linear regression with seasonality equation calculator

A discussion offered in section 5 reveals how the eta regression forecasts could, indeed, have been used in a subjective manner to adjust NGM MOS forecasts closer to the observed maximum temperature.ġ) Estimation of point-specific thickness It was discovered that certain changes in the average relative humidity of the 1000–850-mb layer at Nashville often occurred at times of poorest forecast accuracy.įinally, an investigation was made to determine if eta regression forecasts could have been used during the study period to improve local temperature forecasts made at the Nashville NWSO. Because of these findings, the goals of this study were expanded to include 4) four seasonal regression equations for the Eta Model and NGM and a comparison of model forecast accuracy (results showing the Eta Model to be more accurate), 5) a comparison of the eta regression equations to the NGM Model Output Statistics (MOS) forecasts to determine which numerical predictor was most accurate, 6) an additional study to determine if particular low-level relative humidity profiles and/or changes in such profiles were associated with large eta regression forecast errors (defined to be ≥3☏). There were three initial objectives: 1) to determine if a correlation exists between low-level thickness forecasts and observed maximum temperatures, 2) to use mathematical regression methods to define any such correlations, and 3) to test the accuracy of the regression equations and assess their usefulness in forecasting maximum temperatures at Nashville.Ī strong linear correlation was found between low- level thickness forecasts and observed maximum temperatures. Eta and Nested Grid Model (NGM) 24-h 1000–850-mb thickness forecasts for Nashville were collected and paired with the corresponding observed daily maximum temperatures. This study includes results based on data collected at the National Weather Service Office (NWSO) Nashville, Tennessee, during the 1-yr period of March 1995–96. Indeed, the collection of such data for a particular site during a period of time would likely aid in site-specific temperature forecasting, as well as provide useful information regarding model accuracy. Nevertheless, since a linear relationship has been shown to exist between observed thickness and observed temperature, it is suggested that model thickness forecasts and observed surface temperature may exhibit a similar relationship, assuming the model forecasts to be reasonably accurate. However, NWS forecasters always issue their first forecast for the upcoming afternoon before 1200 UTC and typically must use 0000 UTC model forecast data to produce the forecast.

The method used by Struthwolf (1995) involved the use of 1200 UTC observed 850–700-mb thickness to predict the upcoming afternoon temperature at the U.S. This possibility will be discussed further in section 3.

However, it is noted that when ambient temperature is used to approximate mean layer virtual temperature, the relationship between thickness and temperature may become less linear, especially in environments with high specific humidity. Also, there have been previous studies ( Struthwolf 1995) that have shown linear relationships between ambient surface temperature and observed low-level thickness. First, National Weather Service (NWS) forecasts involve the prediction of ambient air temperature rather than virtual temperature. Although virtual temperature and ambient air temperature sometimes differ by as much as 4☌, there are several reasons why the relationship between ambient air temperature and thickness should be investigated. T v g z R P 1 P 2 (1)where T v is the mean layer virtual temperature (°K), g the acceleration of gravity (m s −2), Δ z the layer thickness (m), R the universal gas constant, and P 1 and P 2 the pressures at the bottom and top of the layer, respectively.

Linear regression with seasonality equation calculator