Quality Of Weather Forecasts Research Articles

The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation

Abstract. Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed.

Ensemble classification as a method for improvement of the long-term weather forecast quality

Ensemble classification as a method for improvement of the long-term weather forecast quality

Exploring the relationship between temperature forecast errors and Earth system variables

Abstract. Accurate subseasonal weather forecasts, from 2 weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models' process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological, and meteorological variables to study their potential for explaining temperature forecast errors at the weekly timescale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the model's difficulties in accurately capturing the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.

Correction of wind bias for the lidar on board Aeolus using telescope temperatures

Abstract. The European Space Agency (ESA) Earth Explorer satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component globally from space. It was successfully launched in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data have already been successfully assimilated into several NWP models and have already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources were necessary. One of them is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of model u-component winds with respect to radiosondes is smaller than 0.3 m s−1. Furthermore, this paper presents the alternative of using Aeolus ground return winds which serve as a zero-wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has a good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.

Международные требования к компетенции метеорологов-прогнозистов

Competency requirements for public weather forecasters, as well as knowledge and skills necessary for their implementation, that were developed and recommended for practical use by the World Meteorological Organization, are presented. Basic skills of working with radar and satellite data are described. The importance of the weather forecaster competency compliance with the presented requirements is emphasized, as well as a need for proper competency assessment and, if necessary, further training in order to improve the quality of weather forecasts and storm warnings. Keywords: competency, weather forecasters, weather forecasting, knowledge and skills, competency assessment, training

Международные требования к компетенции метеорологов-прогнозистов

Competency requirements for public weather forecasters, as well as knowledge and skills necessary for their implementation, that were developed and recommended for practical use by the World Meteorological Organization, are presented. Basic skills of working with radar and satellite data are described. The importance of the weather forecaster competency compliance with the presented requirements is emphasized, as well as a need for proper competency assessment and, if necessary, further training in order to improve the quality of weather forecasts and storm warnings. Keywords: competency, weather forecasters, weather forecasting, knowledge and skills, competency assessment, training

Examination of Optical Processes in The Atmosphere During Upper Air Soundings

AbstractImproving the quality of weather forecasts and the reliability of climate research requires increasing the reliability of measurements. This paper presents results for optical sensors attached to radiosondes. These sensors can measure cloud-top height (CTH) with high accuracy, determine the presence of precipitation particles in clouds and the height of the boundary between the tropospheric and stratospheric air masses. These research findings are especially important in the Arctic, where the reliability of cloud data is poor, especially during polar nights. With the help of a visible range optical sensor, during the daytime, it is possible to measure CTH with an accuracy of 50 meters. Using data from an IR sensor it is possible to measure CTH both day and night. The paper also discusses the possibility of using optical sensors in an observational network. The results from this study could be useful for both weather forecasting and climate research.

Assessing the Impact of Land Use and Land Cover Data Representation on Weather Forecast Quality: A Case Study in Central Mexico

In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified.

Validation of Aeolus wind products above the Atlantic Ocean

Abstract. In August 2018, the first Doppler wind lidar in space called Atmospheric Laser Doppler Instrument (ALADIN) was launched on board the satellite Aeolus by the European Space Agency (ESA). Aeolus measures profiles of one horizontal wind component (i.e., mainly the west–east direction) in the troposphere and lower stratosphere on a global basis. Furthermore, profiles of aerosol and cloud properties can be retrieved via the high spectral resolution lidar (HSRL) technique. The Aeolus mission is supposed to improve the quality of weather forecasts and the understanding of atmospheric processes. We used the opportunity to perform a unique validation of the wind products of Aeolus by utilizing the RV Polarstern cruise PS116 from Bremerhaven to Cape Town in November/December 2018. Due to concerted course modifications, six direct intersections with the Aeolus ground track could be achieved in the Atlantic Ocean west of the African continent. For the validation of the Aeolus wind products, we launched additional radiosondes and used the EARLINET/ACTRIS lidar PollyXT for atmospheric scene analysis. The six analyzed cases prove that Aeolus is able to measure horizontal wind speeds in the nearly west–east direction. Good agreements with the radiosonde observations could be achieved for both Aeolus wind products – the winds observed in clean atmospheric regions called Rayleigh winds and the winds obtained in cloud layers called Mie winds (according to the responsible scattering regime). Systematic and statistical errors of the Rayleigh winds were less than 1.5 and 3.3 m s−1, respectively, when compared to radiosonde values averaged to the vertical resolution of Aeolus. For the Mie winds, a systematic and random error of about 1 m s−1 was obtained from the six comparisons in different climate zones. However, it is also shown that the coarse vertical resolution of 2 km in the upper troposphere, which was set in this early mission phase 2 months after launch, led to an underestimation of the maximum wind speed in the jet stream regions. In summary, promising first results of the first wind lidar space mission are shown and prove the concept of Aeolus for global wind observations.

Cross-Comparison and Methodological Improvement in GPS Tomography

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0–8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions.

How Much Can AI Techniques Improve Surface Air Temperature Forecast? —A Report from AI Challenger 2018 Global Weather Forecast Contest

In August 2018, the Institute of Urban Meteorology (IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest (WFC)—one of the AI (artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than 1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height. Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2% and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.

