Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.

The final published version of this manuscript will replace the preliminary version at the above DOI once it is available.If you would like to cite this EOR in a separate work, please use the following full citation:Ou, M., M. Charles, and D. Collins, 2016: Sensitivity of calibrated week-2 probabilistic forecast skill to reforecast sampling of the NCEP Global Ensemble Forecast System. Wea. Forecasting. Abstract 21 22CPC requires the reforecast-calibrated GEFS to support the production of their 23 official 6-10 and 8-14 day temperature and precipitation forecasts. While a large sample 24 size of forecast-observation pairs is desirable to generate the necessary model 25 climatology and variances, and covariances to observations, sampling by reforecasts 26 could be done to use available computing resources most efficiently. A series of 27 experiments was done to assess the impact on calibrated forecast skill of using a 28 smaller sample size than the current available reforecast dataset. This study focuses on 29 the skill of week-2 probabilistic forecasts of the 7-day mean 2-meter temperature and 30 accumulated precipitation. The tercile forecasts are expressed as being below-, near-, 31 and above-normal temperature/median precipitation over the CONUS. Calibration 32 statistics were calculated using an ensemble regression technique from 25 years of 33 daily, 11-member GEFS reforecasts for 1986-2010, which were then used to post-34 process the GEFS model forecasts for 2011-2013. In assessing the skill of calibrated 35 model output using a reforecast dataset with fewer years and ensemble members, and 36 an ensemble run less frequently than daily, it was determined that reductions in the 37 number of ensemble members to 6 or fewer and reductions in the frequency of 38 reforecast runs from daily to once a week were achievable with minimal loss of skill. 39However, reducing the number of years of reforecasts to less than 25 resulted in a 40 greater skill degradation. The loss of skill was statistically significant using only 18 years 41 3 of reforecasts from 1993-2010 to generate model statistics. 42

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