crtm activities contributions from: paul van delst, david groff, quanhua liu, tong zhu, ming chen,...

CRTM Activities

Contributions From: Paul van Delst, David Groff, Quanhua Liu, Tong Zhu, Ming Chen, Emily Liu,

Andrew Collard

JCSDA Workshop, May 13-15, 2015

JCSDA Workshop May 13-15, 2015

Overview

• Version 2.2.x release• Features for version 2.3• OSS implementation• Issues with current implementation• Features for version 3.0• Transmittance coefficient generation package

– Not enough time to talk about it, but putting the slides in regardless.

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Version 2.2.x

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Version 2.2.x

• Released internally for Q1FY16 GSI implementation• Feature set:

– Overcast radiances.– Turning on the microwave sea surface reflection correction for non-precipitating

clouds.– ATMS snow emissivity model.– FASTEM-6 (rather than fix issues with our FASTEM-5 implementation).– Minor source code changes to address zeus meta-server issues.

• V2.2.0 released 13 April 2015• V2.2.1 released 21 April 2015

– Rolled back the SRF-based ATMS transmittance coefficients.– Parallel test in GSI showed degraded results with new ATMS coefficients.

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Version 2.3

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Version 2.3

• This is the next planned public release.– v2.2.0 was released without some planned features due to time constraints.

• Feature set:– Cloud fraction– netCDF coefficient datafiles– Update of transmittance coefficient data objects– Generic aerosol optical property object– RT solver computation speedup (see also “Issues” slide)

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Version 2.3Cloud Fraction

• Initial implementation (ongoing)– A simple scalar “effective” cloud fraction, C– User is responsible for determining the value– CRTM result RTOA = (1-C)*RCLR + C*RCLD⇒– Code is waiting for review– Repeated clear sky calcs ~5% of computational cost

• Planned implementation– Cloud fraction profile input– The effective cloud fraction will be computed– Multiple overlap schemes (default max-random)

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Version 2.3netCDF Coefficient Files

• Switching all coefficient files to netCDF4– Allows the combination of datasets.– Byte-sex independent.– Easier maintenance.

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SensorCoeff

SensorInfo

SpcCoeff

TauCoeff

ACCoeff(optional)

NLTECoeff(optional)

SensorInfo group in all files and groups for checking.

SpcCoeff

Coeff Data

SensorInfo

TauCoeff

SensorInfo

ODPSCoeff

ZeemanCoeff(optional)

ODASCoeff

ODPSCoeff

Coeff Data

SensorInfo

ODASCoeff

Coeff Data

SensorInfo

ZeemanCoeff

Coeff Data

SensorInfo

Completed

CloudCoeff, AerosolCoeff, andvarious EmisCoeffs alsocompleted


Version 2.3Update of transmittance coefficient data objects

• There are three reason for doing this:– Removal of pointers from the ODAS and ODPS object definitions. Allocatables

provide the same functionality with much less code and zero danger of memory leaks.

– It will enable the CRTM to be built in single-precision mode with a simple switch in the build process. This capability was originally designed into the CRTM, but floating point dependencies crept back in via the object definitions. (Review!)

– Get the transmittance coefficient objects and their methods in the same design as all the other CRTM objects to allow usage of OO features.

• The conversion to netCDF4 is also an opportunity to do this.

• It’s taking a longer than anticipated.– Due to differences between the actual ODAS definition, and a repeated one

embedded within ODPS used for water vapour line absorption.

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Version 2.3Generic Aerosol Optical Property Object

• Currently the CRTM can only use GOCART-based aerosol optical property definitions.

• As part of the update of data objects for netCDF coefficient I/O, the AerosolCoeff object is being changed to allow its use with optical property data from any aerosol model.

• Plan (in concert with the various JCSDA aerosol folks):– Define the data object and file format, used by the CRTM, that will work with any

aerosol model output. [easy]– Generate an application that takes aerosol model output (e.g. OPAC) and creates an AerosolCoeff datafile for use with the CRTM. [harder]

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Version 2.3Cloudy RT Computational Speedup

• The default scattering radiative transfer solver is the ADA.

– It’s fast, but not fast enough compared to simpler algorithms used for “low-stream-count” RT.

• Slow cloudy computations are impacting applications.– NCEP operational post-processing that generates cloudy radiance product.– MiRS retrieval package.

• Two requirements:1. Optimise memory usage of current ADA. Modify internal data objects to not use

MAX_N_ANGLES in allocation for more efficient memory usage. This has been started, but on backburner.

2. Addition of dedicated 2- and 4-stream only RT modules.

• This speed issue really needs to be addressed.

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OSS Implementation

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OSS implementation

• AER delivered an Optimal Spectral Sampling (OSS) model, which was developed using CRTM v2.0.5.

– CRTM-OSS can directly simulate un-apodized radiance– CRTM-OSS computation efficiency depends on applications: fast for smaller ratio of

nodes per channel, slower for larger ratio of nodes per channel.

• Recently, AER delivered new OSS coefficients for AIRS, IASI, CrIS and CrIS high resolution sensors.

• We have performed a preliminary evaluation of the OSS model in terms of bias and standard deviation.

