Numerical Forecast Models Research Articles

DYNAMIC ERROR OF THE TEMPERATURE SENSORS WITH THE SOUNDING OF THE ATMOSPHERE

Radiosounding of the atmosphere is an essential component constituting the base for prognostic aviation authorities. GPR scanning data are the basis for the mapping of baric topography used in the development of aviation weather forecasts. Currently, numerical methods of weather forecast have become especially popular. This is quite justified, as these methods allow to increase the accuracy of weather forecasts and these techniques represent the future. However, the era of "numerical weather prediction" will not come soon. This is primarily due to imperfect numerical forecast models, which do not provide for timeliness and reliability of weather forecasting required for aviation. However, the quality of meteorological support of aircraft flights is largely determined by the timeliness and predictability of the aviation weather forecasts. In this regard, the network of radiosounding functions require the presentation of the theoretical foundations and providing consumers with normalized metrological characteristics of the measuring system of radio sounding, methods of measurement and a reasonable assessment of the reliability of the results of sensing. A number of these problems have been resolved nowadays, however, so far the problem of estimating dynamic measurement errors in the sounding is not solved. The metrological characteristics and the dynamic error of measurement of temperature with the new temperature sensors of foreign production (NТС MFB-5000-3220), recently used in the Russian radiosondes still require the detailed studies. This article is devoted to one of the most important types of errors of radio sounding – the dynamic errors of measurement, to be precise, the dynamic error of the temperature measurement. In the article the problem of determining the value of dynamic errors of radiosondes is being solved alongside with the investigation of the role of this kind of errors when assessing the reliability of the results of the atmosphere radio sounding.

Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers

Data assimilation or filtering combines a numerical forecast model and observations to provide accurate statistical estimation of the state of interest. In this paper we are concerned with accurate data assimilation of a sparsely observed passive tracer advected in turbulent flows using a reduced-order forecast model. The turbulent flows which contain anisotropic and inhomogeneous structures such as jets are typical in geophysical turbulent flows in atmosphere and ocean science, and passive tracers with a mean gradient can exhibit anisotropic transport with intermittent extreme events, as shown below. Stochastic superparameterization, which is a seamless multiscale method developed for large-scale models of atmosphere and ocean models without scale-gap between the resolved and unresolved scales, generates large-scale turbulent velocity fields using a significantly smaller degree of freedom compared to a direct fine resolution numerical simulation. In a large-scale model of the tracer transport, the tracer...

Verification of Mongolian Cyclone-Induced Snowstorm Model Forecast in Jilin Province, China

Situation field forecast and rainfall forecast in typical numerical forecast models including EC (The European Centre for Medium-Range Weather Forecasts), t639 (T639 Global Forecast System) and Japanese model were verified by set statistics and TS (Threat Score) scoring based on 8 cases of Mongolian cyclone-induced snowstorm in Jilin Province in this paper. As shown by the results, for the forecast of Mongolian cyclone location and intensity, EC has significantly higher accuracy than Japanese model and t639, and there is a high likelihood that it forecasts the southerly cyclone location, relatively fast movement and comparatively weak intensity within 72 hours; for snowfall forecast, Japanese model shows significantly higher accuracy than other models, especially it has obviously stronger ability to forecast the heavy rainfall above snowstorm than other models, while WRF model (The Weather Research and Forecasting Model) has strong forecast ability of normal snowfall; for normal snowfall, the 72-hour missing forecast rate is higher than false forecast rate in all the models.

一次高影响小雨过程预报失误的原因初探

2010年冬春之交的4月，因对8~9日发生在上海地区的一次小雨过程的预报失误，导致9日最高气温的预报与实况相差近7度，市民反响强烈。本文对这一“高影响”小雨过程的预报失误原因，进行了初步分析，结果表明：低层(850 hPa)的中尺度锋生，减缓了低层短波槽系统的过境速度，使短波槽系统在过境时出现“前倾”的异常垂直结构，导致高空(500 hPa)槽线虽已过境，但小雨过程却(因低层低值系统尚未过境)不止的令预报员始料未及的现象；预报员的惯性预报思维在前期预报已出现明显偏差的情况下未及时调整，对数值模式的中尺度预报信息分析不够深入，特别是模式对弱天气过程的要素预报能力把握不准等均是最终造成这次预报失误的主要原因。文章最后还给出了上海地区春季小雨过程预报及数值模式对弱天气过程诊断量改进的几点启示。 Due to the failure forecast on a light rain of 8-9 April 2010 in Shanghai, the highest temperature forecast is fault, the error near 7˚C. In this paper, the possible reasons of failure forecast on the high-impact light rain are discussed. The results show that: the lower (850 hPa) mesoscale fron-togenesis slows down the moving speed of low-level shortwave trough, causing the shortwave trough appearing a unusual “forward-lean” vertical structure during the trough movement, lead-ing to the trough line of high-level (500 hPa) passing through Shanghai but the lower-pressure system haven't and the light rain process sustains. Obviously, the unexpected phenomenon is difficult to forecast in operation. Furthermore, the forecast results have not been timely adjusted when the early forecasts have already shown the significant deviations, because of the inertia forecasting thinking. And the mesoscale analysis based on the numerical model products is not in-depth enough. Particularly, the limited capabilities of numerical model forecast on the weak process and its weather elements, are the main reasons for ultimate forecast failure. Finally, some inspirations on the forecast of spring light rain in Shanghai and model using skill on the weak weather process are shown in this paper.

