# Presentation: 529697

## Current Status and Progress of Data Assimilation and Ensemble Forecasting

**Current Status and Progress of Data Assimilation and Ensemble Forecasting**

**M. Tsyrulnikov and E. Astakhova**

**NCEP/EMC, May 15, 2007**

## Outline

-  Operational technology
-  A new 3D-Var scheme under construction
-  First results with the new analysis scheme in the ocean
-  Current research in satellite data assimilation
-  First results in ensemble prediction
-  Model-error stochastic modelling  (a simulation study)

## Operational data assimilation

- (M Tsyrulnikov, A Bagrov and R Zaripov)

**3-D multivariate *****OI***** based analysis (to be replaced by 3D-Var)**
***Observations:***** TEMP, SYNOP, SHIP, BUOY, SATEM, Cloud-motion and Aircraft winds, SHIP and SATOB sea surface temperatures**
***Intermittent assimilation***** **
**	****- global:** **with the semi-Lagrangian or the spectral model of the HMC**
**	****- regional/meso: experiments with WRF, plans with LM**
**	****-  static analyses with UKMO or NCEP 6-h. forecasts as the first guess **

## The Global Spectral Model

-  - Resolution :     T85L31  ( 1.41x 1.41)
-  - Semi-implicit time scheme, time step=900 s
-  - Non-linear normal mode initialisation
-  - MPI parallelization
*Operational runs twice a day *
*00:00 UTC 84-h forecast      12:00 UTC  240-h forecast*
***                    ******	***

## The spectral model:  Physics

-   **Large-scale condensation and precipitation scheme, including    **
**         ****evaporation of rain**
-    **Kuo-type c****onvection scheme**
-    **Surface processes:  *****Land surface temperature, soil moisture***
***                              ******	******       ******and  snow depth are predicted;***
***                                               ******SST is fixed  ***
-     **Vertical diffusion:     Surface layer:   *****Monin-Obukhov theory***         ***   ***
***                                                ******Free atmosphere fluxes depend on the***
-                                                 ***mixing length and the Richardson number***
-     **Radiation  scheme:    *****Mean path length method ***
***                                                ******H******2******O, CO******2******, O******3******, aerosol***
***                                                 ******5  spectral  intervals***
***                                                  ******Diurnal cycle ***
***                                                  ******Albedo depends on the snow height***

## The global semi-Lagrangian model(M Tolstykh)

-   _**Resolution**__ __***:***_***   ****** 1.125/1.40625************ lat/lon, ***
***                            ******28 σ- levels***
_**Model domain**_***:  Globe, option with rotated pole                         ***
**4****th**** order compact differences for discretization of derivatives in non-advective terms, including semi-implicit scheme and U-V reconstruction**
**Physics  from Meteo-France**
**MPI + OpenMP parallelization**
***Quasi-operational runs: ***
***00:00 UTC 84-h forecast      12:00 UTC  240-h forecast***

## RosHydromet modernization project (started in 2007)

| Source  | USD, Millions |
| --- | --- |
| International Bank for Reconstruction and Development | 80 |
| Borrower – Government of Russia | 52.5 |
| Total | 132.5 |

**RosHydromet modernization project**** (started in 2007)**
**Financing****:**

## Project Structure 

-  Technical re-equipment of  computation and communications systems
-  Modernization of observation networks
-  Enhancement of the institutional structure, improvement of information delivery techniques and emergency preparedness
**Hydrometcenter of Russia: a 8-Tfl computer with ~1000 PEs**

## Development of the new 3D-Var scheme

- (M Tsyrulnikov and P Svirenko)

***Requirements:***
**(1) ****Universality.**** The new scheme is being built to be applicable on global, regional, and meso scale**** in the **_**atmosphere**_** ****and**** also**** ****for **_**oceanic**_** data assimilation**
**(2) ****Applicability in a future***** ******EnKF****** *****data assimilation system *****(in particular, c******apability of representing spatially variable flow dependent structures******)***

