# Presentation: 447827

## Tests of a Prototype Ensemble Data Assimilation System at NCEP

-  Jeff Whitaker (_[jeffrey](mailto:jeffrey.s.whitaker@noaa.gov)__[.s.](mailto:jeffrey.s.whitaker@noaa.gov)__[whitaker@noaa](mailto:jeffrey.s.whitaker@noaa.gov)__[.gov](mailto:jeffrey.s.whitaker@noaa.gov)_),
- Tom Hamill (_[tom.hamill@noaa.gov](mailto:tom.hamill@noaa.gov)_),
- NOAA/ESRL / Physical Sciences Division
*With lots of help from collaborators at NCEP, *
*University of Maryland, and NRL.*

- NOAA Earth System
- Research Laboratory

## Competitors

**GSI**: NCEP’s operational grid-point statististical interpolation system, a variant of 3D-Var that allows some anisotropy of background-error covariances.
**LETKF** (an EDA algorithm): U. MD’s Local Ensemble Transform Kalman filter.  An ensemble filter parallelized to update a grid point using observations contained in a surrounding box.
**EnSRF**: (an EDA algorithm): Ensemble square-root filter.  A variant on the classical ensemble Kalman filter that avoids perturbing the observations. Serial processing of observations.

## Previous results

- Code ported to NCEP system, optimized somewhat.
- Comparison with current **operational** GSI code, GFS at higher resolution.
- Focus on forecasts of hydrologic variables (humidity, precipitation). Probabilistic forecast verification.

**What’s new**

- EDA outperforms old SSI at T62L28  resolution when only non-radiance obs assimilated (Whitaker et al. 2008, *MWR*, to appear).? link to page proofs?
- UMD’s LETKF and our serial EnSRF perform similarly. Additive inflation ‘system error’ parameterization slightly better than other methods.
- When radiances included, gap between EDA and SSI narrows (still T62L28).

## Experiments

- Test period: May 15 - June 15, 2007.
- Operational GSI used for forward operator, current operational GFS as forecast model.
- 64 members, T126L64 resolution.  LETKF used to compute analysis increment (serial EnSRF a run-time option).
- All ‘conventional’ data, cloud-track winds, COSMIC GPS, AMSU radiances (haven’t included HIRS, AIRS yet).
- Compare to GSI run at same resolution, with same input observations.
- Single forecasts run to 5 days from GSI analyses and ensemble-mean EDA analyses.
- 20-member ensembles forecasts with EDA perturbations, vs. operational ET perturbations re-centered on EDA ensemble mean.

## Forecast RMS error of Z500, PRMSL

- No significant difference between EDA and GSI.

## Forecast RMS error of precipitable water

-  EDA forecasts significantly more skillful.
-  GSI forecasts have dry bias at early leads, EDA forecasts have slight wet bias.

## Why does EDA do better with moisture?

-  EDA can make corrections to moisture field with non-moisture obs.
-  GSI can only increment moisture with moisture (or moisture channel) obs.

- Single-observation
- experiment. EDA
- ensemble mean
- background PRMSL
- contoured in white.
- PWAT background
- in colors. Obs at
- yellow dot 1 hPa
- smaller than
- background. Red
- contours = moisture
- analysis increment
- (solid +, dashed -)

## Deterministic precipitation forecast metrics (ETS, BIA)

- EDA much better in tropics, significantly better at short leads over CONUS.

**Notes:**
CMORPH is a CPC satellite microwave estimate.

## Probabilistic forecast verification (T850 CRPSS, 1-mm precipitation BSS)

- Note: Why are precipitation forecast skills so small? Verification based on new methods for calculating BSS, CRPSS that do not exaggerate forecast skill due to geographic variations in climatological event probabilities.  See Hamill and Juras, QJRMS, Oct 2006 (Vol C).

- EnKF skill larger at shortest lead, quickly loses to ET.

## Evolution of T850 Spread,EnKF and ET perturbation methods

- EnKF spreads initially more homogeneous.  Tough here
- to visualize how spreads evolve with time, so (next slide)

## Evolution of T850 spread,EnKF and ET perturbation methods

- ET spreads grow quickly in midlatitudes, EnKF does not.
- Hints at possible dynamical imbalances in EnKF IC’s.

## Conclusions

- 1st generation EDA system at NCEP performs similarly to 3D-Var, for ‘standard’ variables such as Z500 and MSLP.
- EDA better than 3D-Var for hydrologic variables, such as precipitation and water vapor.
- Perturbations for forecasts grow faster from operational ET relative to experimental EnKF.
- Computational cost depends on localization radius, ensemble size - for 1400 km and 64 members cost of EDA update about half as expensive as running the first-guess ensemble.