Numerical Forecasts Research Articles

Weather model verification using Sodankylä mast measurements

Abstract. Sodankylä, in the heart of Arctic Research Centre of the Finnish Meteorological Institute (FMI ARC) in northern Finland, is an ideal site for atmospheric and environmental research in the boreal and sub-Arctic zone. With temperatures ranging from −50 to +30 °C, it provides a challenging testing ground for numerical weather forecasting (NWP) models as well as weather forecasting in general. An extensive set of measurements has been carried out in Sodankylä for more than 100 years. In 2000, a 48 m-high micrometeorological mast was erected in the area. In this article, the use of Sodankylä mast measurements in NWP model verification is described. Starting in 2000, with the NWP model HIRLAM and Sodankylä measurements, the verification system has now been expanded to include comparisons between 12 NWP models and seven measurement masts, distributed across Europe. A case study, comparing forecasted and observed radiation fluxes, is also presented. It was found that three different radiation schemes, applicable in NWP model HARMONIE-AROME, produced somewhat different downwelling longwave radiation fluxes during cloudy days, which however did not change the overall cold bias of the predicted screen-level temperature.

Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events

Atmospheric moisture content or humidity is an important analysis variable of any meteorological data assimilation system. The humidity analysis can be univariate, using humidity background (normally short-range numerical forecasts) and humidity observations. However, more and more data assimilation systems are multivariate, analyzing humidity together with wind, temperature and pressure. Background error covariances, with unbalanced velocity potential and humidity in the multivariate formulation, are generated from weather research and forecasting model forecasts, collected over a summer rainy season and a winter dry season. The unbalanced velocity potential and humidity related correlations are shown to be significantly larger, indicating more important roles unbalanced velocity potential and humidity play, in the rainy season than that in the dry season. Three cycling data assimilation experiments of two rainfall events in the middle and lower reaches of the Yangtze River are carried out. The experiments differ in the formulation of the background error covariances. Results indicate that only including unbalanced velocity potential in the multivariate background error covariance improves wind analyses, but has little impact on temperature and humidity analyses. In contrast, further including humidity in the multivariate background error covariance although has a slight negative effect on wind analyses and a neutral effect on temperature analyses, but significantly improves humidity analyses, leading to precipitation forecasts more consistent with China Hourly Merged Precipitation Analysis.

A visualization system for calibrating multimodel ensembles in weather forecast

Sophisticated numerical computation is widely used in weather forecast. Various computation models exist, and each of them is designed for different meteorological elements, areas, or seasons. Therefore, multimodel ensemble forecast is aimed at integrating various numerical model forecasts to obtain an accurate forecast result. The existence of deviations compels forecasters to calibrate ensemble forecast result. Traditional calibration methods are strongly dependent on the experience of the forecaster and are usually performed inefficiently with grid-by-grid or station-by-station mode. Such calibration methods experience several drawbacks, including the high complexity and the low comprehensiveness of searching for similar historical forecast results in a limited dataset and the lack of real-time interactive analysis. To solve these problems and improve the efficiency and the accuracy of calibration, we propose a visualization system for calibrating multimodel ensemble forecast result. This system can assist forecasters to quickly locate potential calibrating stations by visualizing the deviation of historical forecast and observation result. Then, associated regions are calculated automatically under similarity and connectivity constraints, and corresponding coefficients between associated stations are generated based on parameter learning of the factor graph. These associated regions and coefficients are used as a guide for forecasters in setting unified deviation and weight for each numerical model. Finally, automatic calibration is conducted on the rest stations in the associated area. The performance of the visualization system is evaluated with real meteorological data.

Numerical Forecasting Experiment of the Wave Energy Resource in the China Sea

The short-term forecasting of wave energy is important to provide guidance for the electric power operation and power transmission system and to enhance the efficiency of energy capture and conversion. This study produced a numerical forecasting experiment of the China Sea wave energy using WAVEWATCH-III (WW3, the latest version 4.18) wave model driven by T213 (WW3-T213) and T639 (WW3-T639) wind data separately. Then the WW3-T213 and WW3-T639 were verified and compared to build a short-term wave energy forecasting structure suited for the China Sea. Considering the value of wave power density (WPD), “wave energy rose,” daily and weekly total storage and effective storage of wave energy, this study also designed a series of short-term wave energy forecasting productions. Results show that both the WW3-T213 and WW3-T639 exhibit a good skill on the numerical forecasting of the China Sea WPD, while the result of WW3-T639 is much better. Judging from WPD and daily and weekly total storage and effective storage of wave energy, great wave energy caused by cold airs was found. As there are relatively frequent cold airs in winter, early spring, and later autumn in the China Sea and the surrounding waters, abundant wave energy ensues.

