Numerical Forecasts Research Articles

Autonomous Sampling of Ocean Submesoscale Fronts with Ocean Gliders and Numerical Model Forecasting

ABSTRACTSubmesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms.

Role and Impact of the Urban Environment in a Numerical Forecast of an Intense Summertime Precipitation Event over Tokyo

Role and Impact of the Urban Environment in a Numerical Forecast of an Intense Summertime Precipitation Event over Tokyo

T639数值预报产品在克州地区大降水中的检验及应用

T639数值预报产品在克州地区大降水中的检验及应用

Influence of parameterization of soil processes on numerical forecasts of vertical profiles of air potential temperature

Very complex physical processes occurring in the soil have an influence on the structure of the atmospheric boundary layer. These processes, together with sub-grid processes, in general, must be parameterized. A good description of soil (micro)physics, fluid dynamics in porous media, soil dynamics, the water and thermal cycle in soil, and the soil–plants–water–atmosphere relation affects the quality of numerical weather forecasts. The current parameterization of soil's physical processes in the COSMO meteorological model was initially prepared 40 years ago for numerical models with a poor resolution of several kilometres. Nowadays, operational numerical models work with a resolution that has been enhanced significantly. In order to improve the quality of numerical weather prediction, existing parameterizations should be adequately changed. This research was focused on the case of ‘bare soil’. The influence of the changed parameterization of water flux through the soil on forecasts of vertical profiles of potential temperature for the dry and moist cases is presented.

Initiation and Propagation of an Atmospheric Bore in a Numerical Forecast Model: A Comparison with Observations

AbstractIn the autumn of 2016, an undular atmospheric bore passed over southern England. Observations at surface sites as the bore passed gave phase speeds of up to 30 m s−1, surface pressure rises of over 1.5 hPa, and abrupt wind direction changes of 90°, followed by slower changes of a further 180°. Use was made of this event, rarely observed in the United Kingdom, to compare surface and satellite observations of the bore with output from the operational Met Office limited-area 1.5-km numerical weather model and to investigate the bore initiation mechanism within the model. Although the model had timing errors of over an hour and orientation errors of the bore, the bore propagation was simulated fairly well, giving similar bore phase speeds and decay times. There was also a reasonable correlation between surface and satellite observations and the model. One significant difference was the longer bore wavelength in the model simulation.

Comparing an Ensemble Kalman Filter to a 4DVAR Data Assimilation System in Chaotic Dynamics

In this paper, the Ensemble Kalman Filter is compared with a 4DVAR Data Assimilation System in chaotic dynamics. The Lorenz model is chosen for its simplicity in structure and the dynamic similarities with primitive equations models, such as modern numerical weather forecasting. It was examined if the Ensemble Kalman Filter and 4DVAR are effective to track the Control for 10, 20 and 40% of error at the Initial Conditions. With 10% of noise, the trajectories of both are almost perfect. With 20% of noise, the differences between the simulated trajectories and the observations as well as “true trajectories” are rather small for the Ensemble Kalman Filter but almost perfect for 4DVAR. However, the differences are increasingly significant at the later part of the integration period for the Ensemble Kalman Filter, due the chaotic behavior system. However, for the case with 40% error at the Initial Condition, neither the Ensemble Kalman Filter or 4DVAR could track the Control with only 3 observations ingested. To evaluate a more realistic assimilation application, it was created an experiment in which the Ensemble Kalman Filter ingested single observation at the 180th time step in the X, Y, and Z Lorenz variables and only in the X variable. The results show a perfect fit of 4DVAR and the Control during a complete integrations period, but the Ensemble Kalman Filter has a disagreement after the 80th time step. On the other hand, it was shown a considerable disagreement between the Ensemble Kalman Filter trajectories and the Control as well as a total fail of 4DVAR. Better results were obtained for the case in which observation covers all the components of the model vector.

