scholarly journals Biogenic nitrogen oxide emissions from soils – impact on NO<sub>x</sub> and ozone over West Africa during AMMA (African Monsoon Multidisciplinary Experiment): modelling study

2008 ◽  
Vol 8 (9) ◽  
pp. 2351-2363 ◽  
Author(s):  
C. Delon ◽  
C. E. Reeves ◽  
D. J. Stewart ◽  
D. Serça ◽  
R. Dupont ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Nitrogen Oxide ◽  
Lower Troposphere ◽  
Environmental Parameters ◽  
Biogenic Emissions ◽  
Convective System ◽  
Sahel Region ◽  
The Impact ◽  
No Emissions

Abstract. Nitrogen oxide biogenic emissions from soils are driven by soil and environmental parameters. The relationship between these parameters and NO fluxes is highly non linear. A new algorithm, based on a neural network calculation, is used to reproduce the NO biogenic emissions linked to precipitations in the Sahel on the 6 August 2006 during the AMMA campaign. This algorithm has been coupled in the surface scheme of a coupled chemistry dynamics model (MesoNH Chemistry) to estimate the impact of the NO emissions on NOx and O3 formation in the lower troposphere for this particular episode. Four different simulations on the same domain and at the same period are compared: one with anthropogenic emissions only, one with soil NO emissions from a static inventory, at low time and space resolution, one with NO emissions from neural network, and one with NO from neural network plus lightning NOx. The influence of NOx from lightning is limited to the upper troposphere. The NO emission from soils calculated with neural network responds to changes in soil moisture giving enhanced emissions over the wetted soil, as observed by aircraft measurements after the passing of a convective system. The subsequent enhancement of NOx and ozone is limited to the lowest layers of the atmosphere in modelling, whereas measurements show higher concentrations above 1000 m. The neural network algorithm, applied in the Sahel region for one particular day of the wet season, allows an immediate response of fluxes to environmental parameters, unlike static emission inventories. Stewart et al (2008) is a companion paper to this one which looks at NOx and ozone concentrations in the boundary layer as measured on a research aircraft, examines how they vary with respect to the soil moisture, as indicated by surface temperature anomalies, and deduces NOx fluxes. In this current paper the model-derived results are compared to the observations and calculated fluxes presented by Stewart et al (2008).

scholarly journals Nitrogen Oxide biogenic emissions from soils: impact on NO<sub>x</sub> and ozone formation in West Africa during AMMA (African Monsoon Multidisciplinary Analysis)

2007 ◽  
Vol 7 (5) ◽  
pp. 15155-15188 ◽  
Author(s):  
C. Delon ◽  
C. E. Reeves ◽  
D. J. Stewart ◽  
D. Serça ◽  
R. Dupont ◽  
...  
Keyword(s):  
Neural Network ◽  
West Africa ◽  
Nitrogen Oxide ◽  
Lower Troposphere ◽  
Environmental Parameters ◽  
Biogenic Emissions ◽  
Convective System ◽  
Emissions Inventory ◽  
The Impact ◽  
No Emissions

Abstract. Nitrogen Oxide biogenic emissions from soils are driven by soil and environmental parameters. The relationship between these parameters and NO fluxes is highly non linear. A new algorithm, based on a neural network calculation, is used to reproduce the NO biogenic emissions in West Africa during the AMMA campaign, in August 2006. It has been coupled in the surface scheme of a coupled chemistry dynamics model to estimate the impact of the NO emissions on NOx and O3 formation in the lower troposphere. Four different simulations on the same domain and at the same period are compared: CTRL run (without soil NO emissions), YL95 run (with NO emissions inventory, at low time and space resolution), SOILNOx run (with NO emissions from neural network) and ALLNOx run (with NO from neural network). The influence of NOx from lightning is assessed, and is limited to the upper troposphere. Compared to parameterisations generally used at the global and regional scales, the neural network parameterisation can give higher NOx (up to +380 ppt) and ozone (up to +7ppb), closer to the ones measured in aircrafts during the AMMA field campaign. The NO emission from soils calculated with neural network responds to changes in soil moisture giving enhanced emissions over the wetted soil, as observed by aircraft measurements after the passing of a convective system, well reproduced by the model. Consecutive enhancement of NOx and ozone is limited to the lowest layers of the atmosphere in modelling, whereas measurements show higher levels above 500 m. This equation allows an immediate response of fluxes to environmental parameters, on the contrary to fixed emission inventories. The annual cycle of emissions from this algorithm will be simulated in a future work

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

Land–Atmosphere Interaction in Tropical Africa

2020 ◽  
Author(s):  
Cathy Hohenegger
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface State ◽  
Land Surface ◽  
Large Scale ◽  
Moisture Distribution ◽  
Sahel Region ◽  
The Impact

Even though many features of the vegetation and of the soil moisture distribution over Africa reflect its climatic zones, the land surface has the potential to feed back on the atmosphere and on the climate of Africa. The land surface and the atmosphere communicate via the surface energy budget. A particularly important control of the land surface, besides its control on albedo, is on the partitioning between sensible and latent heat flux. In a soil moisture-limited regime, for instance, an increase in soil moisture leads to an increase in latent heat flux at the expanse of the sensible heat flux. The result is a cooling and a moistening of the planetary boundary layer. On the one hand, this thermodynamically affects the atmosphere by altering the stability and the moisture content of the vertical column. Depending on the initial atmospheric profile, convection may be enhanced or suppressed. On the other hand, a confined perturbation of the surface state also has a dynamical imprint on the atmospheric flow by generating horizontal gradients in temperature and pressure. Such gradients spin up shallow circulations that affect the development of convection. Whereas the importance of such circulations for the triggering of convection over the Sahel region is well accepted and well understood, the effect of such circulations on precipitation amounts as well as on mature convective systems remains unclear. Likewise, the magnitude of the impact of large-scale perturbations of the land surface state on the large-scale circulation of the atmosphere, such as the West African monsoon, has long been debated. One key issue is that such interactions have been mainly investigated in general circulation models where the key involved processes have to rely on uncertain parameterizations, making a definite assessment difficult.

Impacts of Assimilating AMMA Soundings on ECMWF Analyses and Forecasts

2010 ◽  
Vol 25 (4) ◽  
pp. 1142-1160 ◽  
Author(s):  
Anna Agustí-Panareda ◽  
Anton Beljaars ◽  
Carla Cardinali ◽  
Iliana Genkova ◽  
Chris Thorncroft
Keyword(s):  
Monsoon Season ◽  
Lower Troposphere ◽  
African Region ◽  
Motion Vectors ◽  
African Easterly Jet ◽  
Model Biases ◽  
Multidisciplinary Analysis ◽  
3D Information ◽  
Sahel Region ◽  
The Impact

Abstract The field experiment of the African Monsoon Multidisciplinary Analysis (AMMA) project during the 2006 wet monsoon season provided an unprecedented amount of radiosonde/dropsonde data over the West African region. This paper explores the usage and impacts of this invaluable dataset in the European Centre for Medium-Range Weather Forecasts analyses and forecasts. These soundings are the only source of data that can provide 3D information on the thermodynamic and dynamic structures of the lower troposphere over continental West Africa. They are particularly important for the Sahel region located between 12° and 20°N, which is characterized by large gradients in temperature and moisture in the lower troposphere. An assimilation experiment comparison between the pre-AMMA and AMMA radiosonde networks shows that the extra AMMA soundings have a significant analysis impact on the low-level temperature over the Sahel and on the structure of the African easterly jet. However, the impacts of the extra AMMA data on the forecast disappear after 24 h. The soundings reveal large model biases in boundary layer temperature over the northern and eastern Sahel, which are consistent with the well-known model biases in cloud, rainfall, and radiation. Large analysis increments in temperature lead to increments in divergence and subsidence, which act to suppress convection. Thus, the analysis increments appear to have an undesirable feedback on the cloud and temperature model biases. The impact of the AMMA soundings on the African easterly jet is to enhance and extend the jet streak to 15°E, that is, toward the eastern part of the Sahel. No observations are assimilated east of 15°E at the level of the African easterly jet to support the jet enhancement farther east. Comparisons with independent atmospheric cloud motion vectors indicate that the African easterly jet in the analysis is too weak over this data-sparse region. This could have implications for the development of African easterly waves in the model forecast. Further experimentation by assimilating atmospheric motion vectors—currently not used—could address this problem.

scholarly journals Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali)

2015 ◽  
Vol 12 (11) ◽  
pp. 3253-3272 ◽  
Author(s):  
C. Delon ◽  
E. Mougin ◽  
D. Serça ◽  
M. Grippa ◽  
P. Hiernaux ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Litter Decomposition ◽  
Emission Model ◽  
No Emission ◽  
Yearly Average ◽  
No Release ◽  
Emission Fluxes ◽  
Few Data ◽  
Sahel Region ◽  
No Emissions

Abstract. This work is an attempt to provide seasonal variation of biogenic NO emission fluxes in a Sahelian rangeland in Mali (Agoufou, 15.34° N, 1.48° W) for years 2004, 2005, 2006, 2007 and 2008. Indeed, NO is one of the most important precursors for tropospheric ozone, and previous studies have shown that arid areas potentially display significant NO emissions (due to both biotic and abiotic processes). Previous campaigns in the Sahel suggest that the contribution of this region in emitting NO is no longer considered as negligible. However, very few data are available in this region, therefore this study focuses on model development. The link between NO production in the soil and NO release to the atmosphere is investigated in this modelling study, by taking into account vegetation litter production and degradation, microbial processes in the soil, emission fluxes, and environmental variables influencing these processes, using a coupled vegetation–litter decomposition–emission model. This model includes the Sahelian Transpiration Evaporation and Productivity (STEP) model for the simulation of herbaceous, tree leaf and faecal masses, the GENDEC model (GENeral DEComposition) for the simulation of the buried litter decomposition and microbial dynamics, and the NO emission model (NOFlux) for the simulation of the NO release to the atmosphere. Physical parameters (soil moisture and temperature, wind speed, sand percentage) which affect substrate diffusion and oxygen supply in the soil and influence the microbial activity, and biogeochemical parameters (pH and fertilization rate related to N content) are necessary to simulate the NO flux. The reliability of the simulated parameters is checked, in order to assess the robustness of the simulated NO flux. Simulated yearly average of NO flux ranges from 2.09 to 3.04 ng(N) m−2 s−1 (0.66 to 0.96 kg(N) ha−1 yr−1), and wet season average ranges from 3.36 to 5.48 ng(N) m−2 s−1 (1.06 to 1.73 kg(N) ha−1 yr−1). These results are of the same order as previous measurements made in several sites where the vegetation and the soil are comparable to the ones in Agoufou. This coupled vegetation–litter decomposition–emission model could be generalized at the scale of the Sahel region, and provide information where few data are available.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Vol 11 (11) ◽  
pp. 1334 ◽  
Author(s):  
Nemesio Rodríguez-Fernández ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

scholarly journals Soil Moisture Dynamics in Response to Precipitation and Thinning in a Semi-Dry Forest in Northern Mexico

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 105
Author(s):  
Argelia E. Rascón-Ramos ◽  
Martín Martínez-Salvador ◽  
Gabriel Sosa-Pérez ◽  
Federico Villarreal-Guerrero ◽  
Alfredo Pinedo-Alvarez ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Water Availability ◽  
Dry Forest ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Northern Mexico ◽  
The Difference ◽  
The Impact ◽  
Moisture Dynamics

Understanding soil moisture behavior in semi-dry forests is essential for evaluating the impact of forest management on water availability. The objective of the study was to analyze soil moisture based in storm observations in three micro-catchments (0.19, 0.20, and 0.27 ha) with similar tree densities, and subject to different thinning intensities in a semi-dry forest in Chihuahua, Mexico. Vegetation, soil characteristics, precipitation, and volumetric water content were measured before thinning (2018), and after 0%, 40%, and 80% thinning for each micro-catchment (2019). Soil moisture was low and relatively similar among the three micro-catchments in 2018 (mean = 8.5%), and only large rainfall events (>30 mm) increased soil moisture significantly (29–52%). After thinning, soil moisture was higher and significantly different among the micro-catchments only during small rainfall events (<10 mm), while a difference was not noted during large events. The difference before–after during small rainfall events was not significant for the control (0% thinning); whereas 40% and 80% thinning increased soil moisture significantly by 40% and 53%, respectively. Knowledge of the response of soil moisture as a result of thinning and rainfall characteristics has important implications, especially for evaluating the impact of forest management on water availability.

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