John M. Forsythe, Stanley Q. Kidder*, Andrew S. Jones, and Thomas H. Vonder Haar

1. Moisture Profile Retrievals from Satellite Microwave Sounders for Weather Analysis Over Land and Ocean John M. Forsythe, Stanley Q. Kidder*, Andrew S. Jones, and Thomas H. Vonder Haar Cooperative Institute for Research in the Atmosphere (CIRA) Colorado State University Fort Collins, CO USA Corresponding author: forsythe@cira.colostate.edu

2. Passive microwave moisture information not fully exploited, even over oceans but especially over land. Hinders potential gains on critical forecast needs like improved quantitative precipitation forecasts (QPF) and forecaster products with vertical water vapor structure. Direct radiance assimilation requires land emissivity knowledge. The CIRA 1-Dimensional Optimal Estimator (C1DOE) was developed to explore these challenges (retrieval approach is that of Rodgers (2000)). Motivation “By virtue of the very tight vertical and horizontal gradients that develop… Moisture-related fields have historically been the most difficult to forecast… this remains true in modern high-resolution models” (Zapotocny et al. 2005).

3. India Passive Microwave Atmospheric Moisture Products are Typically Not Produced Over Land TPW (mm) Global, Blended 6-satellite AMSU / SSM/I Total Precipitable Water Over Oceans (Kidder and Jones; J. Atmos. Oceanic Tech., Jan. 2007)

4. Data – The Advanced Microwave Sounding Unit (AMSU) • Two modules: AMSU – A and AMSU – B (or MHS) • 20 channels: 23.8 to 183 GHz • Spatial resolution from 16 – 48 km at nadir • NEDT values ranging from 0.11 to 1.06 K (very low) • On NOAA satellites and Aqua (and METOP) Microwave Transmittance Spectrum 183 GHz used for moisture sounding

5. 5 AMSU-B moisture channels span troposphere • AMSU-B weighting functions calculated using a near standard tropical atmosphere (TPW = 38 mm), incidence Angle = 53º.

6. C1DOE Basis Cost of retrieved atmosphere versus background atmosphere Cost of satellite measurements versus calculated radiances • Approach: Minimize cost function by iterating on the retrieved atmosphere and surface. • Heritage in satellite sounding work of Rodgers; Engelen and Stephens. Radiative Transfer Model Result Satellite Observations Retrieved atmosphere and surface A priori information

7. C1DOE RTM Agrees Closely With NOAA CRTM CRTM being added as an optional solver into C1DOE

8. CIRA 1DVAR Optimal Estimator (C1DOE) Data Flow • Outputs • ~600 diagnostic fields / retrieval • Mixing ratio profile, temperature profile, cloud liquid water profile at 7 levels from 1000 to 100 hPa • 6 Emissivity bands • TPW • Integrated CLW • Many diagnostics Errors and Correlations (Sa and Sy) 20 channels AMSU-A AMSU-B Instrument Properties C1DOE Retrieval (Capability for SSMIS) Near real-time system has been demonstrated First Guess and a priori data SST / LST Land Emissivity (MEM / AGRMET) Cloud mask / Infrared data (optional) T(p), RH (p), Tsfc (GDAS) Currently MEM model, use retrieved emissivity next

9. Initial Ocean Validation of C1DOE Promising 25 20 850 hPa (g/kg) 1000 hPa (g/kg) 20 15 15 10 Retrieved Retrieved 10 Bias = 1.76 RMS = 2.23 R = 0.90 Bias = 1.19 RMS = 1.91 R = 0.92 5 5 0 0 0 5 10 15 20 25 0 5 10 15 20 Radiosonde Radiosonde Constant 50 % RH first guess Island radiosonde sites 15 8 500 hPa (g/kg) 700 hPa (g/kg) 6 10 Retrieved Retrieved 4 5 Bias = 0.83 RMS = 1.46 R = 0.92 Bias = -0.15 RMS = 0.68 R = 0.88 2 0 0 0 5 10 15 0 2 4 6 8 Radiosonde Radiosonde

10. 30 mm 15 mm 0 0 November 6, 2006 “Atmospheric River” (Major Floods on US West Coast) Different techniques, similar results… NOAA MSPPS TPW C1DOE TPW …but C1DOE provides vertical information TPW (mm) Note different scales 500 – 300 hPa Layer TPW 1000 – 850 hPa Layer TPW Moist boundary layer, dry aloft

11. C1DOE Improves Moisture Over Forecast Model Initialization C1DOE (integration of profile) NOAA MSPPS “TRUTH” (column-only technique) GDAS (a priori) C1DOE captures spatial gradients well in the stratus region GDAS: 3.67 mm bias C1DOE: 1.5 mm bias vs. NOAA MSPPS mm

12. Land emissivity first guess currently derived from NOAA MEM model, then iterated upon further in C1DOE. 89 GHz Land Emissivity from NOAA MEM (Microwave Emisivity Model). June 8, 2006 2030 UTC.

