scholarly journals The solar eclipse: a natural meteorological experiment

2016 ◽  
Vol 374 (2077) ◽  
pp. 20150225 ◽  
Author(s):  
R. Giles Harrison ◽  
Edward Hanna
Keyword(s):  
Solar Radiation ◽  
Solar Eclipse ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Regional Scale ◽  
Electrical Energy ◽  
Atmospheric Effects ◽  
Modern Approach ◽  
Spatial Coverage

A solar eclipse provides a well-characterized reduction in solar radiation, of calculable amount and duration. This captivating natural astronomical phenomenon is ideally suited to science outreach activities, but the predictability of the change in solar radiation also provides unusual conditions for assessing the atmospheric response to a known stimulus. Modern automatic observing networks used for weather forecasting and atmospheric research have dense spatial coverage, so the quantitative meteorological responses to an eclipse can now be evaluated with excellent space and time resolution. Numerical models representing the atmosphere at high spatial resolution can also be used to predict eclipse-related changes and interpret the observations. Combining the models with measurements yields the elements of a controlled atmospheric experiment on a regional scale (10–1000 km), which is almost impossible to achieve by other means. This modern approach to ‘eclipse meteorology’ as identified here can ultimately improve weather prediction models and be used to plan for transient reductions in renewable electricity generation. During the 20 March 2015 eclipse, UK electrical energy demand increased by about 3 GWh (11 TJ) or about 4%, alongside reductions in the wind and photovoltaic electrical energy generation of 1.5 GWh (5.5 TJ). This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

scholarly journals Improved Gridded Wind Speed Forecasts by Statistical Postprocessing of Numerical Models with Block Regression

2016 ◽  
Vol 31 (6) ◽  
pp. 1929-1945 ◽  
Author(s):  
Michaël Zamo ◽  
Liliane Bel ◽  
Olivier Mestre ◽  
Joël Stein
Keyword(s):  
Wind Speed ◽  
Random Forests ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Near Surface ◽  
Numerical Weather ◽  
Spatial Coverage

Abstract Numerical weather forecast errors are routinely corrected through statistical postprocessing by several national weather services. These statistical postprocessing methods build a regression function called model output statistics (MOS) between observations and forecasts that is based on an archive of past forecasts and associated observations. Because of limited spatial coverage of most near-surface parameter measurements, MOS have been historically produced only at meteorological station locations. Nevertheless, forecasters and forecast users increasingly ask for improved gridded forecasts. The present work aims at building improved hourly wind speed forecasts over the grid of a numerical weather prediction model. First, a new observational analysis, which performs better in terms of statistical scores than those operationally used at Météo-France, is described as gridded pseudo-observations. This analysis, which is obtained by using an interpolation strategy that was selected among other alternative strategies after an intercomparison study conducted internally at Météo-France, is very parsimonious since it requires only two additive components, and it requires little computational resources. Then, several scalar regression methods are built and compared, using the new analysis as the observation. The most efficient MOS is based on random forests trained on blocks of nearby grid points. This method greatly improves forecasts compared with raw output of numerical weather prediction models. Furthermore, building each random forest on blocks and limiting those forests to shallow trees does not impair performance compared with unpruned and pointwise random forests. This alleviates the storage burden of the objects and speeds up operations.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

scholarly journals The Diurnal Cycle of Precipitation from Continental Radar Mosaics and Numerical Weather Prediction Models. Part II: Intercomparison among Numerical Models and with Nowcasting

2012 ◽  
Vol 140 (8) ◽  
pp. 2689-2705 ◽  
Author(s):  
Marc Berenguer ◽  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Diurnal Cycle ◽  
Prediction Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Model Performance ◽  
Radar Data ◽  
Skill Scores ◽  
Diurnal Cycle Of Precipitation ◽  
Timing Of Initiation ◽  
Short Term Forecasting

Abstract This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting. The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar. Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill. The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 103°–85°W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

scholarly journals Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain)

2009 ◽  
Vol 13 (10) ◽  
pp. 1907-1920 ◽  
Author(s):  
M. Angulo-Martínez ◽  
M. López-Vicente ◽  
S. M. Vicente-Serrano ◽  
S. Beguería
Keyword(s):  
Soil Erosion ◽  
Prediction Models ◽  
Regional Scale ◽  
Geostatistical Simulation ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Ebro Basin ◽  
Spatial Coverage ◽  
Interpolation Techniques ◽  
Ne Spain

Abstract. Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ~15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty, caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.

scholarly journals Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases

2012 ◽  
Vol 29 (2) ◽  
pp. 267-285 ◽  
Author(s):  
R. Mínguez ◽  
B. G. Reguero ◽  
A. Luceño ◽  
F. J. Méndez
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Numerical Models ◽  
Wave Climate ◽  
Spatiotemporal Resolution ◽  
Statistical Characterization ◽  
Outlier Identification ◽  
Wave Hindcast ◽  
Spatial Coverage ◽  
Instrumental Records

Abstract The development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering important advantages for the statistical characterization of wave climate all over the globe (continuous time series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series). However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient spatiotemporal resolution used. These difficulties make the data related to these events appear as “outliers” when compared with instrumental records. These bad data distort results from calibration and/or correction techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods are applied to a global wave hindcast database and results are compared with existing hurricane and buoy databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean.

scholarly journals A review of operational, regional-scale, chemical weather forecasting models in Europe

2012 ◽  
Vol 12 (1) ◽  
pp. 1-87 ◽  
Author(s):  
J. Kukkonen ◽  
T. Olsson ◽  
D. M. Schultz ◽  
A. Baklanov ◽  
T. Klein ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Regional Scale ◽  
Chemical Species ◽  
Future Research ◽  
Physical Processes ◽  
Forecasting Models ◽  
Air Chemistry