Sustainability of Wind Energy under Changing Wind Regimes—A Case Study

A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-September) of 2006 to 2012 (CESM-P1) and 2026 to 2032 (CESM-P2) to obtain a reference and projected wind-speed climatology, respectively. The wind speeds served to calculate the potential power output and capacity factors of seven turbine types. CESM-P1 wind-speed climatology, power output, and capacity factors were compared to those derived from wind speeds obtained by numerical weather forecasts for reference to known standard to wind-farm managers. Juneau, Alaska served as a virtual testbed as this region is known to experience changes in wind speeds in response to the Pacific Decadal Oscillation. CESM-P2 suggested about 2% decrease for wind speeds between the speeds at cut-in and rated power, and about 8% - 10% decrease in potential wind-power output. This means that in regions of decadal climate variations, the sustainability of wind-energy production should be part of the decision-making process. The study demonstrated that using mean values of wind-speeds can provide qualitative knowledge about decreases/increases in potential energy production, but not about the magnitude. Using the total individual wind-speed data of all seasons provided the same amount of total power output than summing up the power outputs of individual seasons. The main advantage of calculating individual seasonal wind-power outputs, however, is that it theoretically permits assessment of interannual variability in power output and capacity factors. Comparison to a known standard may help stakeholders in understanding of uncertainty and interpretation of projected changes.

How to manage flood forecasting in a catchment with high flood risk - experiences from the Emscher

The Emscher area is a basin of 865 km2 , which includes nearly 2.3 million inhabitants. According to the average of 2.700 inhabitants per km2 nearly 60 % of the basin’s surface is sealed. 100 years of coal mining shaped the basin significantly. Due to that open channel system, the Emscher river shows a very quick reaction on precipitation. What does that mean for our flood forecasting? We have to record our data of the river gauges in an adequate time interval, that is usually 1 min. We have to import this recorded data from the gauges in our timeseries database every 5 minutes. In several other time intervals we’re getting our precipitation data, the radar data and the available numerical weather forecast from the German Weather Service. All this serves as input data for multiple model runs. The flood forecasting models perform continuous calculations every half an hour. All in all we’re offering an hydrological forecasting service for the operational branch of our organization so that they can handle the occurring problems. If necessary we’re performing this duty in a shift work system, so that we can offer a 24 hours service as long as needed.

Computational fluid dynamics analysis and experimental study of sounding temperature sensor

Owing to the fact that the increasing amount of attention has been focused on numerical weather forecast and climate change research, it is desired that the observation error of upper air temperature with using sounding temperature sensors can be reduced down to 0.1 K. However, the temperature measurement errors of bead thermistor sounding temperature sensors, induced by solar radiation, are on the order of 1 K or more, which is a few orders of magnitude larger than the errors produced by the measurement circuits and digital signal processing systems in radiosondes. Hence, the solar radiation error poses an important bottleneck for improving the measurement accuracy. To tackle this problem, a numerical analysis method is proposed in this research. By employing a computational fluid dynamics (CFD) method, the influences of various solar radiation intensity, sensor angles, and air pressures from sea level to 20 km altitude on temperature measurement accuracy are studied. In this CFD model, the boundary conditions of external convection and solar radiation of the bead thermistor are taken into consideration. The modeling results indicate that solar radiation intensity and altitude are important factors that affect the amplitude of the radiation error. With the elevation increasing from sea level, the solar heating error appears to have an exponential correlation with the altitude, which exhibits a growing slop rate. When the sensor angle is 90o, the radiation error of a bead thermistor sensor probe is minimal. The simulation results are fitted by a Levenberg-Marquardt method and a global optimization method. A correction equation of the radiation error is obtained, where the altitude of the sensor and solar radiation intensity act as two major variables in the equation. In order to verify the equation obtained in this study, an experimental platform for solar radiation error, which includes a low-pressure temperature chamber, a rotation apparatus, an LED-based radiation source, and a wireless communication system, is designed and constructed. It can be found that the solar radiation errors of the bead thermistor based on fluid dynamics numerical calculation are generally consistent with experimental data. The average offset and root mean square error between the correction equation and experimental results are 0.017 K and 0.023 K, respectively, which can demonstrate the accuracies of the computational fluid dynamics method, the Levenberg-Marquardt method and the global optimization method proposed in this research. The methods and techniques introduced in this paper may open the way for correcting the solar radiation errors of the bead thermistor sounding temperature sensors.