13. 12 24 36 48 60 72 Emissivity Must Be Constrained to Retrieve Atmosphere TPW (mm) No emissivity constraint: Little atmospheric change Emissivity Variance = 0.5 (very large!) GDAS TPW, 18 UTC June 8, 2006 • Expect increased convergence with: • Dynamic land emissivity background • Infrared cloud detection • Observation / RTM bias reduction Tight emissivity constraint: white areas nonconvergent Emissivity variance = 0.01

14. 0 % 100 % % Variance Due to AMSU Observations: 500 hPa Mixing Ratio Observations have more impact on moisture solution, but more nonconvergent retrievals Observations have almost no impact on moisture solution Emissivity Variance of 0.5 (“loose constraint”) Emissivity Variance of 0.01 (“tight constraint”) Emissivity must be constrained, otherwise error is dumped there

15. Analysis of ~ 300 surface GPS stations over land to provide TPW validation source at high time resolution (few minutes) Blended Total Precipitable Water (mm) 06 UTC Sept 16 to 18 UTC September 17, 2007

16. Conclusions The CIRA 1-Dimensional Optimal Estimator (C1DOE) has been validated over ocean at island sites Emissivity must be tightly constrained over land to retrieve the atmosphere Some retrievals over land are possible at present using the MEM model emissivity with tight constraint GPS Total Precipitable Water is a useful validation dataset Work in Progress Further refinement of the land emissivity approach: Dynamic emissivity database Retrieve emissivity or supply as a fixed value? Multisensor cloud properties from infrared data Future comparisons to NOAA MIRS retrieval system

17. Backup Slides

18. Scatter indicates forecast model initialization does not have correct moisture / clouds. GDAS Simulated TB AMSU TB June 8, 2006 A type of metric for model cloud and moisture performance. GDAS Simulated TB GDAS Simulated TB AMSU TB AMSU TB GDAS computed forward model brightness temperatures versus measured AMSU brightness temperatures in the stratus region.

19. First guess atmosphere and surface Calculate weighting functions (sensitivity) Forward problem solved to yield estimates of the radiance in each channel Millimeter Wave Propagation (MPM92) Model (Liebe et al. 1993) Rayleigh cloud droplet absorption (Liebe et al. 1991) assuming a plane parallel, non-scattering atmosphere Match observed and modeled radiances Iterative process C1DOE Retrieval Methodology Additional details in Rodgers (2000)

20. C1DOE cost function (Φ): Minimize Differences between Observed and Simulated Tbs Minimize Differences between a priori and retrieved states • *Error per channel (<= 3.5 K) • NEDT (noise) • Forward Model error • Biases: sensor - model • *A priori errors • q(p): 25-50% RH • w(p): 0.15 mm • T(p): 1.5 K, ε: 0.01 A priori ensures solution is physical and acts as a virtual measurement to further constrain the problem.

21. Model Bias for 26 vertical RTM levels Minus 7 Levels CH 1 = 23.8 CH 2 = 31.4 CH 3 = 50.3 CH 4-8 = T(p) CH 16 = 89 CH 17 = 150 CH 18-20 = 183 4 4 2 2 26 level – 7 level RTM DTb Obs – Model (K) 0 0 window -2 -2 window windows windows -4 -4 1 2 3 4 5 6 7 8 16 17 18 19 20 1 2 3 4 5 6 7 8 16 17 18 19 20 Channel Channel Bias Correction for RTM Vital All zenith angles • Simulated TB’s calculated from pristine, clear sky, island sonde matchups and compared to AMSU TB’s. • Further refinement in progress

22. Cloudy Sky Retrieved q(p) vs. Radiosonde q(p) 25 20 1000 hPa 850 hPa (g/kg) (g/kg) 20 15 15 Retrieved Retrieved 10 10 Bias = 1.47 RMS = 2.39 R = 0.81 Bias = 1.28 RMS = 1.93 R = 0.88 5 5 Errors increase over clear sky 0 0 0 5 10 15 20 0 5 10 15 20 25 Radiosonde Radiosonde 15 8 700 hPa 500 hPa (g/kg) (g/kg) 6 10 Retrieved Retrieved 4 5 Bias = 0.90 RMS = 1.78 R = 0.82 Bias = -0.13 RMS = 0.78 R = 0.87 2 0 0 0 5 10 15 0 2 4 6 8 Radiosonde Radiosonde

23. RETRIEVAL APPLICATIONS Cloud from 1000 – 950 hPa (100 – 500 m) GOES-10 2030 UTC(visible – ch1) GOES-12 2030 UTC (visible –ch1) Stratus region Gulf of Mexico • AMSU swath on June 8, 2006 – remapped to 25 km • Comparison withNESDISMSPPS TPW (Grody et al. 2001) • Claims 0.9 mm bias, 3.0 mm RMS error vs. RAOBs • Retrieval Diagnostics (Chi-square, A matrix)

24. Total Precipitable Water C1DOE MSPPS “TRUTH” GDAS (a priori) C1DOE captures spatial gradients well in the stratus region GDAS: 3.67 mm bias C1DOE: 1.5 mm bias Vs. MSPPS mm

25. C1DOE vs. GDAS water vapor profiles 700 hPa 1000 hPa 850 hPa C1DOE g/kg 5 12.5 20 0 7.5 15 0 5 10 GDAS C1DOE reduces the “blocky” structure of 1º by 1º GDAS best at 1000 hPa, with increased GDAS contribution as you ascend

26. AMSU % contribution from the A matrix Chi-Square (χ2) “goodness of fit” 700 hPa 200 hPa % 0 850 hPa 300 hPa 0 25 50 75 100+ Reasonable χ2 (< 25) except near coastline 32.5 1000 hPa 500 hPa Higher AMSU contribution to retrieved variance near the surface and aloft 65

27. 12 24 36 48 60 72 CIRA Blended GPS (land)/SSMI/AMSU TPW GDAS TPW, 18 UTC June 8, 2006 CIRA experimental blended GPS TPW product provides validation source over land. TPW (mm) C1DOE retrieves at 7 levels and integrates to obtain TPW Clear regions

28. Total Precipitable Water C1DOE MSPPS GDAS (a priori) C1DOE shows positive biases with respect to MSPPS in clear sky and in regions where clouds may not be captured mm