Forecasting drought revisited – the importance of spectral transformations to dominant atmospheric predictor variables

2020 ◽  
Author(s):  
Ashish Sharma ◽  
Ze Jiang ◽  
Fiona Johnson
Keyword(s):  
Forest Fires ◽  
Drought Index ◽  
Predictor Variables ◽  
Lead Times ◽  
Water Restriction ◽  
Climate Forecasts ◽  
Entire Country ◽  
The Future ◽  
Spectral Transformations ◽  
Decadal Climate

<p>As we write this abstract, Australia is experiencing widespread forest fires, Sydney has declared significant water restriction measures curtailing demand, and the entire country is experiencing a drought that is amongst the worst on record. Formulating a stable and practical approach for predicting drought into the future is being realised as an important need, as we enter an era of warmer climates that complicate this problem to an even greater extent. This study presents a novel basis for forecasting drought into the future. Use is made of a recently developed wavelets based methodology for transforming predictor variables so as to force greater consistency in spectral attributes with the response being modelled. Using a commonly adopted drought index, we demonstrate how the wavelets transformed predictor variables can be used to model the response with greater accuracy than otherwise. These transformed predictor variables are then used in conjunction with CMIP5 decadal climate forecasts to demonstrate the accuracy attainable at longer lead times than is currently possible. While our application focusses on the Australian mainland, the method is generic and can be adopted anywhere.</p>

scholarly journals Long Range Prediction and the Stratosphere

2021 ◽  
Author(s):  
Adam A. Scaife ◽  
Mark P. Baldwin ◽  
Amy H. Butler ◽  
Andrew J. Charlton-Perez ◽  
Daniella I. V. Domeisen ◽  
...  
Keyword(s):  
Long Range ◽  
Numerical Models ◽  
Climate Forecasts ◽  
The Future ◽  
Decadal Climate ◽  
Long Range Prediction ◽  
Range Forecasts

Abstract. Over recent years there have been parallel advances in the development of stratosphere resolving numerical models, our understanding of stratosphere-troposphere interaction and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing new and improved capability in long range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal and decadal climate predictions. We end with an outlook towards the future of climate forecasts and identify areas for improvement that could further benefit these rapidly evolving predictions.

Influence of forest humidity on the distribution of forest fires in the territory of Serbia

2021 ◽  
Vol 14 (9) ◽  
pp. 8-14
Author(s):  
Stanimir Živanović
Keyword(s):  
Forest Fires ◽  
Drought Index ◽  
Aridity Index ◽  
Administrative District ◽  
Positive Correlation

In this study, we examined the dependence of the influence of forest humidity conditions on the variability of forest fires in Serbia. The changes in values of the Forest Aridity Index (FAI) and the De Martonne Drought Index (IDM) in the period 2009-2018 were analyzed, with an emphasis on 2012 and 2014. Data from ground meteorological measurements at 14 main meteorological stations on the territory of Serbia were used. The analysis of the FAI index determines a positive correlation on the activity of forest fires in the territory of Serbia. FAI values indicate marked increases for 2012 and 2017 when the largest number of forest fires was registered in Serbia. The lowest values of this index are for 2014, when we registered the smallest occurrence of forest fires in Serbia. Decrease in the value of the IDM index was observed during 2011, 2012 and 2017 correlated with a larger number of forest fires in the period. The greatest threat to forests from fire is in the administrative district of Kragujevac (region of Šumadija and Western Serbia) and Vranje (region of Southern and Eastern Serbia) and the lowest in the area of Sombor and Kikinda (region of Vojvodina). At nine of the fourteen meteorological stations, the De Martonne Drought Index (IDM) showed stronger connection with the occurrence of forest fires compared to the Forest Aridity Index (FAI).

Global warming and forest fires in Canada

1993 ◽  
Vol 69 (3) ◽  
pp. 290-293 ◽  
Author(s):  
Brian J. Stocks
Keyword(s):  
Global Warming ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Management ◽  
Warming Trend ◽  
Research Activities ◽  
Forest Fire Management ◽  
The Future ◽  
Innovative Thinking ◽  
The Impact

The looming possibility of global warming raises legitimate concerns for the future of the forest resource in Canada. While evidence of a global warming trend is not conclusive at this time, governments would be wise to anticipate, and begin planning for, such an eventuality. The forest fire business is likely to be affected both early and dramatically by any trend toward warmer and drier conditions in Canada, and fire managers should be aware that the future will likely require new and innovative thinking in forest fire management. This paper summarizes research activities currently underway to assess the impact of global warming on forest fires, and speculates on future fire management problems and strategies.

