Medium-range Forecasts Research Articles

Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.

Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series.

Extreme events appear in many complex nonlinear dynamical systems. Predicting extreme events has important scientific significance and large societal impacts. In this paper, a new mathematical framework of building suitable nonlinear approximate models is developed, which aims at predicting both the observed and hidden extreme events in complex nonlinear dynamical systems for short-, medium-, and long-range forecasting using only short and partially observed training time series. Different from many ad hoc data-driven regression models, these new nonlinear models take into account physically motivated processes and physics constraints. They also allow efficient and accurate algorithms for parameter estimation, data assimilation, and prediction. Cheap stochastic parameterizations, judicious linear feedback control, and suitable noise inflation strategies are incorporated into the new nonlinear modeling framework, which provide accurate predictions of both the observed and hidden extreme events as well as the strongly non-Gaussian statistics in various highly intermittent nonlinear dyad and triad models, including the Lorenz 63 model. Then, a stochastic mode reduction strategy is applied to a 21-dimensional nonlinear paradigm model for topographic mean flow interaction. The resulting five-dimensional physics-constrained nonlinear approximate model is able to accurately predict extreme events and the regime switching between zonally blocked and unblocked flow patterns. Finally, incorporating judicious linear stochastic processes into a simple nonlinear approximate model succeeds in learning certain complicated nonlinear effects of a six-dimensional low-order Charney-DeVore model with strong chaotic and regime switching behavior. The simple nonlinear approximate model then allows accurate online state estimation and the short- and medium-range forecasting of extreme events.

Medium range sea ice prediction in support of Japanese research vessel MIRAI’s expedition cruise in 2018

ABSTRACT The Japanese research vessel MIRAI sailed into the Arctic Ocean through the Bering Strait from 4 to 25 November 2018 to study the atmosphere, ocean, and sea ice of the Chukchi Sea. Early winter dynamics and thermodynamics can cause sea ice conditions to change over short timescales. Heavy ice pressure may build up in the compression of compact ice. MIRAI is an ice-strengthened ship that has to avoid thick sea ice and areas of high sea ice coverage, and therefore precise medium-range forecast of sea ice distribution is key to its safe and efficient navigation. A high-resolution (about 2.5 km) coupled ice–ocean model was used to produce medium-range (10-day) forecasts for the expedition. European Center for Medium-Range Weather Forecast’s atmospheric model high-resolution 10-day forecast was used as forcing data. Forecast skill is measured by ice edge error, which is the average distance between forecast and observed ice edges. Using a threshold of 15% sea ice concentration to indicate the ice edge, the maximum ice edge error in the ice–ocean coupled model in the Chukchi Sea is 16 km, indicating that the model is able to provide 10-day forecasts with sufficient accuracy for the vessel’s operation.

Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges

AbstractArtificial intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing, quality control mechanisms, pattern recognition, data fusion, forward and inverse problems, and prediction. Thus, modern AI in general and machine learning (ML) in particular can be positively disruptive and transformational change agents in the fields of satellite remote sensing and NWP by augmenting, and in some cases replacing, elements of the traditional remote sensing, assimilation, and modeling tools. And change is needed to meet the increasing challenges of Big Data, advanced models and applications, and user demands. Future developments, for example, SmallSats and the Internet of Things, will continue the explosion of new environmental data. ML models are highly efficient and in some cases more accurate because of their flexibility to accommodate nonlinearity and/or non-Gaussianity. With that efficiency, ML can help to address the demands put on environmental products for higher accuracy, for higher resolution—spatial, temporal, and vertical, for enhanced conventional medium-range forecasts, for outlooks and predictions on subseasonal to seasonal time scales, and for improvements in the process of issuing advisories and warnings. Using examples from satellite remote sensing and NWP, it is illustrated how ML can accelerate the pace of improvement in environmental data exploitation and weather prediction—first, by complementing existing systems, and second, where appropriate, as an alternative to some components of the NWP processing chain from observations to forecasts.

