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Impact of Precipitation Assimilation on Climate Simulations over Brazil

Impact of Precipitation Assimilation on Climate Simulations over Brazil. Ana Nunes, John Roads and Masao Kanamitsu Scripps Experimental Climate Prediction Center (ECPC) La Jolla, California, USA anunes@ucsd.edu. Summary.

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Impact of Precipitation Assimilation on Climate Simulations over Brazil

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  1. Impact of Precipitation Assimilation onClimate Simulations over Brazil Ana Nunes, John Roads and Masao Kanamitsu Scripps Experimental Climate Prediction Center (ECPC)La Jolla, California, USA anunes@ucsd.edu III LBA Scientific Conference Brasilia, Brazil

  2. Summary IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL Although atmospheric analyses and reanalyses are now providing physical realistic fields for many variables, precipitation remains problematic. Physical initialization (PI) has been proposed as a methodology for improving precipitation and related hydroclimatological simulation skill. For this reason, the Scripps Experimental Climate Prediction Center (ECPC) is now implementing a PI procedure in the Regional Spectral Model (RSM). We summarize here some of the improvements obtained over South America during the rainfall season of the Amazon region and increased activity of the South Atlantic Convergence Zone. III LBA Scientific Conference Brasilia, Brazil

  3. Model IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL The Scripps ECPC RSM, described previously by Juang and Kanamitsu (1994); Anderson et al. (2001); and Roads (2003), used for these experiments had 60-km resolution 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). III LBA Scientific Conference Brasilia, Brazil

  4. Data Set IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL RSM initial and boundary conditions were obtained from the coarser-scale R-2 reanalysis (1.875° resolution) and 28 vertical levels. SSM/I-OLR precipitation estimates were used in the simulations over South America. The SSM/I-OLR estimate was provided on a Gaussian grid of 0.7º. The NOAA/NESDIS SSM/I algorithm (Ferraro and Marks, 1995) was used to estimate the rain rates. All rainfall fields were bi-linearly interpolated to the regional model’s grid. III LBA Scientific Conference Brasilia, Brazil

  5. PI Procedure IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL 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. “OBSERVED” RAIN RATES TIME STEP ASSIMILATED PI-ANALYSIS PHYSICAL INITIALIZATION SCHEME FORECAST DAY -1 ANALYSIS DAY 0 ANALYSIS Fig. 1 - General overview of the PI procedure considering a continuous data assimilation system. III LBA Scientific Conference Brasilia, Brazil

  6. RSM 60-km Experiments IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL South America long- and short-term experiments were performed during January 1999, starting at January 1st, 1999 at 12 UTC, using Simplified Arakawa-Schubert cumulus convection parameterization (SAS) in order to compare with R-2. The control simulations were not initialized. In the PI simulations, the rain rates were updated every 24 hours, and the moisture adjustment took place every time-step which was 3 min. The boundary conditions were updated every 6 hours. III LBA Scientific Conference Brasilia, Brazil

  7. Evaluation IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL A preliminary evaluation of the PI scheme results was based on spatial correlation coefficient (r) and root mean square error (RMSE) given, respectively, by: The variables x and y represent the predicted and observed precipitations, and the subscript i, the grid point on the Mercator projection. III LBA Scientific Conference Brasilia, Brazil

  8. South AmericaPrecipitation (mm/d)Monthly Mean for January 1999 ECPC-RSM 60-km PI Control SSM/I-OLR Reanalysis R-2 ERA-40 III LBA Scientific Conference Brasilia, Brazil

  9. ECPC-RSM 60-kmSouth AmericaJanuary 1999 III LBA Scientific Conference Brasilia, Brazil

  10. South America850 hPa Relative Humidity (%)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  11. South America300 hPa Relative Humidity (%)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  12. South America850 hPa Temperature (K)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  13. South America300 hPa Temperature (K)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  14. South America850 hPa Horizontal Wind (m/s)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  15. South America300 hPa Horizontal Wind (m/s)Monthly Mean for January 1999 ECPC-RSM + PI ECPC-RSM Control ERA-40 R-2 III LBA Scientific Conference Brasilia, Brazil

  16. South America Table 1- Spectral Models x R-2: Monthly Mean for January 1999 III LBA Scientific Conference Brasilia, Brazil

  17. South America Table 2 – ECPC-RSM x SSM/I-OLR Estimates III LBA Scientific Conference Brasilia, Brazil

  18. South AmericaDaily Precipitation (mm)Initial Condition valid at January 11th, 1999 ECPC-RSM + PI (24 h) SSM/I-OLR ECPC-RSM+PI (240 h) III LBA Scientific Conference Brasilia, Brazil

  19. South AmericaECPC-RSM x SSM/I-OLR Estimates III LBA Scientific Conference Brasilia, Brazil

  20. South America24-h Accumulated Precipitation (mm)Simulation valid at 12UTC January 12th, 1999 PI (24 h) PI (240 h) SSM/I-OLR Control (24 h) Control (48 h) III LBA Scientific Conference Brasilia, Brazil

  21. South AmericaECPC-RSM x SSM/I-OLR Estimates III LBA Scientific Conference Brasilia, Brazil

  22. Conclusions IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL Preliminary evaluations of the PI implementations indicate that the RSM was able to successfully assimilate the merged precipitation analysis and SSM/I estimates as well. PI correlation coefficients are increased compared to the control and the spin-up problem was noticeably reduced during the continuous assimilation period. The atmospheric fields are not degraded by using a rainfall nudging and they are well correlated with the R-2 fields. We are now attempting to implement a physically initialized analyses as part of our effort to develop useful downscaled reanalysis fields. III LBA Scientific Conference Brasilia, Brazil

  23. IMPACT OF PRECIPITATION ASSIMILATION ON CLIMATE SIMULATIONS OVER BRAZIL Acknowledgments NCEP/DOE AMIP-II reanalysis data used in this study has been obtained from the NCEP (NOMADS2) data server. ECMWF ERA-40 data used in this study has been obtained from ECMWF data server. SSM/I-OLR estimates were provided by The Florida State University. III LBA Scientific Conference Brasilia, Brazil

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