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In numerical weather prediction (NWP), ensemble forecasting aims to quantify the flow-dependent forecast uncertainty. The focus here is on observation-based verification of the reliability of ensemble forecasting systems. In particular, at short forecast lead times, forecast errors tend to be relatively small compared to observation errors and hence it is very important that the verification metric also accounts for observational uncertainties. This paper studies the so-called filter likelihood score which is deep-rooted in Bayesian estimation theory and fits naturally to the filtering setup of NWP. The filter likelihood score considers observation errors, ensemble mean skill, and ensemble spread in one metric. Importantly, it can be made multivariate and effortlessly expanded to simultaneous verification against all observation types through the observation operators contained in the parental data assimilation scheme. Here observations from the global radiosonde network and satellites (AMSU-A channel 5) are included in the verification of OpenIFS-based ensemble forecasts using different types of initial state perturbations. Our results show that the filter likelihood score is sensitive to the ensemble prediction system quality and compares consistently with other verification metrics such as the relationships between ensemble spread and ensemble mean forecast error, and Dawid-Sebastiani score. Our conclusion is that the filter likelihood score provides a very well-behaving verification metric, that can be made truly multivariate by including covariances, for ensemble prediction systems with a strong foundation in estimation theory.

Ensemble prediction is an indispensable tool in modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has almost exclusively been carried out by operational weather agencies to date. Thus, it has been very hard for academia to contribute to this important branch of NWP research using realistic weather models. In order to open ensemble prediction research up to the wider research community, we have recreated all 50+1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (OpenEnsemble 1.0) is available for use under a Creative Commons licence and is downloadable from an https server. The dataset covers 1 year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (TL159, TL399, and TL639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of Typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside of major global weather forecasting centres.

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O (20) parameters is possible with O (100) algorithm steps using an ensemble of O (20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative.

Electromagnetic signals commonly used in geodetic applications, such as the Global Navigation Satellite System (GNSS), undergo bending and delay in the neutral gas atmosphere of the Earth. The least travel time (LTT) concept is one of the approaches to model signal slant delays via a ray tracing (RT) procedure. In this study, we developed an LTT-based RT algorithm (LTT v2), where the three-dimensional refractivity field of the atmosphere is based on the atmospheric model data. This representation is complete in a sense that the domain of the RT conforms to the native grid geometry of the atmospheric model. In principle, the LTT-based RT algorithm is seen as an extension of an atmospheric model for signal delay evaluation. The atmospheric states are generated using a global numerical weather prediction model, the Open Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts. In the LTT v2 model, some physical and numerical approximations are improved compared to the original implementation, called “LTT v1”. We compare the slant delays products of the two models. Additionally, a comparable modelling setup is created with the state-of-the-art VieVS Ray Tracer (RADIATE). The skill of slant delay estimation is assessed using metrics that are indicative of the quality of GNSS products derived using the GROOPS (Gravity Recovery Object Oriented Programming System) orbit solver software toolkit of the Graz University of Technology. The metrics used are GNSS orbit midnight discontinuities (MDs) and residuals of ground station precise positioning with respect to the IGS14 reference. Employment of slant delay products of the LTT RT algorithm in GNSS processing shows similar performance with v1 and v2. The GNSS orbit MDs are reduced by around 3 % when using the LTT v2 model, while root-mean-square residuals of ground station precise positioning are 5 % lower with LTT v1. The consistency of both metrics is improved slightly using LTT v2, as seen by the metrics' standard deviation values. Intercomparison with RADIATE indicates significantly better performance of LTT v2, which we attribute entirely to the much larger amount and lossless utilization of weather model data as input to LTT v2 versus RADIATE.

Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M(x, θ) denote the time evolution model, where x and θ are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that θ is recovered when the tuning process is initialised with perturbed model parameters θ′ and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in a semi-realistic test setting to obtain reliable results and thus build confidence on the tuning in fully realistic cases. A large set of convergence tests is carried in semi-realistic cases by applying two different ensemble-based parameter estimation methods and the atmospheric forecast model of the Integrated Forecasting System (OpenIFS) model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.

Neutral gas atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing three-dimensional refraction fields to ray-trace the signal delays. In this study, a global numerical weather prediction model (Open Integrated Forecasting System (OpenIFS) licensed for Academic use by the European Centre for Medium-Range Weather Forecast) is used to generate the refraction fields. The ray-traced slant delays are supplied as such – in contrast to mapping – for an orbit solver (GROOPS (Gravity Recovery Object Oriented Programming System) software toolkit of Graz University of Technology) which applies the raw observation method. Here we show that such a close coupling is possible without need for major additional modifications in the solver codes. The main finding here is that the adopted approach provides a very good a priori model for the atmospheric effects on navigation signals. We suspect that removal of the intermediate mapping step allows us to take advantage of the local refraction field asymmetries in the GNSS signal processing. Moreover, the direct coupling helps in identifying deficiencies in the slant delay computation because the modeling errors are not convoluted in the mapping procedures. These conclusions appear robust, despite the relatively small data set of raw code and phase observations covering the core network of 66 ground-based stations of the International GNSS Service over 1-month periods in December 2016 and June 2017. More generally, the new configuration enhances our control of geodetic and meteorological aspects of the orbit problem. This is pleasant because we can, for instance, regulate at will the weather model output frequency and increase coverage of spatiotemporal aspects of weather variations. The direct coupling of a weather model in precise GNSS orbit determination presented in this paper provides a unique framework for benefiting even more widely than previously the apparent synergies in space geodesy and meteorology.