Numerical forecasting of short-term algae-induced black bloom in eutrophic shallow lake:A case study of Lake Taihu

Numerical forecasting of short-term algae-induced black bloom in eutrophic shallow lake:A case study of Lake Taihu

阿勒泰东部地区暴雨过程的成因分析

利用常规气象观测资料、2015年6月29日阿勒泰地区降水资料以及EC模式细网格和T639模式细网格预报产品，对2015年6月29日阿勒泰东部地区暴雨天气过程的成因进行了天气学分析并检验了暴雨过程的数值模式预报的性能，结果表明：这次暴雨过程100 hPa上南亚高压呈双体型，500 hPa上欧洲长波脊是主导系统，而暴雨的主要影响天气系统是西西伯利亚长波槽，高低空环流形势和影响天气系统合理配置，尤其是中尺度系统活动是暴雨形成的主要原因。阿勒泰东部地区上空深厚的不稳定能量、较强的动力抬升作用、充沛的水汽条件和有利的迎风波地形效应是暴雨产生的缘由。在此次暴雨过程中，各家数值预报产品对于此次天气过程的高度场预报性能较好，与实况相差不大；但对东部暴雨动力条件、水汽条件的预报偏差较大，无参考价值；EC细网格对东部暴雨的雨量预报较接近实况。 Based on the conventional meteorological data, EC model and T639 model of fine grid forecast products, we analyzed the cause of the rainstorm weather process in the east of Aletai on June 29, 2015, and analyzed the performance of the numerical model prediction of the rainstorm process. The results showed that the rainfall process on the South Asian high was 100 hPa double size; 500 hPa Europe long wave ridge was the dominant system; the main influence of rainstorm weather system was West Siberia wave trough, the level of air circulation and weather system reasonably; and especially the mesoscale system activity was the main cause of torrential rain. Over the eastern part of Aletai, the deep unstable energy, strong dynamic uplift, abundant water vapor conditions and favorable terrain effect were the causes of heavy rain. In the course of the storm, the performance of various numerical forecast products to predict height field for the weather was good, similar to the actual situation; but for the heavy rain in eastern dynamic conditions and water vapor condition, prediction showed a large deviation, which had no reference value; while EC fine grid rainfall forecast on the eastern rainstorm was closer to the truth.

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

The aim of this paper is to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7km horizontal resolution. The WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Results showed a tendency of the WRF model to overestimate the values of the analyzed parameters in comparison to observations.

PODEn4DVar-based radar data assimilation scheme: formulation and preliminary results from real-data experiments with advanced research WRF (ARW)

The Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as PODEn4DVar) is a hybrid assimilation method that exploits the strengths of both the ensemble Kalman filter (EnKF) and the 4DVar assimilation method. Its feasibility and validity have been demonstrated using ideal models through observing system simulation experiments (OSSEs). In this study, we further utilise this approach to build a PODEn4DVar-based radar data assimilation scheme (PRAS). In a PRAS, radar observations including radial velocity and reflectivity, after some necessary data preprocessing, are assimilated directly to improve model initialisation. A group of single-observation-based OSSEs are first designed to generally evaluate the validity of PRAS. Subsequently, a group of comparison experiments are also carried out between PRAS and an LETKF-based radar assimilation scheme (LRAS), which shows that PRAS is able to produce results better than (at least as good as) LRAS. Thirdly, to evaluate the potential impact for PRAS in the operational context, a group of cycling assimilation experiments of radar data are performed, which demonstrates that PRAS can gradually improve the accuracy of analysis field by cycling assimilation. Finally, a heavy convective-rainfall case study was selected to investigate the performance of PRAS in assimilating real radar observations and the impacts of assimilating radar observations on numerical forecasts, with the Weather Research and Forecasting (WRF) model as our forecasting model. The results show that significant improvements in predicting heavy rainfall can be achieved due to the improved initial conditions for the convective system's dynamics and microphysics after assimilating the radar observations with PRAS. In summary, the results show that the PODEn4DVar is a promising method for atmospheric data assimilation.

