scholarly journals Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications

2021 ◽  
Vol 56 ◽  
pp. 89-96
Author(s):  
Aheli Das ◽  
Somnath Baidya Roy
Keyword(s):  
Renewable Energy ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Numerical Models ◽  
Supply And Demand ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Surface Solar Radiation

Abstract. This study evaluates subseasonal to seasonal scale (S2S) forecasts of meteorological variables relevant for the renewable energy (RE) sector of India from six ocean-atmosphere coupled models: ECMWF SEAS5, DWD GCFS 2.0, Météo-France's System 6, NCEP CFSv2, UKMO GloSea5 GC2-LI, and CMCC SPS3. The variables include 10 m wind speed, incoming solar radiation, 2 m temperature, and 2 m relative humidity because they are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous regions of India for 1994–2016. The target months are April and May when the electricity demand is the highest and June–September when the renewable resources outstrip the demand. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with the ERA5 reanalysis spatially averaged over each region. The fair continuous ranked probability skill score (FCRPSS) is used to quantitatively assess the forecast skill. Results show that incoming surface solar radiation predictions are the best, while 2 m relative humidity is the worst. Overall SEAS5 is the best performing model for all variables, for all target months in all regions at all lead times while GCFS 2.0 performs the worst. Predictability is higher over the southern regions of the country compared to the north and north-eastern parts. Overall, the quality of the raw S2S forecasts from numerical models over India are not good. These forecasts require calibration for further skill improvement before being deployed for applications in the RE sector.

The Impacts of Climate Changes on the Renewable Energy Resources in the Caribbean Region

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
M. E. Angeles ◽  
J. E. González ◽  
D. J. Erickson ◽  
J. L. Hernández
Keyword(s):  
Puerto Rico ◽  
Renewable Energy ◽  
Solar Radiation ◽  
Climate Changes ◽  
Future Climate ◽  
Energy Resources ◽  
Caribbean Region ◽  
Renewable Energy Resources ◽  
Surface Solar Radiation ◽  
The Caribbean

Assessment of renewable energy resources such as surface solar radiation and wind current has great relevance in the development of local and regional energy policies. This paper examines the variability and availability of these resources as a function of possible climate changes for the Caribbean region. Global climate changes have been reported in the last decades, causing changes in the atmospheric dynamics, which affects the net solar radiation balance at the surface and the wind strength and direction. For this investigation, the future climate changes for the Caribbean are predicted using the parallel climate model (PCM) and it is coupled with the numerical model regional atmospheric modeling system (RAMS) to simulate the solar and wind energy spatial patterns changes for the specific case of the island of Puerto Rico. Numerical results from PCM indicate that the Caribbean basin from 2041 to 2055 will experience a slight decrease in the net surface solar radiation (with respect to the years 1996–2010), which is more pronounced in the western Caribbean sea. Results also indicate that the easterly winds have a tendency to increase in its magnitude, especially from the years 2070 to 2098. The regional model showed that important areas to collect solar energy are located in the eastern side of Puerto Rico, while the more intense wind speed is placed around the coast. A future climate change is expected in the Caribbean that will result in higher energy demands, but both renewable energy sources will have enough intensity to be used in the future as alternative energy resources to mitigate future climate changes.

scholarly journals Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

2019 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Power Plants ◽  
Numerical Models ◽  
Regional Climate ◽  
Diffuse Radiation ◽  
Terrestrial Ecosystems ◽  
Global Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).

Evaluation of S2S forecasts over India for renewable energy applications

2021 ◽  
Author(s):  
Aheli Das ◽  
Somnath Baidya Roy
Keyword(s):  
Renewable Energy ◽  
Relative Humidity ◽  
Radiative Forcing ◽  
Lead Time ◽  
Surface Fluxes ◽  
Shortwave Radiation ◽  
Lead Times ◽  
Western Coast ◽  
Western Himalayas ◽  
Probability Score

