After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Meteo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the as- sociated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.

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The AROME-France Convective-Scale Operational Model

. Framed within the Copernicus Climate Change Service (C3S) of the European Commission,
the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies.
This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5
most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models.
A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution
is hourly as in ERA5. Evaluation against independent in situ observations
and global model or satellite-based reference datasets shows the added value
of ERA5-Land in the description of the hydrological cycle, in particular
with enhanced soil moisture and lake description, and an overall better agreement of
river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude.
The description of the
energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as
reference MODIS data, mainly due to the contribution of
coastal points where spatial resolution is important.
Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a
2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021.
The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies,
to initialize NWP and climate models,
and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly ( Munoz-Sabater ,  2019 a ) and monthly ( Munoz-Sabater ,  2019 b ) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30 , respectively.

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ERA5-Land: a state-of-the-art global reanalysis dataset for land applications

This article describes the performance of the variational bias correction system for satellite radiance data in ERA-Interim, and considers implications for the representation of climate signals in reanalysis. We briefly review the formulation of the method and its ability to automatically develop bias estimates when radiance measurements from newly available satellite sensors are first introduced in the reanalysis. We then present several results obtained from the first 19 years (1989–2007) of ERA-Interim. These include the identification of Microwave Sounding Unit (MSU) instrument calibration errors, the response of the system to the Pinatubo eruption in 1991, and the detection of a long-term drift in biases of tropospheric AMSU-A data. We find that our results support the notion that global reanalysis provides an appropriate framework for climate monitoring. Copyright © 2009 Royal Meteorological Society

Variational bias correction of satellite radiance data in the ERA‐Interim reanalysis

Abstract In recent decades, the warming in the Arctic has been much faster than in the rest of the world, a phenomenon known as Arctic amplification. Numerous studies report that the Arctic is warming either twice, more than twice, or even three times as fast as the globe on average. Here we show, by using several observational datasets which cover the Arctic region, that during the last 43 years the Arctic has been warming nearly four times faster than the globe, which is a higher ratio than generally reported in literature. We compared the observed Arctic amplification ratio with the ratio simulated by state-of-the-art climate models, and found that the observed four-fold warming ratio over 1979–2021 is an extremely rare occasion in the climate model simulations. The observed and simulated amplification ratios are more consistent with each other if calculated over a longer period; however the comparison is obscured by observational uncertainties before 1979. Our results indicate that the recent four-fold Arctic warming ratio is either an extremely unlikely event, or the climate models systematically tend to underestimate the amplification. 

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The Arctic has warmed nearly four times faster than the globe since 1979

We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and environment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statistics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geosciences, it has so far remained a topic mostly reserved to experts. We aim this overview article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities.

Data assimilation in the geosciences: An overview of methods, issues, and perspectives

The ERA5 global reanalysis

This paper intends to give a general description of the limited area numerical weather prediction model ARPEGE/ALADIN developed by a collaboration between Meteo-France and some Central- and Eastern-European meteorological services (and also Morocco) including the Hungarian Meteorological Service (HMS). The model is a hydrostatic, spectral limited area model for dynamical adaptation at the limit of the hydrostatic approximation. The main characteristics of the model are discussed like the preparation of initial and lateral boundary conditions, the initialization method, the representation of the meteorological fields, the dynamics, the physics and the post-processing. The first experiences at HMS are discussed in the light of the one year quasi-operational application of the model. Finally, the possible future utilization for nowcasting purposes is mentioned.

ARPEGE/ALADIN : A numerical weather prediction model for Central-Europe with the participation of the Hungarian Meteorological Service

Observing system experiments within the operational ECMWF data assimilation framework have been performed for summer 2008 when the largest recorded number of Global Navigation Satellite System (GNSS) radio occultation observations from both operational and experimental satellites were available. Constellations with 0%, 5%, 33%, 67%, and 100% data volume were assimilated to quantify the sensitivity of analysis and forecast quality to radio occultation data volume. These observations mostly constrain upper-tropospheric and stratospheric temperatures and correct an apparent model bias that changes sign across the upper-troposphere–lower-stratosphere boundary. This correction effect does not saturate with increasing data volume, even if more data are assimilated than available in today’s analyses. Another important function of radio occultation data, namely, the anchoring of variational radiance bias corrections, is demonstrated in this study. This effect also does not saturate with increasing data vo...

GNSS Radio Occultation Constellation Observing System Experiments

Comments on “A Spectral Limited-Area Formulation with Time-Dependent Boundary Conditions Applied to the Shallow-Water Equations”

The development of the ARPEGE/ALADIN modeling system was initiated in 1990 (by Meteo-France). Recently the project encounters 15 partners from Europe and Northern Africa. The main original objective of the cooperation was to develop a numerical weather prediction model for dynamical adaptation, which takes into account all the advantages and constraints coming from its "mother" system ARPEGE/IFS. In a later stage it was natural - based on the inspiration from the ARPEGE/IFS modeling family - to consider the development of all numerical weather prediction related configurations in a single computer code beside the initially established pre-processing (interpolation) and model integration modules. Sophisticated post-processing algorithms were added and then data assimilation procedures were developed (first optimal interpolation and then three-dimensional variational data assimilation). The code has been extended to the tangent linear and adjoint versions, which make possible to apply configurations for sensitivity studies and the computation of singular vectors. The non-hydrostatic version of the model is an essential part of the software: this is the heart of the new AROME model, which is under intensive development for the meso-gamma spatial scales. The article briefly summarizes the most important configurations of the ALADIN model together with some illustration of their practical use at the Hungarian Meteorological Service.

Gabor Radnoti

Papers

The ERA5 global reanalysis

ARPEGE/ALADIN : A numerical weather prediction model for Central-Europe with the participation of the Hungarian Meteorological Service

GNSS Radio Occultation Constellation Observing System Experiments

Comments on “A Spectral Limited-Area Formulation with Time-Dependent Boundary Conditions Applied to the Shallow-Water Equations”

The ARPEGE/ALADIN mesoscale numerical modeling system and its application at the Hungarian Meteorological Service