# Presentation: 928849

## Planetary Atmospheric Data Assimilation System

- Mark I. Richardson and W. Gregory Lawson
*California Institute of Technology*

## Want & Desire

- Gridded accurate, frequent, & dense data
*Certainly* not directly available for planetary science
- Drives us to want to extrapolate the very limited available data

**Want:**

**Desire:**

- Want to understand how planetary atmospheres work:
- Dynamics of atmospheres
- Role of atmospheric motions in planetary climate systems

## Challenge of using planetary atmospheric observations

- Very sparse
- Remote sensing data (no current surface stations)
- Asynchronous
- Poorly known error characteristics (biases and (co-)variances)

- MGS 1999-2006
- MRO 2006-now

## How best to extrapolate, spatially and temporally?

_*Binning and averaging*_ - not really an extrapolation - just trading time and space
_*Functional fitting*_ - can extrapolate in an unphysical way
_*Data assimilation*_ - “done properly,” ought to physically (*i.e.* consistent with physical laws) extrapolate observations

## What our project is

- To develop a ensemble-based _data assimilation_ system for planetary use
- To optimize the system for the data-limited nature of planetary science
- To make the system easily adaptable for new planetary data sets and diverse atmospheric models
- To create a publicly available system amenable to use by (the typically small) planetary science groups
- The goal is _***not***_ forecasting - we’re after “reanalysis” data for study of the atmosphere

## The world of atmospheric DA

- discretized

- high dimensional

- images from Ross Bannister

- very, very
- high dimensional

## Get confidence and run primer experiments with simple model:

- Lorenz 96 ::

- “advection”

- “damping”

- “forcing”

- dynamics

## Ensemble Dispersion -- L96 is Chaotic! (or at least sensitive to 

- initial conditions)

- Courtesy of DART team

- 180° W

- 180° E

- 0°

- Value (units)

## Probabilistic

**Probabilistic**

**Monte Carlo**

## Ensemble-based Kalman filtering

- time

## Example of Data Assimilation using “station” observations

- Courtesy of DART team

- 180° W

- 180° E

- 0°

- Value (units)

- “Boston”

- “Barcelona”

- “Beijing”

## Ensemble approaches

- Pros:
- Estimates state-dependent uncertainty
- Direct use of nonlinear operators (no TLM or AM)
- Theoretically pleasing -- slight non-normality
- Readily parallelizable & competitive with other “state of the art” methods
- Modular / generalizable framework
- Poetry:
- Monte Carlo methods are subject to sampling errors with which one must deal

**Slides from J.L. Anderson**

**Slides from J.L. Anderson**

**Slides from J.L. Anderson**

**Slides from J.L. Anderson**

**Slides from J.L. Anderson**

**Slides from J.L. Anderson**

## What we are specifically doing

- Use a modular data assimilation tool from the National Center for Atmospheric Research (NCAR): the Data Assimilation Research Testbed (DART)
- Put a tested planetary model into DART (planetWRF) - the NCAR-based planetary Weather Research and Forecasting (WRF) model
- Introduce an example planetary dataset (the MGS Thermal Emission Spectrometer forward operator)

## NCAR’s DART

- Explicitly embraces the modular, generalizable nature of ensemble DA, thus providing a framework within which to do DA

- DART Compliance:
- Model must provide specific Fortran 90 interfaces
- Model must be callable as subroutine or drivable via shell script
- Forward Operator must be callable as subroutine
- Observations must be stored in certain format for Observation Sequence file

## NCAR’s DART

- Offers many “bells & whistles”:
- Adaptive covariance inflation
- Tools for estimating proper localization
- An ensemble smoother (!)
- Maintained and supported by NCAR

## planetWRF

- Example model - the DA system does not depend on WRF
- Grid-point, finite difference model
- Adapted for global, planetary (Mars, Venus, Titan) use
- Publicly available via NCAR

## Thermal Emission Spectrometer

## Where are we right now?

- Incorporated a terrestrial version of WRF in DART
- Tested DART for idealized systems and the terrestrial version of our WRF model
- Have obtained the TES forward operator and starting to strip it down for DART
- Have the latest version of planetWRF in testing mode for incorporation in DART

## extra slides

## Ensembles estimate state dependent uncertainty

**Slides from J.L. Anderson**

## Sampling Error from Gaussianity

- Often addressed by **Covariance Inflation**

**Slides from J.L. Anderson**

## Sampling Error from Linear Regression

-  Often addressed by **Covariance Localization**

**Slides from T. Hamill**