impact of precipitation observations on regional climate simulations

October 18-22, 2004

NOAA 29th CD Workshop

Madison, WI1

IMPACT OF PRECIPITATION OBSERVATIONS ON REGIONAL CLIMATE SIMULATIONSIMPACT OF PRECIPITATION OBSERVATIONS ON REGIONAL CLIMATE SIMULATIONS

Impact of Precipitation Impact of Precipitation Observations Observations

ononRegional Climate SimulationsRegional Climate Simulations

Ana Nunes, John Roads, Masao Kanamitsu

Scripps Experimental Climate Prediction Center (ECPC)La Jolla, CA

andPhil Arkin

Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD

[email protected]

October 18-22, 2004


Madison, WI2


Currently available global reanalyses (NCEP/NCAR Reanalysis, NCEP/DOE Reanalysis, ERA-15, ERA-40 and others) provide reasonably accurate analysis of large-scale atmospheric states, the weakest component of those reanalyses is the model-produced precipitation, which has very large errors compared to observations. For this reason, to develop downscaled analysis suitable for regional forecast initial conditions and for consistent energy budget research became a nowadays topic.

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.

SummarySummary

October 18-22, 2004


Madison, WI3


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

ModelModel

October 18-22, 2004


Madison, WI4


(a) Base and boundary conditions:

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.

Data SetsData Sets

October 18-22, 2004


Madison, WI5


““OBSERVED” OBSERVED” RAIN RATESRAIN RATES

TIME STEP ASSIMILATEDTIME STEP ASSIMILATED

DAY -1DAY -1ANALYSISANALYSIS

DAY 0DAY 0ANALYSISANALYSIS

PHYSICAL INITIALIZATIONSCHEME

PI-ANALYSISPI-ANALYSIS

FORECASTFORECAST

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) Physical Initialization (PI)

October 18-22, 2004


Madison, WI6


(1) The North and Central America experiment using 60-km resolution started at July 1st, 1986 at 0 UTC, where RAS was the cumulus convection scheme. July-August-September (JAS) 1988 will be shown. The CPC daily rain rates were used by the assimilation technique.

(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 50- and 60-km ExperimentsRSM 50- and 60-km Experiments

October 18-22, 2004


Madison, WI7


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

Higgins+R-2ControlPI

Area 1

Area 2

October 18-22, 2004


Madison, WI8


RSM-60km (RAS) x Higgins+R2

JAS 1988

PrecipitationMean

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

October 18-22, 2004


Madison, WI9


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

€

ETS =C−A

F +O−C−A

A=F ×ON

October 18-22, 2004


Madison, WI10


€

BIAS =FO

RSM 60-km (RAS)BIAS

October 18-22, 2004


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NCEP North American Regional Reanalysis NCEP North American Regional Reanalysis

(NARR)(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

October 18-22, 2004


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NCEP North American Regional Reanalysis NCEP North American Regional Reanalysis

(NARR)(NARR)

http://wwwt.emc.ncep.noaa.gov/mmb/rreanl/eta_rean_3245.gifThe plot is courtesy of Matt Pyle of EMC.

October 18-22, 2004


Madison, WI13


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

PI Control NARR

October 18-22, 2004


Madison, WI14


RSM 60-km: JAS 1988Temperature (K)

Control NARRPI

October 18-22, 2004


Madison, WI15


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

ControlPI NARR

October 18-22, 2004


Madison, WI16


ECPC-RSM 60-km x NARR: Mean for JAS 1988

Correlation Coefficient/Root Mean Square Error (RMSE)

Variable Specific Humidity (g/kg) Temperature (K) Horizontal Wind (m/s)

Level(hPa)

925 300 925 300 925 300

PI 0.92/1.37 0.92/0.11 0.97/1.01 0.98/0.63 0.78/1.40 0.95/1.90

Control 0.80/2.56 0.71/0.09 0.93/1.53 0.97/1.12 0.72/1.52 0.94/2.23

October 18-22, 2004


Madison, WI17


JAS 1988: Precipitation (mm/d)JAS 1988: Precipitation (mm/d)

Higgins+R-2

ControlPI

NARR

October 18-22, 2004


Madison, WI18


ECPC-RSM 60-km x NARR 0.5-degree

JAS 1988

Mean Precipitation (mm/d) Correlation Coefficient/RMSE (mm/d)

PI 0.82 / 2.36

Control 0.64 / 1.95

October 18-22, 2004


Madison, WI19


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

3-h PI

300-hPa

Control NARR24-h PI

925-hPa

October 18-22, 2004


Madison, WI20


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

3-h PI 24-h PI Control NARR

300-hPa

925-hPa

October 18-22, 2004


Madison, WI21


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

3-h PI 24-h PI Control NARR

925-hPa

300-hPa

October 18-22, 2004


Madison, WI22


ECPC-RSM 50-km x NARR: Mean for JJA 2003

Correlation Coefficient/Root Mean Square Error (RMSE)

Variable Specific Humidity (g/kg) Temperature (K) Horizontal Wind (m/s)

Level(hPa)

925 300 925 300 925 300

PI-3hr 0.93/1.06 0.37/0.08 0.96/1.25 0.99/0.65 0.80/1.57 0.97/1.96

PI-Daily 0.92/1.13 0.42/0.08 0.95/1.29 0.98/0.65 0.78/1.64 0.97/2.06

Control 0.89/1.68 0.24/0.10 0.95/1.32 0.98/0.60 0.81/1.51 0.96/2.00

October 18-22, 2004


Madison, WI23


50-km JJA 2003Precipitation (mm/d)

Daily PI RSM3-hourly PI RSM Control RSM

3-h CMORPH 24-h CMORPH NARR

October 18-22, 2004


Madison, WI24


ECPC-RSM 50-km x NARR 0.5-degree

JJA 2003

Mean Precipitation (mm/d) Correlation Coefficient/RMSE (mm/d)

PI-3hr 0.41 / 2.53

PI-daily 0.42 / 2.45

Control 0.27 / 3.02

October 18-22, 2004


Madison, WI25


Concluding RemarksConcluding 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.

impact of precipitation observations on regional climate simulations

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