CCPA Precipitation Analysis and Its Cross Validation

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 24 January 2011: 4:00 PM
CCPA Precipitation Analysis and Its Cross Validation
612 (Washington State Convention Center)
Dingchen Hou, NOAA/NWS/NCEP/EMC, Camp Springs, MD; and Y. Luo, Y. Zhu, P. Xie, and Y. Lin

Climatology-Calibrated Precipitation Analysis, a new data set of precipitation analysis at about 5km resolution and with 6 hour accumulation period, has been routinely produced at NCEP since July 14, 2010. A historical data set, covering the period from Jan. 2002 to July 2010, has also been generated. The data set is generated by combining two widely used available datasets to take advantage of the higher reliability of the CPC Unified Global Daily Gauge Analysis and the higher temporal and spatial resolution of the Stage IV dataset based on multi-sensor observations. The methodology employed is basically performing statistical adjustment of the Stage IV 6 hour accumulation so that its climatology approaches that of the CPC data at 0.125 degree and 24 hour resolution. A linear regression model is used, with its parameters estimated from a sample of seven years from June 2002 to July 2009.

To evaluate the merit of the methodology and the quality of the CCPA data set, cross validation is performed following a data holding approach. Regression parameters are re-estimated with the same sample pool except that the data for a particular one year period (July 1 to June 30) are excluded, and the analysis for the same period is reproduced with these new parameters. The same procedure is repeated for each of the seven years and the data set reproduced is referred to as cross validation analysis. Comparison between the cross validation analysis and the original Stage IV analysis (both aggregated to 0.125 degree resolution and 24 hours accumulation) against the CPC analysis suggests that the former is statistically closer to the CPC analysis and this is true in most individual cases.

In addition to applications in climate study and forecast verifications, the data set provides a proxy of truth for the bias correction and downscaling of NWP forecast, especially ensemble forecast. Development is in progress at EMC/NCEP to bias correct the precipitation forecast from GEFS and NAEFS.