Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

Abstract. The potential of the shortwave infrared channel of the atmospheric spectrometer SCIAMACHY on Envisat to provide accurate measurements of total atmospheric water vapour columns is explored. It is shown that good quality results can be obtained for cloud free scenes above the continents using the Iterative Maximum Likelihood Method. In addition to the standard cloud filter employed in this method, further cloud screening is obtained by comparing simultaneously retrieved methane columns with values expected from models. A novel method is used to correct for the scattering effects introduced in the spectra by the ice layer on the detector window. The retrieved water vapour total vertical columns for the period 2003–2007 are compared with spatially and temporally collocated values from the European Centre for Mid-Range Weather Forecast (ECMWF) data. The observed differences for individual measurements have standard deviations not higher than 0.3 g/cm2 and an absolute mean value smaller than 0.01 g/cm2 with some regional excursions. The use of recently published spectroscopic data for water vapour led to a significant improvement in the agreement of the retrieved water vapour total columns and the values derived from ECMWF data. This analysis thus supports the superior quality of the new spectroscopic information using atmospheric data.

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Validation of two independent retrievals of SCIAMACHY water vapour columns using radiosonde data

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-665-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 665-702 ◽

Cited By ~ 2

Author(s):

A. du Piesanie ◽

A. J. M. Piters ◽

I. Aben ◽

H. Schrijver ◽

P. Wang ◽

...

Keyword(s):

Water Vapour ◽

Correction Method ◽

Short Wave ◽

Mean Difference ◽

Likelihood Method ◽

Radiosonde Data ◽

Retrieval Algorithm ◽

Infrared Wavelength ◽

Integrated Water Vapour ◽

Iterative Maximum Likelihood

Abstract. Two independently derived SCIAMACHY total water vapour column (WVC) products are compared with integrated water vapour data calculated from radiosonde measurements, and with each other. The two SCIAMACHY WVC products are retrieved with two different retrieval algorithms applied in the visible and short wave infrared wavelength regions respectively. The first SCIAMACHY WVC product used in the comparison is ESA's level 2 version 5.01 WVC product derived with the Air Mass Corrected Differential Absorption Spectroscopy (AMC-DOAS) retrieval algorithm (SCIAMACHY-ESA). The second SCIAMACHY WVC product is derived using the Iterative Maximum Likelihood Method (IMLM) developed by Netherlands Institute for Space Research (SCIAMACHY-IMLM). Both SCIAMACHY WVC products are compared with collocated water vapour amounts determined from daily relative humidity radiosonde measurements obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) radiosonde network, over an 18 month and 2 yr period respectively. Results indicate a good agreement between the WVC amounts of SCIAMACHY-ESA and the radiosonde, and a mean difference of 0.03 g cm−2 is found for cloud free conditions. Overall the SCIAMACHY-ESA WVC amounts are smaller than the radiosonde WVC amounts, especially over oceans. For cloudy conditions the WVC bias has a clear dependence on the cloud top height and increases with increasing cloud top heights larger than approximately 2 km. A likely cause for this could be the different vertical profile shapes of water vapour and O2 leading to different relative changes in their optical thickness, which makes the AMF correction method used in the algorithm less suitable for high clouds. The SCIAMACHY-IMLM WVC amounts compare well to the radiosonde WVC amounts during cloud free conditions over land. A mean difference of 0.08 g cm−2 is found which is consistent with previous results when comparing daily averaged SCIAMACHY-IMLM WVC amounts with ECMWF model data globally. Furthermore, we show that the measurements for cloudy conditions (cloud fraction ≥ 0.5) with low clouds (cloud pressure ≥ 930 hPa) above the ocean and land compare quite well with radiosonde data.

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Statistical analyses and correlation between tropospheric temperature and humidity at Dome C, Antarctica

Antarctic Science ◽

10.1017/s0954102013000564 ◽

2013 ◽

Vol 26 (3) ◽

pp. 290-308 ◽

Cited By ~ 4

Author(s):

P. Ricaud ◽

F. Carminati ◽

Y. Courcoux ◽

A. Pellegrini ◽

J.-L. Attié ◽

...