GEOS-5 seasonal forecast system

Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere–Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9-month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

Principal component analysis and singular value decomposition used for a numerical sensitivity analysis of a complex drawn part

The numerical forecasting of car body construction processes is already being used in industry to provide support in the ramp-up process. However, long calculation times are stretching the finite element method (FEM) to the limit, in particular when analyzing the effect of the variation of an input variable on one or more dependent variables. Moreover, there is still a need for experienced users to separate relevant from irrelevant parameters and to determine their variation. This paper presents a method that makes it possible, based on stochastic experimental design (DOE) in combination with both principle component analysis (PCA) and singular value decomposition (SVD), to create mathematical models that separate relevant from irrelevant input variables and that represent the effect of individual variables on all part or assembly areas by means of a variance-based sensitivity analysis. The method is verified in a case study based on realistic front hood geometry. The study examines the deep-drawing process steps as well as the geometrical accuracy in a measuring device. It is shown that it is possible to represent the effects of the most important variables from these processes on the strain and geometry parameters of the car body part and to vary these, based on a model function, interactively.

Evaluating Forecasts, Narratives and Policy Using a Test of Invariance

Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which would in turn question the validity of the entailed policy implementation. We establish when systematic forecast failure entails failure of the accompanying narrative, which we call forediction failure, and when that in turn implies policy invalidity. Most policy regime changes involve location shifts, which can induce forediction failure unless the policy variable is super exogenous in the policy model. We propose a step-indicator saturation test to check in advance for invariance to policy changes. Systematic forecast failure, or a lack of invariance, previously justified by narratives reveals such stories to be economic fiction.

Backtesting Extreme Value Theory Models of Expected Shortfall

We use stock market data to analyze the quality of alternative models and procedures to estimate Expected Shortfall (ES) at different significance levels. We consider conditional models applied to the full distribution of returns as well as models that focus on tail events using extreme value theory (EVT) under the two-step procedure proposed by McNeil&Frey (2000). The performance of the different models is assessed using a variety of ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1- and 10-day ES forecasts than non-EVT based models. Under either approach, asymmetric probability distributions for return innovations are clearly more appropriate. These qualitative results are also valid for the recent crisis period, even though all models then undervalue the level of risk. Filtered Historic Simulation narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be the best approach to obtain accurate ES forecasts.

China–Sri Lanka Joint Center for Education and Research for the 21st Century Maritime Silk Road

The China–Sri Lanka Joint Center for Education and Research for the 21st Century Maritime Silk Road (CSL-CER) was established in August 2015. The Joint Center is an overseas science and education platform established by Chinese Academy of Sciences. CSL-CER has conducted a series of research activities, including an annual scientific expedition to the eastern Indian Ocean, with many kinds of instruments installed for monsoon and ocean observation. The ‘Numerical Forecast System for the Surrounding Area of Sri Lanka,’ which was developed by the Joint Center, can provide rapid mapping on multiple scales and at multiple resolutions for numerical forecast products serving local society. The annual China–Sri Lanka Joint Workshop on Monsoon Climate and Ocean Environment was successfully held in 2015 and 2016. According to the Memorandum of Understanding (MOU) on joint Master’s degrees, 15 new Master’s students were accepted by University of Chinese Academy of Sciences (UCAS) in 2015.

