Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state-of-the-art of forecasting wind and solar energy. We describe approaches in statistical and physical modelling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modelling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralised way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security.

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The future of forecasting for renewable energy

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

While contemporary Numerical Weather Prediction models represent the large-scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small-scale features such as convective rainfall. Even though high-resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly
nonlinear and dynamically unstable. Importantly, the models’ initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water-related observations – especially at convective scales (also known as storm scales) – this paper discusses the observation and assimilation of water-
related quantities. Special emphasis is placed on background error statistics for variational and
hybrid methods which need special attention for water variables.
The challenges of convective-scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow,
the high volume of water-related observations (e.g. from radar, microwave, and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations
(e.g. cloud and precipitation affected observations), the need to account for the flow-dependent multivariate ‘balance’ between atmospheric water and both dynamical and mass fields, and the inherent non-Gaussian nature of atmospheric water variables.

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Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

A high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

/pdf/imdaa-high-resolution-satellite-era-reanalysis-for-the-wmlhizyx4m.pdf

IMDAA: High-Resolution Satellite-Era Reanalysis for the Indian Monsoon Region

Over the past 100 years, the collaborative effort of the international science community, including government weather services and the media, along with the associated proliferation of env...

/pdf/100-years-of-progress-in-forecasting-and-nwp-applications-2so01css1y.pdf

100 Years of Progress in Forecasting and NWP Applications

Data assimilation methods for convective-scale numerical weather prediction at operational centres are surveyed in this paper. The operational methods include variational methods (3D-Var and 4D-Var), ensemble methods (LETKF) and hybrids between variational and ensemble methods (3DEnVar and 4DEnVar). At several of the operational centres, other assimilation algorithms, like latent heat nudging, are additionally applied to improve the model initial state, with emphasis on convective scales. It is demonstrated that the quality of forecasts based on initial data from convective-scale data assimilation is significantly better than the quality of forecasts from simple downscaling of larger-scale initial data. The duration of positive impact depends however on the weather situation, the size of the computational domain and the data that are assimilated. It is furthermore shown that more-advanced methods applied at convective scales provide improvements compared to simpler methods. This motivates continued research and development in convective-scale data assimilation.

Challenges in research and development for improvements of convective-scale data assimilation are also reviewed and discussed in this paper. The difficulty of handling the wide range of spatial and temporal scales makes development of multi-scale assimilation methods and space-time covariance localization techniques important. Improved utilization of observations is also important. In order to extract more information from existing observing systems of convective-scale phenomena, for example weather radar data and satellite image data, it is necessary to provide improved statistical descriptions of the observation errors associated with these observations.

Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres

https://www.jstage.jst.go.jp/article/jmsj/advpub/0/advpub_2021-076/_pdf

Variational Data Assimilation System for Operational Regional Models at Japan Meteorological Agency

Herein, the impact of a hyperspectral sounder on a geostationary satellite (GeoHSS) in a regional numerical weather prediction system is investigated during the Baiu seasons in 2017, 2018, and 2020, including heavy rainfall cases. The reanalysis-based observing system simulation experiment (OSSE) technique uses ERA5 as the pseudo-truth atmospheric profile. Temperature and relative humidity pseudo-observations are generated using the one-dimensional variational retrieval scheme based on the spectral characteristics of the GeoHSS. Verification against radiosondes shows improvements at various altitudes and forecast times (FTs). Although wind pseudo-observations are not assimilated, winds are also impacted through assimilation cycles and forecasts. The precipitation forecasts also show an improving trend with a notable impact to extend the forecasts' lead time. Case studies show impacts on precipitation primarily during longer FTs, accompanied by an improved prediction of depressions on the Baiu front and upper-level troughs. These are due to large-scale impacts from the pseudo-observations with a comprehensive coverage over clear-sky areas, propagating to precipitation areas through the assimilation cycle and forecasts. However, the prediction of an event of small-scale localized heavy rain is insufficient even at short forecast ranges owing to a limited spatial resolution. Experiments show that extracting information in the lower atmosphere is critical, and that the impact on upper-level environments is sensitive to using observations in cloudy areas. 

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Mesoscale Observing System Simulation Experiment (OSSE) to Evaluate the Potential Impact from a Geostationary Hyperspectral Infrared Sounder

This study examined the changes of attitudes on beliefs about teaching, comparing scores of a questionnaire before and after taking a course on teaching and learning (Educational Psychology). Five hundred and forty-four students taking a teaching profession course at a university participated in this study. One hundred and thirty-three students answered both of the questionnaires before and after taking this course, and their data was analysed. The results were as follows: scores on "need to learn teaching method at university" showed higher scores after taking a course on teaching and learning, while scores on "means and contents of learning on teaching at university" showed no significance before and after taking this course. Possible reasons for these results and future tasks were pointed out at the end of this article. 

Tadashi Fujita

Papers

Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres

Variational Data Assimilation System for Operational Regional Models at Japan Meteorological Agency

Mesoscale Observing System Simulation Experiment (OSSE) to Evaluate the Potential Impact from a Geostationary Hyperspectral Infrared Sounder

Effect of a teaching profession course related to development and learning on the change of beliefs on how to learn or acquire teaching