# Presentation: 885371

## THE IMPACT OF ENSEMBLE SIZE TO  REGIONAL CLIMATE FORECAST Jun Wang and  Hann-Ming Henry JuangEnvironmental Modeling Center, NCEP, Washington DC

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**Verification methodologies**
- Several ensemble verification skills are computed :
- a. Ensemble mean forecast skill
- Anomaly Correlation, Root Mean Square Error, Bias
- b. Ensemble spread
**Verification data**:
- H500: NCEP_DOE reanalysis data
- T2m:CPC T2m regrid 2,5 degree data
- Rain: CPC US_MEX 1 degree rain gauge data** **

**Introduction**
- NCEP Regional Spectral Model (RSM) nested in Global Forecast System (GFS) has been used for regional climate prediction. How to design an effective ensemble  system is essential for the regional climate forecast. In this project, an ensemble climate forecast on CONUS with up to 30 ensemble members  was conducted to explore the impact of ensemble size on both global and regional climate forecasts on CONUS region. The effect of initial conditions in regional ensemble climate forecast is also investigated. The relative importance between the ensemble size and initial conditions is evaluated in these experiments.

**Experiment Setup**
-  NCEP RSM regional climate forecast
-         **Model**:  GSM+RSM
-            GSM: CFS2003 T62L28
-            RSM: RSM2007 45km, 28 levels
-            Domain: US continental
-        **Hindcast**
-        A 30-member ensemble of 4 month integration for 1982-2004
-       Initial Condition: 00Z and 12Z for Feb 09-13, Feb 19-23 and Last two day of Feb and the first three days of March
-       Boundary Condition: Forecasted SST from CFS
-       Forecast period: 4 month forecast from March to Jun
-      **Ensemble experiment sets**:
**Initial conditions for experiments**:
- Exp 2-3: 00Z from Feb09-13, Feb19-13, Fen17/18 Mar 1-3
- Exp 4-5: 12Z from Feb09-13, Feb19-13, Fen17/18 Mar 1-3
- Exp 6-8: choose every 3 from 30 initial conditions
- Exp 9-11: 00/12 Z from Feb09-13(exp9),Feb19-23(exp10) and Feb27/27&Mar1-3(exp11)
- Exp 12-14:00Z from Feb 19-23, Feb 27/28 &Mar1-3(exp12), Feb 09-13, Feb 27/28 &Mar1-3(exp13), Feb09-13,19-23(exp14)
- Exp 15-17:12Z from Feb 19-23, Feb 27/28 &Mar1-3(exp12), Feb 09-13, Feb 27/28 &Mar1-3(exp13), Feb09-13,19-23(exp14)
- Exp 18-19: Every other member from EXP11
- Exp 20-21: Every other member from EXP10
- Exp 22-23: Every other member from EXP8
- Exp 24-25: Every other member from EXP7
- Exp 26: 00Z from Feb 28(or 29), Mar 1-2

| member | 30 | 15 |  |  |  | 10 |  |  |  |  |  |  |  |  |  |  |  | 5 |  |  |  |  |  |  |  | 3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Set # | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 |

**Summary**
-  From the ensemble mean skills (AC and RMS) and ensemble spread skills, at least 10 ensemble members are required to obtain compatible forecast skills to 30 member ensemble forecasts.
-  The forecast skills of RSM are consist with that of GSM except the ensemble spread skill on 2 meter Temperature, where ensemble size has more significant impact on RSM than GSM.
-  With the same ensemble member, Initial conditions have little impact on the ensemble spread skill. The large spread on initial conditions would not produce large spread ensemble climate forecast.
-  Compared to initial condition perturbations, ensemble size has more impact on regional climate forecast.
**Reference**
-  Buizza R. and T.N. Palmer,1998: The impact of ensemble size on ensemble prediction. Mon Wea Rev.,126,2503-2518
-  Kumer, Arun, ,A. Barnston, and M. P. Hoerling, 2001: Seasonal prediction, probabilitistic verifications, and ensemble size. J. Climate. 14.  1671-1776
-  Szunyogh I. and Z. Toth, 2002: The effect of increased horizontal resolution on the NCEP global ensemble mean forecasts
-  Zhu, Y. 2007: Objective evaluation of global precipitation forecast. In special collection of: International symposium on advances in atmospheric and information technology, Beijing, China, 2007 P3-8

- Fig2.30 member ensemble mean precip skill

- Fig4. Precip for Obs, GSM 30 members, 10 members , and RSM 30 members, 10 members

- Fig3. Ensemble spread skill (a)H500 (b)T2m (c) Precip. The red line is experiment 18. Below the red line  are experiments with ensemble member 10, 15 and 30. Above the red line are experiments with ensemble member 5 and 3.

- Fig1. Ensemble mean skills (a). Anomaly correlation (b). Root mean square error (c ). Bias. Black line is for GSM, Green line is for RSM. The means of skills  for experiments with same ensemble member(15, 10 and 5) are in orange (GSM) and in blue (RSM).