# Presentation: 133641

## Impact of Precipitation Observations onRegional Climate Simulations

*Ana Nunes, John Roads, Masao Kanamitsu*
*Scripps Experimental Climate Prediction Center  (ECPC)**La Jolla, CA*
*and*
*Phil Arkin*
*Earth System Science Interdisciplinary Center, *
*University of Maryland, College Park, MD *
- _[anunes@ucsd.edu](mailto:anunes@ucsd.edu)_

## Summary

- In this study, we use a regional climate model to assimilate different precipitation data sets: (a) the .25 deg. National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) daily precipitation analyses; (b) and the new .25 deg NOAA/CPC MORPHed precipitation (CMORPH).  To study the sensitivity of the precipitation assimilation method to these data sets, we chose a large domain, which includes North and Central America.
- To evaluate the performance of the regional spectral model results, we compared them to the North America Regional Reanalysis (NARR) fields.

## Model

- The Scripps ECPC RSM, described previously by Juang and Kanamitsu (1994); Anderson et al.  (2001); and Roads (2003), used for these experiments had 50- and 60-km resolutions and 28 vertical levels. A Mercator projection was used for the projection of the regional grid.  The RSM is a primitive equation model, with similar physics as the driving NCEP-DOE reanalysis II (R-2) Global Spectral Model as reported in Kanamitsu et al. (2002).  This study employed  Simplified and Relaxed Arakawa-Schubert cumulus convection schemes (SAS and RAS).
- .

## Data Sets

-         RSM initial and boundary conditions were obtained from the coarser scale R-2 reanalysis (1.875° resolution) and 28 vertical levels.
-        SST (1 degree resolution) was taken from the Project to Intercompare Regional Climate Simulations (PIRCS)  data set.
- (b) Precipitation data sets:
-         Daily rain rates were provided by the CPC precipitation analysis (see Higgins et al., 2000) over the U. S. domain.  R-2 precipitation fields were used for the rest of the model domain, including Mexico.
-        The 3-hourly and daily CMORPH precipitation analysis was provided on a regular grid of 0.25o. The CPC morphing (CMORPH) technique (Joyce et al, 2004) combines the low earth orbiting satellite passive microwave sensor (PMW) retrievals and the infrared channel of the geostationary satellite, which is used to spatially and temporally transport the rainfall features.

## Physical Initialization (PI) 

**“****OBSERVED” **
**RAIN RATES**
**TIME STEP ASSIMILATED**

**DAY  -1**
**ANALYSIS**

**DAY  0**
**ANALYSIS**

**PHYSICAL INITIALIZATION**
**SCHEME**

- PI-ANALYSIS** **

- FORECAST** **

- Fig. 1 -  General overview of  the  PI procedure considering  a  continuous data assimilation system.

- This scheme basically adjusts the humidity profile using the difference between the “observed” and predicted rain rates as factor of this adjustment.  In order to provide consistent temperature profiles, the cumulus and large-scale parameterizations are then requested.  This methodology differs from the used by the FSU Nested Regional Spectral Model (Nunes and Cocke, 2003), where a modified Kuo parameterization is the convection scheme, however the general PI procedure follows the same structure as shown in Fig. 1.

- Physical Initialization (PI)

## RSM 50- and 60-km Experiments

- (2) The North America experiment was performed with 50-km model resolution, starting at May 1st, 2003 at 0 UTC, using SAS.  June-July-August (JJA) will be shown. The 3-hourly as well as daily CMORPHED precipitation analyses were used.
- The Control simulations do not assimilate precipitation. In the PI simulations, the rain rates were updated every 24-h (1 and 2) and 3-h (2), and the moisture adjustment took place every time-step, which was 2 min. The boundary conditions were updated every 6 hours.

## RSM 60-km (RAS): JAS 1988Precipitation (mm/d)

- Higgins+R-2

- Control

- PI

- Area 1

- Area 2

## RSM-60km (RAS) x  Higgins+R2JAS 1988

| Precipitation
Mean | Correlation Coefficient | RMSE (mm/d) |
| --- | --- | --- |
| PI 
A1/A2 | 0.98/0.98 | 1.13/0.90 |
| Control 
A1/A2 | 0.80/0.57 | 3.19/3.87 |

## RSM 60-km (RAS)Equitable Threat Score (ETS)

## RSM 60-km (RAS)BIAS

## NCEP North American Regional Reanalysis 

- NCEP North American Regional Reanalysis
- (NARR)

- NARR is based on the Eta 32-km/45-layer resolution (see Mesinger et al, 2002).
- NARR assimilates observational data sets, which include temperature, wind, and moisture. However, the major component of the NARR is the assimilation of precipitation.
- The precipitation data set used by NARR comes from different sources, including the CPC Merged Analysis of Precipitation (CMAP), a merged combination of satellite and gauge precipitation.

- http://wwwt.emc.ncep.noaa.gov/mmb/rreanl

## NCEP North American Regional Reanalysis 

- (NARR)

- http://wwwt.emc.ncep.noaa.gov/mmb/rreanl/eta_rean_3245.gif

- The plot is courtesy of Matt Pyle of EMC.

## RSM 60-km: JAS 1988Specific Humidity (g/kg)

- PI

- Control

- NARR

## RSM 60-km: JAS 1988Temperature (K)

- Control

- NARR

- PI

## RSM 60-km: JAS 1988Horizontal wind (m/s)

- Control

- PI

-  NARR

## Slide 16

## JAS 1988: Precipitation (mm/d)

- Higgins+R-2

- Control

- PI

- NARR

## Slide 18

## RSM 50-km (SAS): JJA 2003Specific Humidity (g/kg)

- 3-h PI

- 300-hPa

- Control

- NARR

- 24-h PI

- 925-hPa

## RSM 50-km (SAS): JJA 2003Temperature (K)

- 3-h PI

- 24-h PI

- Control

- NARR

- 300-hPa

- 925-hPa

## RSM 50-km (SAS): JJA 2003Horizontal Wind (m/s)

- 3-h PI

- 24-h PI

- Control

- NARR

- 925-hPa

- 300-hPa

## Slide 22

## 50-km JJA 2003Precipitation (mm/d)

- Daily PI RSM

- 3-hourly PI RSM

- Control RSM

- 3-h CMORPH

- 24-h CMORPH

- NARR

## Slide 24

## Concluding Remarks

- Precipitation assimilation has been used by the ECPC-RSM to improve short- and long-term regional precipitation simulations as well as simulations of prognostic variables, and preliminary results using different sets of precipitation data produced model precipitation fields quite similar to the assimilated precipitation analyses, especially during warmer seasons, which was reported by Mesinger et al. (2003) about the NARR simulations as well.
- The ECPC merged precipitation analysis (CPC daily + R-2) assimilations   were able to bring the prognostic variables closer to the NARR analysis. However,  the specific humidity fields at the high troposphere had increased values.  This could be relate to the R-2 precipitation higher values found at the same area.
- Daily and 3-hourly CMORPH precipitation analyses had slightly different responses, and  increased specific humidity values were not found during any of the CMORPH assimilations.