Cold-air outbreaks over the ocean at high latitudes and associated mesoscale atmospheric circulations: Problems of numerical modelling

A review of the current state of research in the field of numerical modelling and forecasting of cold-air outbreaks over the ocean at high latitudes and associated mesoscale circulations is presented. It is shown that the most relevant tasks are as follows: (1) the improvement of predictability and the adequacy of reproduction of polar mesocyclones, (2) a more adequate representation of the marginal sea-ice zone in the numerical models, and (3) solving problems of the parametrization and explicit reproduction of organized convection and orographic jets in numerical atmosphere models. It is demonstrated that these tasks only can be accomplished as a result of a comprehensive development of different components of the climatic system models and technology of the numerical weather prediction (NWP). One of the most promising approaches to overcome the identified problems is to develop and use methods of satellite remote sensing of the atmosphere and underlying surface in NWP technology. The high potential of analyzing the satellite multisensor data for quantifying parameters of different-scale atmospheric circulations is demonstrated using the example of cold-air outbreaks over the seas of the Far East.

Improved boundary values of ocean wave fields using a data assimilation scheme

The objective of this study was to assess the accuracy of numerical wave forecasts through data assimilation of boundary values. A sequential data assimilation scheme was adopted to utilise altimeter data. The performance of the system in terms of operational applications, specifically for typhoon waves, was investigated. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used as case studies. The proposed data assimilation increased the forecast accuracy of the boundary values in terms of the wave parameters, such as the wave heights and periods. The results showed that after the assimilation, the assimilation model was significantly improved, especially the peak value of the wave and time of occurrence of the peak value.

Ensemble Forecast Uncertainty of the 1 December 2011 Wasatch Windstorm

AbstractA downslope windstorm on 1 December 2011 led to considerable damage along a narrow 50-km swath at the western base of the Wasatch Mountains in northern Utah. Operational forecasts issued by the Salt Lake City National Weather Service Forecast Office provided accurate guidance for the event at 1–2-day lead times, partially based on locally generated high-resolution numerical forecasts. Forecasters highlighted the possibility of the windstorm 4 days in advance. To address the apparent reduced uncertainty for this windstorm, three 11-member three-domain ensemble forecasts were initialized at 0000 UTC 25 November, 0000 UTC 27 November, and 0000 UTC 29 November 2011 using the Weather Research and Forecasting (WRF) Model with initial and boundary conditions supplied by Global Ensemble Forecast System Reforecast, version 2 (GEFS/R2). Eight of the 11 ensemble members from the 29 November 2011 forecast (60 h before the windstorm) generated a strong, localized windstorm with outliers arising from reduced cross-barrier flow. Analysis of kinetic energy error growth suggests that the reduced uncertainty of 60-h forecasts was not primarily a result of the underdispersion of GEFS/R2 initial and boundary conditions but was related to a regional reduction in error growth in midtropospheric flow upstream of northern Utah. The ensemble initialized 2 days earlier (27 November, 108 h before the windstorm) contains fewer members that generate strong windstorms, while no members generate a windstorm in the ensemble initialized on 25 November (156 h prior). This sudden increase in uncertainty with forecast lead time results from the sensitivity of the ensemble solutions to the lateral boundary conditions imposed by the GEFS/R2 between 0000 UTC 29 November and 0000 UTC 30 November.

Real-Time Applications of the Variational Version of the Local Analysis and Prediction System (vLAPS)

Abstract The accurate and timely depiction of the state of the atmosphere on multiple scales is critical to enhance forecaster situational awareness and to initialize very short-range numerical forecasts in support of nowcasting activities. The Local Analysis and Prediction System (LAPS) of the Earth System Research Laboratory (ESRL)/Global Systems Division (GSD) is a numerical data assimilation and forecast system designed to serve such very finescale applications. LAPS is used operationally by more than 20 national and international agencies, including the NWS, where it has been operational in the Advanced Weather Interactive Processing System (AWIPS) since 1995. Using computationally efficient and scientifically advanced methods such as a multigrid technique that adds observational information on progressively finer scales in successive iterations, GSD recently introduced a new, variational version of LAPS (vLAPS). Surface and 3D analyses generated by vLAPS were tested in the Hazardous Weather Testbed (HWT) to gauge their utility in both situational awareness and nowcasting applications. On a number of occasions, forecasters found that the vLAPS analyses and ensuing very short-range forecasts provided useful guidance for the development of severe weather events, including tornadic storms, while in some other cases the guidance was less sufficient.