<p>This study evaluates S2S forecasts of meteorological variables relevant for the renewable energy sector from six global coupled forecast models: ECMWF-SEAS5, DWD- GCFS 2.0, Météo-France’s System 6, NCEP-CFSv2, UKMO- GloSea5-GC2-LI, and CMCC-SPS3. The variables include 10m wind speed, incoming shortwave radiation, 2 m temperature, and relative humidity because these variables are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous climate regions of India for 1994-2016 April and May when energy demand peaks throughout the country. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with ERA5 reanalysis data. In order to assess the forecast quality, deterministic metrics such as bias and correlation and probabilistic metrics such as Ranked Probability Score (RPS) and Continuous Ranked Probability Score (CRPS) are calculated by spatially averaging the forecasts and reanalyses over each region. The tercile limits for each variable are determined separately for each homogenous region from the ERA5 reanalysis using leave-one-out cross-validation. The forecasts show the highest skill at 1-month lead-time and the skill reduces with the increase in lead-time. However, deviations from this pattern are observed in some cases. For example, the 2 m temperature forecasts tend to perform better at longer lead-times over the western Himalayas perhaps because the slowly-varying snow dynamics aids in long-term predictability. The 2 m temperature and relative humidity forecasts generally show high correlations with observations over the western coast of the Indian peninsula in May at all lead-times, indicating the ability of the models to simulate presence of moisture prior to monsoon onset. Results show that the model performance depends on time-period of initialisation, better representation of surface fluxes, interaction between radiation and microphysics schemes, land-surface processes and factors governing radiative forcing such as greenhouse gases and aerosols. Overall, the SEAS5 model performs better than other models, although the Météo-France’s System 6 and UKMO- GloSea5-GC2-LI models also perform well in some regions.</p>

scholarly journals Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate

2009 ◽  
Vol 137 (9) ◽  
pp. 2736-2757 ◽  
Author(s):  
Arindam Chakraborty ◽  
T. N. Krishnamurti
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Skill Score ◽  
Rain Gauge ◽  
Scale Model ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Large Scale Model ◽  
Regional Downscaling ◽  
Seasonal Time Scale

Abstract This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.

scholarly journals Seasonal forecasting of mountain snow resources: evaluation of a climate service prototype

2021 ◽  
Author(s):  
Silvia Terzago ◽  
Giulio Bongiovanni ◽  
Jost von Hardenberg
Keyword(s):  
Climate Change ◽  
Snow Depth ◽  
Skill Score ◽  
Small Scale ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Current Season ◽  
Near Surface ◽  
Forecast Quality

<p>Warming trends in the past decades in mountain regions have resulted in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff (IPCC, 2019), and we expect droughts to become more severe in the future (Haslinger et al., 2014) with consequences for both mountain and downstream economies. Effective adaptation strategies to address and reduce negative climate change impacts involve multiple time scales, from the long-term support of mountain water resource management and the diversification of mountain tourism activities, to the seasonal scale, for the optimization of the available snow resources. </p><p>In the frame of the MEDSCOPE project we developed a prototype to generate seasonal forecasts of mountain snow resources, in order to estimate the temporal evolution of the depth and the water content of the snowpack with lead times of several months. The prototype has been tailored on the needs of water and hydropower plant managers and of mountain ski resorts managers. We present the modelling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S). Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are bias-corrected and downscaled to the site of Bocchetta delle Pisse 2410 m a.s.l. in the North-Western Italian Alps, and finally used as input for a physically-based multi-layer snow model (SNOWPACK, Bartelt and Lehning, 2002). The RainFARM stochastic downscaling procedure (Terzago et al., 2018) is used for precipitation data in order to allow an estimate of uncertainties linked to small-scale variability in the forcing.</p><p>The skills of the prototype in predicting the snow depth evolution from November 1st to May 31st in each season of the hindcast period 1995-2015 are demonstrated using station measurements as a reference. We show the correlation between forecast and observed snow depth anomalies and we quantify the forecast quality in terms of reliability, resolution, discrimination and sharpness using a set of probabilistic measures (Brier Skill Score, the Area Under the ROC Curve Skill Score and the Continuous Ranked Probability Skill Score). Implications of the forecast quality at different lead times on climate services are discussed. </p><p>Real-time snow forecasts for the current season (2020-2021) are available at this link: http://wilma.to.isac.cnr.it/diss/snowpack/snowseas-eng.html</p>

scholarly journals Verification of ECMWF System4 for seasonal hydrological forecasting in a northern climate

