Assessing the WRF-Solar model performance using satellite-derived irradiance from the National Solar Radiation Database

2021 ◽  
Author(s):  
Jaemo Yang ◽  
Ju-Hye Kim ◽  
Manajit Sengupta ◽  
Jimy Dudhia
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Model Performance ◽  
Absolute Error ◽  
Forecast Errors ◽  
Grid Spacing ◽  
Radiative Effects ◽  
High Quality ◽  
Radiation Processes ◽  
Nwp Model

Abstract WRF-Solar is a numerical weather prediction (NWP) model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol-cloud-radiation processes. This flexibility can be argued to make more difficult to improve the model’s performance due to the necessity of inspecting different configurations. To alleviate this, WRF-Solar has a reference configuration to use it as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the CONUS. Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, sensitivities to the grid spacing, and to the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning the year of 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 km to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute increasing the WRF-Solar performance.

scholarly journals Optical Flow for Verification

2010 ◽  
Vol 25 (5) ◽  
pp. 1479-1494 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Optical Flow ◽  
Mesoscale Model ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Performance ◽  
Simulated Data ◽  
General Idea ◽  
Forecast Errors ◽  
Spatial Fields ◽  
Nwp Model

Abstract Modern numerical weather prediction (NWP) models produce forecasts that are gridded spatial fields. Digital images can also be viewed as gridded spatial fields, and as such, techniques from image analysis can be employed to address the problem of verification of NWP forecasts. One technique for estimating how images change temporally is called optical flow, where it is assumed that temporal changes in images (e.g., in a video) can be represented as a fluid flowing in some manner. Multiple realizations of the general idea have already been employed in verification problems as well as in data assimilation. Here, a specific formulation of optical flow, called Lucas–Kanade, is reviewed and generalized as a tool for estimating three components of forecast error: intensity and two components of displacement, direction and distance. The method is illustrated first on simulated data, and then on a 418-day series of 24-h forecasts of sea level pressure from one member [the Global Forecast System (GFS)–fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] of the University of Washington’s Mesoscale Ensemble system. The simulation study confirms (and quantifies) the expectation that the method correctly assesses forecast errors. The method is also applied to a real dataset consisting of 418 twenty-four-hour forecasts spanning 2 April 2008–2 November 2009, demonstrating its value for analyzing NWP model performance. Results reveal a significant intensity bias in the subtropics, especially in the southern California region. They also expose a systematic east-northeast or downstream bias of approximately 50 km over land, possibly due to the treatment of terrain in the coarse-resolution model.

scholarly journals Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

2020 ◽  
Vol 12 (21) ◽  
pp. 3672
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Software Tool ◽  
Absolute Error ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Forecast Horizon ◽  
Novel Approach ◽  
Forecasting System

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

Solar and Thermal Radiation Errors on Upper-Air Radiosonde Temperature Measurements

2013 ◽  
Vol 30 (10) ◽  
pp. 2382-2393 ◽  
Author(s):  
R. Philipona ◽  
A. Kräuchi ◽  
G. Romanens ◽  
G. Levrat ◽  
P. Ruppert ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Thermal Radiation ◽  
Air Temperature ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Radiative Heating ◽  
Temperature Measurements ◽  
High Quality

Abstract Atmospheric temperature and humidity profiles are important for weather prediction, but climate change has increased the interest in upper-air observations asking for very high-quality reference measurements. This paper discusses an experimental approach to determine the radiation-induced error on radiosonde air temperature measurements. On the one hand, solar shortwave and thermal longwave radiation profiles were accurately measured during radiosonde ascents from the surface to 35-km altitude. On the other hand, air temperature was measured with several thermocouples on the same flight, simultaneously under sun-shaded and unshaded conditions. The radiation experiments reveal that thermal radiation errors on the very thin thermocouple of the Meteolabor SRS-C34 radiosonde are similar during night- and daytime. They produce a radiative cooling in the lower troposphere and the upper stratosphere, but a radiative heating in the upper troposphere and lower stratosphere. Air temperature experiments with several thermocouples, however, show that solar radiation produces a radiative heating of about +0.2°C near the surface, which linearly increases to about +1°C at 32 km (~10 hPa). The new solar radiation error profile was then applied to SRS-C34 measurements made during the Eighth WMO Intercomparison of High Quality Radiosonde Systems, held in Yangjiang, China, in July 2010. The effects of thermal and solar radiation errors are finally shown in contrast to the 10 other internationally used radiosonde systems, which were flown during this international campaign.

