Chilly Mother’s Day
As predicated in my May 5th blog, Toronto was battered with strong easterly winds gusting up to 50 km/h. Our Mother’s Day trip to the Toronto Botanical Garden was quite an experience considering the high was only 15.4 deg. C but the lack of sunshine and howling winds made the apparent temperature closer to 10 deg. C. On a positive note, I was happy to see a weather station at the botanical garden.
Luckily, the center of the storm stayed south of us resulting in less impressive rainfall amounts. The American Midwest was inundated with tornado warnings issued in Kansas, Oklahoma and Missouri to name a few. In Newton County, Missouri ten people were reported to be killed while six people lost their lives when a tornado ripped a 20-block area in Picher, Oklahoma. Furthermore, a 4.25 inch (10.8 cm) diameter hail was reported in Baxter Springs, Kansas.
As of 6:00 pm, the low was centered in Missouri with a SLP well below 990 mb. Here in Toronto, I expect the pressure to dip to around 1000 mb and rainfall amounts less than 5 mm.
It’s Sunday and here’s the first report on the temperature accuracy study. The root mean square error (RMSE) is a quadratic scoring rule which measures the average magnitude of the error. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors.
I have also added the mean absolute error (MAE) into the calculation which is the weighted average of the absolute errors (i.e. Ipredicted-actualI). Since we just started our analysis a few days ago, please note that the values below only cover the period of May 9-10. Any statistician will tell you that your analysis is more meaningful if you have more data. Therefore, we will have a better picture of which weather source provides the most accurate forecast once we have a bigger sample. Nevertheless, here is the preliminary result:
So far, Environment Canada (EC), The Weather Network (TWN) and The Weather Channel (Weather.com) have the best record with RMSE and MAE of 1.0 deg. C. Note that the lower the value, the better their forecast (i.e. closest to actual observed temperature).
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