Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia

Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.

The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.

A more accurate Parameterization based on cosmic Age (MAPAge)

Recently, several statistically significant tensions between different cosmological datasets have raised doubts about the standard Lambda cold dark matter (ΛCDM) model. A recent letter (Huang 2020) suggests to use “Parameterization based on cosmic Age” (PAge) to approximate a broad class of beyond-ΛCDM models, with a typical accuracy ∼1% in angular diameter distances at z ≲ 10. In this work, we extend PAge to a More Accurate Parameterization based on cosmic Age (MAPAge) by adding a new degree of freedom η 2. The parameter η 2 describes the difference between physically motivated models and their phenomenological PAge approximations. The accuracy of MAPAge, typically of order 10−3 in angular diameter distances at z ≲ 10, is significantly better than PAge. We compare PAge and MAPAge with current observational data and forecast data. The conjecture in Huang (2020), that PAge approximation is sufficiently good for current observations, is quantitatively confirmed in this work. We also show that the extension from PAge to MAPAge is important for future observations, which typically require sub-percent accuracy in theoretical predictions.

ECOLOGIZATION OF PRODUCTION AND CONSUMPTION AS A FACTOR OF ENSURING OPENNESS OF THE CITY IN CONDITIONS OF SUSTAINABLE DEVELOPMENT

In modern conditions, an adequate philosophy and culture of environmental policy is needed, which should be based on a holistic and balanced strategy for the use of natural resources and environmental protection. The use of open data on the official websites of Ukrainian cities makes it possible to increase the effectiveness of environmental policy and environmental measures, as well as to strengthen public control over compliance with environmental legislation. The article considers the ecological indicators of the level of pollutant emissions into the atmosphere of Ukraine and its regions. It is estimated that the main regions that pollute the air of Ukraine the most are: Donetsk region in the first place — 623086,8 tons per year; second place — Dnipropetrovsk region — 276982 tons per year; the third place is occupied by Ivano-Frankivsk region — 185314 tons per year; the fourth place — Zaporizhzhia region — 150481,4 tons per year, the fifth place is Vinnytsia region — 72948,4 tons per year. Forecast data on the dynamics of emissions of pollutants into the air for 2021-2023 are given. The main types of economic activity that have the greatest negative impact of economic processes on the environment are identified. The necessity of implementing greening areas of production and consumption in the management of enterprises is proved. The nature and degree of influence of the external environment on the greening of production and consumption are given. The main goals, directions, priorities and principles of the “Ecological City” projects are described. The basic operational and analytical principles on which it is possible to build the program “Ecologically open city” are defined: 1) posting on the official website of the cities information on the amount of emissions of pollutants into the air by industrial facilities of the city; 2) creating a platform for discussion and coordination of actions of the city administration and citizens on measures to eliminate the negative impact on the environmental condition of the city; 3) development of programs to attract investment to overcome the ecologically dangerous state of the city and the introduction of mechanisms for greening production and consumption.

Overview of a new Ocean Glider Navigation System: OceanGNS

Ocean gliders are increasingly a platform of choice to close the gap between traditional ship-based observations and remote sensing from floats (e.g., Argo) and satellites. However, gliders move slowly and are strongly influenced by currents, reducing useful battery life, challenging mission planning, and increasing pilot workload. We describe a new cloud-based interactive tool to plan glider navigation called OceanGNS© (Ocean Glider Navigation System). OceanGNS integrates current forecasts and historical data to enable glider route–planning at varying scales. OceanGNS utilizes optimal route–planning by minimizing low current velocity constraints by applying a Dijkstra algorithm. The complexity of the resultant path is reduced using a Ramer-Douglas Pueckler model. Users can choose the weighting for historical and forecast data as well as bathymetry and time constraints. Bathymetry is considered using a cost function approach when shallow water is not desirable to find an optimal path that also lies in deeper water. Initial field tests with OceanGNS in the Gulf of St. Lawrence and the Labrador Sea show promising results, improving the glider speed to the destination 10–30%. We use these early tests to demonstrate the utility of OceanGNS to extend glider endurance. This paper provides an overview of the tool, the results from field trials, and a future outlook.

Development of a deep neural network for predicting 6-hour average PM_2.5 concentrations up to two subsequent days using various training data

This study aims to develop a deep neural network (DNN) model as an artificial neural network (ANN) for the prediction of 6-hour average fine particulate matter (PM2.5) concentrations for a three-day period—the day of prediction (D+0), one day after prediction (D+1) and two days after prediction (D+2)—using observation data and forecast data obtained via numerical models. The performance of the DNN model was comparatively evaluated against that of the currently operational Community Multiscale Air Quality (CMAQ) modelling system for air quality forecasting in South Korea. In addition, the effect on predictive performance of the DNN model on using different training data was analyzed. For the D+0 forecast, the DNN model performance was superior to that of the CMAQ model, and there was no significant dependence on the training data. For the D+1 and D+2 forecasts, the DNN model that used the observation and forecast data (DNN-ALL) outperformed the CMAQ model. The root-mean-squared error (RMSE) of DNN-ALL was lower than that of the CMAQ model by 2.2 μgm−3, and 3.0 μgm−3 for the D+1 and D+2 forecasts, respectively, because the overprediction of higher concentrations was curtailed. An IOA increase of 0.46 for D+1 prediction and 0.59 for the D+2 prediction was observed in case of the DNN-ALL model compared to the IOA of the DNN model that used only observation data (DNN-OBS). In additionally, An RMSE decrease of 7.2 μgm−3 for the D+1 prediction and 6.3 μgm−3 for the D+2 prediction was observed in case of the DNN-ALL model, compared to the RMSE of DNN-OBS, indicating that the inclusion of forecast data in the training data greatly affected the DNN model performance. Considering the prediction of the 6-hour average PM2.5 concentration, the 8.8 μgm−3 RMSE of the DNN-ALL model was 2.7 μgm−3 lower than that of the CMAQ model, indicating the superior prediction performance of the former. These results suggest that the DNN model could be utilized as a better-performing air quality forecasting model than the CMAQ, and that observation data plays an important role in determining the prediction performance of the DNN model for D+0 forecasting, while prediction data does the same for D+1 and D+2 forecasting. The use of the proposed DNN model as a forecasting model may result in a reduction in the economic losses caused by pollution-mitigation policies and aid better protection of public health.

Compressing atmospheric data into its real information content

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed. Here we define the bitwise real information content from information theory for the Copernicus Atmospheric Monitoring Service (CAMS). Most variables contain fewer than 7 bits of real information per value and are highly compressible due to spatio-temporal correlation. Rounding bits without real information to zero facilitates lossless compression algorithms and encodes the uncertainty within the data itself. All CAMS data are 17× compressed relative to 64-bit floats, while preserving 99% of real information. Combined with four-dimensional compression, factors beyond 60× are achieved. A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data.

Forecasting of fuel oil supply using the transfer function approach (Case study: PT. Agrabudi Karyamarga Gas Station Division 64.706.07)

In everyday life, fuel oil is quite important. The need for fuel is increasing every day, which means that the supply of fuel oil must keep up with the demand. As a reason, we require a way for predicting future fuel needs. The forecasting method is one that is frequently utilized. Forecasting is a method for predicting future conditions based on historical data. The transfer function approach is one way to forecast data with several variables in time series analysis. The objective of this research is to estimate the parameters of the transfer function model and use a transfer function approach to predict the movement of fuel, particularly pertalite. The parameter estimation results in this research are ω ^ 0 = 0.033 ; ω ^ 1 = − 0.0358 ; ω ^ 2 = 0.0627 ; δ ^ 1 = − 0.9713 ; δ ^ 2 = 1 ; θ ^ 1 = − 0.9141 , and the forecast value for the 214th period is 8762.61, based on the data used, namely for 213 periods starting from the 1st period until the 213th period.

Peramalan Pencemaran Udara Di Kota Surabaya Menggunakan Metode DSARIMA dengan Pendekatan Percentile Error Bootstrap (PEB)

<p>Peramalan pencemaran udara yang akurat diperlukan untuk mengurangi dampak pencemaran udara. Peramalan yang belum akurat akan berdampak kurang efektifnya tindakan yang dilakukan untuk mengantisipasi dampak pencemaran udara. Sehingga diperlukan sebuah pendekatan yang dapat mengetahui keakuratan plot data hasil peramalan. Penelitian ini dilakukan dengan tujuan melakukan peramalan pencemaran udara berdasarkan parameter PM₁₀, NO₂, CO, SO₂, dan O₃dengan metode DSARIMA. Data dalam penelitian ini sebanyak 8.760 data yang berasal dari Dinas Lingkungan Hidup Kota Surabaya. Berdasarkan hasil peramalan selama 168 jam kadar parameter PM₁₀, NO₂, SO₂ dan O₃ cenderung menurun. Hasil peramalan selama 168 jam dengan menggunakan DSARIMA memberikan hasil peramalan yang nilainya mendekati data aktual terbukti dari polanya yang sesuai atau mirip dengan grafik plot data aktual dengan hasil ramalan. Dengan pendekatan PEB, selisih antara data aktual dan data ramalan kecil dan plot grafik PEB mengikuti plot grafik di data aktual, sehingga dapat dikatakan bahwa model sudah sesuai. Hasil akurasi terbaik yang dihasilkan adalah model DSARIMA dengan RMSE terkecil 0,59 didapatkan dari parameter CO yaitu ARIMA(0,1,[1,2,3])(0,1,1)²⁴(0,1,1)¹⁶⁸.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Accurate air pollution forecasting is needed to reduce the impact of air pollution. Inaccurate forecasting will result in less effective actions taken to anticipate the impact of air pollution. So we need an approach that can determine the accuracy of the forecast data plot. This research was conducted with the aim of forecasting air pollution based on the PM₁₀, NO₂, CO, _SO2, and O₃ parameters using the DSARIMA method. The data in this study were 8.760 data from the Surabaya City Environmental Service. Based on the results of forecasting for 168 hours, the levels of PM₁₀, NO_2,SO₂, and O₃ parameters tend to decrease. Forecasting results for 168 hours using DSARIMA provide forecasting results whose values are close to the actual data as evidenced by the pattern that matches or is similar to the actual data plot graph with the forecast results. With the PEB approach, the difference between the actual data and the forecast data is small and the PEB graph plot follows the graph plot in the actual data, so it can be said that the model is appropriate. The best accuracy result is DSARIMA with the smallest RMSE 0,59 obtained from the CO parameter, namely </em>ARIMA(0,1,[1,2,3])(0,1,1)²⁴(0,1,1)¹⁶⁸.</p><p> </p><p> </p>