China
Africa
Explore chapters and articles related to this topic
Strategies in Naturalistic Decision Making: A Cognitive Task Analysis of Naval Weather Forecasting
Published in Schraagen Jan Maarten, Laura G. Militello, Tom Ormerod, Lipshitz Raanan, Naturalistic Decision Making and Macrocognition, 2017
Schraagen Jan Maarten, Laura G. Militello, Tom Ormerod, Lipshitz Raanan
How do weather forecasters arrive at their decisions? What information do they use and how do they combine it to come up with the forecast? In general, weather-forecasting decisions, like other naturalistic decisions, depend heavily on the preliminary stage of situation assessment (Klein and Calderwood, 1991; Wohl, 1981). In weather forecasting, the current state of the atmosphere is determined first by examining large amounts of observational data. When the current state of the atmosphere is ascertained to the degree possible, it is then projected forward in time to obtain the future conditions. Forecasters are aided in this endeavor by numerical weather-prediction models—computer programs that predict weather by taking current weather conditions as input and transforming them algorithmically into future weather conditions according to known principles of atmospheric physics. Computer predictions are fairly accurate for large-scale phenomena over short time periods. However, they still make large errors, especially for small-scale, local phenomena and for forecasts extending further into the future. Thus, the computer predictions cannot be applied directly. Human forecasters must evaluate the computer predictions and adjust them when necessary, based in part on their understanding of local conditions and known weaknesses in the numerical models. It is interesting to note that weather forecasting is one of the few domains in which humans have improved on the decisions made by decision aids such as computer weather-prediction models (Swets, Dawes, and Monohan, 2000). However, computer models are constantly improving with advances in atmospheric science and, as their accuracy increases, the human contribution is reduced (Baars, Mass, and Albright, 2004).
Literature review
Published in Isnaeni Murdi Hartanto, Integrating Multiple Sources of Information for Improving Hydrological Modelling: An Ensemble Approach, 2019
The term “Numerical Weather Prediction” refers to application of computer models of atmospheric processes and ocean dynamics to predictions of weather conditions. Global and regional models simulate weather in many regions in the world. There is distinction between short-term, mid-term, and long-term weather forecasts; short-term concerns weather predictions for the coming hours or days, while the long-term is used, for example, for climate change analysis.
Selecting components in a probabilistic hydrological forecasting chain: the benefits of an integrated evaluation
Published in LHB, 2021
Joseph Bellier, Guillaume Bontron, Isabella Zin
Historically, the meteorological community has paved the way for probabilistic forecasting with ensemble forecasting, which consists in running a numerical weather prediction (NWP) model multiple times with slightly perturbed atmosphere initial conditions, and sometimes different modelling assumptions (Bauer et al., 2015). More generally, we refer in this paper to the ensemble approach as any method that, for a given physical process to be modelled, constructs an ensemble of simulations whose dispersion represents the uncertainty of that process. The introduction of the ensemble approach into hydrological forecasting has most often consisted in propagating meteorological ensemble forecasts, issued by a given operational centre, into a hydrological model (see Cloke & Pappenberger, 2009 for a review). But other practices exist, such as using “poor man’s ensembles”, which refers to picking multiple deterministic forecasts from independent meteorological centres (Ebert, 2001), or “grand ensembles”, which merge together ensembles issued by different meteorological centres (He et al., 2009; Pappenberger et al., 2008). The objective is to benefit from the differences between the NWP models (e.g. in resolution, physical parametrisation, assimilation or perturbation methods) to widen the range of possible meteorological scenarios. Yet a meteorological ensemble forcing only exhibits the uncertainty associated with the atmospheric processes. To encompass uncertainties linked to hydrological processes and to their modelling, a common practice consists in using multiple hydrological models instead of a single one, an approach referred to as “multi-model”. Most often, several models with different structures and modelling assumptions are selected (Thiboult et al., 2016; Velázquez et al., 2011), although one could also consider different parameter sets for the same model (e.g. the generalized likelihood uncertainty estimation [GLUE] method, Beven & Freer, 2001). One may remark that the multi-model approach follows the same principle as the “poor man’s ensemble” (i.e. different deterministic simulations are gathered to form an ensemble), although it has never been named as such, the use of physically perturbed members in hydrological modelling being still in the early stages.