The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System

Abstract The aim of this article is to describe the reference configuration of the convection-permitting numerical weather prediction (NWP) model HARMONIE-AROME, which is used for operational short-range weather forecasts in Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the Netherlands, Norway, Spain, and Sweden. It is developed, maintained, and validated as part of the shared ALADIN–HIRLAM system by a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. HARMONIE–AROME is based on the model AROME developed within the ALADIN consortium. Along with the joint modeling framework, AROME was implemented and utilized in both northern and southern European conditions by the above listed countries, and this activity has led to extensive updates to the model’s physical parameterizations. In this paper the authors present the differences in model dynamics and physical parameterizations compared with AROME, as well as important configuration choices of the reference, such as lateral boundary conditions, model levels, horizontal resolution, model time step, as well as topography, physiography, and aerosol databases used. Separate documentation will be provided for the atmospheric and surface data-assimilation algorithms and observation types used, as well as a separate description of the ensemble prediction system based on HARMONIE–AROME, which is called HarmonEPS.

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Limited-area short-range ensemble predictions targeted for heavy rain in Europe

Hydrology and Earth System Sciences ◽

10.5194/hess-9-300-2005 ◽

2005 ◽

Vol 9 (4) ◽

pp. 300-312 ◽

Cited By ~ 9

Author(s):

K. Sattler ◽

H. Feddersen

Keyword(s):

Short Range ◽

Weather Prediction ◽

Heavy Rain ◽

River Basins ◽

Ensemble Prediction ◽

Limited Area ◽

Target Area ◽

Extreme Rainfall Events ◽

Ensemble Prediction System ◽

Area Of Interest

Abstract. Inherent uncertainties in short-range quantitative precipitation forecasts (QPF) from the high-resolution, limited-area numerical weather prediction model DMI-HIRLAM (LAM) are addressed using two different approaches to creating a small ensemble of LAM simulations, with focus on prediction of extreme rainfall events over European river basins. The first ensemble type is designed to represent uncertainty in the atmospheric state of the initial condition and at the lateral LAM boundaries. The global ensemble prediction system (EPS) from ECMWF serves as host model to the LAM and provides the state perturbations, from which a small set of significant members is selected. The significance is estimated on the basis of accumulated precipitation over a target area of interest, which contains the river basin(s) under consideration. The selected members provide the initial and boundary data for the ensemble integration in the LAM. A second ensemble approach tries to address a portion of the model-inherent uncertainty responsible for errors in the forecasted precipitation field by utilising different parameterisation schemes for condensation and convection in the LAM. Three periods around historical heavy rain events that caused or contributed to disastrous river flooding in Europe are used to study the performance of the LAM ensemble designs. The three cases exhibit different dynamic and synoptic characteristics and provide an indication of the ensemble qualities in different weather situations. Precipitation analyses from the Deutsche Wetterdienst (DWD) are used as the verifying reference and a comparison of daily rainfall amounts is referred to the respective river basins of the historical cases.

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Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

Hydrology and Earth System Sciences ◽

10.5194/hess-23-493-2019 ◽

2019 ◽

Vol 23 (1) ◽

pp. 493-513 ◽

Cited By ~ 7

Author(s):

Samuel Monhart ◽

Massimiliano Zappa ◽

Christoph Spirig ◽

Christoph Schär ◽

Konrad Bogner

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Added Value ◽

Ensemble Prediction ◽

Late Winter ◽

Streamflow Prediction ◽

Ensemble Prediction System ◽

Mountainous Regions ◽

Temperature And Precipitation ◽

Nwp Model

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

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Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

Monthly Weather Review ◽

10.1175/mwr2911.1 ◽

2005 ◽

Vol 133 (7) ◽

pp. 1825-1839 ◽

Cited By ~ 36

Author(s):

A. Arribas ◽

K. B. Robertson ◽

K. R. Mylne

Keyword(s):

Short Range ◽

Weather Prediction ◽

Ensemble Prediction ◽

Small Subset ◽

Prediction System ◽

Ensemble Forecasts ◽

Sea Level Pressure ◽

Ensemble Prediction System ◽

To Come ◽

Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

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Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2013.0284 ◽

2014 ◽

Vol 372 (2018) ◽

pp. 20130284 ◽

Cited By ~ 23

Author(s):

Glenn Shutts ◽

Alfons Callado Pallarès

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Model Error ◽

Horizontal Resolution ◽

Phase Changes ◽

Ensemble Prediction ◽

Condensation Temperature ◽

Ensemble Prediction System ◽

The Mean ◽

Temperature Tendency

The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.