Systematic errors in the WRF model planetary boundary layer schemes for two contrasting monsoon seasons over the state of Odisha and its neighborhood region

This study evaluates the characteristics of systematic errors (up to day 4) in the planetary boundary layer (PBL) parameterization schemes over the state of Odisha and its neighboring regions for two contrasting monsoon seasons, i.e., 2013 (normal) and 2014 (deficit), using the Weather Research and Forecasting (WRF) model. A total of 1112 numerical experiments are carried out, each with initial conditions from May 28 to September 29 using four PBL schemes, i.e., Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino (MYNN), Asymmetric Convective Model version 2 (ACM2), and Medium Range Forecast (MRF) with horizontal resolutions of 27 and 9 km with a lead time up to 96 h. It is found that the PBL parameterizations significantly impact the basic atmospheric variables (i.e., wind, temperature, moisture, and rainfall) and associated convective processes such as moist static energy. The wind and rainfall forecast skill of MRF showed better agreement with European Re-Analysis (ERAI). However, YSU has better skills for 2-m temperature and specific humidity compared to others. All PBL schemes produced a moist bias at the surface except MRF. The principle cause of this moist bias with mid tropospheric warming is attributed to inherent tendency of the model to overestimate moist static energy leading to intense vertical motion and convective instability. The magnitude of errors for wind forecast is higher for the deficit year compared to the normal monsoon year. However, the strength of the cross equatorial flow is found to be weaker in all the experiments for both the seasons. In general, the spread and magnitude of errors are large for 2013 (normal) as compared to 2014 (deficit) year.

Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits

ABSTRACTThe operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the basis for information services about the marine environment and its ecosystem, and can provide boundary data for weather predictions. Bluelink information services are available to marine industries (e.g. commercial fishing, aquaculture, shipping, oil and gas, renewable energy), government agencies (e.g. search and rescue, defence, coastal management, environmental protection), and other stakeholders (e.g. recreation, water sports, artisanal and sport fishing) who depend on timely and accurate information about the marine environment. This review highlights the last 15 years of Bluelink achievements delivering mesoscale (eddy-resolving) to sub-mesoscale and short- to medium-range (days to weeks) ocean forecasts and reanalyses. Key achievements include the development of a global ocean forecasting and reanalysis system, a relocatable ocean-atmosphere model and a littoral zone analysis and forecasting capability. Beyond the traditional short-term forecasting of physical ocean properties (temperature, salinity, surface height, currents, waves), marine activities such as water quality and habitat management as well as climate monitoring increasingly rely on operational oceanographic data and products. These are areas of active research of the Bluelink team in collaboration with national and international partners.

Evaluating the Forecast Performance of the Meiyu Front Rainbelt Position: A Case Study of the 30 June to 4 July 2016 Extreme Rainfall Event

An impactful and poorly forecasted heavy rainfall event was observed in association with the Meiyu front over the Yangtze River valley of China from 30 June–4 July 2016. Operational global numerical weather prediction models for almost all forecast lead times beyond 24 h incorrectly forecasted the location and intensity of the precipitation associated with this event. This study presents the first examination of this poleward bias in the operational models for the Meiyu front, which has been frequently noted by meteorologists at the Chinese Meteorological Administration, and explores areas of forecast error and uncertainty in the prediction of the position of the primary frontal rainbelt that is crucial to the placement and intensity of the heavy rainfall. A new zonal mean maximum accumulated precipitation index is introduced and utilized to identify members in the European Centre for Medium-Range Forecasts (ECMWF) Ensemble Prediction System (EPS) that either perform well or perform poorly in forecasting the location of the Meiyu front. Using this new precipitation metric, five-member subgroups representing the EPS members that were most accurate and those that incorrectly displace the Meiyu front the furthest north were identified. An analysis of composite mean fields for the EPS subgroups and the correlation between the rain band placement and the 500 hPa heights was performed for several EPS model runs. We showed that a successful prediction of the location of the Meiyu front rainbelt position by the EPS is most sensitive to the intensity of the 500 hPa trough located over eastern China for the event. The ensemble members that had the largest northward error in the location of the rain band were found to have a more intense 500 hPa trough than the members that more accurately predicted the rainbelt. The more intense upper level trough was found to have enhanced the lower tropospheric southerly flow equatorward of the front and led to a less zonal-oriented Meiyu front, resulting in a northward displacement of both the rainbelt and the regions of more intense precipitation rates. Finally, an examination of the evolution of the differences between the subgroups shows that the primary differences in 500 hPa intensity propagate in-phase with the 500 hPa trough. We show that it is the intensity of the trough, rather than the rate of propagation, that is the most important source of forecast dissimilarities between the successful and failed forecasts.

Evaluation of physical changes (temperature and salinity) in the Persian Gulf waters due to climate change using field data and numerical modeling

Salinity and temperature are two important physical parameters of seawater that determine the life conditions, development of herbal creatures and sea animals and the class of human activity at the sea. They are also important due to their effects on chemical and biochemical processes that are vital to sea organisms. In this study, the effects of climate change on these physical characteristics of the Persian Gulf waters have been evaluated from 1992 to 2018. A one-dimensional numerical oceanic model, called Program for Boundary layer Environment, and some field data were used to determine the long-term changes of these characteristics. The input data of this model include meteorological information obtained from European Center for Medium Range Forecasting. For vertical turbulence simulation, Axell–Liungman, Omstedt, Rodi, Pacanowski and Marchuk schemes were used for some stations located in the Persian Gulf and the results were compared to some measurements of temperature and salinity at three other stations in the Persian Gulf. The results showed that Omstedt scheme was the best at stimulating the vertical turbulence in the Persian Gulf. Statistical homogeneity tests on the predicted time series on temperature and salinity showed that they are homogeneous. The results clearly indicate the annual variations of temperature (ranging between 20 and 36 °C), as well as showing mild increasing trends of about 1–2 °C in 10–30 years which is rather dramatic for this sea.

Impact of Increased Vertical Resolution on Medium-Range Forecasts in a Global Atmospheric Model

Abstract This study aims to investigate the impact of increased vertical resolution on global medium-range forecasts. For this purpose, the dependencies of simulated atmospheric temperature and specific humidity on the lowest model level height and vertical grid spacing are investigated. The reduced first model level increases vertical turbulent mixing by determining a higher planetary boundary layer (PBL) height and associated turbulent diffusivities and velocity scales. This contributes to warming/drying within the PBL and cooling/moistening above the PBL. Resulting dryness near the surface enhances an increase in surface moisture flux from the ocean, which results in the apparent moistening of the troposphere. Consequently, large-scale precipitation increases due to more humid atmospheric conditions, while convective precipitation decreases because of drier conditions near the starting level of convection. Meanwhile, the reduced vertical grid spacing resolves the overshooting layer well in the cumulus convection process, which decreases the detrained moisture at the convective cloud top. This leads to a noticeable downward shift of ice clouds in the upper troposphere, and further contributes to the enhancement of longwave cooling via cloud–radiation processes. In medium-range forecasts, the increased vertical resolution exerts a significant impact on the simulated features of tropospheric temperature and humidity, while changes in the prediction accuracy precipitation are negligible owing to compensation between convective and large-scale precipitation. Finally, the discussion of possible methods to minimize the sensitivities of model’s physics to vertical resolution is presented.

Developing an Automated Medium-Range Flood Awareness System for Switzerland Based on Probabilistic Forecasts of Integrated Water Vapor Fluxes

Abstract Floods in the Alpine region can be destructive and cause large economic losses. Many rivers and lakes in Switzerland are regulated and flood damage can be mitigated through an optimal management of lake levels and runoff. This requires high-quality forecasts of atmospheric flood precursors extending beyond short-range (forecast days 1–5) predictions. In several places around the world atmospheric rivers or extreme integrated vapor transport (IVT) are causally related to flood events. Also in Switzerland, extreme IVT oriented perpendicular to the main orography heralds extreme flood events. This relationship is exploited in an operational flood warning system on the medium-range (here forecast days 6–10) time scale based on probabilistic medium-range forecasts of IVT and precipitation over Switzerland provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). This entails first a comprehensive probabilistic verification of the direction and magnitude of (extreme) IVT and second the development of compact visualizations for the operational use by hydrologists. Based on 20 years of probabilistic reforecasts, we show that both regular and extreme IVT has a better predictability than precipitation and IVT is predictable out to day 8. As the direction of IVT is of central importance for flood risk in Switzerland, we develop a visualization that summarizes probabilistic information on both the direction and magnitude of the IVT together with users of the product. The result is an operational flood warning system based solely on atmospheric flood precursors to extend flood warning information beyond the range of high-resolution deterministic weather forecasts.