## The new covariance model: SARMA

** ****Cf. the NCEP’s approach:**

**The general design:**

**ARMA in the vertical****:**

**The model implemented:**

## Ref.: 

**Tsyroulnikov M.D. Proportionality of scales: an isotropy-like property of geophysical fields. - Quart. J****. ****Roy****.**** Meteorol****.**** Soc****.****, 2001, v. 127,  N 578, 2741-2760.**

## The new covariance model: operators

** **

**The stochastic model:**

**The horizontal operators:**

**or**

## Ref.: 

**Tsyroulnikov M.D. Proportionality of scales: an isotropy-like property of geophysical fields. - Quart. J****. ****Roy****.**** Meteorol****.**** Soc****.****, 2001, v. 127,  N 578, 2741-2760.**

## The analysis formulation

**In the computation of the gain matrix:**

## The new covariance model: implementation

** **

** ****Compactly supported covariance generating functions**
** ****Sparse numerical linear algebra is used**
**- The resulting univariate 3D-Var analysis scheme takes 5-10 min. on a single Itanium processor**
**- Horizontal isotropy is built in, but can be relaxed**
**- Full degree of non-separability in 3-D correlations**
**- Spatial variability of the field structure**

## Vertically variable horizontal length scale 

**(a realization of the SARMA pseudo-random field)**** **

## Vertically variable vertical length scale

## Horizontally variable horizontal length scale

**In the spot at the center of the plot, the horizontal length scale is specified to be 1/3 of that over the rest of the globe**

## Locally tilted structures:

**Locally **_**tilted **_**structures:**
**The vertical cross-section of a realization of the random field**

## Local horizontal anisotropy. Single-obs. test results

**Isotropic case**

**Anisotropy 45 deg. from N**

## Review of the new 3D-Var system

** **

**- The new covariance model based on 3-D spatial filters is proposed, tested, and implemented in a 3D-Var system**
**- The model is formulated constructively and so is guaranteed to be well posed**
**- The model is coordinate-free and so can be used for data assimilation in the atmosphere or in the ocean on any domain and in any geometry**
**- The model is capable of representing realistic covariances with full degree of 3-D non-separability**
**- The capability to model various spatially variable field structures is demonstrated**

- M.Tsyrulnikov and P.Svirenko

## Oceanic data assimilation

-  (A Zelenko, Yu Resnyansky, M Tsyrulnikov, B Strukov and P Svirenko)

** **

** ****A primitive-equations oceanic forecast model (global, 2 deg. resolution) (similar to GFDL z-coordinate model with ‘rigid lid’ at the surface)**
** ****Upper ocean: mixed layer (integral) model similar to that of Kraus & Turner (1967) with updated parameterization of TKE budget and special algorithm for imbedding into OGCM (Resnyansky & Zelenko, 1991)**
**- A simplified 2D-Var SARMA analysis scheme**
** ****Operational observations (GTS messages ****BUOY**, **BATHY****,** **TESAC****;** **Argo**** floats transmitted in TESAC being the most influential)**
** ****Runs operationally since August 2006**

## Salinity analyses (surface layer)

**HMC 2D-Var**

**MERCATOR**

## Analyses of the speed of currents (surface layer)

**HMC 2D-Var**

**MERCATOR**

## Satellite data assimilation research: 

**first attempts**

** **

**- GPS data assimilation (implementation of the research by M. Gorbunov) **
**- A study aimed at the estimation of the likelihood function for scatterometer wind observations is underway**
** ****An investigation into spatial correlations for microwave satellite radiance observations is planned**
** ****Current plans: (1) AMSU-A and (2) GPS**

## The observation operator(M Gorbunov)

- 1. The Canonical Transform technique for GPS data processing based on Fourier Integral Operators was proposed by
***Ref:***  **M. E. Gorbunov and K. B. Lauritsen. Analysis of wave fields by Fourier Integral Operators and its application for radio occultations, *****Radio Science*****, 39(4), RS4010, 10.1029/2003RS002971, 2004.**
- 2.** **The observational operator relies on the Canonical Transform technique and  provides high accuracy and vertical resolution of 50-100 m when retrieving bending angle profiles.
***Ref:***  **M. E. Gorbunov and L. Kornblueh. Principles of Variational Assimilation of GNSS Radio Occultation Data, *****Report No. 350, Max Planck Institute for Meteorology, Hamburg*****, December, 2003.**

