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Line-by-line model development in support of the JCSDA PI – Eli Mlawer Co-PI – Jean-Luc Moncet Atmospheric and Environmental Research AER contributors:

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Presentation on theme: "Line-by-line model development in support of the JCSDA PI – Eli Mlawer Co-PI – Jean-Luc Moncet Atmospheric and Environmental Research AER contributors:"— Presentation transcript:

1 Line-by-line model development in support of the JCSDA PI – Eli Mlawer Co-PI – Jean-Luc Moncet Atmospheric and Environmental Research AER contributors: Yingtao Ma, Gennady Uymin, Matt Alvarado, Karen Cady- Pereira, Dan Gombos, Alan Lipton Other contributors to the material presented: Vivienne Payne (JPL), David Turner (NOAA-NSSL), Maria Cadeddu (DOE-ARL), Stefan Kneifel (U. of Cologne), Paul van Delst (JCSDA), Quanhua Liu (JCSDA), Sid Boukabara (JCSDA) Joint Center for Satellite Data Assimilation Workshop May 21, 2014 Copyright 2014, Supported by JCSDA external research program through NOAA contract NA13NES – “Modernizing the Community line-by-line radiative transfer models”

2 Satellite data assimilation depends on accurate spectroscopy –Reducing uncertainties in spectroscopic line parameters and continua is critical to improving the use of satellite data in numerical weather prediction (NWP) and climate models. JCSDA Community Radiative Transfer Model (CRTM) is trained using AER line-by-line models –Both OPTRAN-based CRTM and new version developed with AER’s OSS approach –Two LBL models – LBLRTM and MonoRTM (Clough et al., 2005) _____________________________________________ Transition of our research focus in 2014 –Formerly focused on improving model quality – accuracy, capabilities,... –Current focus is on modernizing the coding in the models Lesser focus on efforts with respect to LBLRTM, MonoRTM, OSS enhancements JCSDA and Line-by-Line Modeling at AER

3 Developing a Modern Community LBL Model –Motivation –Plan MonoRTM Developments –Release of MonoRTM_v5.0 Current LBLRTM Efforts –H 2 O ν2 band ( cm -1 ) Planned Enhancement to MonoRTM –Improved Microwave Liquid Absorption Model Overview of Talk

4 AER LBL codes are highly respected in community –Train fast codes used operationally – e.g. CRTM, RRTMG –Reference LBL calculations for RT code intercomparisons Code quality not up to modern standards –LBLRTM written in 1980’s for that era’s computing environment Superb scientific achievement, as attested to by its prevalence and importance today. However: Code assumes available memory is small Extensive use of common blocks, equivalence statements, etc. Difficult to enhance and maintain Goal: Keep the physics, modernize the code. Motivation for Modernizing AER’s LBL Codes

5 Code example Equivalence between two very different vectors Variable with dimension 2 equivalenced to multiple variables Numerous instances of writing out and reading from disk Motivation for Modernizing AER’s LBL Codes

6 First step – analysis of the existing code e.g. Construct calling trees for each of the 19 modules in LBLRTM Developing a Community Line-By-Line Model


8 Calling trees include the 301 subroutines in LBLRTM Analysis also performed for common blocks in LBLRTM –214 distinct common blocks –76 common blocks shared by more than one module 18 data blocks used by LBLRTM Developing a Community Line-By-Line Model

9 Goals of effort –Develop a high-quality well-documented supportable code –Developmental approach modeled after inclusive paradigm used for CRTM –Maintain the code’s current scientific integrity –Support future scientific advancement Extensive collaboration with JCSDA –Recommendations from JCSDA and wider community (selected) Refactor LBLRTM in modern Fortran (e.g. 2003/2008) Input and output files in netcdf – more appropriate names for I/O files Allow use of any line parameter file in “standard” format To the extent possible, eliminate max 2000 cm -1 restriction Parallel computing capability Developing a Community Line-By-Line Model

