GOAL To build an efficient OBSERVATIONAL OPERATOR with the following features

1. Efficient All-Weather (Cloudy and Clear) Observational Operator for Satellite Radiance Data Assimilation October 2004 – September 2006 Dr. Manajit Sengupta, Dr. Tomislava Vukicevic and Dr. Thomas H. Vonder Haar CIRA/CSU Dr. K. Franklin Evans CU

2. GOAL To build an efficient OBSERVATIONAL OPERATOR with the following features (1) Capability to handle all visible and infrared satellite channels (2) Capability to handle all types of weather (cloudy and clear) (3) Can be used with any data assimilation system (1DVar, 3DVar, 4DVar, EnKF) (4) Can use output from any NWP model

3. Forward Anomalous diffraction: Compute hydrometeor optical properties pCRTM: Compute gaseous transmission Compute cost function Observations Adjoint of RT Models Adjoint of ADT Adjoint in pCRTM Schematic of the Observational Operator NWP Model Output RT Models: Compute radiance Adjoint

4. Features of the Observational Operator Required Inputs to Forward Model Profiles of Pressure(P), Temperature (T), WV Mixing ratio (r) Surface Temperature(Ts), Surface Type Hydrometeor Mixing Ratio (rc) Number Concentration (Nc) for various hydrometeor types (currently 7) (Cloudwater, pristine ice, aggregates, graupel, hail, snow and rain) Solar zenith angle (date and time of day) Satellite and channel, location of grid point, satellite position for computing zenith and azimuth with respect to sun

5. Components of the Forward Model pCRTM: Inputs: P, T, r, Ozone(O) Output: gaseous transmittance(t) used to compute gaseous absorption optical depth (a) Anomalous Diffraction Theory: Inputs: rc and Nc, Assumptions: mass- area-dimensional power law, gamma distribution, tunneling factor Outputs: Optical depth (c), Single scatter albedo (o), Asymmetry parameter (g) Radiative Transfer: SHDOMPPDA (visible, near-IR) or Delta-eddington (IR) Inputs: T, cumulative a, c, o, legendre-coefficients using HG phase function using g Output: Radiance, Reflectance (visible channels)

6. Adjoint modelling Adjoints constructed using Transformation of Algorithms in Fortran (TAF) software available from FastOpt Radiative Transfer: ADJ SHDOMPPDA or ADJ EDDINGTON Inputs: radiance_ad Outputs: T_ad, a_ad, c_ad, o_ad, g_ad, Ts_ad Adjoint pCRTM: Inputs: t_ad from a_ad Outputs: P_ad, T_ad, r_ad, O_ad Adjoint MADT: Inputs: c_ad, o_ad, g_ad Outputs: rc_ad and Nc_ad P_ad, T_ad

7. Status of Proposed Upgrades to the Observational Operator Previous Proposed Research version of OPTRAN and its adjoint pCRTM forward and adjoint (implemented) SHDOMPPDA has forward and adjoint (being implemented) SHDOM with tangent linear Standalone version (being implemented) Coupled to RAMS output Exact scattering: Mie theory for cloudwater and Ice scattering tables (under development) Anomalous diffraction theory

8. SHDOMPPDA: A Radiative Transfer Tool for Assimilation Goal: Flexible, fast, and accurate forward and adjoint models for cloudy scattering radiative transfer Based on SHDOMPP forward model Solves solar and/or thermal emission RT Arbitrary accuracy depending on angular and spatial resolution parameters chosen SHDOM modified for plane-parallel RT Source function represented with spherical harmonics on an optical depth grid Solution method: source function iteration using spherical harmonics and discrete ordinates Automatic adaptive layers to minimize error

9. Building the SHDOMPPDA Adjoint Philosophy: minimize hand coding of adjoint by modifying forward model to persuade TAF compiler to generate a decent adjoint Solution iterations with adapative grid Solution iterations with fixed grid, compute series acceleration parameters Divide solution iteration routine into three Solution iterations with fixed grid, use series acceleration parameters Generate tangent linear and adjoint with TAF operating only on last solution routine Trick TAF into avoiding extensive loop recomputations by using separate arrays for iterated variables and putting in unneeded STORE directives

10. Testing SHDOMPPDA Tests made for solar and thermal RT with multiple particle species Tangent linear compared with finite difference of forward model; < 1% differences (single precision) Adjoint tested with tangent linear by comparing inner products: (dy H dx) = (dx HT dy) to machine precision for many random input vectors dx (profiles of LWC, rmm, T; surface temp/albedo) and output vectors dy (radiances).

11. Status, Expectations and Limitations Final product will be state of the art Observational Operator with complete visible and infrared capabilities in both clear and cloudy scenarios Capable of handling different hydrometeor types and their combinations Dependent on availability of CRTM coefficients and therefore can handle visible channels only if no gaseous absorption is assumed. Will depend on the availability of input variables from NWP model

15. Sensitivity of GOES Sounder Channels in presence of clouds

16. Sensitivity of Water Vapor channels in ice clouds Ch 11 Mid-level Moisture Ch 12 Upper-level Moisture

17. Sensitivity of Temperature channels in ice clouds Low-level temperature Ch 5 Ch 13

18. Conclusions Modeled ice clouds significantly improved by GOES IR observations Modeled liquid clouds not improved as imager IR observations have little to no information below ice clouds Need water vapor information below ice cloud to improve forecast of multi-layer clouds Sounder channel have sensitivity below certain ice clouds and can potentially provide additional information.

19. References Vukicevic, T., M. Sengupta, A. S. Jones, T. Vonder Haar, 2005: Cloud Resolving Satellite Data Assimilation: Information content of IR window observations and uncertainties in estimation; J. Atmos. Sci, under review. Vukicevic, T., T, Greenwald, M. Zupanski, D. Zupanski, T. Vonder Haar, and, A. Jones, 2004: Mesoscale cloud state estimation from visible and infrared satellite radiances. Mon. Wea. Rev. , 132, 3066-3077. Greenwald, T. J., T. Vukicevic, and, L. D. Grasso and T. H. Vonder Haar, 2004: Adjoint sensitivity analysis of an observational operator for cloudy visible and infrared radiance assimilation. Q. J. R. Met. Soc. 130, 685-705. Greenwald, T. J., R. Hertenstein, and T. Vukicevic, 2002: An all-weather observational operator for radiance data assimilation with mesoscale forecast models. Mon. Wea. Rev., 130, 1882-1897.