## Occultation 0041, 2004.01.18UTC 04:09, 26.0S 19.4E   (by M Gorbunov)

## Occultation 0097, 2004.01.18UTC 09:50, 78.8N 125.6W

## Summary of the data assimilation R/D

- (M Tsyrulnikov, P Svirenko, R Zaripov, M Gorbunov, A Zelenko, Yu Resnyansky and B Strukov)

** **

** ****The unified SARMA covariance model is proposed and implemented in the analysis system**
** ****The 3-D univariate atmospheric analysis OK, the multivariate analysis under construction**
** ****The oceanic 2D-Var is operational since 2006**
** ****The satellite assimilation is at an early stage**

## Ensemble forecasting with the breeding method: first attempts

**Based on T85L31 model**
**Breeding with global rescaling and the total energy norm**
**Breeding step 12 h**
**Analysis increment as an initial perturbation**
**9 members**
**Implemented on a 2-processor (Xeon, 1.6 GHz) server**

**Ensemble forecasting with the breeding method: first attempts**
- (E Astakhova)

## 516  552 576 contours at 500 hPa. 120h forecast. Dec. 1, 2006

## Meso-scale multi-model ensemble

- (N Veltishev)

** **

** ****MM5**
** ****WRF**
** ****Eta**
** ****LM**
** ****Two domestic models (****regional and meso****)**
**+ GTS transmitted ECMWF, NCEP, UKMO, and DWD forecasts**
**= emphasis on near-surface fields **

## Ensemble forecasts by system simulation

***System simulation*****:**
**(a) simulate analysis errors**
**(b) simulate model tendency errors**
**Need for adequate stochastic models**

## Stochastic modelling of model errors

- (M Tsyrulnikov)

**A *****simulation study*****: “truth”   = shallow-water model “model”= vorticity equation**
**1. Critical importance of state-dependent model errors**

**2. The general design of the stochastic model**

## Stochastic modelling of model errors

**Model errors appear to be *****advected*****, with the effective advection velocity very different from the wind**

## Stochastic modelling of model errors

**In ensemble prediction experiments, the identified and estimated model-error model had almost perfect performance**

## Stochastic modelling of model errors: practical conclusions of the simulation study

**When modelling model errors:**
**(1) Look for a locally (in physical space) state-dependent model errors component (state-dependence of the first kind)**
**(2) The residual (after subtraction of state-dependent errors) model errors can be regarded as stochastic, satisfying a linear advection-dissipation equation forced by the white-in-time noise **
**(3) Parameters of this equation (variance and local spatial length-scales of the driving noise, the effective advection velocity vector, and the dissipation time scale) are, most likely, also dependent on the local flow structure (state-dependence of the second kind)**

## Stochastic modelling of model errors

**- Practical stochastic modelling: is planned for 2008-2009**
**- Implementation in an ensemble prediction system (2010)**
**- Implementation in an EnKF data assimilation system (2010-2011)**
***Ref.:***
- Tsyrulnikov M.D. Stochastic modelling of model errors: A simulation study. -*** ****Quart. J**. **Roy**.** Meteorol**.** Soc**.,**** ***2005, v. 131, 3345-3371 .

## Conclusions

- The data assimilation and ensemble forecasting research and development at the HMC will be carried out along the following lines:
** ****The 3D-Var analysis scheme (2007-2008)**
** ****The** **Ensemble Prediction System based on system simulation (2009-2010)**
** ****The** **Ensemble Kalman Filter based data assimilation system (2009-2011)**

**Mikhail Tsyrulnikov and Elena Astakhova**

## Areas of possible joint research

** ****Flow-dependent covariance modelling**
** ****Satellite data assimilation (specification of obs-error covariances)**
** ****Ensemble prediction**
** ****Model-error modelling**