Assessing the impact of observations in a multi‐year reanalysis

Abstract

Operational and quasi‐operational weather prediction centres have been routinely assessing the contribution from various observing systems to reducing errors in short‐range forecasts for a number of years now. The original technique, Forecast Sensitivity‐based 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 assessment here finds that, for example, 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 2000's onward after the increased availability of wind observation from aircraft and atmospheric motion vectors, 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. The dependence of results on the chosen error measure is emphasized throughout.

A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

“…The other reason for the reduced observation impact (not shown here) is the increased quality in certain types of observations (particularly brightness temperatures derived from microwave [MW] observations; e.g., Microwave Sounding Unit versus Advanced Microwave Sounding Unit A [AMSU-A]). Diniz &Todling, 2019 (2019, andreferences therein) have shown this to be associated with the considerable reduction in forecast error over the same regions and the same period. Clearly, these are directly related to the reason (ii) listed in section 1 for how observation impacts can be found to be small.…”

“…In many ways, reanalysis provides an ideal environment for conducting FSOI studies. Diniz and Todling (2019) rely on the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2; Gelaro et al, 2017), which is an ongoing exercise with now nearly 40 years of assimilated products available for climate studies. Though not used in its ongoing integration, MERRA-2 has all necessary ingredients to perform FSOI, namely, an adjoint model Holdaway et al (2014) of its nonlinear general circulation model and an adjoint Trémolet (2008) of its three-dimensional variational (3D-Var) analysis.…”

“…In a recent study, Diniz and Todling (2019) provide a comprehensive assessment of the impact of the observations on short-range (24-hr) forecasts from a multiyear reanalysis. Unlike typical examination of root-mean-square error of observation residuals that inherit the unit of the variables being examined and are thus not suitable for cross-comparison, the Forecast Sensitivity-based Observation Impact (FSOI) technique employed by Diniz and Todling (2019) standardizes units and thus allows for cross-comparison. FSOI was introduced by Langland and Baker (2004), and it has since become a standard tool for assessing the plethora of observations assimilated in global operational numerical weather prediction (NWP) systems.…”

The Impact of the Observation Data Assimilation on Atmospheric Reanalyses over Tibetan Plateau and Western Yunnan-Guizhou Plateau

“…The significant effect of conventional observations on a multi-year reanalysis (the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)) was also noted in some recent studies which employed the forecast sensitivity-based observation impact (FSOI) technique [36,37]. Diniz and Todling [36] indicated that conventional observations played a major role in reducing forecast errors throughout the nearly 40-year reanalysis period. Diniz et al [36] applied the same FSOI technique to MERRA-2 over the Amazon basin and found that more than half of the forecast error reduction was related to conventional observations after 1999.…”

“…Diniz and Todling [36] indicated that conventional observations played a major role in reducing forecast errors throughout the nearly 40-year reanalysis period. Diniz et al [36] applied the same FSOI technique to MERRA-2 over the Amazon basin and found that more than half of the forecast error reduction was related to conventional observations after 1999. Excepting the differences in input observations, differences in the forecast models and data assimilation methods also affect the quality of reanalyses.…”