. Appl. Optics, 38(6), 916-923. Ma, X. L., T.
Schmit, and W. L. Smith, 1999 A non-linear
physical retrieval algorithm - its application to
the GOES-8/9 sounder. J. Appl. Meteor., 38,
501-513. Schmit, T. J., M. M. Gunshor, W. Paul
Menzel, J. Gurka, J. Li, and S. Bachmeier, 2005
Introducing the next-generation advanced baseline
imager (ABI) on GOES-R. Bull. Amer. Meteorol.
Soc. 86, 1079-1096. Seemann, S.W., E. E.
Borbas, R. O. Knuteson, G. R. Stephenson, H.-L.
Huang, 2008 Development of a Global Infrared
Land Surface Emissivity Database for Application
to Clear Sky Sounding Retrievals from
Multi-spectral Satellite Radiance Measurements.
J. Appl. Meteorol., 47, 108 - 123.
120
Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

121
Summary

• The purpose of the sounding products is to
  monitor the changes in the atmosphere related to
  water vapor and temperature (and derived
  products, such as total precipitable water).
• Using ABI data, products will be produced to
  continue the legacy products that currently are
  being done with the GOES Sounder.
• The team has met the schedules (ATBD, CDR, TRR)
  and is progressing. Both SEVIRI and simulated ABI
  data have been used.
• The retrieved products are within spec, with the
  possible exception of low-level temperature
  (depends on the status of the specs and
  definition of boundary layer).

122
Outline

• Executive Summary
• Introduction
• Requirements
• Algorithm Package
• ATBD
• Validations
• Summary
• Relevant Posters

123
Relevant Posters

• LAP
• J. Li et al.
• Combination with polar high spectral data
• William L. Smith Sr. Stanislav Kireev Hampton
  U
• Surface Emissivity
• J. Li et al.

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AWG ABI Surface Emissivity (SE) Algorithm Status
and Accomplishments

• Algorithm Development Status
• Algorithm Design Review held in June 2009
• Initial ATBD delivered in September 2008
• Proxy simulated products tested SEVIRI
  measurements
• Accomplishments So Far
• Algorithm is under development
• ATBD delivery milestone met
• Algorithm tested with proxy (SEVIRI)

125
AWG ABI Legacy Atmospheric Profile (LAP)
Algorithm Status and Accomplishments
8.7-um surface emissivity
MODIS emissivity - one-day composite
SEVIRI emissivity - 20002300 UTC, Aug01, 2006
(RAOB)
LAP Algorithm Developers Jun Li, Tim Schmit, Xin
Jin, Jinlong Li, Zhenglong Li, Elisabeth Weisz
126
Achieving GOES-R Mesoscale Sounding Objectives
Studies Using JAIVEx NAST-I Hyperspectral
Data William L. Smith Sr. Stanislav Kireev
Hampton U
Mesoscale Sounding Combining ABI and Polar
Hyperspectral Sounding (PHS) Radiances
April 29, 2007
May 4, 2007
The Studies are performed using JAIVEx NAST-I
data to validate an algorithm to better define
the mesoscale atmospheric thermodynamic state
using a combination of ABI and the operational
Polar Hyperspectral Sounders (e.g., IASI and
CrIS). Results for two cases study days, April
29, 2007 and May 4, 2007 are presented.
127
Open Discussion

• The time is now open for discussion