Plansforinves-ga-ng-medependent biasesinAIRStemperatureprofiles - - PowerPoint PPT Presentation
Plansforinves-ga-ng-medependent biasesinAIRStemperatureprofiles BillIrion JetPropulsionLaboratory,CaliforniaIns-tuteofTechnology Withthanksto
Bill Irion
Jet Propulsion Laboratory, California Ins-tute of Technology
With thanks to Chris Barnet, Murty Divakarla, Eric Maddy
NOAA
John Blaisdell, Thomas Hearty, Gyula Molnar
GSFC
George Aumann, Eric Fetzer, Evan Fishbein, Steve Friedman, Bjorn Lambrigtsen, Sung‐Yung Lee, Evan Manning, Ed Olsen, Joao Teixeira
Jet Propulsion Laboratory, California Ins-tute of Technology
Courtesy of Eric Maddy
V5.0.14 + AVN Tq a priori + CarbonTracker CO2 + no AMSU + CO2 noise covariance matrix ON
NO CO2 NCV
Eric Maddy
V5.0.14 + AVN Tq a priori + CarbonTracker CO2 + no AMSU + CO2 noise covariance matrix OFF
Eric Maddy
– Turning CO2 NCV off brings bias rate down to ~ ‐50mK yr‐1
L0 MW LO IR L1A MW L1A IR
MW Calibra-on IR Calibra-on
L1B MW L1B IR
MW only Retrieval MW Only Product Cloudy Regression Cloud‐ clearing #1 Regression Regression sol’n products Physical Retrieval #1 Error Est. & Quality Control
L2 product
Cloud. Parameter Retrieval #1 Cloud. Parameter Retrieval #2 Cloud‐ clearing #2 Cloud. Parameter Retrieval #3 Cloud‐ clearing #3 Physical Retrieval #2 AMSU Retrieval #1 AMSU Retrieval #2
L0 MW LO IR L1A MW L1A IR
MW Calibra-on IR Calibra-on
L1B MW L1B IR
MW only Retrieval MW Only Product Cloudy Regression Cloud‐ clearing #1 Regression Regression sol’n products Physical Retrieval #1 Error Est. & Quality Control
L2 product
Cloud. Parameter Retrieval #1 Cloud. Parameter Retrieval #2 Cloud‐ clearing #2 Cloud. Parameter Retrieval #3 Cloud‐ clearing #3 Physical Retrieval #2 AMSU Retrieval #1 AMSU Retrieval #2
Next Steps:
rou-nes to write out results (incl. Jacobians etc.) from each processing step.
process. This problem appears to have more than
tes-ng and evalua-on.
L0 MW LO IR L1A MW L1A IR
MW Calibra-on IR Calibra-on
L1B MW L1B IR
MW only Retrieval MW Only Product Cloudy Regression Cloud‐ clearing #1 Regression Regression sol’n products Physical Retrieval #1 Error Est. & Quality Control
L2 product
Cloud. Parameter Retrieval #1 Cloud. Parameter Retrieval #2 Cloud‐ clearing #2 Cloud. Parameter Retrieval #3 Cloud‐ clearing #3 Physical Retrieval #2 AMSU Retrieval #1 AMSU Retrieval #2
Next Steps:
and ECMWF days (i.e. focus days) to compare AIRS results against each other.
consistent valida-on set across different tests will be important.
comparison allows tests under more atmospheric states (different regions, seasons, cloud condi-ons, etc.)
L0 MW LO IR L1A MW L1A IR
MW Calibra-on IR Calibra-on
L1B MW L1B IR
MW only Retrieval MW Only Product Cloudy Regression Cloud‐ clearing #1 Regression Regression sol’n products Physical Retrieval #1 Error Est. & Quality Control
L2 product
Cloud. Parameter Retrieval #1 Cloud. Parameter Retrieval #2 Cloud‐ clearing #2 Cloud. Parameter Retrieval #3 Cloud‐ clearing #3 Physical Retrieval #2 AMSU Retrieval #1 AMSU Retrieval #2
Next Steps:
calcula-ng biases and trends.
region (say under the AIRS resolu-on) may produce an erroneous trend while too thick a region may mask it.
groups, so we need assurance that results can be properly compared to each other.