Bayesian Updating of Solar Panel Fragility Curves and Implications of Higher Panel Strength for Solar Generation Resilience

Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few studies have assessed its resilience to extreme events, and none have used empirical data to characterize the fragility of solar panels. This paper develops fragility functions for rooftop and ground-mounted solar panels calibrated with solar panel structural performance data in the Caribbean for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating hurricane wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground-mounted panels based on observations supplemented with existing numerical studies on solar panel vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane hazards in Miami-Dade, Florida, highlighting that panels perform below the code requirements, especially rooftop panels. We also illustrate that strength increases can improve the panels' structural performance effectively. However, strength increases by a factor of two still cannot meet the reliability stated in the code. Our results advocate reducing existing panel vulnerabilities to enhance resilience but also acknowledge that other strategies, e.g., using storage or deploying other generation sources, will likely be needed for energy security during storms.

Observing Profiles of Derived Kinematic Field Quantities Using a Network of Profiling Sites

Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely advection, vorticity, divergence, and deformation, can be obtained by applying Green’s Theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50 -80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well-suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely-spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.

Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation

Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses. The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellite columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources for anthropogenic methane (CH4) emissions in Europe. Here, we report on CH4 emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pair-wise upwind-downwind observations of the column-average dry-air mole fractions of CH4 (XCH4) by a network of four portable, ground-based, sun-viewing Fourier Transform Spectrometers of the type EM27/SUN operated during the CoMet campaign in May/June 2018. The EM27/SUN were deployed in the four cardinal directions around the USCB in approx. 50 km distance to the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips-Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad-hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the averaging kernels is essential to detected correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for the south-eastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 and 36 % dominated by the error contribution from uncertain wind fields.

Applying a Random Time Mapping to Mann modelled turbulence for the generation of intermittent wind fields

A new approach to derive a synthetic wind field model which combines spatial correlations from the Mann model and intermittency is introduced. The term intermittency describes the transition from Gaussian to non-Gaussian velocity increment statistics at small scales, where non-Gaussian velocity increment statistics imply a higher probability for extreme values than a Gaussian distribution. The presented new model is named the Time-mapped Mann model. The intermittency is introduced by applying a special random time-mapping procedure to the regular Mann model. The Time-mapping procedure is based on the so-called Continuous-time random walk model. As will be shown, the new Time-mapped Mann field reflects spatial correlations from the Mann model in the plane perpendicular to flow direction and temporal intermittency. In a first wind turbine study, the new Time-mapped Mann field and a regular Mann field are used as inflow to a wind turbine in a Blade Element Momentum simulation. It is shown that the wind field intermittency carries over to the loads of the wind turbine, and, thus, shows the importance of carefully modeling synthetic wind fields.

Role of tropical mid-latitude interaction in the genesis of a monsoon depression - A detailed case study

During northern summer, a monsoon stationary wave which maintains as part of its baroclinic structure three well-defined troughs, one each in the region of the Arabian sea, the Bay of Bengal and South China sea, frequently interacts with the mid-latitude baroclinic waves which amplify during their eastward passage with profound influence on the development of the monsoon troughs. The paper discusses the mechanism of this wave-wave interaction as suggested by the temporal evolution of the themla1 and wind fields associated with the waves and reports the findings of a detailed study of a case of tropical-mid latitude interaction in which the development of a monsoon trough led to the birth of a westward-propagating monsoon depression over South China.

Application of Sasaki's numerical variational technique to the analysis of height and wind fields over Indian region

An objective analysis method based on Sasaki's numerical variational analysis technique has been taken up for the analysis of geopotential height and wind over the Indian region. The univariate optimum interpolation (UOI) method is used to generate the initial or input fields. These fields are then adjusted by the variational method. A study of this method over Indian and adjoining region for 850, 700, 500, 300 and 200 hPa levels is made from 4 to 8 July 1979 and the analyses obtained using this method are compared with the FGGE analyses.

