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Karl-Ivar Ivarsson

Abstract

A score for verifying probabilistic forecasts is presented. It is called the continuous specific score (CSS) and is intended for binary events only. The score is based on the user’s cost–loss relations and their relative importance. The relative importance is determined by a continuous function of the user’s density of loss for various cost–loss relations. One may also consider CSS as the result of the expected mean value based on a probability function of the various loss values for all possible cost–loss relations for one single user. The CSS is a negatively oriented score and has the following properties: perfect forecasts yield the score zero, and 100% probability for adverse weather (AW) when AW does not occur leads to the score one. The result of a 0% forecast of AW when AW occurs is the inverted value of the ratio of the average cost to the average loss minus one. Different possible usages of the score are discussed. An effective cost–loss ratio (ECLR) is defined. It measures how important low cost–loss ratios are compared to higher ones.

Open access
Karl-Ivar Ivarsson

Abstract

A score for verifying probabilistic forecasts is presented. It is called the continuous specific score (CSS) and is intended for binary events only. The score is based on the user’s cost–loss relations and their relative importance. The relative importance is determined by a continuous function of the user’s density of loss for various cost–loss relations. One may also consider CSS as the result of the expected mean value based on a probability function of the various loss values for all possible cost–loss relations for one single user. The CSS is a negatively oriented score and has the following properties: perfect forecasts yield the score zero, and 100% probability for adverse weather (AW) when AW does not occur leads to the score one. The result of a 0% forecast of AW when AW occurs is the inverted value of the ratio of the average cost to the average loss minus one. Different possible usages of the score are discussed. An effective cost–loss ratio (ECLR) is defined. It measures how important low cost–loss ratios are compared to higher ones.

Open access
Tage Andersson and Karl-Ivar Ivarsson

Abstract

A new model for making probability forecasts of accumulated spot precipitation from weather radar data is presented. The model selects a source region upwind of the forecast spot. All pixels (horizontal size 2 × 2 km2) within the source region are considered, having the same probability of hitting the forecast-spot. A pixel hitting the forecast spot is supposed to precipitate there a short time (about 10 min.). A drawing is performed, and a frequency distribution of accumulated precipitation during the first time step of the forecast is obtained. A second drawing gives the frequency distribution of accumulated precipitation during the first to second time step, a third one during the first to third, and so on until the end of the forecast period is reached. A number of forecasts for 1-h accumulated precipitation, with lead times of 0, 1, and 2 h, have been performed and verified. The forecasts for 0-h lead time got the highest Brier skill scores, +50% to 60% relative to climatological forecasts for accumulated precipitation below 1 mm.

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Karl-Ivar Ivarsson, Rune Joelsson, Erik Liljas, and Allan H. Murphy

Abstract

This paper describes new operational and experimental forecasting programs at the Swedish Meteorological and Hydrological Institute (SMHI) designed to provide users with more detailed and more useful weather forecasts. User groups currently served by these programs include construction contractors, farmers, electric power companies, street and highway departments, and ski resorts. The programs represent a major component of a SMHI-wide effort to develop products to meet the needs of the public and private sectors in Sweden for meteorological and hydrological information.

An important feature of these programs is that many of the forecasts are expressed in probabilistic terms, and some results of the probability forecasting components of four programs are presented here. These subjective forecasts specify the likelihood of occurrence of various precipitation, wind speed, temperature, and cloud amount events, and they generally involve relatively short lead times and/or valid periods. The probabilistic forecasts of measurable precipitation are found to be reasonably reliable and definitely skillful. Some forecasts of larger precipitation amounts and the wind speed forecasts for shorter lead times also demonstrate positive skill, and the probabilistic temperature forecasts appear to be quite reliable. On the other hand, most of the experimental and operational probability forecasts reveal some degree of overforecasting, which tends to increase as lead time increases and as the climatological probability of the event decreases. As a result, the wind speed forecasts for longer lead times, some forecasts of precipitation amount, and the cloud amount forecasts exhibit negative skill.

Some factors that may have contributed to the deficiencies in the forecasters' performance are identified. The need to refine various components of the forecasting system is emphasized, and current efforts to implement such refinements at SMHI are outlined.

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Malte Müller, Mariken Homleid, Karl-Ivar Ivarsson, Morten A. Ø. Køltzow, Magnus Lindskog, Knut Helge Midtbø, Ulf Andrae, Trygve Aspelien, Lars Berggren, Dag Bjørge, Per Dahlgren, Jørn Kristiansen, Roger Randriamampianina, Martin Ridal, and Ole Vignes

Abstract

Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

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Lisa Bengtsson, Ulf Andrae, Trygve Aspelien, Yurii Batrak, Javier Calvo, Wim de Rooy, Emily Gleeson, Bent Hansen-Sass, Mariken Homleid, Mariano Hortal, Karl-Ivar Ivarsson, Geert Lenderink, Sami Niemelä, Kristian Pagh Nielsen, Jeanette Onvlee, Laura Rontu, Patrick Samuelsson, Daniel Santos Muñoz, Alvaro Subias, Sander Tijm, Velle Toll, Xiaohua Yang, and Morten Ødegaard Køltzow

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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