Keyword(s):

Water Vapour ◽

Pearson Correlation ◽

Microwave Radiometer ◽

Weather Forecast ◽

Tropospheric Temperature ◽

Infrared Sensors ◽

Atmospheric Infrared Sounder ◽

Medium Range Weather Forecast ◽

Integrated Water Vapour ◽

Highly Correlated

AbstractThe Dome C (Concordia) station in Antarctica (75°06′S, 123°21′E, 3233 m above mean sea level) has a unique opportunity to test the quality of remote-sensing measurements and meteorological analyses because it is situated well inside the Eastern Antarctic Plateau and is less affected by local phenomena. Measurements of tropospheric temperature and water vapour (H2O) together with the integrated water vapour (IWV) performed in 2010 are statistically analysed to assess their quality and to study the yearly correlation between temperature and H2O over the entire troposphere. The statistical tools include yearly evolution, seasonally-averaged mean and bias, standard deviation and linear Pearson correlation. The datasets are made of measurements from the ground-based microwave radiometer H2O Antarctica Microwave Stratospheric and Tropospheric Radiometer (HAMSTRAD), radiosonde, in situ sensors, the space-borne infrared sensors Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A platform and the Atmospheric InfraRed Sounder (AIRS) on the Aqua platform, and the analyses from the European Centre for Medium-Range Weather Forecast (ECMWF). Despite some obvious biases within all these datasets, our study shows that temperature and IWV are generally measured with high quality whilst H2O measurement quality is slightly worse. The AIRS and IASI measurements do not have the vertical resolution to correctly probe the lowermost troposphere, whilst HAMSTRAD loses sensitivity in the upper troposphere-lower stratosphere. Within the entire troposphere over the whole year, it is found that the time evolution of temperature and H2O is highly correlated (> 0.8). This suggests that, in addition to the variability of solar radiation producing an obvious diurnal cycle in the planetary boundary layer in summer and an obvious seasonal cycle over the year, the H2O and temperature intra-seasonal variabilities are affected by the same processes, e.g. related to the long-range transport of air masses.

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Probabilistic observational method for estimating wall displacements in excavations

Canadian Geotechnical Journal ◽

10.1139/cgj-2013-0116 ◽

2014 ◽

Vol 51 (10) ◽

pp. 1111-1122 ◽

Cited By ~ 6

Author(s):

Shih-Hsuan Wu ◽

Jianye Ching ◽

Chang-Yu Ou

Keyword(s):

Bayesian Analysis ◽

Optimization Problem ◽

Maximum Likelihood Method ◽

Likelihood Method ◽

Observational Method ◽

Case Histories ◽

Estimation Errors ◽

Wall Displacement ◽

Novel Method ◽

Updating Procedure

In this study, a novel method for updating the probability distribution of the maximum wall displacement at the ith excavation stage ([Formula: see text]) based on the measurements at earlier stages is proposed. The main novelty of the proposed method is in the updating procedure, which incorporates the correlation among the estimation errors at various stages. This “stage correlation” is evident from a database of wall displacement data from 22 case histories. By incorporating the stage correlation, it is shown that the uncertainty in [Formula: see text] can be effectively reduced through a Bayesian analysis. Furthermore, the calculation steps for such updating can be easily implemented by practical engineers because these calculation steps involve only algebraic computations and chart checking. Sophisticated analyses, such as solving an optimization problem (required by the maximum likelihood method) and probabilistic analyses are not necessary because all of the Bayesian analysis results are summarized in the charts.

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Can turbulence within the field of view cause significant biases in radiative transfer modelling at the 183 GHz band?

10.5194/amt-2018-181 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xavier Calbet ◽

Niobe Peinado-Galan ◽

Sergio DeSouza-Machado ◽

Emil Robert Kursinski ◽

Pedro Oria ◽

...

Keyword(s):

Radiative Transfer ◽

Water Vapour ◽

Scale Up ◽

Field Of View ◽

Uniform Field ◽

Upper Troposphere ◽

Air Turbulence ◽

Water Vapour Absorption ◽

Novel Method ◽

Transfer Modelling

Abstract. The hypothesis whether turbulence within the passive microwave sounders field of view can cause significant biases in radiative transfer modelling at the 183 GHz water vapour absorption band is tested. A novel method to calculate the effects of turbulence in radiative transfer modelling is presented. It is shown that the turbulent nature of water vapour in the atmosphere can be a critical component of radiative transfer modelling in this band. Radiative transfer simulations are performed comparing a uniform field with a turbulent one. These comparisons show frequency dependent biases which can scale up to several Kelvin in brightness temperature. These biases can match experimentally observed biases, such as the ones reported in Brogniez et al. (2016). Our simulations show that those biases could be explained as an effect of high intensity turbulence in the upper troposphere. This kind of turbulence is common in clear air turbulence, storm or cumulus cloud situations.

