scholarly journals Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

2018 ◽  
Vol 11 (1) ◽  
pp. 8 ◽  
Author(s):  
Gaëlle Veyssière ◽  
Fatima Karbou ◽  
Samuel Morin ◽  
Matthieu Lafaysse ◽  
Vincent Vionnet
Keyword(s):  
Numerical Simulations ◽  
Radar Data ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Radar Backscatter ◽  
French Alps ◽  
Snow Conditions ◽  
The Impact

This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km 2 ) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and the radiative transfer model Microwave Emission Model for Layered Snowpacks (MEMLS3&a), operating at a spatial resolution of 250-m. The simulations, without any bias correction, were evaluated against 141 Sentinel-1 synthetic aperture radar observation scenes with a resolution of 20 m over three snow seasons from October 2014 to June 2017. Results show that there is good agreement between observations and simulations under snow-free or dry snow conditions, consistent with the fact that dry snow is almost transparent at C-band. Under wet snow conditions, although the changes in time and space are well correlated, there is a significant deviation, up to 5 dB, between observations and simulations. The reasons for these discrepancies were explored, including a sensitivity analysis on the impact of the liquid water percolation scheme in Crocus. This study demonstrates the feasibility of performing end-to-end simulations of radar backscatter over extended geographical region. This makes it possible to envision data assimilation of radar data into snowpack models in the future, pending that deviations are mitigated, either through bias corrections or improved physical modeling of both snow properties and corresponding radar backscatter.

scholarly journals Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

scholarly journals Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

2015 ◽  
Vol 16 (3) ◽  
pp. 1109-1134 ◽  
Author(s):  
H. Lievens ◽  
A. Al Bitar ◽  
N. E. C. Verhoest ◽  
F. Cabot ◽  
G. J. M. De Lannoy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Land Surface ◽  
Microwave Emission ◽  
Cumulative Distribution ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
The Impact ◽  
Forward Operator

Abstract The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

scholarly journals Evaluation of a Microwave Emissivity Module for Snow Covered Area with CMEM in the ECMWF Integrated Forecasting System

2020 ◽  
Vol 12 (18) ◽  
pp. 2946
Author(s):  
Yoichi Hirahara ◽  
Patricia de Rosnay ◽  
Gabriele Arduini
Keyword(s):  
Radiative Transfer ◽  
Weather Prediction ◽  
Low Frequency ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Snow Covered Area ◽  
Covered Area ◽  
Forecasting System

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling.

scholarly journals Effects of Various parameters on Radiative Transfer Theory Based Microwave Emission Model

2017 ◽  
Vol 27 (1) ◽  
pp. 95-107
Author(s):  
Mazen E. Assiri Mazen E. Assiri
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Stem Volume ◽  
Transfer Model ◽  
Emission Model ◽  
Radiative Transfer Theory ◽  
Transfer Theory

This paper outlines research that is currently being carried out to model the interaction of electromagnetic radiation with earth and atmosphere. Among many others, passive microwave (PM) imagery represents a useful source of data for mapping Earth features. Since, signal of a microwave radiometer is composed of surface and atmospheric contributions, for proper interpretation of the data these effects should be quantified. This research presents analysis of radiative transfer model contributors, which include; the ground based parameters, forest area, water area, and meteorological parameters. The principal objective of this study is to analyze the degree to which brightness temperature can be affected by various earth and atmospheric features. A sensitivity analysis is performed to test the contributing effects of various parameters in radiative transfer theory based microwave emission model. The results of the study show that soil temperature and forest stem volume are the main contributing parameters in estimating brightness temperature values. The results further show that both the earthly located features and atmospheric parameters are important factors that must be taken into account in the development and application of radiative transfer theory based models

scholarly journals The impact of residential combustion emissions on atmospheric aerosol, human health, and climate

2016 ◽  
Vol 16 (2) ◽  
pp. 873-905 ◽  
Author(s):  
E. W. Butt ◽  
A. Rap ◽  
A. Schmidt ◽  
C. E. Scott ◽  
K. J. Pringle ◽  
...  
Keyword(s):  
Eastern Europe ◽  
Human Health ◽  
Atmospheric Aerosol ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
So2 Emissions ◽  
Residential Sector ◽  
Residential Combustion ◽  
Combustion Emissions ◽  
The Impact

Abstract. Combustion of fuels in the residential sector for cooking and heating results in the emission of aerosol and aerosol precursors impacting air quality, human health, and climate. Residential emissions are dominated by the combustion of solid fuels. We use a global aerosol microphysics model to simulate the impact of residential fuel combustion on atmospheric aerosol for the year 2000. The model underestimates black carbon (BC) and organic carbon (OC) mass concentrations observed over Asia, Eastern Europe, and Africa, with better prediction when carbonaceous emissions from the residential sector are doubled. Observed seasonal variability of BC and OC concentrations are better simulated when residential emissions include a seasonal cycle. The largest contributions of residential emissions to annual surface mean particulate matter (PM2.5) concentrations are simulated for East Asia, South Asia, and Eastern Europe. We use a concentration response function to estimate the human health impact due to long-term exposure to ambient PM2.5 from residential emissions. We estimate global annual excess adult (>  30 years of age) premature mortality (due to both cardiopulmonary disease and lung cancer) to be 308 000 (113 300–497 000, 5th to 95th percentile uncertainty range) for monthly varying residential emissions and 517 000 (192 000–827 000) when residential carbonaceous emissions are doubled. Mortality due to residential emissions is greatest in Asia, with China and India accounting for 50 % of simulated global excess mortality. Using an offline radiative transfer model we estimate that residential emissions exert a global annual mean direct radiative effect between −66 and +21 mW m−2, with sensitivity to the residential emission flux and the assumed ratio of BC, OC, and SO2 emissions. Residential emissions exert a global annual mean first aerosol indirect effect of between −52 and −16 mW m−2, which is sensitive to the assumed size distribution of carbonaceous emissions. Overall, our results demonstrate that reducing residential combustion emissions would have substantial benefits for human health through reductions in ambient PM2.5 concentrations.

