Estimating Contrail Climate Effects from Satellite Data

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate

A Long-Term Overshooting Convective Cloud Top Detection Database over Australia Derived from MTSAT Japanese Advanced Meteorological Imager Infrared Observations

A 10-yr geostationary (GEO) overshooting cloud-top (OT) detection database using Multifunction Transport Satellite (MTSAT) Japanese Advanced Meteorological Imager (JAMI) observations has been developed over the Australian region. GEO satellite imagers collect spatially and temporally detailed observations of deep convection, providing insight into the development and evolution of hazardous storms, particularly where surface observations of hazardous storms and deep convection are sparse and ground-based radar or lightning sensor networks are limited. Hazardous storms often produce one or more OTs that indicate the location of strong updrafts where weather hazards are typically concentrated, which can cause substantial impacts on the ground such as hail, damaging winds, tornadoes, and lightning and to aviation such as turbulence and in-flight icing. The 10-yr OT database produced using an automated OT detection algorithm is demonstrated for analysis of storm frequency, diurnally, spatially, and seasonally relative to known features such as the Australian monsoon, expected regions of hazardous storms along the southeastern coastal regions of southern Queensland and New South Wales, and the preferential extratropical cyclone track along the Indian Ocean and southern Australian coast. A filter based on atmospheric instability, deep-layer wind shear, and freezing level was used to identify OTs that could have produced hail. The filtered OT database is used to generate a hail frequency estimate that identifies a region extending from north of Brisbane to Sydney and the GoldfieldsEsperance region of eastern Western Australia as the most hail-prone regions

Toksyny oraz substancje toksyczne obecne w budynkach mieszkalnych - działanie, źródła oraz zapobieganie.

XXI wiek pełen jest informacji o zagrożeniach, na które narażeni są ludzie – smog, gluten, globalne ocieplenie, żywność nafaszerowana lekami, konflikty pomiędzy państwami czy terroryzm. Mimo to, bardzo rzadko poruszany jest temat toksyn oraz substancji toksycznych, na których działanie ludzie narażeni są każdego dnia we własnych domach i mieszkaniach. Celem niniejszej pracy jest omówienie kwestii źródeł oraz działania najpowszechniejszych z nich. W artykule opisano także metody minimalizacji ryzyka powstawania toksyn i substancji toksycznych oraz ekspozycji na te substancje

A CERES-Consistent Cloud Property Climate Data Record Using AVHRR Data: Version 1 Delivery Results

No abstract availabl

Fragmentation Increases Impact of Wind Disturbance on Forest Structure and Carbon Stocks in a Western Amazonian Landscape

Tropical second-growth forests could help mitigate climate change, but the degree to which their carbon potential is achieved will depend on exposure to disturbance. Wind disturbance is common in tropical forests, shaping structure, composition, and function, and influencing successional trajectories. However, little is known about the impacts of extreme winds in fragmented landscapes, though second-growth forests are often located in mosaics of forest, pasture, cropland, and other land cover types. Though indirect evidence suggests that fragmentation increases risk of wind damage, few studies have found such impacts following severe storms. In this study, we ask whether fragmentation and forest type (old vs. second growth) were associated with variation in wind damage after a severe convective storm in a fragmented production landscape in western Amazonia. We applied linear spectral unmixing to Landsat 8 imagery from before and after the storm, and combined it with field observations of damage to map wind effects on forest structure and biomass (Figure 4, 5). We also used Landsat 8 imagery to map land cover with the goals of identifying old- and second-growth forest and characterizing fragmentation. We used these data to assess variation in wind disturbance across 95,596 hectares of forest, distributed over 6,110 patches. We find that fragmentation is significantly associated with wind damage, with damage severity higher at forest edges and in edgier, more isolated patches (Figure 7). Damage was more severe in old-growth than in second-growth forests, but this effect was weaker than that of fragmentation (Figure 8). These results illustrate the importance of considering spatial configuration and landscape context in planning tropical forest restoration and predicting carbon sequestration in second-growth forests. Future research should address the mechanisms behind these results, to minimize wind damage risk in second-growth forests so their carbon potential can be maximally achieved

Comparison Between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports

Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-micrometer infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (less than 200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage

Detección automática de ascendentes intensas mediante imágenes satelitales y su relación con tiempo severo

Fil: Vidal, Luciano. Servicio Meteorológico Nacional. Dirección Nacional de Ciencia e Innovación en Productos y Servicios. Dirección de Productos de Modelación Ambiental y de Sensores Remotos; Argentina. Universidad de Buenos Aires.Numerosos estudios basados en datos de sensores remotos, especialmente de satélites, muestran que la frecuencia de convección húmeda profunda asociada con una variedad de fenómenos meteorológicos severos (ej., granizo grande, lluvias intensas, vientos dañinos, entre otros) es muy alta en la parte sur de Sudamérica, especialmente en Argentina. En presencia de tormentas severas, las imágenes satelitales generalmente presentan ciertas características o patrones espaciales muy útiles al momento de diagnosticar cuán probable es la ocurrencia de algún fenómeno severo como los mencionados, en el marco del proceso de elaboración de un alerta a muy corto plazo en una oficina operativa de pronóstico. Por ende, el desarrollo de algoritmos que ayuden a la detección automática de estos patrones ha sido foco de atención de numerosos investigadores alrededor del mundo. En particular, el grupo de investigación liderado por el Dr. Kristopher Bedka ha desarrollado un producto denominado "NASA LaRC Gridded Overshooting Cloud Top Detection", el cual, en el marco de un proyecto de colaboración con el Servicio Meteorológico Nacional de Argentina se tuvo acceso para realizar una primera evaluación en nuestra región para la estación cálida 2019-2020.Numerous studies based on remote sensing data, especially from satellites, show that the frequency of deep moist convection associated with a variety of severe weather events (e.g., large hail, heavy rain, damaging winds, etc.) is very high in the southern South America, especially in central and northern Argentina. In the presence of severe storms, satellite images generally present particular signatures or spatial patterns that are very useful when diagnosing how likely the occurrence of a severe phenomenon such as those mentioned is, within the framework of the process of preparing a very short-term alert in an operational forecasting office. Therefore, the development of algorithms that help to automatically detect these severe signatures has been the focus of attention of many researchers around the world. In particular, the research group led by Dr. Kristopher Bedka has developed a product called "NASA LaRC Gridded Overshooting Cloud Top Detection", which within the framework of a collaboration project with the National Meteorological Service of Argentina, was accessed to carry out a first evaluation in our region for the warm season 2019-2020

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe