Long-range Forecasts Research Articles

Temporal variation of solar flare index during solar cycles 21 − 24

The present investigation attempts to quantify the temporal variation of Solar Flare Index (SFI) with other activity indices during solar cycles 21 – 24 by using different techniques such as linear regression, correlation, cross-correlation with phase lag-lead, etc. Different Solar Activity Indices (SAI) considered in this present study are Sunspot Number (SSN), 10.7 cm Solar Radio Flux (F10.7), Coronal Index (CI) and MgII Core-to-Wing Ratio (MgII). The maximum cycle amplitude of SFI and considered SAI has a decreasing trend from solar cycle 22, and cycle 24 is the weakest solar cycle among all other cycles. The SFI with SSN, F10.7, CI and MgII shows hysteresis during all cycles except for solar cycle 22 where both paths for ascending and descending phases are intercepting each other, thereby representing a phase reversal. A positive hysteresis circulation exists between SFI and considered SAI during solar cycles 22 and 23, whereas a negative circulation exists in cycles 21 and 24. SFI has a high positive correlation with coefficient values of 0.92, 0.94, 0.84 and 0.81 for SSN, F10.7, CI and MgII respectively. According to cross-correlation analysis, SFI has a phase lag with considered SAI during an odd-number solar cycle (solar cycles 21 and 23) but no phase lag/lead during an even-numbered solar cycle (solar cycles 22 and 24). However, the entire smoothed monthly average SFI data indicate an in-phase relationship with SSN, F10.7 and MgII, and a one-month phase lag with CI. The presence of those above characteristics strongly confirms the outcomes of different research work with various solar indices and the highest correlation exists between SFI and SSN as well as F10.7 which establishes that SFI may be considered as one of the prime activity indices to interpret the characteristics of the Sun’s active region as well as for more accurate short-range or long-range forecasting of solar events.

Развитие системы долгосрочного прогноза Гидрометцентра России в 2020 году

A brief description is given for the works carried out in 2020 to implement the longrange forecast technology based on the SLAV072L96 multiscale hydrodynamic atmosphere model. The purpose of these works was an improvement in simulating the deep convection and stratosphere dynamics. The works comprised the improvement and verification of the parameterizations for subgrid-scale processes and the whole model using long-range forecasts computed from historical initial data. As a result, the model correctly reproduces the main features of the annual mean precipitation field and zonal mean wind in the stratosphere. Keywords: long-range forecasts, global atmosphere model, parameterizations of subgrid-scale processes

Экстремальные метеорологические явления на сезонных и внутрисезонных интервалах времени в контексте изменения климата

The automatic identification of objects associated with various extreme weather events (EWE) on seasonal and intraseasonal timescales is done based on surface air temperature and precipitation datasets (NCEP/NCAR daily reanalysis fields for the Northern Hemisphere). Some features of the spatial and temporal variability of the extreme characteristics of temperature and precipitation regimes are considered in the context of climate change. An inventory of extreme events is carried out for the Northern Hemisphere in 1981–2019 depending on the spatial extent, duration, and intensity of EWEs. The years with the most striking events are noted, and a brief description of their specific features is given. The results will be used to analyze the EWE predictability in the context of the verification of long-range weather forecasts. Keywords: extreme weather events, climate change, identification of extreme events, long-range weather forecasts

An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability

Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long-range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~60km) and lead (2–5 months) considered here, the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June–August) scale. Eight-member ensemble-average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.

An error model for long-range ensemble forecasts of ephemeral rivers

Few ensemble streamflow forecasting systems are designed to operate for ephemeral rivers. In this study, we revise our error model for generating Forecast Guided Stochastic Scenarios (FoGSS) to produce statistically reliable long-range (12-month) forecasts for ephemeral rivers. FoGSS features an error model with four stages: data transformation, bias-correction, an autoregressive error model and the statistical distribution of residuals. We revise the fourth stage of FoGSS with a parameter estimation method that uses data censoring to account for zero values in both observations and forecasts. This allows FoGSS to produce statistically reliable ensemble forecasts in even highly ephemeral streams (with >50% zero flows). We apply FoGSS to conventional ensemble hydrological prediction (ESP) forecasts for 50 Australian catchments, including 26 ephemeral rivers. We show that FoGSS improves the accuracy of ESP forecasts at short lead times, while at long lead times FoGSS forecasts transition to climatology-like forecasts. FoGSS forecasts are reliable in ensemble spread at individual lead times and for volumes aggregated over lead times, even in highly ephemeral rivers. FoGSS forecasts pave the way for operational long-range forecasts in ephemeral rivers, meeting a key need for improved water management.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.