Weak constraint four-dimensional variational data assimilation in a model of the California Current System

Abstract. A new approach is explored for computing estimates of the error covariance associated with the intrinsic errors of a numerical forecast model in regions characterized by upwelling and downwelling. The approach used is based on a combination of strong constraint data assimilation, twin model experiments, linear inverse modeling, and Bayesian hierarchical modeling. The resulting model error covariance estimates Q are applied to a model of the California Current System using weak constraint four-dimensional variational (4D-Var) data assimilation to compute estimates of the ocean circulation. The results of this study show that the estimates of Q derived following our approach lead to demonstrable improvements in the model circulation estimates and isolate regions where model errors are likely to be important and that have been independently identified in the same model in previously published work.

Evidence for gravity wave drag in the boundary layer of a numerical forecast model: a comparison with observations

The main effect of gravity wave drag is generally considered to be an increased drag on upper tropospheric flow. However, the nocturnal boundary layer overland is often extremely stable and in this case there can be considerable gravity wave drag on near‐surface flow, affecting turbulent fluxes and screen temperatures.In order to determine whether this type of drag is correctly represented in numerical forecast models, a dataset of winds and temperatures output from a forecast model is compared with an archive of observations from two surface sites. Comparison with the output from a one‐dimensional model is used to differentiate gravity wave drag from form drag and flow blocking. The results indicate that a gravity wave drag component is probably present in the stable boundary‐layer flows of the forecast models. However the strength of the drag is not as great as that observed. This results in larger model turbulent fluxes and increased surface temperatures from those measured overnight.

Parallelism and optimization of numerical ocean forecasting model

According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China (MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation (SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface (MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation (172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.

Critical weather situations for renewable energies – Part A: Cyclone detection for wind power

A constantly increasing share of weather dependent renewable energies in Germany's power mix poses new challenges concerning grid management and security of energy supply. An evaluation of the three year period from 2012 to 2014 reveals, that 60% of days with largest errors in the day-ahead wind power forecasts for Germany are linked to cyclones and troughs traversing the North Sea, the Baltic Sea or Germany. A cyclone detection algorithm has been developed to automatically indicate these critical weather situations. The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure. The cyclone detection is used to design an automated weather information tool for end-users such as Transmission System Operators (TSOs). For 2014, it identified a critical weather development in 38% of all days. The root mean square error of day-ahead wind power forecasts increased by 1% of installed capacity during these periods. A real time application of the tool is being implemented in order to support a sustainable and save integration of the increasing wind power production. It will then be provided to, and will be tested by, three German TSOs with the purpose of an operative usage to guarantee the security of supply.

A Coupled Numerical and Artificial Neural Network Model for Improving Location Specific Wave Forecast

As more than a quarter of India’s population resides along the coastlines, it is of utmost importance to predict the significant wave height as accurately as possible to cater the needs of safe and secure life. Presently Indian National Centre for Ocean Information Services (INCOIS) provides wave height forecasts on regional as well as local level ranging from 3hours to 7days ahead using numerical models. It is evident from numerical model forecasts at specific locations that the significant wave heights are not predicted very accurately. The obvious reason behind this is the ‘wind’ used in these models as a forcing function is itself forecasted wind (ECMWF wind (European Centre for Medium-range Weather Forecasting)) and hence many times the forecasts, differ very largely from the actual observations. These models work on larger grid size making it as major impediment in employing them particularly for location specific forecasts even though they work reasonably well for regional level. Present work aims in reducing the error in numerical wave forecast made by INCOIS at four stations along Indian coastline. For this ‘error’ between forecasted and observed wave height at current and previous time steps was used as input to predict the error 24hr ahead in advance using ANN since it has been effectively used for wave forecasting (univariate time series forecasting in general) since last two decades or so. This predicted error was then added or subtracted from numerical wave forecast to improve the prediction accuracy. It is observed that numerical model forecast improved considerably when the predicted error was added or subtracted from it. This hybrid approach will add to the usefulness of the wave forecasts given by INCOIS to its stake holders. The performance of improved wave heights is judged by correlation coefficient and other error measures like RMSE, MAE and CE.