An ensemble forecast method combining CNOPs with BVs

Numerical forecasting can estimate the future state of the atmosphere or oceans. Because the atmosphere and oceans are complex and nonlinear systems, very small errors in the initial field can be amplified nonlinearly during integration of the numerical model, which may ultimately result in large errors. The true initial field cannot be obtained, and it is not possible to construct a perfect model that simulates the development of the atmospheric or oceanic state. Therefore, initial uncertainties and system instabilities result in large forecast errors. However, ensemble forecasting can be used to estimate the forecasting uncertainty. The ensemble initial perturbation has a crucial impact on the ensemble forecasting result. To obtain the initial perturbation that can capture initial field uncertainties and system instabilities, conditional nonlinear optimal perturbation (CNOP) is applied to generate initial perturbations for ensemble forecasting. CNOP is the initial perturbation that satisfies certain physical constraints and causes the largest forecast error at forecast time. More specifically, it is a generalization of the singular vector (SV) in nonlinear field. Using the Lorenz-96 model, we generate initial perturbations by combining CNOPs and breeding vectors (BVs), and then compare it with the traditional breeding growing modes (BGM) method. The spectral projected gradient 2 (SPG2) optimal algorithm is used to compute CNOPs based on basic states, and we sort the BVs according to the corresponding L 2 norm magnitude. We design two ensemble samples for the forecast experiments: sample 1 (S1) and sample 2 (S2). Ensemble initial perturbations all consist of BVs in S1. CNOPs and BVs are combined to form ensemble initial perturbations in S2; we use CNOPs to replace the several larger BVs in S1 according to the BVs’ L 2 norm magnitude order, and keep the remaining BVs unchanged. Both S1 and S2 are consist of 17 ensemble members, including one control forecast. Under the perfect model assumption, many ensemble forecasting numerical experiments are run on the Lorenz-96 model. The root mean square error (RMSE) and anomaly correlation coefficient (ACC) are used to evaluate the forecast results. We find that the forecast skill of S2 is higher than that of S1 in most experiments. This result suggests that initial perturbations generated using the new method better overcome the adverse impact from initial field uncertainty and system instability, and provide higher ensemble forecasting skill than the BGM method. When the initial analysis errors are fast-growing, ensemble forecasts generated by the new method can improve the control forecasts results. Furthermore, the rate of improvement increases with increasing forecast time, and is more obvious in the medium range (6–14 d). Ensemble forecasts generated using the new method can extend the effective forecast time about four days relative to the control forecasts based on the standard that the effective ACC forecast is not <0.6. Nevertheless, for slowly-growing initial analysis errors, ensemble forecasts generated using the new and BGM method may be worse than the control forecasts. The initial analysis errors that show much faster growth behavior are easily influenced by nonlinearity, and extreme events due to strong nonlinear instability may be more likely to cause fast growth of initial analysis errors. Therefore, ensemble forecasts generated by combining CNOPs and BVs will have higher forecasting skill than the BGM method, and can improve the forecast effect of control forecast.

Testing and improving the performance of the Common Land Model: A case study for the Gobi landscape

Land surface processes take place on the interface between the earth and atmosphere, exerting significant influences on the weather and climate. Correct modeling of these processes is important to numerical weather forecast and climate prediction. In order to obtain a more thorough understanding of the land surface processes over the Gobi landscape, we evaluated the performance of the Common Land Model (CoLM) at Dunhuang station in Gansu Province of China to determine whether the model formulation, driven by observational data, is capable of simulating surface fluxes over the underlying desert surface. In comparison with the enhanced observation data collected at Dunhuang station over the period 22–28 August 2008, the results showed that the surface albedo simulated by CoLM was larger than that in the observation, and the simulated surface temperature was lower than the observed. After the measured values were used to correct the surface albedo, the solar radiation absorbed by the ground surface was more consistent with the measurements. A new empirical relationship of the surface thermal exchange coefficient rah was used to modify the thermal aerodynamic impedance. The simulated soil surface temperature became significantly closer to the observed value, and the simulated surface sensible heat as well as net radiative fluxes were also improved.

Regional Retrieval Processor for Direct Broadcast High-Resolution Infrared Data

AbstractThe Space Science Engineering Center, in collaboration with the Mauna Kea Weather Center at the University of Hawai’i at Mānoa, has developed a regional retrieval processor for high-spectral-resolution infrared data. The core of the processor makes use of an inversion system, referred to as Mirto, which combines, in a Bayesian way, the a priori knowledge of the atmospheric state, based on available numerical weather prediction forecasts, with the physical information embedded in satellite observations. Forecast temperature and water vapor mixing ratio fields over the central North Pacific Ocean are adjusted to produce synthetic radiances closer and closer to the Suomi NPP Cross-track Infrared Sounder (CrIS) observations taken in clear-sky conditions. The paucity of synoptic observations over this area and the highly homogeneous background represented by the ocean provide a good framework for the implementation of this hyperspectral data inversion system. Nearly real-time (less than 60 min from overpass time) Internet publication of retrieved atmospheric profiles is made possible by the availability of a direct broadcast system that provides data from the Suomi NPP platform (CrIS and VIIRS). The main goal for the implemented system is to provide the forecasting community with products suitable for nowcasting applications and for optimal data assimilation. The implemented processor has been running routinely since August 2013. Validation based on the comparisons of retrievals with rawinsonde data from Hilo, Hawaii, and Lihue, Hawaii, and GPS-derived total precipitable water from four stations, performed over a time period of more than 1 year, shows a statistically significant improvement on the background atmospheric state used as a priori information.

