On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction

TL;DR

This paper reviews methods for evaluating the influence of observations in convection-permitting numerical weather prediction, addressing challenges posed by nonlinearity, limited data, and novel observations, with a focus on ensemble systems.

Contribution

It compares assessment methods for observation value in convection-permitting NWP and discusses operational considerations and future research directions.

Findings

Comparison of assessment methods for convection-permitting NWP

Discussion on operational applicability of these methods

Identification of future research areas like ensemble size effects

Abstract

In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, the assessment can be challenging in convection-permitting NWP. First, the strong nonlinearity in the forecast model limits the methods available for the assessment. Second, convection-permitting NWP typically uses a limited area model and provides short forecasts, giving problems with verification and our ability to gather sufficient statistics. Third, convection-permitting NWP often makes use of novel observations, which can be difficult to simulate in an observing system simulation experiment (OSSE). We compare methods that can be used to assess the value of observations in convection-permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection-permitting NWP. We also identify several future research directions: comparison of forecast validation using analyses and observations, the effect of ensemble size on assessing the value of observations, flow-dependent covariance localization, and generation and validation of the nature run in an OSSE.