Classification of surface atmospheric pressure fields in the Laptev and East Siberian seas

The need for classifying surface atmospheric pressure fields over the Arctic seas arose as a method was being developed for predicting the characteristics of discontinuities (leads) in the sea ice cover. Wind, which is determined by the atmospheric pressure field, acts on the ice cover and causes it to drift. Leads are formed in the ice cover due to the irregularity of ice drift. Ice drift can be caused by several factors, such as skewed sea level, tidal waves and currents. However, the main cause of ice drift in the Arctic seas is wind. Each typical field of surface atmospheric pressure corresponds to a certain field of leads in the ice cover. This makes it possible to predict the characteristics of leads in the ice cover by selecting fields similar to predictive fields of atmospheric pressure based on archived data.The variety of atmospheric pressure fields makes it difficult to find an analogue to a given field by simply going through all the corresponding data available in the electronic archive. Classification of atmospheric pressure fields makes it possible to simplify the process of selecting an analogue.To develop the classification, we used daily surface pressure maps at 00 hours GMT for the cold seasons (from mid- October to the end of May) 2016–2021. The atmospheric pressure fields, which were similar in configuration, and hence the wind fields, belonged to the same type. In total, 27 types were identified, applicable both to the Laptev Sea and the East Siberian Sea. Within one type, a division into subtypes was made, depending on the speed of the geostrophic wind.The wind intensity was estimated by the number of isobars multiples of 5 mb on the surface atmospheric pressure map. All the surface pressure fields observed over the waters of the Laptev and East Siberian Seas over the past 5 years have been assigned to one of the types identified using cluster analysis. Each type of atmospheric pressure within the framework of the forecasting method being developed is supposed to correspond to a field of discontinuities in the ice cover.

Simulation of severe storms of tornadic intensity over Indo-Bangla region

Many severe thunderstorms of tornadic intensity were reported in the northwestern parts of Bangladesh during 30 August to 14 September, 2008. Two among them occurred at Nilphamari and Kurigram districts on 30th August, and at Nilphamari district on 3rd September. The tornadic storms are studied based on a field survey, surface data, radar and satellite observations and model simulations. Low level moisture influx by southerly flow from the Bay of Bengal coupled with an upper level westerly jet stream causing intense instability and shear in the wind fields triggered a series of storms for two weeks. The exact time and locations of the storms are investigated by using the hourly precipitation data retrieved from a S-band radar of Bangladesh Meteorological Department (BMD) located at Dhaka. Subsequently, the storms are simulated by using the WRF-ARW model on double nested domains at 9 and 3 km horizontal resolutions based on 6 hourly FNL analyses and boundary conditions of NCEP. Among the typical characteristics of the storms, the CAPE, Storm-Relative Environment Helicity (SREH), Bulk Richardson Number Shear (BRNSHR), dew point depression, and potential vorticity are studied. Results show that while there are differences of 2-3 hours between the observed and simulated time of the storms, the distances between observed and simulated locations of the storms are several tens of kilometers. The maximum CAPE is generally above 2400 J kg-1. The maximum amount of vorticity transferred by directional shear in the storm updraft (helicity) due to convective motion simulated by the model is 766 m2 sec-2, and the highest value of BRNSHR that define the region in which low-level mesocyclogenesis is more likely is 168 m2 sec-2 among the 2 cases, which is generally supposed to produce rotating storms according to the prescribed range.

A Systematic Study on Berthing Capacity Assessment of Sanya Yazhou Fishing Port by Typhoon Prediction Model

This paper sheds light on the effect of combination modes on the evaluation of berthing capacity for Sanya Yazhou Fishing Port (SYFP) under hypothetical typhoon conditions. By statistically analysing the maximum probability of moving speeds and directions of historical typhoons passing through the fishing port, the representative typhoon path was determined with the nonparametric regression method. The designed typhoon wind fields of levels 12–17 were generated based on Holland’s parametric wind model. Then, the MIKE 21 BW model was used to obtain the high-precision wave distribution in the fishing port. The boundary conditions (significant wave height and peak period) of the MIKE 21 BW model were calculated by combining the MIKE 21 SW model with the designed typhoon wind fields. In SYFP, ships usually adopt the modes of multi-ship side-by-side and single anchor mooring during typhoons. In fair weather, approximately 158 vessels can be berthed if they are all large ones, while approximately 735 vessels can be moored if they are all small ones. However, with an increase in typhoon levels, the anchoring area for small vessels decreases. From the perspective of wave distribution in the fishing port, the number of large vessels moored was hardly affected by typhoons. This can be attributed to the breakwater, which significantly decreases the large wave height in the fishing port. Finally, a study on the framework of a method for hazard assessment of berthing capacity in the coming typhoon-driven storm waves was set up.