Impact of Tropical Initial Water Vapor from MT-SAPHIR Observations on Medium-Range Forecasts Using the KMA Operational Model

The proper representation of water vapor helps set accurate initial conditions in numerical weather prediction (NWP) models. Water vapor levels have been observed over the tropics by the Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry (SAPHIR) on the Megha-Tropiques (MT) satellite. To determine how tropical convection, which is relevant to water vapor levels, can affect medium-range forecasts in both the tropics and extratropics, we compare the differences between simulations with and without MT-SAPHIR data using the operational NWP model from the Korea Meteorological Administration (KMA), which includes a hybrid four-dimensional variational process in data assimilation. More observed water vapor in the lower tropical troposphere leads to stronger tropical convection, which is well represented in the 300 hPa velocity potential, and strengthens mean meridional circulation. Root-mean-square errors (RMSEs) for relative humidity, temperature, and wind fields in the tropics are reduced by up to 7%. This improvement enhances the forecast field in the middle latitudes. In particular, RMSEs are reduced by up to 3.5% in the humidity field in the extratropics. Overall, the correct simulation of tropical water vapor via MT-SAPHIR data improves medium-range forecasts in the extratropics.

The Predictability of Blocking Character in the Northern Hemisphere Using an Ensemble Forecast System

Introduction:Some weather extremes are the result of atmospheric blocking, which can be responsible for the stagnation of weather patterns. These large-scale quasi-stationary mid-latitude flow regimes can result in significant temperature and precipitation anomalies over the regions that the blocking event impacts or in the upstream and downstream regions.Methods:The ability to predict periods of anomalous weather conditions due to atmospheric blocking is a major problem for medium-range forecasting. Analyzing the National Centers for Environmental Prediction (NCEP) Ensemble 500-hPa pressure level heights (240 hrs.) ten-day forecasts, and using the University of Missouri blocking archive to identify blocking events, the forecasted onset, duration, and intensity of model blocking events are compared to observed blocks.Results and Discussion:The observed blocking events were identified using the University of Missouri blocking archive. Comparing these differences using four Northern Hemisphere case studies occurring over a one-year period across the Northern Hemisphere has shown the continued need for improvement in the duration and intensity of blocking events. Additionally, a comparison of the block intensity to a diagnostic known as the Integrated Regional Enstrophy (IRE) was performed in order to determine if there is a correlation between IRE and these quantities.Conclusion:Having a better understanding of block persistence and their associated anomalies can help society prepare for the damage they can cause.

An Occupational Heat-Health Warning System for Europe: The HEAT-SHIELD Platform.

Existing heat–health warning systems focus on warning vulnerable groups in order to reduce mortality. However, human health and performance are affected at much lower environmental heat strain levels than those directly associated with higher mortality. Moreover, workers are at elevated health risks when exposed to prolonged heat. This study describes the multilingual “HEAT-SHIELD occupational warning system” platform (https://heatshield.zonalab.it/) operating for Europe and developed within the framework of the HEAT-SHIELD project. This system is based on probabilistic medium-range forecasts calibrated on approximately 1800 meteorological stations in Europe and provides the ensemble forecast of the daily maximum heat stress. The platform provides a non-customized output represented by a map showing the weekly maximum probability of exceeding a specific heat stress condition, for each of the four upcoming weeks. Customized output allows the forecast of the personalized local heat-stress-risk based on workers’ physical, clothing and behavioral characteristics and the work environment (outdoors in the sun or shade), also taking into account heat acclimatization. Personal daily heat stress risk levels and behavioral suggestions (hydration and work breaks recommended) to be taken into consideration in the short term (5 days) are provided together with long-term heat risk forecasts (up to 46 days), all which are useful for planning work activities. The HEAT-SHIELD platform provides adaptation strategies for “managing” the impact of global warming.

Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

Medium range forecasts using cut-cells: a sensitivity study

In this study, 10-day forecasts of Atlantic lows are investigated by comparing the forecasts of the standard Climate version of the Lokal Modell using terrain-following coordinates to that of the cut-cell Lokal Modell with z coordinates (LMZ). As indicated by idealized tests of the atmosphere at rest and mountain wave tests, LMZ exhibits a much better approximation of the lower boundary condition due to the use of the cut-cell approximation. An important difference to the terrain-following coordinate is that with cut-cells, a single steep mountain as defined different from 0 at only one point, has a reasonable up–downwind scheme and does not become unstable. We are interested in the large-scale structure of Atlantic lows, which are under the influence of the high orography of Greenland. The steep orography of Greenland has a considerable impact on these lows, even though they are positioned to a large part over water. It is found that at Day 6, both models yield similar forecasts, which is consistent with the fact that Day 6 is in the useful forecast range of both models. At Day 10, the forecasts are quite different, indicating a sensitivity of the 10-day forecast to the discretization in question.

Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.