Role of Regional Parameters in Inter-annual Variation of Indian Summer Monsoon Rainfall

The Indian summer monsoon affects almost all the facets of life in India. Variability in the onset and duration of monsoon has profound impacts on water resources, human life, agriculture, economics and ecosystems. That is why predictions of the monsoon behavior are eagerly watched by government planners and agronomists. However, every monsoon has its own unique characteristics, which makes it more enigmatic and complex. Predicting the likely physical behavior of the monsoon system and the resulting distribution of rainfall over different parts of India is an extremely challenging task. Sometimes, the necessity of producing long-range forecasts has compelled investigators to develop empirical relationships between different facets of the coupled atmosphere-ocean land system. The present study also presents some empirical evidence of relationships between summer monsoon rainfall and sea-surface temperature over the Indian Ocean. Sea surface temperatures exert significant control over the atmosphere, particularly over the monsoon circulation. The study has identified four pockets in the Indian Ocean, which can be used as potential predictor zones for summer monsoon rainfall (June to September) over India. To achieve this objective, correlation analysis with lags in months was carried out to establish association between the sea surface temperature over the four pockets and summer monsoon rainfall. Strong significant association can be found between the SST of these four pockets and Indian summer monsoon rainfall. The study also reveals a phase-change in the relationship between the sea surface temperature and Indian summer monsoon rainfall, particularly in the recent period.

Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

Опыт использования фильтра Калмана для коррекции численного прогноза приземной температуры воздуха

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

AbstractDifferent multi‐model ensemble (MME) methods were investigated for their potential to improve the skill of 1‐month lead seasonal forecast products, based on six models from Global Producing Centers (GPCs) for long‐range forecasts (LRFs) designated by the World Meteorological Organization (WMO). We first compared the hindcast performance of seven MME methods (simple composite method, SCM; simple linear regression, SLR; multiple linear regression, MLR; best selection anomaly, BSA; multilayer perceptron, MLP; radial basis function, RBF; genetic algorithm, GA) for the global 2‐m temperature and precipitation for 1983–2009. The reference datasets for 2‐m temperature and precipitation are the ERA‐Interim from European Centre for Medium‐Range Weather Forecasts (ECMWF) and Global Precipitation Climatology Project (GPCP) for hindcast verification. For real‐time verification, the data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1 for 2‐m temperature and climate anomaly monitoring system and outgoing longwave radiation precipitation index (CAMS OPI) for precipitation are used. According to our analysis, GA was the most successful MME method in predicting both the global 2‐m temperature and precipitation for all four seasons. GA also showed good performance in predicting the 2‐m temperature and precipitation over the 13 regional climate outlook forum (RCOF) regions in all four seasons, but the range in performance among the RCOF regions varied significantly. In a real‐time forecast period (MAM 2012‐DJF 2015/16), GA outperformed in terms of time‐averaged anomaly pattern correlation coefficient (ACC) and root‐mean‐square error (RMSE) of the 2‐m temperature, although the forecast skill difference (0.02) between GA and SCM was not statistically significant. For the precipitation, both SCM and GA also reveal better performance than other MME methods. During the very strong El Niño event in 2015, individual models show better performance than other years. Nonetheless, these two MME methods outperform all the individual models.

