Open AccessA comparative verification of forecasts from two operational solar wind models
Author(s) -
Norquist Donald C.,
Meeks Warner C.
Publication year - 2010
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2010sw000598
Subject(s) - meteorology , standard deviation , environmental science , solar wind , space weather , climatology , statistics , physics , mathematics , geology , quantum mechanics , magnetic field
The solar wind (SW) and interplanetary magnetic field (IMF) have a significant influence on the near‐Earth space environment. In this study we evaluate and compare forecasts from two models that predict SW and IMF conditions: the Hakamada‐Akasofu‐Fry (HAF) version 2, operational at the Air Force Weather Agency, and Wang‐Sheeley‐Arge (WSA) version 1.6, executed routinely at the Space Weather Prediction Center. SW speed (V sw ) and IMF polarity (B pol ) forecasts at L1 were compared with Wind and Advanced Composition Explorer satellite observations. Verification statistics were computed by study year and forecast day. Results revealed that both models' mean V sw are slower than observed. The HAF slow bias increases with forecast duration. WSA had lower V sw forecast‐observation difference (F‐O) absolute means and standard deviations than HAF. HAF and WSA V sw forecast standard deviations were less than observed. V sw F‐O mean square skill rarely exceeds that of recurrence forecasts. B pol is correctly predicted 65%–85% of the time in both models. Recurrence beats the models in B pol skill in nearly every year forecast day category. Verification by “event” (flare events ≤5 days before forecast start) and “nonevent” (no flares) forecasts showed that most HAF V sw bias growth, F‐O standard deviation decrease, and forecast standard deviation decrease were due to the event forecasts. Analysis of single time step V sw increases of ≥20% in the nonevent forecasts indicated that both models predicted too many occurrences and missed many observed incidences. Neither model had skill above a random guess in predicting V sw increase arrival time at L1.