Regional simulations of deep convection and biomass burning over South America: 1. Model evaluations using multiple satellite data sets

2011 ◽  
Vol 116 (D17) ◽  
Author(s):  
Longtao Wu ◽  
Hui Su ◽  
Jonathan H. Jiang
Keyword(s):  
South America ◽  
Biomass Burning ◽  
Satellite Data ◽  
Deep Convection ◽  
Data Sets

scholarly journals Model study of the cross-tropopause transport of biomass burning pollution

2007 ◽  
Vol 7 (14) ◽  
pp. 3713-3736 ◽  
Author(s):  
B. N. Duncan ◽  
S. E. Strahan ◽  
Y. Yoshida ◽  
S. D. Steenrod ◽  
N. Livesey
Keyword(s):  
Biomass Burning ◽  
El Niño ◽  
Fossil Fuels ◽  
Extreme Event ◽  
El Nino ◽  
Deep Convection ◽  
Tropical Tropopause Layer ◽  
Tropical Tropopause ◽  
El Niño Event ◽  
The Impact

Abstract. We present a modeling study of the troposphere-to-stratosphere transport (TST) of pollution from major biomass burning regions to the tropical upper troposphere and lower stratosphere (UT/LS). TST occurs predominately through 1) slow ascent in the tropical tropopause layer (TTL) to the LS and 2) quasi-horizontal exchange to the lowermost stratosphere (LMS). We show that biomass burning pollution regularly and significantly impacts the composition of the TTL, LS, and LMS. Carbon monoxide (CO) in the LS in our simulation and data from the Aura Microwave Limb Sounder (MLS) shows an annual oscillation in its composition that results from the interaction of an annual oscillation in slow ascent from the TTL to the LS and seasonal variations in sources, including a semi-annual oscillation in CO from biomass burning. The impacts of CO sources that peak when ascent is seasonally low are damped (e.g. Southern Hemisphere biomass burning) and vice-versa for sources that peak when ascent is seasonally high (e.g. extra-tropical fossil fuels). Interannual variation of CO in the UT/LS is caused primarily by year-to-year variations in biomass burning and the locations of deep convection. During our study period, 1994–1998, we find that the highest concentrations of CO in the UT/LS occurred during the strong 1997–1998 El Niño event for two reasons: i. tropical deep convection shifted to the eastern Pacific Ocean, closer to South American and African CO sources, and ii. emissions from Indonesian biomass burning were higher. This extreme event can be seen as an upper bound on the impact of biomass burning pollution on the UT/LS. We estimate that the 1997 Indonesian wildfires increased CO in the entire TTL and tropical LS (>60 mb) by more than 40% and 10%, respectively, for several months. Zonal mean ozone increased and the hydroxyl radical decreased by as much as 20%, increasing the lifetimes and, subsequently TST, of trace gases. Our results indicate that the impact of biomass burning pollution on the UT/LS is likely greatest during an El Niño event due to favorable dynamics and historically higher burning rates.

scholarly journals Double cores of the Ozone Low in the vertical direction over the Asian continent in satellite data sets

2019 ◽  
Vol 3 (2) ◽  
pp. 93-101 ◽  
Author(s):  
Zhou Tang ◽  
◽  
Dong Guo ◽  
YuCheng Su ◽  
ChunHua Shi ◽  
...  
Keyword(s):  
Satellite Data ◽  
Vertical Direction ◽  
Data Sets ◽  
Asian Continent

scholarly journals Sources of light-absorbing aerosol in arctic snow and their seasonal variation

2010 ◽  
Vol 10 (6) ◽  
pp. 13755-13796 ◽  
Author(s):  
D. A. Hegg ◽  
S. G. Warren ◽  
T. C. Grenfell ◽  
S. J. Doherty ◽  
A. D. Clarke
Keyword(s):  
Seasonal Variation ◽  
Biomass Burning ◽  
Chemical Constituents ◽  
Dominant Role ◽  
Data Sets ◽  
Source Attribution ◽  
Data Set ◽  
Pmf Model ◽  
The North ◽  
Absorbing Aerosols

Abstract. Two data sets consisting of measurements of light absorbing aerosols (LAA) in arctic snow together with suites of other corresponding chemical constituents are presented; the first from Siberia, Greenland and near the North Pole obtained in 2008, and the second from the Canadian arctic obtained in 2009. A preliminary differentiation of the LAA into black carbon (BC) and non-BC LAA is done. Source attribution of the light absorbing aerosols was done using a positive matrix factorization (PMF) model. Four sources were found for each data set (crop and grass burning, boreal biomass burning, pollution and marine). For both data sets, the crops and grass biomass burning was the main source of both LAA species, suggesting the non-BC LAA was brown carbon. Depth profiles at most of the sites allowed assessment of the seasonal variation in the source strengths. The biomass burning sources dominated in the spring but pollution played a more significant (though rarely dominant) role in the fall, winter and, for Greenland, summer. The PMF analysis is consistent with trajectory analysis and satellite fire maps.

scholarly journals Day-time identification of summer hailstorm cells from MSG data

2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Convection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Phase Method ◽  
Data Sets ◽  
Liquid Water Path ◽  
Two Phase ◽  
Logistic Regression Models ◽  
Ebro Valley