Interactions Between the Nighttime Valley-Wind System and a Developing Cold-Air Pool

The Weather Research and Forecast numerical model is used to characterize the influence of a thermally-driven down-valley flow on a developing cold-air pool in an idealized alpine valley decoupled from the atmosphere above. Results for a three-dimensional (3D) valley, which allows for the formation of a down-valley flow, and for a two-dimensional (2D) valley, where the formation of a down-valley flow is inhibited, are analyzed and compared. A key result is that advection leads to a net cooling in the 2D valley and to a warming in the 3D valley, once the down-valley flow is fully developed. This difference stems from the suppression of the slope-flow induced upward motions over the valley centre in the 3D valley. As a result, the downslope flows develop a cross-valley circulation within the cold-air pool, the growth of the cold-air pool is reduced and the valley atmosphere is generally warmer than in the 2D valley. A quasi-steady state is reached for which the divergence of the down-valley flow along the valley is balanced by the convergence of the downslope flows at the top of the cold-air pool, with no net contribution of subsiding motions far from the slope layer. More precisely, the inflow of air at the top of the cold-air pool is found to be driven by an interplay between the return flow from the plain region and subsidence over the plateaux. Finally, the mechanisms that control the structure of the cold-air pool and its evolution are found to be independent of the valley length as soon as the quasi-steady state is reached and the down-valley flow is fully developed.

Quantification of the errors associated with the representation of surface emissivity in the RRTMG_LW

Recent studies have shown that representation of surface spectral emissivity has a non-negligible impact on the polar radiation budget and on simulated climate. This fact motivates the inclusion of surface spectral emissivity into the radiative transfer scheme employed in the climate models. Using a line-by-line radiative transfer model, LBLRTM, as a benchmark, we quantify the errors due to the approximation of surface spectral emissivity in the RRTMG_LW, a radiation scheme widely used by climate models and numerical forecast models. Error quantifications are done using representative sounding profiles in the tropics, mid-latitude summer, sub-arctic winter and Sahara desert interfaced with either ocean or desert spectral emissivities. Though including surface emissivity causes little difference in broadband outgoing longwave radiation, it can cause noticeable differences in longwave radiative cooling rate from surface to 700hPa. Compared to the benchmark, the largest deviation in radiative cooling rate usually happens at the lowest atmospheric layers next to the surface, ranging from −0.23K/day to 0.19K/day with the largest fractional difference being −16.22%. The discrepancies caused solely by the representation of surface emissivity are confined at the lowest atmospheric layer. Using the emissivity at the diffusive angle or the hemispherically averaged emissivity can affect the calculation accuracy, especially for strongly anisotropic surface emission. The band-by-band contribution to the discrepancies between the RRTMG_LW and benchmark reveals compensating errors among the bands used in the RRTMG_LW.

Representation of the grey zone of turbulence in the atmospheric boundary layer

Abstract. Numerical weather prediction model forecasts at horizontal grid lengths in the range of 100 to 1 km are now possible. This range of scales is the "grey zone of turbulence". Previous studies, based on large-eddy simulation (LES) analysis from the MésoNH model, showed that some assumptions of some turbulence schemes on boundary-layer structures are not valid. Indeed, boundary-layer thermals are now partly resolved, and the subgrid remaining part of the thermals is possibly largely or completely absent from the model columns. First, some modifications of the equations of the shallow convection scheme have been tested in the MésoNH model and in an idealized version of the operational AROME model at resolutions coarser than 500 m. Secondly, although the turbulence is mainly vertical at mesoscale (> 2 km resolution), it is isotropic in LES (< 100 m resolution). It has been proved by LES analysis that, in convective boundary layers, the horizontal production of turbulence cannot be neglected at resolutions finer than half of the boundary-layer height. Thus, in the grey zone, fully unidirectional turbulence scheme should become tridirectional around 500 m resolution. At Météo-France, the dynamical turbulence is modelled by a K-gradient in LES as well as at mesoscale in both MésoNH and AROME, which needs mixing lengths in the formulation. Vertical and horizontal mixing lengths have been calculated from LES of neutral and convective cases at resolutions in the grey zone.