General Assessment of the Operational Utility of National Water Model Reservoir Inflows for the Bureau of Reclamation Facilities

This work investigates the utility of the National Oceanic and Atmospheric Administration’s National Water Model (NWM) for water management operations by assessing the total inflow into a select number of reservoirs across the Central and Western U.S. Total inflow is generally an unmeasured quantity, though critically important for anticipating both floods and shortages in supply over a short-term (hourly) to sub-seasonal (monthly) time horizon. The NWM offers such information at over 5000 reservoirs across the U.S., however, its skill at representing inflow processes is largely unknown. The goal of this work is to understand the drivers for both well performing and poor performing NWM inflows such that managers can get a sense of the capability of NWM to capture natural hydrologic processes and in some cases, the effects of upstream management. We analyzed the inflows for a subset of Bureau of Reclamation (BoR) reservoirs within the NWM over the long-term simulations (retrospectively, seven years) and for short, medium and long-range operational forecast cycles over a one-year period. We utilize ancillary reservoir characteristics (e.g., physical and operational) to explain variation in inflow performance across the selected reservoirs. In general, we find that NWM inflows in snow-driven basins outperform those in rain-driven, and that assimilated basin area, upstream management, and calibrated basin area all influence the NWM’s ability to reproduce daily reservoir inflows. The final outcome of this work proposes a framework for how the NWM reservoir inflows can be useful for reservoir management, linking reservoir purposes with the forecast cycles and retrospective simulations.

A Long-Range State-Level Forecast of the 2020 Presidential Election

A Long-Range State-Level Forecast of the 2020 Presidential Election

Configuration of Bayesian Model Averaging Training Window to Improve Seasonal Rainfall Ensemble Prediction

Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensemble prediction in the form of predictive Probability Density Function (PDF). It is known that BMA is able to improve the reliability of the probabilistic forecast of short-and medium-range rainfall forecasts. This study aims to develop the application of BMA to calibrate long-range forecast in order to improve the quality of the seasonal forecast in Indonesia. The seasonal forecast that has been used is monthly rainfall from the output of the ensemble prediction European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated against observational data at 26 stations of Agency for Meteorology, Climatology and Geophysics of Republic of Indonesia (BMKG) over Java Island in 1981-2018. BMA predictive PDF was generated with a Gamma distribution which was obtained based on Training Window Sequential (TWS) and Conditional (TWC) training windows. Output of BMA-TWC was slightly better than BMA-TWS. Nevertheless, both of them were superior to raw model ECS4. BMA-TWC or BMA-TWS output was varying depend on spatial and temporal, but in general, the best result was in the dry season and during the El Nino phase. BMA was able to improve the distribution characteristics of ensemble prediction. BMA also increased the skill, resolution, and reliability of probability forecast of Below Normal (BN) and Above Normal (AN). Furthermore, the reliability of BN and AN of BMA output were also have the categories of “still very useful” and “perfect” compared to raw model ECS4 that were in the “dangerous” and “not useful” categories. The reliability “still very useful” and “perfect” show that the probabilistic forecast of BN and AN event can be used for making decisions related to seasonal forecast especially over Java island.

Long-range forecasting in feature-evolving data streams

Accurate long-range forecasting in high-dimensional feature-evolving time series is a pressing issue with multiple applications in optimal resource allocation, monitoring and budget planning. Stochastic nature of feature-evolving time series coupled with their temporal dependency pose a great challenge in their forecasting. This is because the length of input sequence (rows) may vary as data points evolve with their feature values (columns) changing. In high-dimensional feature-evolving heterogeneous time series, it is impractical to train a forecasting model per single time series across millions of metrics, leave alone space required to maintain the forecasting model and evolving time series in memory for timely streaming processing. Thus this paper proposes O̲ne sketchF̲itsA̲llT̲ime series algorithm, which is a stochastic deep neural network framework to address stated problems collectively. Extensive experiments on real-life datasets and rigorous evaluation showcases that OFAT is fast, robust, accurate and superior to the state-of-the-art methods.

