Abstract
This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.
Highlights
This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs)
It is conceivable that someday the data-driven approach will beat the numerical weather prediction (NWP) using the laws of physics, several fundamental breakthroughs are needed before this goal comes into reach [1,2,3]
The data utilization and behavior of the network will be different whether the NNs are used for short- or long-term forecasts—this is why the analysis was performed for a wide range of forecast lead times going from 0 to 500 days into the future
Summary
This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). Not many attempts were made at constructing end-to-end workflows, i.e., taking the observations as an input and generating an end-user forecast [3] Some examples of such approaches are Jiang et al [19], which tried to predict wind speed and power, and Grover et al [20], which attempted to predict multiple weather variables from the data of the US weather balloon network. We attempt to develop a model based on the NN that takes a single vertical profile measurement from the weather balloon as an input and tries to forecast the daily
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