scholarly journals Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany

2017 ◽  
Vol 17 (21) ◽  
pp. 13391-13415 ◽  
Author(s):  
Daniel Rieger ◽  
Andrea Steiner ◽  
Vanessa Bachmann ◽  
Philipp Gasch ◽  
Jochen Förstner ◽  
...  
Keyword(s):  
Power Generation ◽  
Solar Radiation ◽  
Numerical Weather Prediction ◽  
Mineral Dust ◽  
Weather Prediction ◽  
Aerosol Particles ◽  
Saharan Dust ◽  
Radiative Effects ◽  
Numerical Weather ◽  
The Impact

Abstract. The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.

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 Influence of Parameterization of Some Physical Processes in Soils on Numerical Meteorological Forecasts of Surface Fields

2016 ◽  
Vol 20 (4) ◽  
pp. 48-58
Author(s):  
Andrzej Mazur ◽  
Grzegorz Duniec
Keyword(s):  
Boundary Layer ◽  
Numerical Models ◽  
Weather Prediction ◽  
Water System ◽  
Soil Processes ◽  
Plant Water ◽  
Physical Processes ◽  
High Quality ◽  
Nwp Model ◽  
Surface Fields

Abstract Physical processes in soil-plant-water system are very complicated. Complex physical processes in soil, in particular interaction between soil-plant-water system have significant influence on processes in Planetary Boundary Layer. Changes of soil state can significantly modify processes in the PBL and meteorological fields. Since numerical models are to determine the forecast of high quality, the physical processes occurring in soil should be properly described and then appropriately introduced into a model. Every process in soil occurs on a smaller scale than original model’s domain, so it should be described via adequate parameterization. Overall, soil parameterizations implemented in current numerical weather prediction (NWP) model(s) were prepared almost 40 years ago, when NWP models worked with very poor resolution mesh. Since nowadays NWP works over domains of high resolution, these “old” schemes parameterization must be adequately revised. In this paper preliminary results of changes of parameterization of soil processes will be presented.

Interpretation of clearness day-ahead forecast errors using novel cloud classification

2021 ◽  
Author(s):  
Matthias Zech ◽  
Lueder von Bremen
Keyword(s):  
Solar Radiation ◽  
Water Content ◽  
Radiative Forcing ◽  
Weather Prediction ◽  
Climate Science ◽  
High Water ◽  
High Water Content ◽  
Forecast Errors ◽  
High Solar Radiation ◽  
Unsupervised Deep Learning

<p>        The formation and dissipation of clouds are one of the longest studied and yet least understood phenomenon in nature. This is crucial in atmospheric and climate science as clouds have a significant impact on radiative forcing. In numerical weather prediction, solar radiation forecasts have lower skill than other parameters as temperature forecasts despite recent progresses. This study aims at better understanding cloud situations over Europe and how solar radiation forecast errors are related to these situations. Therefore, an enhanced cloud class algorithm based on unsupervised Deep Learning and hierarchical clustering is introduced. By using the MODIS optical cloud thickness product, the algorithm is able to classify 14 different daily cloud situations which are applied on defined tile regions (approximately 70,000 km²) of Europe. These different classes differ in both optical cloud phase and the overall structure of the cloud shape. The usefulness of the cloud classes is illustrated by showing regional differences of cloud type frequencies over the last 20 years. To better understand solar radiation forecast errors, the cloud classes are assigned to ECMWF IFS clearness day-ahead forecast errors. We show that high-water content and mixed-cloud phase situations lead to highest absolute forecast errors for single sites. Summed up over an area, we observe an accumulation of forecast errors for mixed-cloud phase situations whereas for other cloud situations forecast errors are more likely to cancel each other out (e.g. broken high-water content clouds). This study is useful for researchers and practitioners to better understand situations of high solar radiation errors by using the developed cloud product.</p>

