Comparing diagnosed observation uncertainties with independent estimates: A case study using aircraft‐based observations and a convection‐permitting data assimilation system

Abstract

Aircraft can report in situ observations of the ambient temperature by using aircraft meteorological data relay (AMDAR) or these can be derived using modeselect enhanced tracking data (Mode-S EHS). These observations may be assimilated into numerical weather prediction models to improve the initial conditions for forecasts. The assimilation process weights the observation according to the expected uncertainty in its measurement and representation. The goal of this paper is to compare observation uncertainties diagnosed from data assimilation statistics with independent estimates. To quantify these independent estimates, we use metrological comparisons, made with in-situ research-grade instruments, as well as previous studies using collocation methods between aircraft (mostly AMDAR reports) and other observing systems such as radiosondes. In this study we diagnose a new estimate of the vertical structure of the uncertainty variances using observation-minus-background and observation-minus-analysis statistics from a Met Office limited area three-dimensional variational data assimilation system (3 km horizontal grid-length, 3-hourly cycle). This approach for uncertainty estimation is simple to compute but has several limitations. Nevertheless, the resulting diagnosed variances have a vertical structure that is like that provided by the independent estimates of uncertainty. This provides confidence in the uncertainty estimation method, and in the diagnosed uncertainty estimates themselves. In the future our methodology, along with other results, could provide ways to estimate the uncertainty for the assimilation of aircraft-based temperature observations.

Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics

“…(2015) and Mirza et al . (2021) have found good qualitative and quantitative agreement with diagnosed estimates, respectively. None of these studies has considered the effects of statistical sampling error on the diagnosed estimates, although noisy estimates have been observed (Waller et al ., 2019).…”

“…The assumed error statistics refer to those used in the assimilation system, which are approximations of the true error statistics. Nevertheless, empirical metrological studies by Chun et al (2015) and Mirza et al (2021) have found good qualitative and quantitative agreement with diagnosed estimates, respectively. None of these studies has considered the effects of statistical sampling error on the diagnosed estimates, although noisy estimates have been observed (Waller et al, 2019).…”

Progress, challenges, and future steps in data assimilation for convection‐permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

“…Li (2021) showed that assimilating Mode‐S winds has a positive benefit on the forecast skill in wind profiles in the first 6 h of the forecast, and for hourly precipitation accumulations up to 9 h into the forecast. The assimilation of Mode‐S EHS temperature data is more challenging as the temperature observations have been shown to be of lower quality, particularly in the boundary layer (Mirza et al, 2016; Mirza et al, 2019; Mirza et al, 2021). However, these data can be used after some processing (de Haan, 2013; de Haan & Stoffelen, 2012).…”

The relationship between two methods for estimating uncertainties in data assimilation

“…It might be desirable to corroborate error observation estimates from either DBCP or 3CH in alternative ways by, for example, making use of independent observations (e.g., Ménard and Deshaies-Jacques, 2018;Mirza et al, 2021. The generality of two of the corners of 3CH offers yet another possibility for a methodology to obtain alternative estimates. This is the gist of the work of Anthes et al (2022).…”