1. 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. 2 Motivation 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)).The CIRA 1-Dimensional Optimal Estimator (C1DOE) was developed to explore these challenges (retrieval approach is that of Rodgers (2000)). “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. 3 TPW (mm) India Passive Microwave Atmospheric Moisture Products are Typically Not Produced Over Land Global, Blended 6-satellite AMSU / SSM/I Total Precipitable Water Over Oceans (Kidder and Jones; J. Atmos. Oceanic Tech., Jan. 2007)

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

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

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

8. 8 CIRA 1DVAR Optimal Estimator (C1DOE) Data Flow C1DOE Retrieval AMSU-AAMSU-B SST / LST Land Emissivity (MEM / AGRMET) 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 (S a and S y ) Instrument Properties (Capability for SSMIS) T(p), RH (p), T sfc (GDAS) Cloud mask / Infrared data (optional) First Guess and a priori data Currently MEM model, use retrieved emissivity next 20 channels Near real-time system has been demonstrated

9. 9 Initial Ocean Validation of C1DOE Promising 25 20 15 10 5 0 Retrieved 0 Radiosonde 510152025 Radiosonde 20 15 10 5 0 05 1520 Retrieved 15 10 5 0 15105002468 2 4 6 8 0 Bias = 1.76 RMS = 2.23 R = 0.90 Bias = 1.19 RMS = 1.91 R = 0.92 Bias = 0.83 RMS = 1.46 R = 0.92 Bias = -0.15 RMS = 0.68 R = 0.88 1000 hPa (g/kg) 850 hPa (g/kg) 700 hPa (g/kg) 500 hPa (g/kg) Constant 50 % RH first guess Island radiosonde sites

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

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

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

13. 13 Emissivity Variance = 0.5 (very large!) Expect increased convergence with: Dynamic land emissivity background Infrared cloud detection Observation / RTM bias reduction Emissivity variance = 0.01 Emissivity Must Be Constrained to Retrieve Atmosphere GDAS TPW, 18 UTC June 8, 2006 No emissivity constraint: Little atmospheric change Tight emissivity constraint: white areas nonconvergent TPW (mm) 122436486072

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

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

16. 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. 17 Backup Slides

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

19. 19 C1DOE Retrieval Methodology 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 Additional details in Rodgers (2000)

20. 20 C1DOE cost function (Φ): *Error per channel (<= 3.5 K) NEDT (noise) Forward Model error Biases: sensor - model Minimize Differences between Observed and Simulated Tbs Minimize Differences between a priori and retrieved states *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. 21 Bias Correction for RTM Vital Channel -4 -2 0 2 4 windows DTb Obs – Model (K) 26 level – 7 level RTM 1 2 3 4 5 6 7 8 16 17 18 19 20 Channel 0 -2 -4 2 4 1 2 3 4 5 6 7 8 16 17 18 19 20 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 = 150CH 18-20 = 183 window windows window Simulated TB’s calculated from pristine, clear sky, island sonde matchups and compared to AMSU TB’s. Further refinement in progress All zenith angles

22. 22 Cloudy Sky Retrieved q(p) vs. Radiosonde q(p) 25 20 15 10 5 0 Retrieved 0 Radiosonde 510152025 Radiosonde 20 15 10 5 0 05 1520 Retrieved 15 10 5 0 15105002468 2 4 6 8 0 1000 hPa (g/kg) Bias = 1.47 RMS = 2.39 R = 0.81 Bias = 1.28 RMS = 1.93 R = 0.88 850 hPa (g/kg) Bias = 0.90 RMS = 1.78 R = 0.82 700 hPa (g/kg) Bias = -0.13 RMS = 0.78 R = 0.87 500 hPa (g/kg) Errors increase over clear sky

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

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

25. 25 1000 hPa850 hPa 700 hPa C1DOE vs. GDAS water vapor profiles C1DOE GDAS 07.5150510512.520g/kg C1DOE reduces the “blocky” structure of 1º by 1º GDAS best at 1000 hPa, with increased GDAS contribution as you ascend

26. 26 700 hPa 850 hPa 1000 hPa 200 hPa 300 hPa 500 hPa 0 65 32.5 % AMSU % contribution from the A matrix Chi-Square (χ 2 ) “goodness of fit” Reasonable χ 2 (< 25) except near coastline 050100+ Higher AMSU contribution to retrieved variance near the surface and aloft 2575

27. 27 122436486072 TPW (mm) GDAS TPW, 18 UTC June 8, 2006CIRA Blended GPS (land)/SSMI/AMSU TPW C1DOE retrieves at 7 levels and integrates to obtain TPW CIRA experimental blended GPS TPW product provides validation source over land. Clear regions

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