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Camp Springs, MD, United States

Baigorria G.A.,University of Florida | Chelliah M.,Climate Prediction Center NOAA | Mo K.C.,Climate Prediction Center NOAA | Romero C.C.,University of Florida | And 3 more authors.
Agronomy Journal | Year: 2010

We developed methods of forecasting cotton (Gossypium hirsutum L. var. hirsutum) yields at a county level 3 mo before harvest for the states of Alabama and Georgia. Cotton yield historical records for 57 counties were obtained from NASS and detrended using a low-pass spectral filter. A Canonical Correlation Analysis regression-based model was annually recalibrated to incor- porate the year-by-year accumulating data: (i) April-June (during vegetative growth) observed rainfall for the forecasting year, and (ii) July-September (during reproductive growth) global-scaled 2-m mean temperatures for years before the forecasting year, beginning with 1970. We produced two types of forecasts: short range and medium range. The short-range near-term yield forecast (just before initiating harvest in the region) used gridded assimilated observed 2-m mean temperatures obtained from the NCEP-NCAR CDAS Reanalysis data. The medium-range forecast (3 mo before harvest) used 2-m mean temperature retro- spective forecasts from the operational NOAA/NWS/NCEP Climate Forecasts System coupled global circulation model. The short-range, near-term forecast performance was measured by leave-one-out cross-validation and retroactive validation, whereas medium-range forecast performance used the previous two methods plus a proposed coral-reef validation method. The agree- ment between short-range near-term forecast and actual cotton yield was statistically significant at the 0.05 level in 31 out of 57 counties. For 48% of these 31 counties, the agreements between medium-range forecasts and actual cotton yields were statisti- cally significant at the 0.05 level. The goodness-of-fit index for those 15 counties was 0.51 and the RMSE ranged from 13 to 31% of the annual yield. © 2010 by the American Society of Agronomy.

Hou D.,Environmental Modeling Center NOAA | Charles M.,Environmental Modeling Center NOAA | Charles M.,SAIC | Charles M.,Climate Prediction Center NOAA | And 13 more authors.
Journal of Hydrometeorology | Year: 2014

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 1/ 8° 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.Asimple linear regressionmodel 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 linearmodel 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. © 2014 American Meteorological Society.

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