10 Design considerations –Remove obsolete modules (e.g. Lowtran aerosols, NCAR graphics plotting packages) –Eliminate shared common blocks – replace with Fortran structures –Minimize I/O May not be possible to completely eliminate Dynamically allocate memory at run time? –Explore utility of keeping different spectral spacing for each layer –All program options need to be tested – extensive test suite –Incremental approach to development – one subroutine at a time, ensuring that calculations don’t change Developing a Community Line-By-Line Model

11 MonoRTM upgrade Basic model features Monochromatic radiative transfer model - Designed for use at a single frequency, same physics as LBLRTM - Particularly appropriate for use in microwave region Line shape: Voigt evaluated with Humlicek algorithm (Van-Vleck Weiskopf) Line parameters in MW are from HITRAN 2012 with selected exceptions Continuum: MT_CKD_2.5 Liquid water absorption model from Liebe et al. (1991)

12 MonoRTM upgrade MonoRTM_v5.0 released in December 2013 ( -46 downloads (e.g. NOAA, NASA, numerous US universities and foreign countries) More modular coding - supports OSS-CRTM development -directly calls more LBLRTM subroutines -utilizes same binary line parameter file as LBLRTM Upgraded spectroscopy -HITRAN 2012 in MW with key exceptions (e.g. strengths of 22/183 GHz H 2 O lines) -Changes in self widths of 22 GHz and 183 GHz H 2 O lines led to reanalysis of foreign widths as in Payne et al. (2008) -Many lines added to default line list (accuracy 0.1 K for upwelling calculations) New capability to utilize line broadening coefficients due to collisions from a specific molecule (when available) -e.g. O 2 broadening due to H 2 O in microwave (Drouin et al., 2014), CO 2 due to H 2 O Speed dependent line shape option implemented (Boone et al., 2007, 2011) Output option for spectral layer optical depths (netcdf) added

13 MonoRTM upgrade Differences between TOA brightness temperatures calculated with MonoRTM_v5.0 and MonoRTM_v4.2 for a moist case (3.5 cm PWV) and SSMIS viewing geometry

14 Current version: LBLRTM_v12.2 Recent study - Alvarado et al., ACPD, 2013 –Analysis of recent spectroscopic updates to LBLRTM with respect to IASI measurements Ongoing studies –Determination of near-infrared water vapor self continuum coefficients from ground- based measurements – motivated by needs of Orbiting Carbon Observatory – II (OCO-II) –Evaluation of HITRAN 2012 line parameters in infrared using IASI and AERI measurements –Evaluation of sub-millimeter, far-infrared, and mid-infrared measurements from the DOE-sponsored Radiative Heating in Underexplored Bands Campaign – II in Chile Planned upgrades to LBLRTM capabilities –Line broadening coefficients due to collisions from a specific molecule –Allow specification of vertical profiles of isotopologue abundances Update on LBLRTM

15 Rigorous validation of recent spectroscopic updates to LBLRTM against a global dataset of 120 near-nadir measurements from IASI. The performance of LBLRTM v12.1 was compared to a previous version (LBLRTM v9.4+) to test the impact of the latest updates to the line parameters Conclusions: –The improved CO 2 spectroscopy in LBLRTM v12.1 can alter the retrieved temperature profiles by 0.5 K or more. –The LBLRTM v12.1 CO 2 spectroscopy is remarkably consistent between the CO 2  2 and  3 bands. –The H 2 O spectroscopy is improved in both the P- and R-branches of the  2 band in LBLRTM v12.1, but significant systematic residuals remain. Alvarado et al. validation study of LBLRTM

16 Residuals in the H 2 O  2 band are improved Observation Mean Residuals LBLRTM v12.1 Mean Residuals LBLRTM v9.4+

17 Residuals in the 616 JCSDA assimilated IASI channels are substantially improved Mean Residuals LBLRTM v12.1 Mean Residuals LBLRTM v9.4+ Observation