Policy Responses to El Niño 1997-1998 : Implications for Forecast Value and the Future of Climate Services

2000 ◽  
Author(s):  
Roger A., Jr. Pielke
Keyword(s):  
El Niño ◽  
El Nino ◽  
Federal State ◽  
Policy Makers ◽  
Climate Anomalies ◽  
Future Production ◽  
Climate Forecasts ◽  
The Future ◽  
Climate Event ◽  
State And Local

El Niño 97-98 will be remembered as one of the strongest ever recorded (Glantz, 1999). For the first time, climate anomalies associated with the event were anticipated by scientists, and this information was communicated to the public and policy makers to prepare for the “meteorological mayhem that climatologists are predicting will beset the entire globe this winter. The source of coming chaos is El Niño . . .” (Brownlee and Tangley, 1997). Congress and government agencies reacted in varying ways, as illustrated by the headlines presented in Figure 7-1. The link between El Niño events and seasonal weather and climate anomalies across the globe are called teleconnections (Glantz and Tarlton, 1991). Typically, during an El Niño cycle hurricane frequencies in the Atlantic are depressed, the southeast United States receives more rain than usual (chapter 2), and parts of Australia, Africa, and South America experience drought. Global attention became focused on the El Niño phenomenon following the 1982-1983 event, which, at that time, had the greatest magnitude of any El Niño observed in more than a century. After El Niño 82-83, many seasonal anomalies that had occurred during its two years were attributed, rightly or wrongly, to its influence on the atmosphere. As a consequence of the event, societies around the world experienced both costs and benefits (Glantz et al., 1987). Another lasting consequence of the 1982-1983 event was an increase in research into the phenomenon. One result of this research in the late 1990s has been the production of forecasts of El Niño (and La Niña) events and the seasonal climate anomalies associated with them. This chapter discusses the use of climate forecasts by policy makers, drawing on experiences from El Niño 97-98, which replaced the 1982-1983 eventas the” climate event of the century.” The purpose of this chapter is to draw lessons from the use of El Niño -based climate forecasts during the 1997-1998 event in order to improve the future production, delivery, and use of climate predictions. This chapter focuses on examples of federal, state, and local responses in California, Florida, and Colorado to illustrate the lessons.

The decadal climate prediction skill with focus on the North Atlantic region

2020 ◽  
Author(s):  
Shuting Yang ◽  
Bo Christiansen
Keyword(s):  
North Atlantic ◽  
Large Degree ◽  
Climate Prediction ◽  
Prediction Skill ◽  
Lead Times ◽  
The North ◽  
Ensemble Mean ◽  
The North Atlantic ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>The skill of the decadal climate prediction is analyzed based on recent ensemble experiments from the CMIP5 and CMIP6 decadal climate prediction projects (DCPP) and the Community Earth System Model (CESM) Large Ensemble (LENS) Project. The experiments are initialized every year at November 1 for the period of 1960-2005 in the CMIP5 DCPP experiments and 1960-2016 for the CMIP6 DCPP models as well as the CESM LENS decadal prediction. The CMIP5/6 ensemble has 10 members for each model and the CESM ensemble has 40 members. For the considered models un-initialized (historical) ensembles with the same forcings exist. The advantage of initialization is analyzed by comparing these two sets of experiments.<br><br>We find that the models agree that for lead-times between 4-10 years little effect of initialization is found except in the North Atlantic sub-polar gyre region (NASPG). This well-known result is found for all the models and is robust to temporal and spatial smoothing. In the sub-polar gyre region the ensemble mean of the forecast explains 30-40 % more of the observed variance than the ensemble mean of the historical non-initialized experiments even for lead-times of 10 years.<br><br>However, the skill in the NASPG seems to a large degree to be related to the shift towards warmer temperatures around 1996. Weak or no skill is found when the sub-periods before and after 1996 are considered. We further analyze the characteristics of other climate indicators than surface temperature as well as the NAO to understand the cause and implication of the prediction skill.</p>

scholarly journals Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1848 ◽  
Author(s):  
Yuehjen Shao ◽  
Yi-Shan Tsai
Keyword(s):  
Moving Average ◽  
Multivariate Adaptive Regression Splines ◽  
Predictor Variables ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Explanatory Variables ◽  
Forecasting Models ◽  
Hybrid Approaches ◽  
The Future ◽  
Hybrid Forecasting

Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models.

scholarly journals Model Runtut waktu untuk Peramalan Indeks Kekeringan Daerah Lombok Utara

2019 ◽  
Vol 5 (2) ◽  
pp. 123
Author(s):  
Humairo Saidah ◽  
I Wayan Yasa ◽  
Muh. Bagus Budianto ◽  
Syamsul Hidayat ◽  
I.D.G Jayanegara
Keyword(s):  
Drought Index ◽  
Good Accuracy ◽  
Index Method ◽  
Sarima Model ◽  
The Future ◽  
Drought Pattern ◽  
Lombok Island

PDSI is the drought index method which has good accuracy to be applied in Lombok Island. However, this method is only able to hindcast the drought without any procedure to predict the drought index in the future. So, this model aims to recognize the characteristics of drought in North Lombok for early mitigation and anticipating drought disasters purposes in this region. The results obtained from this study are that the drought pattern in North Lombok has the SARIMA model of (0,1,2) (0,1,1)12. The drought in North Lombok mainly occurs between May-October with an increasing of drought index tends for over last 20 years.

scholarly journals Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles

PLoS Medicine ◽  
2021 ◽  
Vol 18 (3) ◽  
pp. e1003542
Author(s):  
Felipe J. Colón-González ◽  
Leonardo Soares Bastos ◽  
Barbara Hofmann ◽  
Alison Hopkin ◽  
Quillon Harpham ◽  
...  
Keyword(s):  
Outbreak Detection ◽  
Lead Times ◽  
Dengue Case ◽  
Baseline Model ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Dengue Outbreaks ◽  
Forecasting System ◽  
Case Data ◽  
Seasonal Climate Forecasts

Background With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

scholarly journals THE MANIFESTATION OF A GENERAL PLANETARY CRISIS AT THE BEGINNING OF THE THIRD MILLENNIUM

2021 ◽  
Author(s):  
Valeriy Kovalyov ◽  
Olena Ataeva
Keyword(s):  
Forest Fires ◽  
Social Order ◽  
Transition Period ◽  
Human Life ◽  
Industrial Relations ◽  
Economic Restructuring ◽  
The United States ◽  
The Third ◽  
Third Millennium ◽  
The Future

The article reveals the essence and preconditions of the global crisis in nature and society at the beginning of the third millennium. The purpose of human life as a manifestation of its society is defined in accordance with the requirements of the objectively existing laws of human evolution on the way to extracurricular society. In particular, attention is paid to such cataclysms of the planet that interfere with human life in the present and future, such as earthquakes, devastating typhoons, temperature fluctuations, downpours, tsunamis, or, conversely, droughts that lead to vegetation, crop failures, forest fires and more , to eliminate which humanity has limited opportunities. A significant impact on the state of natural conditions of our planet is caused by changes in solar activity. Changes in the survival of mankind, such as the emergence of viral diseases, including plague, Ebola, coronavirus and others, which endanger human life and lead to changes in production and living conditions, which in turn forces people to join forces in the struggle for survival. Possible ways to eliminate or mitigate the devastating effects of the planetary crisis in the context of human existence through socio-economic restructuring within the modern era, the trends of which are currently manifested in the socio-economic confrontation of such countries, on the one hand as the United States, on the other – Russia and China, which are heading to the social and economic space. The main provisions of the methodology for quantifying social changes on the path of socio-economic restructuring of mankind in the future by indicators: the level of labor potential, human and physical capital, wages, as representatives of existing industrial relations in the world. In particular, methods have been invented to analyze and calculate the level of wages as an economic category of the transition period to the direct social order in the future of mankind on such elements as the minimum, additional and stimulating wages. The scientific approaches to determining the degree of social maturity of society in the present and future, which distort the progress of mankind to its ultimate goal – the restructuring of extracurricular, direct community structure, its existence on the planet.

Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method

2020 ◽  
Vol 59 (3) ◽  
pp. 497-515 ◽  
Author(s):  
Ming Li ◽  
Huidong Jin ◽  
Jaclyn N. Brown
Keyword(s):  
Climate Models ◽  
Predictive Distribution ◽  
Forecast Skill ◽  
Maximum Temperature ◽  
Dependence Structure ◽  
Lead Times ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Australian Community ◽  
Farm Scale

AbstractSeasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.

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