Convection is key to better polar low forecasts

2020 ◽  
Author(s):  
Matilda Hallerstig ◽  
Linus Magnusson ◽  
Erik Kolstad
Keyword(s):  
Prediction Models ◽  
Deterministic Model ◽  
Rapid Development ◽  
Weather Prediction ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Added Value ◽  
Grid Spacing ◽  
Polar Lows ◽  
Polar Low

<p>ECMWF HRES and Arome Arctic are the operational Numerical Weather Prediction models that forecasters in northern Norway use to predict Polar lows in the Nordic and Barents Seas. These type of lows are small, but intense mesoscale cyclones with strong, gusty winds and heavy snow showers. They cause hazards like icing, turbulence, high waves and avalanches that threaten offshore activity and coastal societies in the area. Due to their small size and rapid development, medium range global models with coarser resolutions such as ECMWF have not been able to represent them properly. This was only possible with short range high resolution regional models like Arome. When ECMWF introduced their new HRES deterministic model with 9 km grid spacing, the potential for more precise polar low forecasts increased. Here we use case studies and sensitivity tests to examine the ability of ECMWF HRES to represent polar lows. We also evaluate what added value the Arome Arctic model with 2.5 km grid spacing gives. For verification, we use coastal meteorological stations and scatterometer winds. We found that convection has a greater impact on model performance than horizontal resolution. We also see that Arome Arctic produces higher wind speeds than ECMWF HRES. To improve performance during polar lows for models with a horizontal grid spacing less than 10 km, it is therefore more important to improve the understanding and formulation of convective processes rather than simply increasing horizontal resolution.</p>

scholarly journals GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 215-222
Author(s):  
R. A. A. Flores
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Time Frame ◽  
Model Verification ◽  
Climate Type ◽  
Surface Precipitation ◽  
Numerical Weather ◽  
Geovisual Analytics ◽  
Nwp Model

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

scholarly journals A Fetch-Based Statistical Method to Bias Correct and Downscale Wind Speed over Unresolved Water Bodies

2017 ◽  
Vol 32 (4) ◽  
pp. 1637-1657 ◽  
Author(s):  
Bryan P. Holman ◽  
Steven M. Lazarus ◽  
Michael E. Splitt
Keyword(s):  
Wind Speed ◽  
Statistical Method ◽  
Bias Correction ◽  
Weather Prediction ◽  
Absolute Error ◽  
Water Bodies ◽  
Bias Error ◽  
Forecast Errors ◽  
Wind Fields ◽  
Water Characteristics

Abstract This paper presents a method to bias correct and downscale wind speed over water bodies that are unresolved by numerical weather prediction (NWP) models and analyses. The dependency of wind speeds over water bodies to fetch length is investigated as a predictor of model wind speed error. Because model bias is found to be related to the forecast wind direction, a statistical method that uses the forecast fetch to remove wind speed bias is developed and tested. The method estimates wind speed bias using recent forecast errors from similar stations (i.e., those with comparable fetch lengths). As a result, the bias correction is not tied to local observations but instead to locations with similar land–water characteristics. Thus, it can also be used to downscale wind fields over inland and coastal water bodies. The fetch method is compared to four reference bias correction methods using one year’s worth of wind speed output from three NWP analyses in Florida. The fetch method yields a bias error near zero and results in a reduction of the mean absolute error that is comparable to the reference methods. The fetch method is then used to bias correct and downscale a coarse analysis to 500-m grid spacing over a coastal estuary in central Florida.

Sign in / Sign up

Export Citation Format

Share Document