18 Evaluating HITRAN 2012 in H 2 O ν2 band Mean Residuals aer_v3.3 line file Mean Residuals HITRAN 2012 Observation

19 Radiative Heating in Underexplored Bands Campaign (RHUBC) Radiative heating/cooling in the mid-troposphere occurs primarily in water vapor absorption bands that are opaque at the surface –Includes far-infrared, which contains ~40% of OLR - essentially unvalidated Upper troposphere radiative processes are critical in understanding the radiative balance of the tropical tropopause layer and the transport of air into the stratosphere These processes need to be parameterized accurately in climate simulations (GCMs) RHUBC-II (PIs – Mlawer and Turner) Cerro Toco (~5400 m), Atacama, Chile August - October far-IR / IR interferometers ~100 radiosondes, extremely low PWV (as low as 0.2 mm) 1 microwave radiometer for PWV

20 Transmission in the Infrared

21 Atmospheric Emitted Radiance Interferometer (AERI) ARM Instrument developed at U. Wisconsin cm -1 (resolution ~0.5 cm -1 ) Far-infrared Spectroscopy of the Troposphere (FIRST) PI - Marty Mlynczak, NASA-LaRC Michelson interferometer cm -1 (resolution ~0.64 cm -1 ) Radiation Explorer in the Far Infrared (REFIR) Italian collaboration (RHUBC lead - Luca Palchetti) Fourier Transform Spectrometer cm -1 (resolution ~0.50 cm -1 ) RHUBC-II Infrared Instruments

22 Spectral Observations 170 GHz (5.6 cm -1 ) to 3 µm (3000 cm -1 ) SAO FTS (Smithsonian) FIRST (NASA/LaRC) REFIR (Italy) AERI (UW) First ever measurement of the entire infrared spectrum from 3 to 1780 μm!

23 Comparison between LBLRTM and RHUBC-II AERI 54 cases with PWV < 0.3 mm AERI LBLRTM (aer_v3.3 line file) AERI-LBLRTM Stdev of resids AERI uncertainty

24 IASI and RHUBC-II AERI present consistent pictures IASI-LBLRTM (K) HITRAN 2012 aer_v3.3 Stdev (dotted) AERI-LBLRTM Stdev of resids AERI uncertainty

25 IASI and RHUBC-II AERI present consistent pictures Very, very preliminary conclusions -A number of apparent line width, strength, position issues -No apparent issues with respect to water vapor continuum in R- branch ( cm -1 ) and in between P- and R-branches ( cm -1 ) of ν 2 band -Water vapor continuum may be ~5% too strong in P-branch of ν 2 band ( cm -1 ) Analysis approach under consideration: use both IASI and AERI datasets in an optimal estimation framework to retrieve key line parameters (width, strength,...)

26 Future work – upgrade MW liquid absorption model Research (e.g. Cadeddu and Turner, 2011) have shown that existing liquid water absorption models have issues, including the model (Liebe et al., 1991) used by MonoRTM New study by Kneifel, Turner, Cadeddu, et al. has derived a new absorption model Plan: introduce in MonoRTM later this year Next few slides from material presented in “Improving Supercooled Liquid Water Absorption Models in the Microwave Using Multi- Wavelength Ground-based Observations,” by Turner et al. at the 2014 Science Team Meeting of the DOE Atmospheric System Research program

27 Motivation Accurate quantification of liquid water path (LWP) in clouds critical for many atmospheric applications A large fraction of liquid-bearing clouds are supercooled (T cloud < 0°C) There are very few laboratory observations of water vapor absorption coefficient in microwave at supercooled temps Microwave absorption models use semi-empirical models that are fit to warm lab data and extrapolate to supercooled temps Translation: a lot of uncertainty in LWP for T cloud < 0°C !! MEI: Meissner and Wentz (2004) RAY: Ray (1972) LIE: Liebe et al. (1991, 1993) STO: Stogryn et al. (1995) ELL06: Ellison (2006) ELL07: Ellison (2007) Turner, Kneifel, Cadeddu (2014)

