# Presentation: 436159

## Efficient All-Weather (Cloudy and Clear) Observational Operator for Satellite Radiance Data Assimilation 

**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**

## 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

## Schematic of the Observational Operator

**NWP Model Output**

**RT Models: Compute radiance**

**Adjoint**

## 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

## 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)

## 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

## Status of Proposed Upgrades to the Observational Operator

- Previous

- Proposed

- Research version of OPTRAN and its adjoint

- pCRTM forward and adjoint (implemented)

- Anomalous diffraction theory

- SHDOM with tangent linear

- Coupled to RAMS output

- Standalone version (being implemented)

- SHDOMPPDA has forward and adjoint (being implemented)

- Exact scattering: Mie theory for cloudwater and Ice scattering tables (under development)

## 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

## Building the SHDOMPPDA Adjoint

_**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

- Solution iterations with fixed grid,
- use series acceleration parameters

- Divide solution iteration
- routine into three

- 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

## 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 H****T**** dy)  to machine precision for many random**
**input vectors dx (profiles of LWC, r****mm****, T; surface temp/albedo) and output vectors dy (radiances).**

## 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**

## Slide 12

## Slide 13

## Slide 14

## Sensitivity of GOES Sounder Channels in presence of clouds

**Sensitivity of GOES Sounder Channels in presence of clouds**

## Sensitivity of Water Vapor channels in ice clouds

**Ch 11**

**Ch 12**

_**Sensitivity of Water Vapor channels in ice clouds**_

**Mid-level Moisture**

**Upper-level Moisture**

## Sensitivity of Temperature channels in ice clouds

**Ch 5**

**Ch 13**

**Low-level temperature**

## 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.**

## 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.**