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.

A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model

In the Application of Research to Operations at Mesoscale (AROME) numerical weather forecast model used in operations at Meteo-France, five mass-flux schemes are available to parametrize shallow convection at kilometre resolution. All but one are based on the eddy-diffusivity–mass-flux approach, and differ in entrainment/detrainment, the updraft vertical velocity equation and the closure assumption. The fifth is based on a more classical mass-flux approach. Screen-level scores obtained with these schemes show few discrepancies and are not sufficient to highlight behaviour differences. Here, we describe and use a new experimental framework, able to compare and discriminate among different schemes. For a year, daily forecast experiments were conducted over small domains centred on the five French metropolitan radio-sounding locations. Cloud base, planetary boundary-layer height and normalized vertical profiles of specific humidity, potential temperature, wind speed and cloud condensate were compared with observations, and with each other. The framework allowed the behaviour of the different schemes in and above the boundary layer to be characterized. In particular, the impact of the entrainment/detrainment formulation, closure assumption and cloud scheme were clearly visible. Differences mainly concerned the transport intensity thus allowing schemes to be separated into two groups, with stronger or weaker updrafts. In the AROME model (with all interactions and the possible existence of compensating errors), evaluation diagnostics gave the advantage to the first group.

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.

Review of wave‐turbulence interactions in the stable atmospheric boundary layer

AbstractFlow in a stably stratified environment is characterized by anisotropic and intermittent turbulence and wavelike motions of varying amplitudes and periods. Understanding turbulence intermittency and wave‐turbulence interactions in a stably stratified flow remains a challenging issue in geosciences including planetary atmospheres and oceans. The stable atmospheric boundary layer (SABL) commonly occurs when the ground surface is cooled by longwave radiation emission such as at night over land surfaces, or even daytime over snow and ice surfaces, and when warm air is advected over cold surfaces. Intermittent turbulence intensification in the SABL impacts human activities and weather variability, yet it cannot be generated in state‐of‐the‐art numerical forecast models. This failure is mainly due to a lack of understanding of the physical mechanisms for seemingly random turbulence generation in a stably stratified flow, in which wave‐turbulence interaction is a potential mechanism for turbulence intermittency. A workshop on wave‐turbulence interactions in the SABL addressed the current understanding and challenges of wave‐turbulence interactions and the role of wavelike motions in contributing to anisotropic and intermittent turbulence from the perspectives of theory, observations, and numerical parameterization. There have been a number of reviews on waves, and a few on turbulence in stably stratified flows, but not much on wave‐turbulence interactions. This review focuses on the nocturnal SABL; however, the discussions here on intermittent turbulence and wave‐turbulence interactions in stably stratified flows underscore important issues in stably stratified geophysical dynamics in general.

Exposure of farm workers to electromagnetic radiation from cellular network radio base stations situated on rural agricultural land

The electromagnetic field (EMF) levels generated by mobile telephone radio base stations (RBS) situated on rural-agricultural lands were assessed in order to evaluate the exposure of farm workers in the surrounding area. The expected EMF at various distances from a mobile telephone RBS was calculated using an ad hoc numerical forecast model. Subsequently, the electric fields around some RBS on agricultural lands were measured, in order to obtain a good approximation of the effective conditions at the investigated sites. The viability of this study was tested according to the Italian Regulations concerning general and occupational public exposure to time-varying EMFs. The calculated E-field values were obtained with the RBS working constantly at full power, but during the in situ measurements the actual power emitted by RBS antennas was lower than the maximum level, and the E-field values actually registered were much lower than the calculated values.

태풍 진로예측을 위한 다중모델 선택 컨센서스 기법 개발

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON`s except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON`s except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON`s at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

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.

Statistical downscaling model for late-winter rainfall over Southwest China

A statistical downscaling model is built for the late-winter rainfall over Southwest China (SWC). A partial-correlation method is used for selecting factors. The results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe (SNAO) and sea surface temperature in Western Pacific (WPT). SNAO is related to the southern pole of North Atlantic Oscillation (NAO) and excites Southern Eurasian teleconnection, which influences the development of the southern branch trough and the water vapor transport to SWC. WPT indicates the variability of ENSO in the tropical Western Pacific. WPT excites Pacific-East Asia teleconnection and an anticyclone (cyclone) is formed in the southern part of China and suppresses (enhances) rainfall over SWC. A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station, and the model also shows strong robustness in the independent validation. The statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.