2017 ◽  
Author(s):  
Rachel Bazile ◽  
Marie-Amélie Boucher ◽  
Luc Perreault ◽  
Robert Leconte
Keyword(s):  
Climate Models ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Hydro Power ◽  
Hydrological Forecasting ◽  
Northern Climate

Abstract. Hydro-power production requires optimal dam management. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Center for Medium-Range Forecast)'s System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of Continuous Ranked Probability Score. Then, three seasonal ensemble hydrological forecasting systems are compared: 1) the climatology of simulated streamflow, 2) the ensemble hydrological forecasts based on climatology (ESP) and 3) the hydrological forecasts based on bias-corrected ensemble meteorological fore- casts from System4 (corr-DSP). Simulated streamflows are used as observations. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead-times from 1-month to 5-month depending on the season and watershed. Corr-DSP appears quite reliable but sometimes suffer from under- dispersion. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead-time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts per- formance for spring is close to the performance of ESP. For longer lead-times, results are mixed and the CRPS skill score is close to 0 in most cases. Bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting.

The Impact of Climate Changes in the Renewable Energy Resources in the Caribbean Region

Solar Energy ◽  
2006 ◽  
Author(s):  
M. E. Angeles ◽  
J. E. Gonza´lez ◽  
D. J. Erickson ◽  
J. L. Herna´ndez
Keyword(s):  
Puerto Rico ◽  
Renewable Energy ◽  
Solar Radiation ◽  
Climate Changes ◽  
Future Climate ◽  
Energy Resources ◽  
Caribbean Region ◽  
Renewable Energy Resources ◽  
Surface Solar Radiation ◽  
The Caribbean

Assessment of renewable energy resources, such as surface solar radiation and wind current has great relevance in the development of local and regional energy policies. This paper examines the variability and availability of these resources as a function of possible climate changes for the Caribbean region. Global climate changes have been reported in the last decades, causing changes in the atmospheric dynamics which affects the net solar radiation balance at the surface and the wind strength and direction. For this investigation, the future climate changes for the Caribbean are predicted using the Parallel Climate Model (PCM) and it is coupled with the Regional Atmospheric Modeling System (RAMS) to simulate the solar and wind energy spatial patterns changes for the specific case of the island of Puerto Rico. Numerical results from PCM indicate that the Caribbean basin from 2041 to 2055 will experience a slight decrease in the net surface solar radiation (with respect to the years 1996–2010), which is more pronounced in the western Caribbean Sea. Results also indicate that the easterly winds have a tendency to increase in its magnitude, especially from the years 2070 to 2098. The regional model showed that important areas to collect solar energy are located in the eastern side of Puerto Rico, while the more intense wind speed is placed around the coast. A future climate change is expected in the Caribbean that will result in higher energy demands, but both renewable energy sources will have enough intensity to be used in the future as an alternative energy sources to mitigate future climate changes.

scholarly journals A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia)

2019 ◽  
Vol 58 (8) ◽  
pp. 1709-1723 ◽  
Author(s):  
Dian Nur Ratri ◽  
Kirien Whan ◽  
Maurice Schmeits
Keyword(s):  
Lead Time ◽  
Economic Value ◽  
Daily Rainfall ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Good Reliability ◽  
Loss Analysis ◽  
Skill Scores ◽  
Accumulated Rainfall

AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

scholarly journals Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Correlation Coefficient ◽  
Numerical Models ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

Abstract Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

scholarly journals Model Output Statistics (MOS) applied to CAMS O<sub>3</sub> forecasts: trade-offs between continuous and categorical skill scores

2021 ◽  
Author(s):  
Hervé Petetin ◽  
Dene Bowdalo ◽  
Pierre-Antoine Bretonnière ◽  
Marc Guevara ◽  
Oriol Jorba ◽  
...  
Keyword(s):  
Numerical Models ◽  
Skill Score ◽  
Gradient Boosting ◽  
Lead Times ◽  
Forecast Errors ◽  
Systematic Bias ◽  
Model Output ◽  
Trade Offs ◽  
Skill Scores ◽  
Model Output Statistics

Abstract. Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed). Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

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