Assimilation of Weather Radar Data Using WRF 3DVar Modelling for Rainfall Prediction

Data assimilation is one method of improving the initial condition of the atmosphere in numerical weather predictions. The weather-based radar data assimilation, which generates high-resolution data of about 250 meters with coverage of hundreds of kilometers, is expected to improve the quality of weather forecasts and analysis, especially in determining the precipitation forecasting or Quantitative Precipitation Forecasting (QPF). This study aims to investigate the role of assimilation of Doppler weather radar data in mesoscale models for the forecast of heavy rain events occurring in the Greater Jakarta area on 20 February 2017 and 13 June 2017. For this purpose, the reflectivity data and radial radar speeds obtained from weather radar Tangerang will be assimilated with the 3DVar system into the Weather Research Forecasting (WRF) numerical model version 3.9.1 and Tropical Physics Suite parameterized configuration recommended for the tropics. The output of assimilation model of radar data and without assimilation of the numerical model of WRF is verified by point and spatial with some observation data of Automatic Weather System (AWS) and GSMAP rainfall in Jabodetabek region. In general, the WRF model assimilation of radar data provides a better simulation of spatial rain events and better points compared to the WRF model without assimilation.

Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control

Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.

Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe

Abstract. Global navigation satellite systems (GNSSs) have revolutionised positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is now an established atmospheric observing system, which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60–70 % of atmospheric warming. In Europe, the application of GNSS in meteorology started roughly two decades ago, and today it is a well-established field in both research and operation. This review covers the state of the art in GNSS meteorology in Europe. The advances in GNSS processing for derivation of tropospheric products, application of GNSS tropospheric products in operational weather prediction and application of GNSS tropospheric products for climate monitoring are discussed. The GNSS processing techniques and tropospheric products are reviewed. A summary of the use of the products for validation and impact studies with operational numerical weather prediction (NWP) models as well as very short weather prediction (nowcasting) case studies is given. Climate research with GNSSs is an emerging field of research, but the studies so far have been limited to comparison with climate models and derivation of trends. More than 15 years of GNSS meteorology in Europe has already achieved outstanding cooperation between the atmospheric and geodetic communities. It is now feasible to develop next-generation GNSS tropospheric products and applications that can enhance the quality of weather forecasts and climate monitoring. This work is carried out within COST Action ES1206 advanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC, http://gnss4swec.knmi.nl).

The Remote Monitoring of Earth’s Atmosphere Based on Operative Processing GNSS Data in the UA-EUPOS/ZAKPOS Network of Active Reference Stations

The system of remote monitoring of atmosphere is designed to obtain information about the state of atmosphere. The principle of the remote monitoring of atmosphere is based on registering and processing GLONASS/GPS radio signals. Modern networks of active reference stations allow us to solve both practical problems of geodesy, navigation, and purely scientific problems that are important in all geosciences. The paper investigates a spatiotemporal instability in the atmosphere, based on 845 temporal measurements of tropospheric delay over the territory covered by 20 active reference stations of the UA-EUPOS/ZAKPOS network. The method elaborated by the authors for the determination of tropospheric delay in the UA-EUPOS/ZAKPOS network in real time takes relief of the region into account. The results are very good, since mapping tropospheric delay can be made with an average RMSE of 1.5 mm. The method developed in this research can be used to improve the quality of weather forecasts and the prevention of natural disasters.

The development of the commercial weather services market in Europe: 1970-2012

This study explores the development of commercial weather services in Europe over the past 40 years. The market is traced from its very low levels in the 1970s to its present size of ∼ €300 million per annum (excluding aviation). Ways in which growth in the European weather market has been affected by government policies relating to the organization of weather services and the philosophical argument over the status of ‘public sector information’ (PSI), the improvements in weather forecast quality and the advent of mobile web technology are discussed. Despite some significant growth among the private sector providers, the current structure of the European commercial weather market on the supply side restrains its overall development and the ability of both the private and public sectors to stimulate and meet the market needs. It is pointed out that, as a consequence of the present governmental policies in many European countries, there are emerging risks to the overall meteorological infrastructure and data quality, while in the commercial weather sector of the economy there has been and remains a clear loss of economic value to central exchequers. Ways to improve this position are proposed.

Parallel Multi-level Genetic Ensemble for Numerical Weather Prediction Enhancement

The need for reliable predictions in environmental modelling is well-known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental modelling. Additionally, right decisions to prevent damages and save lives could be taken depending on a reliable meteorological prediction process. Lack and uncertainty of input data and parameters constitute the main source of errors for most of these models. In recent years, evolutionary optimization methods have become popular to solve the input parameter problem of environmental models. We propose a new parallel meteorological prediction scheme that uses evolutionary optimization methods based on Multi-Chromosome Genetic Algorithm to enhance the quality of weather forecasts by focusing on the calibration of input parameters. This new scheme is parallelized and executed on a HPC environment in order to reduce the time needed to obtain the final prediction. The new approach is called Multi-Level Genetic Ensemble (M-Level G-Ensemble) and it has been tested using historical data of a wellknown weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Results obtained with our approach provide both significant improvements in weather prediction and a significant reduction in the execution time.

Supplement to MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region: Additional Applications of the D-Phase Datasets

Supplement to MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region: Additional Applications of the D-Phase Datasets

MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

On the possibility of getting economically sound forecasts of rare space weather events

There is a problem of setting criteria of space weather forecast quality that allows estimation of the economic effectiveness of forecasts in comparison with other methods for real users. The overwhelming majority of such users (airlines, power lines, pipelines, space exploration, navigation, ground-induced currents, medical services, etc.), are primarily interested in large space weather disturbances that affect the operation of their systems. But powerful disturbances happen rather seldom and so the traditional criteria of quality estimation give very little useful information for an estimate of economic effectiveness of the forecast. This work proposes a specially constructed value “A” for every customer (task) and for each method (or kind) of the forecast, which allows the estimation of the comparative economic effectiveness. Special attention is paid to the statistical significance in reference to the cyclic nature of the solar activity, and there are also indicated some numeral limits, which have to be considered during such a check.