• We are working on merging the delivered CRTM-OSS code into the current CRTM trunk.

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OSS ImplementationCRTM-OSS unapodised radiance simulation

Unapodised radiances provide more spectral information

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OSS ImplementationOSS/ODPS speed comparison

• Clear-sky forward simulation for two gases (water vapour and ozone).

L == localised training; G == global training

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Sensors OSS (seconds)

ODPS (seconds)

CPU time Ratio

IASI_B1_metop-a_L 2260 ch., 1012 nodes 2.712 5.730 2.11

IASI_8461_metop-a_L8461 ch., 3680 nodes

8.662 22.006 2.54

CrIS_npp_G1146 ch. (B1+B2), 638 nodes

1.686 3.303 1.96

CrIS_npp_L1305 ch., 7665 nodes

17.643 3.719 0.21


OSS ImplementationIASI B1 OSS/ODPS radiance simulations

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Profile 1Warm Atmosphere

Tskin = 302.56K

Profile 2Moderate Atmosphere

Tskin = 272.99K

Profile 3Cold Atmosphere

Tskin = 236.07K

Pretty good agreement Pretty good agreement Large CO2 and O3 differences


Issues with current implementation

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Issues with current implementation

• CIRA problem with 3.9μm solar reflectivity when clouds are present

• Cold temperature bias over winter pole in GDAS• Radiative transfer computation speed in scattering

atmosphere (mentioned previously, planned for v2.3)– Memory issue– Algorithm implementation

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IssuesCIRA-identified SWIR cloudy simulation problem

• CIRA researchers identified serious problem with 3.9μm solar reflectivity when clouds are present.

– CRTM is producing much too cold temperatures, by up to 50K!– CIRA OO (uses SHDOM) produces good results compared to observations.

• Two changes made– Fix to ADA solar reflectivity application to include solar affected channels.

Previously only visible channels had solar reflectivity included.– Updated the infrared ice cloud optical properties (Ping Yang’s group, TAMU)

• The changes sort of improved results, but not really.• The implementation of solar reflectivity in the presence

of clouds in the CRTM needs to be fixed.

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Data provided by Louis Grasso, CIRA



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Data provided by Louis Grasso, CIRA


IssuesCold temperature bias over winter pole

• This is a potential issue that needs investigation.– Jim Jung (CIMSS/JCSDA) is trying to increase use of water vapour channel

information– Added water vapour channels for CrIS and both IASIs– Parallel experiments show a cold temperature bias over the winter pole– Need to assess the performance of the CRTM for infrared channels in cold dry

atmospheres to determine if any contribution.

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Skin Temperature, August 2014, EXP - CNTRL


Version 3.0

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Version 3.0

• Nascent feature set– Only mentioning because main interface will change

• CSEM/CRTM integration– For CSEM science, see Ming Chen’s talk.

• Change to main CRTM interface– Multiple surface subtypes.– Shift to more complete OO design/usage.

• An interface description document is being developed.– CRTM requirements.

• FWD, TL, and AD models.• Microwave, infrared, and visible spectral coverage.

– CSEM requirements.– Proposed interface to CSEM procedures.

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Version 3.0CSEM integration

• Goal is to allow CSEM to be developed completely separately from CRTM. (Duh!)

• Main issue is getting information into CSEM from CRTM.– Definition of input CSEM data structures need to be made available to CRTM.

• Two approaches are being considered– Use the CSEM structures as a component of CRTM structures.– Use the CSEM structures as the base and extend them with CRTM structures.

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• CSEM objects as components in CRTM surface structure.

TYPE :: CRTM_Surface_type ...other components... TYPE(CSEM_Land_Surface_type), ALLOCATABLE :: Land(:) ...other components...END TYPE CRTM_Surface_type

– Downside here is that the CRTM will be completely dependent upon the CSEM definition.

– Adding extra CRTM-specific land info will be difficult. Or square-peg-in-round-hole-y.

– This tightly couples the models (A Bad Thing™)

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• CSEM objects as base types for equivalent CRTM objects

TYPE, EXTENDS(CSEM_Land_Surface_type) :: CRTM_Land_Surface_type

...any CRTM specific components...

END TYPE CRTM_Land_Surface_type

TYPE :: CRTM_Surface_type

...other components...

TYPE(CRTM_Land_Surface_type), ALLOCATABLE :: Land(:)

...other components...

END TYPE CRTM_Surface_type

– CRTM definition can change as required– More loosely coupled models

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Closing remarks

• Huge amount of work performed by David Groff and Emily Liu over the year with CRTM user support, and implementation and testing of CRTM in GSI.

– Yong Chen, Andrew Collard, and John Derber too.

• Releases– V2.3 is planned for Q1FY16.– CRTM-OSS release likely for Q2FY16.

• Testing schedule in GSI needs to be firmed up.

– V3.0 is planned for 2016.• Actual date depends on what else is added.

• Issues mentioned need to be addressed in the meantime.

– Same for TauCoeff generation package.

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crtm activities contributions from: paul van delst, david groff, quanhua liu, tong zhu, ming chen,...

Documents

xjcsda workshop

andrew collard jcsda

crtm objects

new atms coefficients

fix issues

planned features

odps object definitions

emily liu