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Day-Ahead Wind Power Forecast Through High-Resolution Mesoscale Model: Local Computational Fluid Dynamics Versus Artificial Neural Network Downscaling

Journal of Solar Energy Engineering ◽

10.1115/1.4045740 ◽

2020 ◽

Vol 142 (3) ◽

Author(s):

Matteo Mana ◽

Davide Astolfi ◽

Francesco Castellani ◽

Cathérine Meißner

Keyword(s):

High Resolution ◽

Wind Farm ◽

Mesoscale Model ◽

Weather Prediction ◽

Ensemble Prediction ◽

Cfd Model ◽

Forecast Performance ◽

Ensemble Prediction System ◽

Nwp Model ◽

The Impact

Abstract The importance of accurately forecasting the power production of wind farms is boosting the development of meteorological models and their processing. This work is a discussion of different forecast configurations for predicting the day ahead production of a wind farm sited in a moderately complex terrain. The numerical weather prediction (NWP) model MetCoOp Ensemble Prediction System with 2.5 km resolution focusing on the wind farm area is dynamically downscaled by the computational fluid model (CFD) model WindSim. The transfer of the NWP model to the CFD model can be done using NWP results from various heights above ground and using all or parts of the nodes of the NWP model within the wind farm area. In this work, many different forecasting configurations are validated and the impact on the forecast performance is discussed. The NWP-CFD downscaling results are compared to a day ahead forecast obtained through ANN methods and to the observed production. The main result of this work is that a deterministic downscaling method like CFD simulations can perform as good or better than statistical approaches when using high-resolution NWP models and more NWP model data.

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Higher Resolution in an Operational Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-13-00138.1 ◽

2014 ◽

Vol 142 (3) ◽

pp. 1143-1162 ◽

Cited By ~ 71

Author(s):

P. L. Houtekamer ◽

Xingxiu Deng ◽

Herschel L. Mitchell ◽

Seung-Jong Baek ◽

Normand Gagnon

Keyword(s):

Kalman Filter ◽

Temporal Resolution ◽

Ensemble Kalman Filter ◽

Forecast Model ◽

Horizontal Resolution ◽

Ensemble Prediction ◽

Time Step ◽

Ensemble Prediction System ◽

Medium Range ◽

The Impact

Abstract Recently, the computing facilities available to the Meteorological Service of Canada were significantly upgraded. This provided an opportunity to improve the resolution of the global ensemble Kalman filter (EnKF) and the medium-range Global Ensemble Prediction System (GEPS). In the EnKF, the main upgrades include improved horizontal, vertical, and temporal resolution. With the introduction of the higher horizontal resolution, it was decided to use a filtered topography in order to address an occasional instability problem. At the same time, the number of assimilated radiance observations was increased via a relaxation of the data-thinning procedures. In the medium-range GEPS, which already used the higher horizontal resolution, the filtered topography was also adopted. Likewise, the temporal resolution was increased to be the same as in the short-range integrations of the EnKF. With these changes, the grid used by the Canadian EnKF has 600 × 300 points in the horizontal and 74 vertical levels. The forecast model uses a 20-min time step and, for time interpolation of the model trajectories, model states are stored every hour. The EnKF uses an ensemble having 192 members. This paper sequentially examines the impact of these implemented changes. The upgraded EnKF became operational at the Canadian Meteorological Centre in mid-February 2013.

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Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

10.5194/ems2021-232 ◽

2021 ◽

Author(s):

Pauline Martinet ◽

Frédéric Burnet ◽

Alistair Bell ◽

Arthur Kremer ◽

Matthias Letillois ◽

...

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Weather Prediction ◽

Ensemble Prediction ◽

Initial State ◽

Ensemble Prediction System ◽

Radar Observations ◽

Stratus Clouds ◽

Cloud Radar ◽

Physical Parameterizations

Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System

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Defining the Influence of Horizontal Grid Spacing on Ensemble Uncertainty within a Regional Modeling Framework

Weather and Forecasting ◽

10.1175/waf-d-16-0030.1 ◽

2016 ◽

Vol 31 (6) ◽

pp. 1997-2017 ◽

Cited By ~ 4

Author(s):

Jamie Dyer ◽

Christopher Zarzar ◽

Philip Amburn ◽

Robert Dumais ◽

John Raby ◽

...

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Computational Time ◽

Ensemble Prediction ◽

Grid Spacing ◽

Model Framework ◽

Modeling Framework ◽

Nwp Model ◽

The Individual ◽

Horizontal Grid

Abstract Numerical weather prediction (NWP) models are limited with respect to initial and boundary condition data and possess an incomplete description of underlying physical processes. To account for this, modelers have adopted the method of ensemble prediction to quantify the uncertainty within a model framework; however, the generation of ensemble members requires considerably more computational time and/or resources than a single deterministic simulation, especially at convection-allowing horizontal grid spacings. One approach to solving this issue is the development of both a large and small horizontal grid spacing model framework over the same domain for ensemble and deterministic simulations, respectively. This approach assumes that model grid spacing has no influence on model uncertainty; therefore, the objective of this paper is to quantify the influence of horizontal grid spacing on the statistical spread of NWP model ensembles over a regional domain. A series of 24-h simulations using the Weather Research and Forecast (WRF) Model are generated over a static domain with horizontal grid spacings of 35, 25, 15, and 9 km, using both a stochastic kinetic energy backscatter scheme and a multiphysics ensemble approach. Results indicate that horizontal grid spacing does influence the magnitude of uncertainty within an ensemble, although the exact magnitude and type of statistical relationship (direct versus inverse) varies by case. As such, at shorter lead times (<12 h) the dominant atmospheric process associated with each event and the type of ensemble being used outweigh the individual impacts of horizontal grid spacing on ensemble spread.

