† The contribution of C. Smith was written in the course of his employment at the Met Office, UK and is published with the permission of the controller of HMSO and the Queen's Printer for Scottland.Drawing from the results of theoretical studies about the behaviour of constantcoefficients semi-implicit schemes, the dynamical kernel of the Aladin-NH spectral limited-area numerical weather prediction (NWP) model has been modified in order to allow for a stable and efficient integration of the fully elastic Euler equations. The resulting dynamical kernel offers the possibility to use semi-Lagrangian transport schemes together with two-time-level discretizations at kilometric scales for NWP purposes. The main characteristics of the adiabatic part of the model formulation and the space and time discretization are described in this article. In order to illustrate the dependence of the results on adjustable parameters of the dynamical kernel, some real-case dynamical-adaptation forecasts performed with a basic physical parameterization package are presented. The results obtained with this model in real-case experiments fully confirm the conclusions drawn in previous numerical analysis studies. The good quality of the results is found to be compatible with a routine exploitation in a NWP framework. The Aladin-NH dynamical kernel has been used in the operational NWP 'AROME' model since December 2008 at the kilometric scale, with an appropriate physical parameterization package and data assimilation system.
Abstract. The ALADIN System is a numerical weather prediction (NWP) system developed by the international AL-ADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 partner institutes of this consortium. These configurations are called the ALADIN canonical model configurations (CMCs). There are currently three CMCs: the AL-ADIN baseline CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations.The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs, (iii) to document their most recent versions, and (iv) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the partner institutes of the AL-ADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.
Spectral integration is the most time consuming part of solar radiative transfer codes used in numerical weather prediction. Routinely used approaches usually incline to one of two extremes – expensive and very accurate correlated k‐distribution method made affordable by doing radiative transfer calculations with reduced temporal and/or spatial resolution, or cheaper but less accurate broadband approach affordable at every grid‐point and time‐step. Both approaches have their pros and cons, but hybrid solutions do not seem very promising. The presented work improves accuracy of full spectrum broadband approach by parameterizing secondary saturation of gaseous absorption, optical saturation of Rayleigh scattering and of cloud absorption as well as non‐random gas‐cloud spectral overlap. In order to isolate the problem of spectral integration from other approximations, one builds a narrowband reference using the same delta‐two stream framework as the broadband scheme. Using this reference reveals the surprising fact that saturation effect of cloud absorption for one single layer and for the whole solar spectrum can be parameterized in a rather compact way, with one simple formula for liquid clouds and one for ice clouds. One then introduces the concept of effective cloud optical depth, which extends the applicability of parameterized cloud optical saturation to multi‐layer cases, accommodating also effects of gas‐cloud spectral overlap in the near‐infrared. A scheme with all the above parameterizations indeed pushes accuracy limits of broadband approach to the level where a single shortwave interval can be used. This opens the possibility to reduce costs by using selective intermittency, where slowly evolving gaseous transmissions are updated on the timescale of hours, while quickly varying cloud optical properties are recomputed at every model time‐step. In a companion article it will be demonstrated that the above core strategy is applicable also to thermal radiative transfer, with perhaps even better cost effectiveness there.
Abstract. The ALADIN System is a numerical weather prediction system (NWP) developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 Partner Institutes of this consortium. These configurations are called the ALADIN Canonical Model Configurations (CMCs). There are currently three CMCs: the ALADIN baseline-CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs and to document their most recent versions, and (iii) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the Partner Institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.