28 Impact on retrieved LWP Turner, Kneifel, Cadeddu (2014)

29 Datasets used 31, 52, 90, and 150 GHz obs at three sites 225 GHz also available at Summit Summit station, Greenland, 3250mZugspitze, Germany, 2650m Black Forest, Germany, 511m Turner, Kneifel, Cadeddu (2014)

30 Opacity ratios: Models vs. Obs A method from Mätzler et al. (2010) is used to separate from liquid from other optical depths using the temporal variability of the liquid. Turner, Kneifel, Cadeddu (2014)

31 Absorption coefficient: Models vs. Obs Anchor these absorption coefficients in the 90 GHz coefficients from the STO model, which were validated in Cadeddu and Turner (2011) Turner, Kneifel, Cadeddu (2014)

32 Building a new model Optimal estimation used to fit new parameters for a double-Debye model (9 parameters), using –Lab observations, typically at temps between 0 and 30°C (up to 100°C), most at frequencies below 60 GHz (up to 900 GHz) –Derived opacity ratios at supercooled temps –Absorption coefficients from Stogryn model at 90 GHz Results with respect to microwave radiometer observations Turner, Kneifel, Cadeddu (2014)

33 Summary Transition of main focus from LBL model quality/capabilities to code modernization Project underway to create a modern Community LBL (CLBL) model –Based on LBLRTM and MonoRTM –Design being done at AER, with key input from CRTM team –Requirements, project plan being finalized –Implementation mainly to be done in College Park by Yingtao Ma (AER) Major upgrade of MonoRTM recently released –Urge use of MonoRTM instead of models without up-to-date spectroscopy Future upgrades of LBLRTM and MonoRTM –Challenging due to transition of focus our JCSDA external research project –Merging HITRAN 2012 and aer_v3.3 based on IASI and RHUBC-II AERI data –Liquid water absorption coefficient model –Upgrades of line broadening method and isotopologue handling

34 Acknowledgements S. A. Clough, Clough Radiation Associates Marco Matricardi, ECMWF Joint Center for Satellite Data Assimilation (JCSDA) NASA

35 IASI Closure Study Methodology Schematic of the retrieval procedure. The dashed arrows show additional retrievals performed to assess the consistency of CO 2 in the IASI spectral range. 120 clear-sky, nighttime, over ocean IASI profiles (a subset of Matricardi, 2009) - minimize potential errors from clouds, surface emissivity, and NLTE effects. Systematic spectral residuals after retrievals indicate errors in the spectroscopy. A priori profiles: Temperature: ECMWF adjusted between 10 mbar and 0.1 mbar (Masiello et al., 2011). H 2 O: ECMWF model. O 3 : ECMWF model scaled by OMI. CO 2, N 2 O, CH 4, and CO: Aura TES climatology

36 The addition of P- and R-branch line coupling improved performance in the  2 band of CO 2 IASI Scan #754 Mean Residuals LBLRTM v12.1 Mean Residuals LBLRTM v9.4+

37 The spectroscopy updates alter the temperature profiles retrieved using the  2 band Right: Mean and std. dev. of the differences between the temperature profiles retrieved with LBLRTM v12.1 and v9.4+. Only the cases that converged for all four model/band combinations are included.

38 Updates to the MT_CKD continuum have improved performance at the  3 bandhead IASI Scan #754 Mean Residuals LBLRTM v12.1 Mean Residuals LBLRTM v9.4+ Note that green ν 3 region was not used in temperature retrievals here

39 The  2 and  3 temperature retrievals in LBLRTM v12.1 are remarkably consistent. Mean and std. dev. of the differences between the retrieved temperature profiles. Right panel: the  2 retrieval was smoothed with the  3 averaging kernel and retrieval(Rodgers and Connor, 2003).

40 Site location RHUBC-II, Cerro Toco, Chile

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