Observed slump of sea land breeze in Brisbane under the effect of aerosols from remote transport during 2019 Australian mega fire events

The 2019 Australian mega fires were unprecedented considering their intensity and consistency. There has been much research on the environmental and ecological effects of these mega fires, most of which focused on the effect of huge aerosol loadings and the ecological devastation. Sea land breeze (SLB) is a regional thermodynamic circulation closely related to coastal pollution dispersion, yet few have looked into how it is influenced by different types of aerosols transported from either nearby or remote areas. Mega fires provide an optimal scenario of large aerosol emissions. Near the coastal site of Brisbane Archerfield during January 2020, when mega fires were the strongest, reanalysis data from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) showed that mega fires did release huge amounts of aerosols, making aerosol optical depth (AOD) of total aerosols, black carbon (BC) and organic carbon (OC) approximately 240 %, 425 % and 630 % of the averages in other non-fire years. Using 20 years' wind observations of hourly time resolution from a global observation network managed by the National Oceanic and Atmospheric Administration (NOAA), we found that the SLB day number during that month was only 4, accounting for 33.3 % of the multi-years' average. The land wind (LW) speed and sea wind (SW) speed also decreased by 22.3 % and 14.8 % compared with their averages respectively. Surprisingly, fire spot and fire radiative power (FRP) analysis showed that heating effects and aerosol emission of the nearby fire spots were not the main causes of the local SLB anomaly, while the remote transport of aerosols from the fire centre was mainly responsible for the decrease of SW, which was partially offset by the heating effect of nearby fire spots and the warming effect of long-range transported BC and CO2. The large-scale cooling effect of aerosols on sea surface temperature (SST) and the burst of BC contributed to the slump of LW. The remote transport of total aerosols was mainly caused by free diffusion, while the large-scale wind field played a secondary role at 500 m. The large-scale wind field played a more important role in aerosol transport at 3 km than at 500 m, especially for the gathered smoke, but free diffusion remained the major contributor. The decrease of SLB speed boosted the local accumulation of aerosols, thus making SLB speed decrease further, forming a positive feedback mechanism.

A Theory for Electromagnetic Radiation and Coupling

This is latest version of my theory. I have (1)revised the abstract and the Introduction section; (2) added a section for mutual coupling to show that EM radiation and EM mutual coupling are almost the same issue, including figures for mutual couplings, and detailed expressions for the mutual coupling energies; (3)added the real radiative power for the Hertzian dipole; (4) added example of a Yagi antenna; (5) added some detailed parameters for numerical implementation.

NORMALISASI DATA UNTUK EFISIENSI K-MEANS PADA PENGELOMPOKAN WILAYAH BERPOTENSI KEBAKARAN HUTAN DAN LAHAN BERDASARKAN SEBARAN TITIK PANAS

Kawasan indonesia merupakan bagian dari daerah tropis yang memiliki potensi kebakaran sangat tinggi terlebih pada musim kemarau, sehingga perlunya sebuah langkah kongkrit untuk dilakukan mitigasi supaya potensi-potensi kebakaran hutan itu menjadi terminimalisir. Untuk melakukan itu dibutuhkan suatu metode teknologi yang lebih mumpuni dan terbaru untuk memetakan wilayah-wilayah yang mempunyai potensi besar terjadinya kebakaran hutan. Sistem pencitraan dan Informasi dari sistem satelit (MODIS) adalah salah satu informasi tentang kondisi permukaan bumi, yaitu parameter Latitude, Longitude, Brightness, FRP (Fire Radiative Power), dan Confidence dapat dijadikan dasar pengelompokan suatu wilayah memiliki potensi kebakaran atau tidak. K-Means adalah salah satu metode dalam machine learning yang bisa digunakan sebagai salah satu metode dalam pengelompokan wilayah-wilayah tersebut. Akurasi dalam menguji hasil pengelompokan K-Means dapat diuji dengan metode Davies Bouldin Index (DBI) dan Silhouette Coefficient.

