A 40-year perspective on the contribution of observations to forecast error reduction - Fábio Luiz Rodrigues Diniz (2019)
Operational and quasi-operational weather prediction centers have been routinely assessing the contribution from various observing systems to reducing errors in shortrange forecasts for a number of years now. The original technique, Forecast Sensitivity Observation Impact (FSOI), involves definition of a forecast error measure and evaluation of sensitivities with respect to changes in the observations that require adjoint operators of both the underlying tangent linear model and corresponding analysis technique. The present work applies FSOI to reanalysis and aims at providing an expanded view of the contribution of various observing systems over nearly 40 years of assimilation. Specifically, this study uses MERRA-2 given that its supporting software includes all ingredients necessary to calculate FSOI. Part of this work shows how the quality of forecasts improves over the course of the reanalysis, and examines forecast sensitivities relevant to FSOI. The global assessment here finds, for example, that: conventional observations are a major player in reducing forecast error throughout the 40 years of reanalysis, even when their volume reduces from 45% in the earlier periods to about 5% in the modern era; satellite radiances, especially microwave instruments, are major contributors to error reduction from the early single platform TIROS-N days to the current multi-platform scenario; though their fractional contribution reduces slightly from the early 2000s onward after the increased availability of wind observation from aircraft and atmospheric motion vector (AMV), and the introduction of GPSRO; infrared instruments play a secondary role to microwave but are significant still, with the peculiar result of fractional impacts contribution from modern hyperspectral instruments being roughly similar to those from early infrared instruments. This work also provides an assessment of these impacts over the Amazon basin. Over this region, under self-verification, forecast errors are found to slightly increase from the early data-sparse days to more recent years, when data dramatically increases. Throughout the reanalysis, satellite radiances dominate in volume, but only in before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). AMVs are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to AMVs, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all-sky treatment of radiances. The dependence of results on the chosen error measure is emphasized throughout.