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

scholarly journals Long-range Transport of Aerosols from Biomass Burning over Southeastern South America and their Implications on Air Quality

2018 ◽  
Vol 18 (7) ◽  
pp. 1734-1745 ◽  
Author(s):  
Leila Droprinchinski Martins ◽  
Ricardo Hallak ◽  
Rafaela Cruz Alves ◽  
Daniela S. de Almeida ◽  
Rafaela Squizzato ◽  
...  
Keyword(s):  
Air Quality ◽  
South America ◽  
Long Range ◽  
Biomass Burning ◽  
Long Range Transport ◽  
Range Transport

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid scale physical parameterizations

2015 ◽  
Vol 15 (8) ◽  
pp. 11853-11888
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Deep Convection ◽  
Chemical Transport ◽  
Global Scale ◽  
Methane Emissions ◽  
Inverse Modelling ◽  
Chemical Transport Model ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modelling face new challenges. Indeed, the chemical transport model used to link the flux space with the mixing ratio space must be able to represent these different types of constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid scale physical parameterization errors on the global methane budget inferred by inverse modelling using the same inversion set-up but different physical parameterizations within one chemical-transport model. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer are tested. Different atmospheric methane datasets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid scale parameterizations. Inversions using satellite total-column retrieved by GOSAT satellite are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradient in methane emissions. At regional scale, the use of different sub-grid scale parameterizations induces uncertainties ranging from 1.2 (2.7%) to 9.4% (14.2%) of methane emissions in Africa and Eurasia Boreal respectively when using only surface measurements from the background (extended) surface network. When using only satellite data, we show that the small biases found in inversions using GOSAT-CH4 data and a coarser version of the transport model were actually masking a poor representation of the stratosphere–troposphere gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT-CH4 satellite data, which largely amplifies inconsistencies between surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relatively to physical parameterizations included in chemical-transport models.

scholarly journals Interannual variability of global biomass burning emissions from 1997 to 2004

2006 ◽  
Vol 6 (2) ◽  
pp. 3175-3226 ◽  
Author(s):  
G. R. van der Werf ◽  
J. T. Randerson ◽  
L. Giglio ◽  
G. J. Collatz ◽  
P. S. Kasibhatla ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Biomass Burning ◽  
Satellite Data ◽  
Net Primary Production ◽  
Terrestrial Ecosystems ◽  
Total Carbon ◽  
Burned Area ◽  
Biogeochemical Model ◽  
Fuel Wood ◽  
Fire Emissions

Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m burned area datasets from MODIS following the approach described by Giglio et al. (2005). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (Rh), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year−1 was fixed by plants, and approximately 95% of this was returned back to the atmosphere via Rh. Another 4%, or 2.5 Pg C year−1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year−1, with a maximum in 1998 (3.2 Pg C year−1) and a minimum in 2000 (2.0 Pg C year−1).

scholarly journals Inter-comparison of CO measurements from TROPOMI, ACE-FTS, and a high-Arctic ground-based FTS

2021 ◽  
Author(s):  
Tyler Wizenberg ◽  
Kimberly Strong ◽  
Kaley Walker ◽  
Erik Lutsch ◽  
Tobias Borsdorff ◽  
...  
Keyword(s):  
Satellite Data ◽  
Polar Region ◽  
High Arctic ◽  
Equatorial Region ◽  
The Arctic ◽  
Data Sets ◽  
Positive Bias ◽  
Clear Sky ◽  
Global Comparison ◽  
The Mean

Abstract. ACE/TROPOMI Abstract for AMT submission The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially-resolved (initially 7 × 7 km2, upgraded to 7 × 5.6 km2 in August 2019) global data set of CO columns, however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite inter-comparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier Transform Spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05° N, 86.42° W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 10 November 2017 to 31 May 2020 displays excellent agreement between the two data sets (R = 0.93), and a small relative bias of −0.68 ± 0.25 % (bias ± standard error). Additional comparisons were performed within five latitude bands; the north Polar region (60° N to 90° N), northern Mid-latitudes (20° N to 60° N), the Equatorial region (20° S to 20° N), southern Mid-latitudes (60° S to 20° S), and the south Polar region (90° S to 60° S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R = 0.93 (southern Mid-latitudes) to R = 0.85 (Equatorial region) between the CO products, but display a dependence of the mean differences on latitude. Positive mean biases of 7.92 ± 0.58 % and 7.98 ± 0.51 % were found in the northern and southern Polar regions, respectively, while a negative bias of −9.16 ± 0.55 % was observed in the Equatorial region. To investigate whether these differences are introduced by cloud contamination which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, and for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear+cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite data sets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R = 0.88), however a positive mean bias of 14.3 ± 0.16 % was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R = 0.82), and a mean positive bias of 9.83 ± 0.22 % in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all inter-comparisons in this work, as well as with recent ground-based validation efforts, suggesting that current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15 %, providing further confirmation that the data quality in these remote high-latitude regions meets this specification.

Satellite Remote Sensing of Cirrus

Cirrus ◽  
2002 ◽  
Author(s):  
Patrick Minnis
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Climate Models ◽  
Cirrus Clouds ◽  
Data Sets ◽  
Visible Radiation ◽  
Accurate Identification ◽  
Spatial And Temporal Scales ◽  
Sensing Systems ◽  
Particle Size And Shape

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.

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