scholarly journals Analysis of actinic flux profiles measured from an ozonesonde balloon

2015 ◽  
Vol 15 (8) ◽  
pp. 4131-4144 ◽  
Author(s):  
P. Wang ◽  
M. Allaart ◽  
W. H. Knap ◽  
P. Stammes
Keyword(s):  
Atmospheric Chemistry ◽  
Low Cost ◽  
Green Light ◽  
Single Layer ◽  
Radiative Transfer Model ◽  
Cloud Base ◽  
Transfer Model ◽  
Actinic Flux ◽  
Clear Sky ◽  
The Impact

Abstract. A green light sensor has been developed at KNMI to measure actinic flux profiles using an ozonesonde balloon. In total, 63 launches with ascending and descending profiles were performed between 2006 and 2010. The measured uncalibrated actinic flux profiles are analysed using the Doubling–Adding KNMI (DAK) radiative transfer model. Values of the cloud optical thickness (COT) along the flight track were taken from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Cloud Physical Properties (CPP) product. The impact of clouds on the actinic flux profile is evaluated on the basis of the cloud modification factor (CMF) at the cloud top and cloud base, which is the ratio between the actinic fluxes for cloudy and clear-sky scenes. The impact of clouds on the actinic flux is clearly detected: the largest enhancement occurs at the cloud top due to multiple scattering. The actinic flux decreases almost linearly from cloud top to cloud base. Above the cloud top the actinic flux also increases compared to clear-sky scenes. We find that clouds can increase the actinic flux to 2.3 times the clear-sky value at cloud top and decrease it to about 0.05 at cloud base. The relationship between CMF and COT agrees well with DAK simulations, except for a few outliers. Good agreement is found between the DAK-simulated actinic flux profiles and the observations for single-layer clouds in fully overcast scenes. The instrument is suitable for operational balloon measurements because of its simplicity and low cost. It is worth further developing the instrument and launching it together with atmospheric chemistry composition sensors.

scholarly journals Data-Driven Localization Mappings in Filtering the Monsoon–Hadley Multicloud Convective Flows

2018 ◽  
Vol 146 (4) ◽  
pp. 1197-1218
Author(s):  
Michèle De La Chevrotière ◽  
John Harlim
Keyword(s):  
Learning Algorithm ◽  
Data Driven ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Errors ◽  
Accurate Analysis ◽  
Perfect Model ◽  
Nonlinear Stochastic Model ◽  
Correlation Statistics ◽  
The Impact

This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature measurements is simulated using an idealized radiative transfer model and assimilated to the monsoon–Hadley multicloud model, a nonlinear stochastic model containing several thousands of model coordinates. A serial ensemble Kalman filter is implemented in which the empirical correlation statistics are improved using localization maps obtained from a supervised learning algorithm. The impact of the localization mappings is assessed in perfect-model observing system simulation experiments (OSSEs) as well as in the presence of model errors resulting from the misspecification of key convective closure parameters. In perfect-model OSSEs, the localization mappings that use adjacent correlations to improve the correlation estimated from small ensemble sizes produce robust accurate analysis estimates. In the presence of model error, the filter skills of the localization maps trained on perfect- and imperfect-model data are comparable.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

The impact of time-dependent stellar activity on the atmospheric chemistry and observability of exoplanets

2021 ◽  
Author(s):  
Amy Louca ◽  
Yamila Miguel ◽  
Shang-Min Tsai
Keyword(s):  
Atmospheric Chemistry ◽  
Emission Spectra ◽  
Time Dependent ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Stellar Spectrum ◽  
Stellar Spectra ◽  
Chemical Tracers ◽  
Atmospheric Escape ◽  
The Impact

<p class="p1">Observations of exoplanets used to characterize the chemistry and dynamics of atmospheres have developed considerably throughout the years. Nonetheless, it remains a difficult task to give a full and detailed description using solely observations. With future space missions such as JWST and ARIEL, both expected to be launched within this decade, it becomes even more crucial to be able to fully explain and predict the underlying chemistry and physics involved. In this research, we focus on modeling star-planet interactions by using synthetic flare spectra to predict chemical tracers for future missions. We make use of a chemical kinetics code that includes synthetic time-dependent stellar spectra and thermal atmospheric escape to simulate the atmospheres of known exoplanets. Using a radiative transfer model we then retrieve emission spectra. This ongoing study is focused on various known planetary systems of which the stellar spectrum has been obtained by the (mega-)MUSCLES collaboration. Preliminary results on these systems show that stellar flares and thermal escape can have a significant effect on the chemistry in atmospheres. </p>

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