Jonathan Edwards-Opperman.  Importance of climate-weather interface ◦ Seasonal forecasting  Agriculture  Water resource management.

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Presentation transcript:

Jonathan Edwards-Opperman

 Importance of climate-weather interface ◦ Seasonal forecasting  Agriculture  Water resource management

 Climate Data – Monthly Indexes ◦ PNA,PDO,ENSO,NAO,AO ◦ Climate Prediction Center (NOAA)  Temporal Coverage – 1955 to present  Temporal Resolution - Monthly  Precipitation Data ◦ NOAA's PRECipitation REConstruction Dataset (PREC)  Temporal Coverage – 1948 to present  Temporal Resolution - Monthly  Spatial Coverage – Global  Spatial Resolution – 2.5° latitude x 2.5° longitude

 Precipitation data split into six regional datasets which cover the Northwest, Southwest, North-central, South-central, Northeast, and Southeast United States  Grid points in each region are summed to obtain a single monthly precipitation anomaly for each region

LocationPNAPDOSOINino-4Nino-3NAOAO Northwest e Southwest e North-Central South-Central Northeast Southeast

LocationPNAPDOSOINino-4Nino-3NAOAO Northwest Southwest North-Central South-Central Northeast Southeast LocationPNAPDOSOINino-4Nino-3NAOAO Northwest Southwest North-Central South-Central Northeast e-4 Southeast

LocationPNAPDOSOINino-4Nino-3NAOAO Northwest Southwest North-Central South-Central Northeast Southeast LocationPNAPDOSOINino-4Nino-3NAOAO Northwest Southwest North-Central South-Central Northeast Southeast

 The largest correlations were found during the winter months (DJF) LocationPNAPDOSOINino-4Nino-3NAOAO Northwest Southwest North-Central South-Central Northeast Southeast  The following analyses will cover the relationship between Nino-3 and precipitation over the North-Central United States

 Some significant correlations between precipitation and teleconnection patterns  Better spatial resolution of the analysis might improve results ◦ Perform correlations and regressions at each gridpoint