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Verification of Probabilistic Precipitation Forecasts in Metropolitan Area of Valley of Mexico Using the ECMWF Ensemble Prediction System

10.5194/egusphere-egu2020-12046 ◽

2020 ◽

Author(s):

Marco Rodrigo López López ◽

Adrian Pedrozo-Acuña

Keyword(s):

Mexico City ◽

Weather Prediction ◽

Extreme Rainfall ◽

Ensemble Prediction ◽

Brier Score ◽

Climate Projections ◽

Good Representation ◽

Time Step ◽

Ensemble Prediction System ◽

Study Case

Security against extreme rainfall events is a basic need for social and economic development. The climate projections suggest a changing world in the rainfall patterns, forecasting increasingly extreme rainfall and droughts events; nevertheless, there is a lot of uncertainty in the future hydrologic cycle of the basins, where rainfall is the more complicated weather phenomena to predict. To deal with this difficulty, process such as assimilation, a better description of weather phenomena and the use of ensembles have been developed. Such technologic advances have resulted in the use of Numerical Weather Prediction Models (NWP) and its chain with Ensemble Prediction Systems (EPS), which have been recognized as valuable tools for a good Warning System. Currently, Mexico City is one of the largest metropolis of the world with more than 22 million of inhabitants and serious difficulties oh hydraulic infrastructure. The city depends completely on the sewage system to prevent and mitigate floods. For these reasons, this work proposes to evaluate the deterministic and meteorological ensemble precipitation forecasts issued by the European Centre for Medium Range Weather Forecasting (ECMWF) for two study cases: 1) Mexico Valley Basin and 2) Mexico City. For study case 1, the precipitation forecasts were compared against 24 hours accumulated observed rainfall, issued by CLICOM System (clicom-mex.cicese.mx) and for 2007 to 2014 period time. For study case 2, the forecast were compared against observed real-time precipitation data issued by the Hydrological Observatory of Engineering Institute (OHIIUNAM), using a lead-time and time step of 90 hours and 6 hours respectively; and carried out for the rainy season of years 2017 and 2018. For this, deterministic and probabilistic verification metrics were applied (Relative Operating Characteristic, Reliability Diagram and the Brier Score) in order to measure the quality and performance of the forecasts products and its potential use for floods prediction in Mexico City. The evaluation of the results shows that the observed events are within the range of the probability distribution, which means that the EPS constitutes a good representation of the possible atmospheric scenarios along the time horizon. Metrics establish a greater reliability for forecast in the range of 2 to 10 mm of accumulated rainfall in 24 hours; in the other hand, there is a good discrimination and accuracy of observed and unobserved events of accumulated precipitation of 1 mm in 6 hours.

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Can the Direct Effect of Aerosols Improve Subseasonal Predictability?

Monthly Weather Review ◽

10.1175/mwr-d-17-0282.1 ◽

2018 ◽

Vol 146 (10) ◽

pp. 3481-3498 ◽

Cited By ~ 8

Author(s):

Angela Benedetti ◽

Frédéric Vitart

Keyword(s):

Weather Prediction ◽

Ensemble Prediction ◽

Radiation Balance ◽

Carbonaceous Aerosols ◽

Madden Julian Oscillation ◽

Reasonable Accuracy ◽

Ensemble Prediction System ◽

Radiative Fluxes ◽

Aerosol Effect ◽

Definite Proof

Abstract The fact that aerosols are important players in Earth’s radiation balance is well accepted by the scientific community. Several studies have shown the importance of characterizing aerosols in order to constrain surface radiative fluxes and temperature in climate runs. In numerical weather prediction, however, there has not been definite proof that interactive aerosol schemes are needed to improve the forecast. Climatologies are instead used that allow for computational efficiency and reasonable accuracy. At the monthly to subseasonal range, it is still worth investigating whether aerosol variability could afford some predictability, considering that it is likely that persisting aerosol biases might manifest themselves more over time scales of weeks to months and create a nonnegligible forcing. This paper explores this hypothesis using the ECMWF’s Ensemble Prediction System for subseasonal prediction with interactive prognostic aerosols. Four experiments are conducted with the aim of comparing the monthly prediction by the default system, which uses aerosol climatologies, with the prediction using radiatively interactive aerosols. Only the direct aerosol effect is considered. Twelve years of reforecasts with 50 ensemble members are analyzed on the monthly scale. Results indicate that the interactive aerosols have the capability of improving the subseasonal prediction at the monthly scales for the spring/summer season. It is hypothesized that this is due to the aerosol variability connected to the different phases of the Madden–Julian oscillation, particularly that of dust and carbonaceous aerosols. The degree of improvement depends crucially on the aerosol initialization. More work is required to fully assess the potential of interactive aerosols to increase predictability at the subseasonal scales.

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