Finding Rare Quasars: VLA Snapshot Continuum Survey of FRI Quasar Candidates Selected from the LOFAR Two-Metre Sky Survey (LoTSS)

The radiative and jet power in active galactic nuclei is generated by accretion of material on to supermassive galactic-centre black holes. For quasars, where the radiative power is by definition very high, objects with high radio luminosities form ∼10 per cent of the population, although it is not clear whether this is a stable phase. Traditionally, quasars with high radio luminosities have been thought to present jets with edge-brightened morphology (Fanaroff-Riley II−FR II) due to the limitations of previous radio surveys (i.e., FRIs were not observed as part of the quasar population). The LOw Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) with its unprecedented sensitivity and resolution covering wide sky areas has enabled the first systematic selection and investigation of quasars with core-brightened morphology (Fanaroff-Riley I−FR). We carried out a Very Large Array (VLA) snapshot survey to reveal inner structures of jets in selected quasar candidates; 15 (25 per cent) out of 60 sources show clear inner jet structures that are diagnostic of FRI jets and 13 quasars (∼22 per cent) show extended structures similar to those of FRI jets. Black hole masses and Eddington ratios do not show a clear difference between FRI and FRII quasars. FRII quasars tend to have higher jet powers than FRI quasars. Our results show that the occurrence of FRI jets in powerful radiatively efficient systems is not common, probably mainly due to two factors: galaxy environment and jet power.

High-resolution smoke forecasting for the 2018 Camp Fire in California

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for two weeks, creating poor air quality in the “unhealthy” range for millions of people. The NOAA Global System Laboratory’s HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model grid at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors’ knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of US wildfires continues to increase in scope and magnitude.

CGHS Black Hole Analog Moving Mirror and Its Relativistic Quantum Information as Radiation Reaction

The Callan–Giddings–Harvey–Strominger black hole has a spectrum and temperature that correspond to an accelerated reflecting boundary condition in flat spacetime. The beta coefficients are identical to a moving mirror model, where the acceleration is exponential in laboratory time. The center of the black hole is modeled by the perfectly reflecting regularity condition that red-shifts the field modes, which is the source of the particle creation. In addition to computing the energy flux, we find the corresponding moving mirror parameter associated with the black hole mass and the cosmological constant in the gravitational analog system. Generalized to any mirror trajectory, we derive the self-force (Lorentz–Abraham–Dirac), consistently, expressing it and the Larmor power in connection with entanglement entropy, inviting an interpretation of acceleration radiation in terms of information flow. The mirror self-force and radiative power are applied to the particular CGHS black hole analog moving mirror, which reveals the physics of information at the horizon during asymptotic approach to thermal equilibrium.

ACCURACY ASSESSMENT OF FY-4A FIRE/HOTSPOT (FHS) PRODUCT IN WILDFIRE DETECTION

The Fengyun-4A (FY-4A) is a relatively new geostationary satellite launched by the National Satellite Meteorological Center of China in 2016. With its Advanced Geosynchronous Radiation Imager (AGRI) instrument, FY-4A was able to provide a Fire and Hotspot product (FHS). This study explored the use of the FHS product in detecting wildfires and was compared to the similar fire detection product of the Visible Infrared Imaging Radiometer Suite (VIIRS) with the goal of assessing its effectiveness in the early detection and monitoring of wildfires. The FY-4A FHS and the VIIRS fire detection products have spatial resolutions of 2 km and 375 m, and temporal resolutions of 15 minutes and 12 hours, respectively. The results of the comparative study showed that the FY-4A FHS product generated false negative results for detecting wildfires smaller than 20 pixels of VIIRS data (∼2.82 km2), at less than 4 MW of radiative power, and brightness temperature lower than 330 K. The FY-4A FHS product was also shown to be 50% accurate (1 correct and 1 false negative out of 2 samples) in detecting large wildfires (>2.5 km2) with high radiative power (>4 MW) and high brightness temperature (>330 K). Lower accuracy may also be attributed to the presence of clouds that tend to obscure satellite images leading to an even lower accuracy of wildfire detection. For future studies, it is recommended that a comparison of the FY-4A FHS product be made with a more similar instrument, for example, the Advanced Himawari Imager 8/9 (AHI 8/9). It is also recommended to improve the fire and hotspot algorithm by incorporating a Normalized Brightness Temperature Difference Index (NBTDI) or by incorporating diurnal temperature cycle modelling for the older FY-2G data. Lastly, if available, a more reliable accuracy assessment can be done using FHS products of higher spatial resolution (at least 500 m).

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Forecasting the regional fire radiative power for regularly ignited vegetation fires

This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.