Editorial: The use of unconventional observations in numerical weather prediction

Abstract

This virtual issue brings together articles that discuss the need for high resolution observations and the use of opportunistic data in numerical weather prediction (NWP). The issue was assembled to complement the Royal Meteorological Society's "Big data assimilation workshop" held in the autumn of 2019 (https://www. rmets.org/event/assimilation-big-data). Over recent decades advances in computing techniques and power have allowed the resolution of NWP models to

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

“…Understanding the characteristics of opportunistic observations is key to their effective use in NWP (Waller, 2020). For data assimilation, an understanding of the instrument and representation errors that contribute to the total observation uncertainty is required.…”

“…However, due to the cost of installation, management, and maintenance of observing instrumentation, it may be impractical to extend traditional scientific observing networks to provide sufficient additional relevant observations. A potential alternative source of inexpensive observations is from opportunistic data generated by the public or other organizations (Blair et al, 2021; Waller, 2020).…”

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction

“…However, due to the cost of installation, management, and maintenance of observing instrumentation, it may be impractical to extend traditional scientific observing networks to provide sufficient additional relevant observations. A potential alternative source of inexpensive observations is from opportunistic data generated by the public or other organisations (Waller, 2020;Blair et al, 2021).…”