Low-visibility forecasts for different flight planning horizons using tree-based boosting models

Abstract. Low-visibility conditions enforce special procedures that reduce the operational flight capacity at airports. Accurate and probabilistic forecasts of these capacity-reducing low-visibility procedure (lvp) states help the air traffic management in optimizing flight planning and regulation. In this paper, we investigate nowcasts, medium-range forecasts, and the predictability limit of the lvp states at Vienna International Airport. The forecasts are generated with boosting trees, which outperform persistence, climatology, direct output of numerical weather prediction (NWP) models, and ordered logistic regression. The boosting trees consist of an ensemble of decision trees grown iteratively on information from previous trees. Their input is observations at Vienna International Airport as well as output of a high resolution and an ensemble NWP model. Observations have the highest impact for nowcasts up to a lead time of +2 h. Afterwards, a mix of observations and NWP forecast variables generates the most accurate predictions. With lead times longer than +7 h, NWP output dominates until the predictability limit is reached at +12 d. For lead times longer than +2 d, output from an ensemble of NWP models improves the forecast more than using a deterministic but finer resolved NWP model. The most important predictors for lead times up to +18 h are observations of lvp and dew point depression as well as NWP dew point depression. At longer lead times, dew point depression and evaporation from the NWP models are most important.

Characterising uncertainty in precipitation downscaling using a Bayesian approach

Statistical downscaling of GCM simulations is widely used for examining future changes in precipitation at different spatial and temporal scales. However, the downscaling process is affected by uncertainty associated with the downscaling model, its parameters, and also the use of different reanalysis products for model calibration. This study develops a Bayesian approach to calibrating a statistical downscaling model. The study investigates the impact of using two different reanalysis products, the National Centre for Environmental Prediction/National Centre for Atmospheric Research Reanalysis 2 (NCEP2) and the European Centre for Medium-Range Forecasts Interim Reanalysis (ERAI), in precipitation downscaling over the Tibetan plateau, a region with sparse ground precipitation coverage. The selected reanalysis products are used for modelling precipitation at selected locations with long, high quality records and diverse geographic characteristics. An assessment of the downscaled precipitation results using atmospheric variables from the ACCESS 1.3 GCM to drive the downscaling model calibrated using a reanalysis dataset is also performed and the impact of calibration uncertainty quantified. The outcomes of this study reveal that the choice of the data length used and the type of Reanalysis product adopted have a significant effect in downscaled precipitation characteristics and their uncertainties, such as the wetness fraction and average annual precipitations over the study locations. These findings point to a common problem in statistical downscaling applications and one that has not been recognised until now. The results show that downscaling model considering ERAI reproduce observed precipitation attributes to a better extent than NCEP2.

Prediction of North Atlantic Oscillation Index with Convolutional LSTM Based on Ensemble Empirical Mode Decomposition

The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is vital to predict NAO events. Since the NAO event can be quantified by the NAO index, an effective neural network model EEMD-ConvLSTM, which is based on Convolutional Long Short-Term Memory (ConvLSTM) with Ensemble Empirical Mode Decomposition (EEMD), is proposed for NAO index prediction in this paper. EEMD is applied to preprocess NAO index data, which are issued by the National Oceanic and Atmospheric Administration (NOAA), and NAO index data are decomposed into several Intrinsic Mode Functions (IMFs). After being filtered by the energy threshold, the remaining IMFs are used to reconstruct new NAO index data as the input of ConvLSTM. In order to evaluate the performance of EEMD-ConvLSTM, six methods were selected as the benchmark, which included traditional models, machine learning algorithms, and other deep neural networks. Furthermore, we forecast the NAO index with EEMD-ConvLSTM and the Rolling Forecast (RF) and compared the results with those of Global Forecast System (GFS) and the averaging of 11 Medium Range Forecast (MRF) model ensemble members (ENSM) provided by the NOAA Climate Prediction Center. The experimental results show that EEMD-ConvLSTM not only has the highest reliability from evaluation metrics, but also can better capture the variation trend of the NAO index data.

Housing Construction in Russia: Trends and Medium-Range Forecasts

The article provides a comparative analysis of the growth in the volume of deliverable housing in the Russian Federation over the period of the Russian economic reforms (1991–2017), reflecting the general development trend and potential growth in the medium-range term. The factors affecting the growth of the annual volume of deliverable housing in terms of the target state housing policy are identified and systematized. The influence of these factors on the growth in the housing volume is estimated using economic and statistical modeling based on official statistical data for 1991–2016. Three forecast scenarios for the growth of annual supply of the total residential area in the Russian Federation for 2017–2023 is developed with allowance for all sources of funding.