Numerical weather prediction revisions using the locally trained differential polynomial network

Meso-scale forecasts result from global numerical weather prediction models, which are based upon the differential equations for atmospheric dynamics that do not perfectly determine weather conditions near the ground. Statistical corrections can combine complex numerical models, based on the physics of the atmosphere to forecast the large-scale weather patterns, and regression in post-processing to clarify surface weather details according to local observations and climatological conditions. Neural networks trained with local relevant weather observations of fluctuant data relations in current conditions, entered by numerical model outcomes of the same data types, may revise its one target short-term prognosis (e.g. relative humidity or temperature) to stand for these methods. Polynomial neural networks can compose general partial differential equations, which allow model more complicated real system functions from discrete time-series observations than using standard soft-computing methods. This new neural network technique generates convergent series of substitution relative derivative terms, which combination sum can define and solve an unknown general partial differential equation, able to describe dynamic processes of the weather system in a local area, analogous to the differential equation systems of numerical models. The trained network model revises hourly-series of numerical prognosis of one target variable in sequence, applying the general differential equation solution of the correction multi-variable function to corresponding output variables of the 24-hour numerical forecast. The experimental results proved this polynomial network type can successfully revise some numerical weather prognoses after this manner.

FROST-2014 project and meteorological support of the Sochi-2014 Olympics

In order to provide weather services to the 2014 Winter Olympics, the observational network in the Sochi region was strengthened with more than 40 automatic weather stations, the dual polarization Doppler radar, temperature and wind profilers, micro-rain radars, ets. These observation facilities allowed using the Sochi testbed for the development, testing, and intercomparison of various technologies of nowcasting and short-range numerical weather forecast. It was accomplished in the framework of the international project of the World Weather Research Program of WMO called FROST-2014 (Forecast and Research in the Olympic Sochi Testbed). The prognostic products of the FROST-2014 project was used for the operational meteorological servicing of Olympic events. Presented are the main research directions and the project preliminary results.

Rapid, short‐term ensemble forecast adjustment through offline data assimilation

Rapid updates of short‐term numerical forecasts remain limited by the time it takes to assimilate observations and run a dynamical model to produce new forecasts. Here we use an ensemble‐based statistical method to adjust a user‐defined subspace of forecast grids rapidly as observations become available. Specifically, an ensemble Kalman filter is used to adjust forecast variables based on covariances between the ensemble estimate of the observation and the forecast variables. This approach allows rapid adjustment of forecast fields, or functions of those fields, ‘offline’, without the expense or time of running the full dynamical model. Furthermore, by updating an ensemble, forecast uncertainty is also adjusted. The technique is tested using operational ensemble forecasts from the European Centre for Medium‐Range Weather Forecasts and Canadian Meteorological Centre. Results show that the method is effective at reducing forecast errors in surface pressure at least 18–24 h after the observation time, with a maximum impact of 9–15% for 12 h forecasts. Results for surface temperature show an error reduction 6–12 h after the observation time. Incorporating time‐lagged ensembles provides even greater reduction in error and a novel covariance localization technique that operates in space and time based on statistical significance is evaluated.

Verifying Forecast Precipitation Type with mPING*

Abstract In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

Sensitivity of Real-Data Simulations of the 3 May 1999 Oklahoma City Tornadic Supercell and Associated Tornadoes to Multimoment Microphysics. Part I: Storm- and Tornado-Scale Numerical Forecasts

Abstract Numerical predictions of the 3 May 1999 Oklahoma City, Oklahoma, tornadic supercell are performed within a real-data framework utilizing telescoping nested grids of 3-km, 1-km, and 250-m horizontal spacing. Radar reflectivity and radial velocity from the Oklahoma City WSR-88D are assimilated using a cloud analysis procedure coupled with a cycled 3DVAR system to analyze storms on the 1-km grid for subsequent forecast periods. Single-, double-, and triple-moment configurations of a multimoment bulk microphysics scheme are used in several experiments on the 1-km and 250-m grids to assess the impact of varying the complexity of the microphysics scheme on the storm structure, behavior, and tornadic activity (on the 250-m grid). This appears to be the first study of its type to investigate single- versus multimoment microphysics within a real-data context. It is found that the triple-moment scheme overall performs the best, producing the smallest track errors for the mesocyclone on the 1-km grid, and stronger and longer-lived tornado-like vortices (TLVs) on the 250-m grid, closest to the observed tornado. In contrast, the single-moment scheme with the default Marshall–Palmer rain intercept parameter performs poorly, producing a cold pool that is too strong, and only weak and short-lived TLVs. The results in the context of differences in latent cooling from evaporation and melting between the schemes, as well as implications for numerical prediction of tornadoes, are discussed. More generally, the feedbacks to storm thermodynamics and dynamics from increasing the prognostic detail of the hydrometeor size distributions are found to be important for improving the simulation and prediction of tornadic thunderstorms.