Error characterization of ARW model in Forecasting tropical cyclone rainfall over North Indian Ocean

The hydrological extremes due to the landfalling tropical cyclones (TCs) pose a severe threat to the coastal communities over the North Indian Ocean (NIO) region. Advanced Research Weather Research and Forecasting (ARW) model is obscure in rainfall prediction, while it is extensively evaluated for track and intensity prediction over the NIO. This study focuses on estimating the model rainfall errors based on a total of 280 TC forecast cases from 42 TCs from 2007 to 2018. The model rainfall errors are studied against rain gauge and Tropical Rainfall Measuring Mission (TRMM) data as a function of TC intensity stage and model forecast length.The short-range (24 h) rainfall guidance yields fewer errors than the long-range (48–96 h) forecast when the model is initialized at any TC intensity stage. The root mean square error (RMSE) and bias of ARW rainfall is higher when the model is initialized at weaker intensity (DD or CS) stages than initialized at stronger intensity (SCS and VSCS) stages. The inland rainfall errors increase with forecast lead. The model exhibited higher errors (~2 mm h−1) in the inner-core region (0–100 km) and lesser errors (~0.5 mm h−1) in the TC environment (200–400 km). The ARW model replicates the observed radial profiles of rainfall up to 400 km with 2–5 mm h−1 overestimation at different intensity stages.Rainfall error decomposition of contiguous rain area (CRA) analysis indicates that the pattern errors contribute the maximum (~50%) to the total error, followed by the displacement error (~35%). In comparison, the volume and rotational errors are less (10% and 2%, respectively). The mean CRA horizontal shift in rainfall decreases from weaker to stronger stage initialization. The radial-distance error of categorical rainfall distribution between the ARW model and TRMM is ~ 150–200 km. This error reduced to 20–50 km after correcting the model rainfall for CRA displacement errors. The RMSE of model-rainfall after CRA correction has reduced by 3–15% for any forecast length. This study helps to advance the regional hydrological model’s ability by improved model rainfall inputs at 72–96 h lead.

Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques

Predicting complex nonlinear turbulent dynamical systems using partial observations is an important topic. Despite the simplicity of the forecast based on the ensemble mean time series, several critical shortcomings in the ensemble mean forecast and using path-wise measurements to quantify the prediction error are illustrated in this article. Then, a new ensemble method is developed for improving the long-range forecast. This new approach utilizes a mixture of the posterior distributions from data assimilation and is more skillful in predicting non-Gaussian statistics and extreme events than the traditional method by simply running the forecast model forward. Next, a systematic framework of improving forecast models is established, aiming at advancing the predictions at all ranges. The starting model in this new framework belongs to a rich class of nonlinear systems with conditional Gaussian structures. These models allow an efficient nonlinear smoother for state estimation using partial observations, which in turn facilitates a rapid parameter estimation based on an expectation–maximization algorithm. Conditioned on the partially observed time series, the nonlinear smoother further advances an efficient backward sampling of the hidden trajectories, the dynamical and statistical characteristics from which allow a systematic quantification of model error through information theory. The sampled trajectories then serve as the recovered observations of the hidden variables that promote the use of general nonlinear data-driven modeling techniques for a further improvement of the forecast model. A low-order model of the layered topographic equations with regime switching and rare events is used as a test example to illustrate this framework.

Monthly Reservoir Inflow Forecasting for Dry Period Using Teleconnection Indices: A Statistical Ensemble Approach

Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used to develop monthly reservoir inflow forecasting models. These models have been verified using leave-one-out cross-validation with expected error S as a measure of forecast skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52 and the SVM from 0.21 to 0.56 for different months. When used as an ensemble, Bayesian model averaging was more accurate than simple model averaging and naïve forecast for four target years tested. These were considered to be decent prediction skills, indicating that teleconnection-based models have the potential to be used as a tool to make a decision for reservoir operation in preparing for droughts.

A Synoptic Climatological Analysis of the Atmospheric Drivers of Water Clarity Variability in the Great Lakes

AbstractA historical water clarity index (Kd index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (Kd; m−1) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Abstract. Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553±50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.

Retrieving Decadal Climate Change from Satellite Radiance Observations-A 100-year CO2 Doubling OSSE Demonstration.

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000–2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique.