Many fire weather index products have been developed to assist land managers, weather forecasters, and firefighters with anticipating weather conditions that may impact existing or potential new wildland fires in coming days. Most of these indices are designed to provide a single value for an entire 24-h period. Extreme wildfire activity in the western United States in recent years, including the impact of mesoscale and microscale phenomena such as thunderstorm gust frontal passages, radiative shading by dense smoke plumes, and pyrocumulonimbus development and collapse, as well as the advent of operational convection-allowing model forecasts, has highlighted the need for a more frequently updated index. In this study, we present a proof of concept for an hourly fire weather index developed specifically for application within a rapidly updating convection-allowing model. The index, termed the hourly wildfire potential (HWP), is developed based on observations of fire radiative power (FRP) from polar-orbiting satellites during large western U.S. wildfires in 2018 and 2020 and is evaluated against a merged dataset of FRP from polar-orbiting and geostationary satellites. The index is computed based on meteorological output from the NOAA operational High-Resolution Rapid Refresh (HRRR) model. The HWP index exhibits an improved representation of hourly FRP compared to a climatological approach and also shows promise for distinguishing between conditions associated with flaming and smoldering combustion. This work paves the way for improved prediction of wildfire smoke emissions in the coming hours and days.

Fire radiative power (FRP), which represents the instantaneous rate of radiative energy emitted by fires, has been successfully used to characterize fire events. A widely used FRP retrieval method is based on the midwave infrared (similar to 4 mu m) radiance difference between the fire pixel and the background. The METImage sensor onboard the new MetOp-Second Generation satellite missions is an advanced multispectral imaging radiometer covering 0.44 mu m to 13.34 mu m in wavelength. The 500 m spatial resolution measurements from METImage will enable fire detections at a higher sensitivity than the current medium resolution sensors flown on missions on the mid-morning polar orbit (e.g. Terra MODIS, Sentinel-3 SLSTR). However, METImage does not have a dedicated "fire" band and large fires are expected to trigger saturation in the 4 mu m band. In this study, we used a machine learning (ML) regression tree (RT) method and Visible Infrared Imaging Radiometer Suite (VIIRS) M-band data as a proxy of METImage to examine the potential and limitations of METImage FRP retrievals when band saturation occurs in the 4 mu m band. The approach is based on first estimating actual 4 mu m radiances for saturated measurements, followed by the traditional FRP retrieval using those estimated radiances. 13 VIIRS M-band daytime data, which have the corresponding METImage bands, and related geometry information were obtained from the large fire events in California (8021 samples in total). METImage band saturation levels were applied to the 13 VIIRS band radiances for those 8021 samples to generate the proxy METImage data. 4172 samples were saturated at the 4 mu m band which were used as the radiance ML RT model training and testing data. The developed radiance ML model was validated using independent fire sample data collected from USA and Australia. Results indicate that the radiance ML RT models can successfully predict the 4 mu m radiance for a wide range of conditions, with a 0.97 correlation coefficient between the predicted and the actual radiance and an average prediction error of 17.8 %. The derived saturated daytime FRPs varied based on the multispectral information, resulting in an average error of 21.7 % to predict reference VIIRS M-band FRP retrievals.

ABSTRACT The current generation of geostationary Earth-observing satellites provide spectral bandpass, spatial resolution and imaging frequency characteristics well suited to near-continuous active fire detection and monitoring. The earliest of these systems-SEVIRI on-board EUMETSAT’s MSG series-has operated since 2004, and more recently the capability has been expanded globally with the ABI on-board NOAA’s GOES-16 and GOES-17 satellites, and the AHI on-board JMA’s Himawari-8 and Himawari-9. At present, the NOAA and EUMETSAT operational geostationary active fire products are available based on two different algorithms: the Fire Detection and Characterization (FDC) product operating with data from GOES-16 and −17, and FRP-PIXEL active fire products from GOES, Himawari and MSG. We have conducted a comprehensive accuracy assessment of these geostationary fire products across two seasons (1 January–31 March 2020 and 1 July–30 September 2020), based on comparison to Landsat active fire detections made simultaneously (±5 minutes of geostationary overpass time) with the geostationary data. Compared to Landsat we find (i) low false alarm rates, ranging between 4%–7% (FDC) and 2%-6% (FRP-PIXEL)- depending on the season and hemispheric-disk for high confidence pixels, (ii) a reduction in this false alarm rate for FDC due to algorithm changes made since our prior (2018) validation effort (48% false alarms in summer 2018 compared to 4% in summer 2020 for high confidence pixels), and (iii) comparable active fire pixel detection rates for the FDC product (high confidence fire pixel classes only) and the matching FRP-PIXEL product (all fire pixel confidence classes). Overall, the performance of these geostationary products is shown to be strong and complementary in that the FRP-PIXEL product has fewer false alarms but a lower detection rate, whereas the FDC product detects more fire pixels but with a much higher false alarm rate.

Background. Accurately estimating burned area from satellites is key to improving biomass burning emission models, studying fire evolution and assessing environmental impacts. Previous studies have found that current methods for estimating burned area of fires from satellite active-fire data do not always provide an accurate estimate.Aims and methods. In this work, we develop a novel algorithm to estimate hourly accumulated burned area based on the area from boundaries of non-convex polygons containing the accumulated Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections. Hourly time series are created by combining VIIRS estimates with Fire Radiative Power (FRP) estimates from GOES-17 (Geostationary Operational Environmental Satellite) data.Conclusions, key results and implication. We evaluate the performance of the algorithm for both accumulated and change in burned area between airborne observations, and specifically examine sensitivity to the choice of the parameter controlling how much the boundary can shrink towards the interior of the area polygon. Results of the hourly accumulation of burned area for multiple fires from 2019 to 2020 generally correlate strongly with airborne infrared (IR) observations collected by the United States Forest Service National Infrared Operations (NIROPS), exhibiting correlation coefficient values usually greater than 0.95 and errors <20%.

Biomass burning influences atmospheric composition and regional air quality. The hourly biomass-burning emissions are usually required by air quality models, yet most available emission inventories provide daily or monthly estimates in 0.1 degrees or coarser grids, limiting the prediction accuracy. The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellites - R Series (GOES-R) observes fires across the conterminous United States (CONUS) every 5 min at a spatial resolution of 2 km, which allows for characterizing fires and emissions on diurnal scale. In this study, we developed a new operational algorithm to generate regional hourly 3 km fire emission across the CONUS by fusing temporally resolved ABI fire radiative power (FRP) and fine spatial-resolution (375 m) FRP from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) satellites. To do this, ABI FRP was first calibrated against and fused with VIIRS FRP in 3 km grids. Then, FRP diurnal cycles at an interval of 5 min were reconstructed using the fused ABI-VIIRS FRP and the land cover-ecoregion-specific FRP diurnal climatologies. The reconstructed FRP diurnal cycles were applied to estimate hourly emissions of eight species (e.g., carbon monoxide (CO) and fine particulate matter with di-ameters <2.5 mu m (PM2.5)). The accuracy was verified by comparing with CO observations from the TROPO-spheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite, and with PM2.5 emissions from eight other inventories. The results of the ABI-VIIRS estimates during one year from April 2020 to March 2021 indicate that fires burned 221 Tg dry matter and emit 2.25 Tg PM2.5 emissions across the CONUS. The seasonal and diurnal patterns of emissions vary with land cover types. The largest and smallest seasonal variations are shown in forest and agriculture fire emissions, respectively. The diurnal emission patterns of different land cover types share similar shapes but differ largely in magnitude. Moreover, the diurnal pattern of forest fire emissions suggests that emissions are dominated during daytime in the eastern U.S. but strong during both daytime and nighttime in the western U.S. The evaluation shows that the fused ABI-VIIRS based CO agrees well with the TROPOMI CO, with a difference of 11%. However, the agreement between fused ABI-VIIRS emissions and other inventories varies for different fire events.

Current operational weather satellites from the United States provide high temporal (GOES), spatial, spectral, and radiometric resolution data and derived products for fire detection and monitoring. These include GOES-16/17 for geostationary missions and Suomi NPP and NOAA-20 for low earth orbiting satellites. The Advanced Baseline Imager (ABI) on the latest geostationary satellites provide 16 channels, including a dedicated fire detection channel along with near-infrared channels which can aid with fire detection at night [1]. A similar sensor is currently being used on Himawari-8/9 satellites as well as on other geostationary satellites, offering similar capabilities as the GOES-East (16) and GOES-West (17) satellites.

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.