Abstract. Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

scholarly journals Implementation of Bessel's method for solar eclipses prediction in the WRF-ARW model

2016 ◽  
Vol 16 (9) ◽  
pp. 5949-5967 ◽  
Author(s):  
Alex Montornès ◽  
Bernat Codina ◽  
John W. Zack ◽  
Yolanda Sola
Keyword(s):  
Solar Radiation ◽  
Solar Eclipse ◽  
Weather Prediction ◽  
Grid Point ◽  
Absolute Error ◽  
Resource Assessment ◽  
Surface Radiation ◽  
Computational Time ◽  
Arw Model ◽  
Solar Eclipses

Abstract. Solar eclipses are predictable astronomical events that abruptly reduce the incoming solar radiation into the Earth's atmosphere, which frequently results in non-negligible changes in meteorological fields. The meteorological impacts of these events have been analyzed in many studies since the late 1960s. The recent growth in the solar energy industry has greatly increased the interest in providing more detail in the modeling of solar radiation variations in numerical weather prediction (NWP) models for the use in solar resource assessment and forecasting applications. The significant impact of the recent partial and total solar eclipses that occurred in the USA (23 October 2014) and Europe (20 March 2015) on solar power generation have provided additional motivation and interest for including these astronomical events in the current solar parameterizations.Although some studies added solar eclipse episodes within NWP codes in the 1990s and 2000s, they used eclipse parameterizations designed for a particular case study. In contrast to these earlier implementations, this paper documents a new package for the Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model that can simulate any partial, total or hybrid solar eclipse for the period 1950 to 2050 and is also extensible to a longer period. The algorithm analytically computes the trajectory of the Moon's shadow and the degree of obscuration of the solar disk at each grid point of the domain based on Bessel's method and the Five Millennium Catalog of Solar Eclipses provided by NASA, with a negligible computational time. Then, the incoming radiation is modified accordingly at each grid point of the domain.This contribution is divided in three parts. First, the implementation of Bessel's method is validated for solar eclipses in the period 1950–2050, by comparing the shadow trajectory with values provided by NASA. Latitude and longitude are determined with a bias lower than 5  ×  10−3 degrees (i.e.,  ∼  550 m at the Equator) and are slightly overestimated and underestimated, respectively. The second part includes a validation of the simulated global horizontal irradiance (GHI) for four total solar eclipses with measurements from the Baseline Surface Radiation Network (BSRN). The results show an improvement in mean absolute error (MAE) from 77 to 90 % under cloudless skies. Lower agreement between modeled and measured GHI is observed under cloudy conditions because the effect of clouds is not included in the simulations for a better analysis of the eclipse outcomes. Finally, an introductory discussion of eclipse-induced perturbations in the surface meteorological fields (e.g., temperature, wind speed) is provided by comparing the WRF–eclipse outcomes with control simulations.

scholarly journals Coupling a weather model directly to GNSS orbit determination

2021 ◽  
Author(s):  
Angel Navarro Trastoy ◽  
Sebastian Strasser ◽  
Lauri Tuppi ◽  
Maksym Vasiuta ◽  
Markku Poutanen ◽  
...  
Keyword(s):  
Orbit Determination ◽  
Prediction Models ◽  
Weather Prediction ◽  
Direct Coupling ◽  
Atmospheric Effects ◽  
Satellite Orbits ◽  
Small Data ◽  
Neutral Atmosphere ◽  
Data Set ◽  
Weather Model

Abstract. Neutral atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing 3-dimensional refraction fields to estimate signal delay in the zenith direction and determine a low-dimensional mapping of this delay to desired azimuth and elevation angles. In this study, a global numerical weather prediction model (OpenIFS licensed for Academic use by ECMWF) is used to generate the refraction fields. The ray-traced slant delays are supplied as such – in contrast to mapping – for an orbit solver (GROOPS software toolkit of TUG) which applies the raw observation method. Here we show that such a close coupling is possible without need for major additional modifications in the solver codes. The main finding here is that the adopted approach provides a very good a priori model for the atmospheric effects on navigation signals, as measured with the midnight discontinuity of GNSS satellite orbits. Our interpretation is that removal of the intermediate mapping step allows to take advantage of the local refraction field asymmetries in the GNSS signal processing. Moreover, the direct coupling helps in identifying deficiencies in the slant delay computation because the modelling errors are not convoluted in the precision-reducing mapping. These conclusions appear robust, despite the relatively small data set of raw code and phase observations covering the core network of 66 ground-based stations of the International GNSS Service over one-month periods in December 2016 and June 2017. More generally, the new configuration enhances our control of geodetic and meteorological aspects of the orbit problem. This is pleasant because we can, for instance, regulate at will the weather model output frequency and increase coverage of spatio-temporal aspects of weather variations. The direct coupling of a weather model in precise GNSS orbit determination presented in this paper provides a unique framework for benefiting even more widely than previously the apparent synergies in space geodesy and meteorology.

scholarly journals Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7970
Author(s):  
Abdel-Rahman Hedar ◽  
Majid Almaraashi ◽  
Alaa E. Abdel-Hakim ◽  
Mahmoud Abdulrahim
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Root Mean Square ◽  
Prediction Models ◽  
Weather Prediction ◽  
Hybrid Models ◽  
Mean Square ◽  
Learning Models ◽  
Mean Square Errors ◽  
Machine Learning Models

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to 5.6% and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.

Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

<p>Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.</p>

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