E. Fishbein, E. Fetzer , S. Friedman, S-Y Lee and B. Kahn

1. Climate Data Record Assessments for the Cross-track Infrared Microwave Sounder SuiteAnalysis of ATMS EDRs E. Fishbein, E. Fetzer, S. Friedman, S-Y Lee and B. Kahn

2. Conclusions • Fix the microwave retrieval first! • Cloud-clearing can not recover from a bad microwave retrieval • c2is dominated by residuals in near-surface channels and rain contamination in G-band channels is a poor indicator of profile quality. • c2is generally a poor indicator of profile quality. • Scene-dependent EOF representation of profiles introduces unphysical vertical and horizontal structures • Strive to produce measurements which reflect information content of observations

3. Global Comparisons with ECMWFTemperature Global statistics on microwave retrieved temperature compared with ECMWF (ATMS-ECMWF) Mean Differences − proxy for accuracy • No significant bias below 3 hPa • ECMWF assimilates NOAA-19 BTs • NOAA19 is practically in the same orbit • Minimal temperature bias near tropopause Standard deviation − proxy for precision • 2K difference across the region of sensitivity • Increases to 3K near surface Tropopause • MW temperature profile IP • No Q//C applied, but makes no difference • 17 Oct 2012

4. Zonal-Mean Comparisons with ECMWFTemperature • Upper panel is all data, • Lower is Q/C using rain flag and MW retrieval status • Large mean differences above 1 hPa because profile relaxes to background • Quality control has no impact on comparison • Large vertical rippling consequence of EOF vertical representation • Representation of the tropopause • Basic properties of the tropopause • Equatorial tropopause is higher, lower temperature, sharper minimum • Polar tropopause is lower, warmer temperatures and weak minimum • A retrieved profile with limited vertical resolution smoothes minima producing a warm bias • Global ensemble doesn’t show warm bias because of “climatology-added” information adds a “climatological tropopause” • Warm bias follows tropopause from equator to pole EOF representations are not conducive to producing climate data records Mean All Q/C

5. Zonal Standard Deviation Comparisons with ECMWF Temperature • Results similar to mean comparisons • Quality control has no impact on comparison • Absence of rippling in standard deviation points to spatially correlated error source • Background state is independent of location • Large vertical rippling consequence of EOF vertical representation • Larger standard deviation near tropopause arises from shallow variability not captured in ATMS product Standard Deviation All Q/C

6. Global Comparisons with ECMWFWater Vapor Global statistics on microwave retrieved water vapor compared with ECMWF • ECMWF water vapor is less reliable than temperature, especially in upper troposphere • ATMS is insensitive to water vapor above 400 hPa. • Jump in standard deviation arises from hard constraint on saturation • The mean bias is independent of c2 • Noisy profiles are removed by Q/C, but c2 is not effective near surface where water vapor sensitivity arises from channels 1 and 2 • Scattering , saturation constraint and stiff background covariance degrades fit and increases c2. Hard-constraints should be replaced by penalty functions or find source of anomalous water vapor Investigate increased background covariance • Percentages are calculated relative to global-mean ECMWF vertical profile • 17 Oct 2012 Caveat op emptor

7. Zonal Mean Comparisons with ECMWF • The effect of saturation hard constraint is primarily located in the tropics above 300 hPa • Q/C filters out tropical large bias region, but has little impact at other locations. • Increased bias in tropic arises from global-mean background drying solution. Mean All Q/C

8. Zonal Standard Deviation Comparisons with ECMWF • The effect of saturation hard constraint is primarily located in the tropics above 300 hPa • Q/C filters out tropical large bias region, but has little impact at other locations and regions. • Possible sources of tropical large errors • Radiance forward model error for tropical conditions • Stiff background covariance matrix and global background solution • Hard constraint on saturation coupled with global profile entering solution High correlations between large c2and poor solution suggest information in radiances is not properly interpreted. Recommend algorithm changes rather than improved Q/C filtering. Standard Deviation All Q/C

9. Global Distribution of χ2 • MW is dominated by residuals in low-noise near surface channels • Roughly 20% of footprints are contained in tail with c2> 1 • Precipitation does not affect 20% of observations Log10 (c2) c2axis is log

10. Temperature Quality Control Using c2 • How well is χ2 a predictor of temperature quality? • Temperature statistics, compared with ECMWF conditioned on χ2 • Statistics are not conditional on χ2except at large χ2and near the surface. • Vertical rippling doesn’t depend on χ2 • Mean difference (accuracy) degrades at larger χ2 χ2 is not a useful predictor of quality and probably never will be Mean Standard Deviation

11. Water Vapor Quality Control Using c2 • Water vapor statistics, compared with ECMWF conditioned on χ2 • No obvious relation between χ2 and except at high χ2 values • Clamped high saturation is filtering on χ2, but nothing else • Once this is fixed other problems will be more evident • When the retrieval is unstable, the final product is biased. • High χ2 correlated with clamped 300 hPa water vapor is a good indicator that background covariance matrix to too tight for background state Mean Standard Deviation

12. Atmospheric Representation Projection of retrieval parameters on the vertical coordinate against geophysical parameters on the horizontal axis • Using EOFs has the advantage of producing a “good-looking” profile with a minimal number of EOFs. • Using EOFs adds climatology to the resulting products with little control • EOFs transfer information from a region of the atmosphere where CrIMSS has information to where it does not • For example for ATMS, • Water vapor above 400 hPa • Temperature above 2 hPa • Bayesian algorithms control the amount of climatology entering the final solution, but EOF’s negate this control • MW surface emissivity is represented with EOFs, but a physical MW model should be used over ocean • IR emissivity and reflectivity are represented by hinge points, • not enough and no physical emissivity model over ocean • Reflectivity representation needs further evaluation Water vapor vertical representation functions

13. Scene-Dependent Atmospheric Representation • Four lowest order temperature profile EOF. • EOFs are scene dependent • 8 scene types • Adjacent footprints, e.g. water and land have systematic differences owing to different set of EOFs First four temperature profile representation functions

14. Atmospheric Representations Tropical Temperature Comparisons of ATMS and ECMWF Equatorial Swath Maps • Above tropopause ATMS temperature does not show spatial variability arising from mesoscale variability • Variability could be shallow and confined to tropopause transition zone (ATMS would not be sensitive) • Retrieval uses difference EOF representation over land and water • 4K bias between land and water (even inland lakes) 1.5 km above the surface is not realistic • Next page shows partial correlation between Q/C indicators and profile quality 100 hPa 800 hPa ATMS ECMWF

15. Atmospheric Representations Tropical Temperature Comparisons of ATMS and ECMWF Equatorial Swath Maps 100 hPa 800 hPa ATMS ECMWF

16. Atmospheric Representations Tropical • Filtering on χ2,shows precipitation regions, except χ2 are not that high • EOF representation propagates information where there isn’t any 10 hPa 100 hPa 500 hPa ATMS ECMWF

17. Conclusions EOFs used in atmospheric representation lead to unphysical vertical correlations not supported by measurements c2is dominated by residuals in near-surface channels and is a poor indicator of profile quality. EOF representation of surface emissivity provides poor constraints on scan-angle and channel dependence

18. Water Vapor Channel Residuals