Landscape fire is a widespread, somewhat unpredictable phenomena that plays an important part in Earth's biogeochemical cycling. In many biomes worldwide fire also provides multiple ecological benefits, but in certain circumstances can also pose a risk to life and infrastructure, lead to net increases in atmospheric greenhouse gas concentrations, and to degradation in air quality and consequently human health. Accurate, timely and frequently updated information on landscape fire activity is essential to improve our understanding of the drivers and impacts of this form of biomass burning, as well as to aid fire management. This information can only be provided using satellite Earth Observation (EO) approaches, and remote sensing of active fire is one of the key techniques used. This form of EO is based on detecting the signature of the (mostly infrared) electromagnetic radiation emitted as biomass burns. Since the early 1980's, active fire (AF) remote sensing conducted using low Earth orbit (LEO) satellites has been deployed in certain regions of the world to map the location and timing of landscape fire occurrence, and from the early 2000's global-scale information updated multiple times per day has been easily available to all. Geostationary (GEO) satellites provide even higher frequency AF information, more than 100 times per day in some cases, and both LEO- and GEO-derived AF products now often include estimates of a fires characteristics, such as its fire radiative power (FRP) output, in addition to the fires detection. AF data provide information relevant to fire activity ongoing when the EO data were collected, and this can be delivered with very low latency times to support applications such as air quality forecasting. Here we summarize the history of achievements in the field of active fire remote sensing, review the physical basis of the approaches used, the nature of the AF detection and characterization techniques deployed, and highlight some of the key current capabilities and applications. Finally, we list some important developments we believe deserve focus in future years.

Imaging radiometers such as the advanced baseline imager and the advanced Himawari imager on the new generation geostationary environmental satellites provide new opportunities for several non-meteorological applications. Examples of three such applications in fire detection, monitoring atmospheric aerosols for air quality and modelling downwelling solar radiation for power plants are presented here.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a new generation global observing system designed to replace the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectro-radiometer (MODIS). It has been deployed onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite and the first Joint Polar Satellite System (JPSS-1, now renamed NOAA-20). SNPP VIIRS data have been used to derive global annual surface type (AST) products at the 1km resolution for 2012 and 2014-2017. These products use a 17-type global classification scheme developed by the International Geosphere Biosphere Programme (IGBP). Their overall accuracies varied from 77.6% to 78.6%, but the among-year differences were not statistically significant. New AST maps will be produced using future VIIRS observations, with an anticipated release of the 2018 product in late 2019. VIIRS AST products are available from ftp://ftp.star.nesdis.noaa.gov/pub/smcd/JPSS/VIIRS-AST.

The Joint Polar Satellite System (JPSS) atmospheric composition products are derived from an array of instruments aboard the JPSS satellites, and provide continued support for atmospheric concentrations of both greenhouse gases and aerosols in the context of air quality, furthering scientific understanding, improving environmental monitoring, and enhancing operational applications. This paper presents an outline of the JPSS atmospheric composition products that are operationally available from the JPSS Suomi National Polar-orbiting Partnership (S-NPP) suite of instruments. Experimental products currently under consideration and exploration, continuity and product upgrades for NOAA-20, future plans on collaborations, special data needs by user communities, and upgrades to the data products in support of near real time applications are included in this paper. Continued efforts towards reprocessing to generate mission-long high quality science products, adapting to enterprise algorithms, and providing calibrated radiances and geophysical data products via direct broadcast networks are discussed for user awareness and international collaborations.

In 1985, the National Science Foundation (NSF) founded NSFNet. It was higher education’s first Internet research-and-development backbone connecting faculty at lead research institutions on one coast to faculty at lead research institutions on the other coast. NSF soon provided funding for the development of regional networks, since the only way many institutions could afford connectivity to NSFNet was by collaborating with other institutions to create a critical mass of users and to share the cost of accessing NSFNet.

Satellite‐based active fire data are a viable tool to understand the role of global fires in the biosphere and atmosphere. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua and Terra satellites are nearing the end of their lives. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar‐orbiting Partnership satellite and the subsequent Joint Polar Satellite System series is expected to extend the MODIS active fire record. Thus, understanding the similarities of and discrepancies between the two data sets during their overlap period is important for existing applications. This study investigated the dependence of the MODIS and VIIRS fire characterization capabilities on satellite view zenith angle and the relationship between the two sensors' fire radiative power (FRP) from individual fire clusters to fire data on continental and global scales. The results indicate that the VIIRS fire characterization capability is similar across swath, whereas MODIS is strongly dependent on view zenith angle. Statistical analyses reveal that the VIIRS and MODIS FRP relationship varies between different spatial scales. In fire clusters, MODIS and VIIRS FRP estimates are very comparable, except for large boreal forest fires where VIIRS FRP is approximately 47% smaller. At the continental scale, the contemporaneous FRP retrievals from MODIS and VIIRS are generally comparable and strongly correlated, but VIIRS FRP is slightly larger and their differences vary across seasons. At global 1° × 1° grids, the FRP difference between the two sensors is, on average, approximately 20% in fire‐prone regions but varies significantly in fire‐limited regions.