Validation of Reanalysis Daily Precipitation over the Americas

1. Validation of Reanalysis Daily Precipitation over the Americas Viviane Silva, Vernon Kousky, Wayne Higgins and Emily Becker CTB Seminar Series 9 March 2009

2. Motivation • The Climate Prediction Center (CPC) plans to replace the current generation of reanalysis products in use for operational monitoring and prediction activities with a new generation of reanalysis products currently under development at the NCEP/ EMC. • Before CPC can confidently base its operational climate monitoring and prediction activities on a new generation of reanalysis and reforecast products, a comprehensive intercomparison of the old and new products is required .

3. Background - 1 • Since the mid-1990’s the CPC has used the NCEP/NCAR reanalysis products and their real-time extension forward in time via the Climate Data Assimilation System (CDAS) for operational climate monitoring and prediction activities. • The current generation of reanalysis products is among the most popular and widely used climate data sets currently in existence.

4. Background - 2 • The NCEP is currently developing the next generation of reanalysis products as part of the Climate Forecast System Reanalysis and Reforecast (CFSRR) project. • The project is driven by NCEP’s intraseasonal-to-interannual prediction needs.

5. EMC’s Plans • The Environmental Modeling Center (EMC) plans call for the CFSRR to extend over the period 1981-present.

6. CPC’s Plans • The CPC plans call for : - the extension of the CFS reanalysis backward in time to 1948 and - forward in real-time in order to satisfy CPC’s operational climate monitoring and prediction needs.

7. Advantages of the CFS Reanalysis Extension Backward in Time • Will increase the number of cases of the low frequency modes of climate variability, such as ENSO, for a proper comparison of the CFSR to the current generation of reanalysis.

8. NOTE • We do not expect seasonal re-forecasts out to 8 months, during 1948-1980, to be as skillful as those for the period 1981-present, because the ocean states generated prior to 1981 are not as reliable. • Not enough ocean observations.

9. What we have done so far …

10. NCEP/NCAR Reanalysis (R1) & NCEP/DOE Reanalysis (R2) • We documented the biases in R1 via comparison to observations (gridded analysis) for US and South America. • Current Focus: Quality of R1 daily precipitation statistics. • The NCEP/DOE Reanalysis (R2), obtained using an updated forecast model and data assimilation system, is also used at CPC, so we evaluated both R1 and R2.

11. Three Basic Statistics(1979-2006) • Difference in Means • Ratio of Variances • Temporal Correlation

12. Preliminary ResultsUnited States

13. Mean Daily Precipitation Difference 13

14. Probability P ≥ 1 mm 14

15. Annual cycle of the probability of daily precipitation ≥ 1 mm for selected locations in the Southeast • Additional Details: • R1 and R2 probabilities are: • greater than observed during the warm season (especially June to September), with greater biases in R1, • and smaller than observed during the cold season (especially November to February), with greater biases in R2. • The largest biases are during the warm season in R1 (and to a lesser extent in R2). 15

16. Probability P ≥ 10 mm 16

17. Annual cycle of the probability of daily precipitation ≥ 10 mm for selected locations in the Southeast • The observed probabilities remain relatively constant through the annual cycle (including the warm season). • while the reanalysis probabilities show a distinct maximum during the warm season as they did for prob. > 1mm. • Again, this is an indication that the overestimates in R1 and R2 are for the relatively heavy (convective) precipitation events. 17

18. Ratio of Variance of Daily Precipitation b) R2/OBS a) R1/OBS • During OCT-APR, both R1 and R2 exhibit less variability than OBS on daily timescales over eastern TX, the Gulf Coast states and Tennessee Valley. • The reanalyses display more variability than observed over the West throughout the year (exception along the immediate West Coast) 18

19. Ratio of Variance of Daily Precipitation c) R2/R1 • R2 variance is greater than R1 variance across the entire CONUS during May-October (greatest differences during July-September). • In addition, R2 variance exceeds R1 variance along the Gulf Coast during November-April. • Since the R2 and R1 variances are less than OBS during November-April (previous slide), the R2/R1 ratio is >1 shown in this slide indicates that R2 is closer to OBS in the Gulf region. 19

20. Temporal Correlation (x100) R2 & OBS R1 & OBS • High correlation in winter and lower correlation in summer when convection is dominant. • Throughout the year, the correlations between R2 and OBS are lower than those for R1 and OBS. • So, in spite of improvements in the mean bias of daily precipitation in R2 (shown before), the correlation with observations is not as good. 20

21. Mean Daily Precipitation (mm) during El Niño OBS R1 R2 Expressed as departures from the appropriate period mean. 21

22. Mean Daily Precipitation (mm) during La Niña Expressed as departures from the appropriate period mean. 22

23. Annual cycle of the probability of daily precipitation for selected locations in the Southeast - El Niño • El Niño-related probability anomalies are too large in the Reanalyses, especially during April-September, with R1 larger than R2, except for region 6 (Florida). • Similar results are obtained for daily precipitation greater than 10 mm. 23

24. Annual cycle of the probability of daily precipitation for selected locations in the Southeast - La Niña During La Niña, the probability anomalies are again larger in R1 than R2 during the warm season. 24

25. Preliminary ResultsSouth America

26. Probability P> 1mm Difference: R1- OBS Difference: R2 - OBS 26

27. Probability P> 5mm Difference: R1- OBS Difference: R2- OBS 27

28. Annual cycle of the probability of daily precipitation ≥ 1 mm for selected locations in South America 1 2 3 4 5 28

29. Expected Outcome • A careful validation of the current generation of reanalysis products will provide a benchmark for determining improvements in the new generation of reanalysis (CFSR) products. • Results from this study will be used: • to investigate the frequency distribution of the CFS daily precipitation forecasts and • to identify and correct biases in the statistics of daily precipitation within a season to improve CPC operational monitoring and climate forecast products.

30. Future Work - 1 • Once the CFS Reanalysis is available, the associated precipitation statistics will be compared to those presented here. • we also plan comparisons of the circulation features, especially for fields used in CPC’s suite of real-time monitoring products. • Comparisons of circulation features will be done to shed light on the degree of uncertainty in the analyses, rather than to validate circulation features, which would require an independent circulation data set.

31. Future Work - 2 • To investigate the dependence of daily precipitation statistics in R1, R2, CFS-R and observations on major climate modes, including ENSO, MJO and AO, and on teleconnection patterns such as the Pacific-North American pattern. • One focus will be on the behavior of the precipitation frequency distributions during developing or decaying phases of the major climate modes.

32. Questions ?Suggestions ? Viviane.Silva@noaa.gov Modeling • • Tropical Research Topics Research Topics oscillations oscillations • • Model Model physics physics • • Etc. Etc.