Validation and application of the monitoring method for degraded land-area-based on a dust erodibility in eastern Asia

ABSTRACTArid regions are very sensitive to climate change and human activity, two critical drivers of change that are degrading environmental conditions. Part of the world’s drylands lie in eastern Asia, including China and Mongolia, where the problems of desertification, drought, and Asian dust events (ADEs) are frequent. To help prevent economic damage from these problems, an early warning and monitoring system based on numerical models, remote sensing, and weather forecasts is needed. I define a degraded land area as ‘the area where dust can easily occur’ and make exclusive use of satellite data to identify land that meets certain conditions of vegetation and aridity. I then validate this definition against a dust erodibility map and occurrences of ADEs over Japan, which was closely related to the extent and severity of dust areas in Mongolia and parts of China, especially in March (coefficient of determination R2 = 0.856). The yearly change of degraded land area indicates a clear decreasing trend in China (R2 = 0.210), but an overall negative trend in Mongolia (R2 = 0.010). Years of major droughts in China and Mongolia correspond well to large positive deviations in degraded land area.

Synoptic Analysis and Hindcast of an Intense Bow Echo in Western Europe: The 9 June 2014 Storm

Abstract On Pentecost Monday, 9 June 2014, a severe linearly organized mesoscale convective system (MCS) hit Belgium and western Germany. This storm was one of the most severe thunderstorms in Germany in decades. The synoptic-scale and mesoscale characteristics of this storm are analyzed based on remote sensing data and in situ measurements. Moreover, the forecast potential of the storm is evaluated using sensitivity experiments with a regional climate model. The key ingredients for the development of the Pentecost storm were the concurrent presence of low-level moisture, atmospheric conditional instability, and wind shear. The synoptic and mesoscale analysis shows that the outflow of a decaying MCS above northern France triggered the storm, which exhibited the typical features of a bow echo like a bookend vortex and a rear-inflow jet. This resulted in hurricane-force wind gusts (reaching 40 m s−1) along a narrow swath in the Rhine–Ruhr region leading to substantial damage. Operational numerical weather prediction models mostly failed to forecast the storm, but high-resolution regional model hindcasts enable a realistic simulation of the storm. The model experiments reveal that the development of the bow echo is particularly sensitive to the initial wind field and the lower-tropospheric moisture content. Adequate initial and boundary conditions are therefore essential for realistic numerical forecasts of such a bow echo event. It is concluded that the Pentecost storm exhibited a comparable structure and a similar intensity to observed bow echo systems in the United States.

Himawari-8 Atmospheric Motion Vectors - Operational Generation and Assimilation

In October 2014 the Japanese Meteorological Agency (JMA) launched the new generation geostationary satellite Himawari-8. This satellite provides ten minute imagery in sixteen wavebands over the Asian and Australasian region. The imagery has been navigated, calibrated and subsequently used in the Bureau of Meteorology (BoM) to generate Atmospheric Motion Vectors (AMVs) over the full earth disk viewed from the satellite every ten minutes. Each vector has been error characterised and assigned an expected error. In preparation for the operational assimilation of the ten minute data, these high temporal and spatial resolution data were used with the BoM operational database to provide forecasts from the next generation operational forecast model ACCESS APS2 using 4D Var. Results from these tests indicate these locally generated Himawari-8 ten minute AMVs are of high density and quality and have the potential to improve numerical weather prediction (NWP) model initialisation and forecasts. The forecasts undertaken include cases associated with extreme weather. The results also provided the appropriate times, data selection and application methods for the effective use of these high temporal resolution data. As a result of these studies these wind data were approved for inclusion in the BoMs operational database and are used in operational forecasting.

Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

New Data Buoys Watch Typhoons from Within the Storm

Advanced real-time data buoys have observed nine strong typhoons in the northwestern Pacific Ocean since 2015, providing high-resolution data and reducing the uncertainty of numerical model forecasts.

Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context

This work presents a multi-methodological approach to address the dynamic and stochastic challenges that underlie the problem of international logistic network reconfiguration induced by the One Belt-One Road initiative. A spatial-temporal logistics interaction model integrated with Markov chain is proposed to forecast time-varying logistic distribution flows for a three-layer supply chain framework. Further, numerical forecasts based on two cases of Chinese oil supply chain is carried out to account for the effectiveness of the proposed model. Analytical results suggest various development strategies for the practitioners and the policy makers in optimizing their logistics and transportation decisions in the OBOR context.