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Potential of INSAT-3D sounder-derived total precipitable water product for weather forecast

Atmospheric Measurement Techniques ◽

10.5194/amt-11-6003-2018 ◽

2018 ◽

Vol 11 (11) ◽

pp. 6003-6012 ◽

Cited By ~ 2

Author(s):

Shailesh Parihar ◽

Ashim Kumar Mitra ◽

Mrutyunjay Mohapatra ◽

Rajjev Bhatla

Keyword(s):

Water Vapour ◽

Weather Forecasting ◽

Meteorological Data ◽

Processing System ◽

Correlation Coefficients ◽

India Meteorological Department ◽

Precipitable Water ◽

Weather Forecast ◽

Satellite System ◽

Total Precipitable Water

Abstract. The objectives of the INSAT-3D satellite are to enhance the meteorological observations and to monitor the Earth's surface for weather forecasting and disaster warning. One of the weather-monitoring capabilities of the INSAT-3D sounder is the estimation of water vapour in the atmosphere. The amount of water vapour present in the atmospheric column is derived as the total precipitable water (TPW) product from the infrared radiances measured by the INSAT-3D sounder. The present study is based on TPW derived from INSAT-3D sounder, radiosonde (RS) observations and the corresponding National Oceanic and Atmospheric Administration (NOAA) satellite. To assess retrieval performances of INSAT-3D sounder-derived TPW, RS TPW observations are considered for the validation from May to September 2016 from 34 stations belonging to the India Meteorological Department (IMD). The analysis is performed on daily, monthly, and subdivisional bases over the Indian region. The comparison of INSAT-3D TPW with RS TPW on daily and monthly bases shows that the root mean square error (RMSE) and correlation coefficients (CC) are ∼8 mm and 0.8, respectively. However, on subdivisional and overall scales, the RMSE found to be in the range of 1 to 2 mm and CC was around 0.9 in comparison with RS and NOAA. The spatial distribution of INSAT-3D TPW with actual rainfall observation is also investigated. In general, INSAT-3D TPW corresponds well with rainfall observation; however, it has found that heavy rainfall events occur in the presence of high TPW values. In addition, the cases of thunderstorm events were assessed using TPW from INSAT-3D and network of Global Navigation Satellite System (GNSS) receiver. This shows the good agreement between TPW from INSAT-3D and GNSS during the mesoscale activity. The improvement in the estimation of TPW is carried out by applying the GSICS calibration corrections (Global Space-based Inter-Calibration System) to the radiances from infrared (IR) channels of the sounder, which is used by IMDPS (INSAT Meteorological Data Processing System). The current TPW from INSAT-3D satellite can be utilized operationally for weather monitoring and forecast purposes. It can also offer substantial opportunities for improvement in nowcasting studies.

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Validation of two independent retrievals of SCIAMACHY water vapour columns using radiosonde data

Atmospheric Measurement Techniques ◽

10.5194/amt-6-2925-2013 ◽

2013 ◽

Vol 6 (10) ◽

pp. 2925-2940 ◽

Cited By ~ 13

Author(s):

A. du Piesanie ◽

A. J. M. Piters ◽

I. Aben ◽

H. Schrijver ◽

P. Wang ◽

...

Keyword(s):

Water Vapour ◽

Wavelength Range ◽

Short Wave ◽

Mean Difference ◽

Likelihood Method ◽

Radiosonde Data ◽

Air Mass ◽

Infrared Wavelength ◽

Short Wave Infrared ◽

Good Agreement

Abstract. Two independently derived SCIAMACHY total water vapour column (WVC) products are compared with integrated water vapour data calculated from radiosonde measurements, and with each other. The two SCIAMACHY WVC products are retrieved with two different retrieval algorithms applied in the visible and short-wave infrared wavelength regions respectively. The first SCIAMACHY WVC product used in the comparison is ESA's level 2 version 5.01 WVC product derived with the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) retrieval algorithm applied in the visible wavelength range (SCIAMACHY-ESA). The second SCIAMACHY WVC product is derived using the iterative maximum likelihood method (IMLM) in the short-wave infrared wavelength range and developed by Netherlands Institute for Space Research (SCIAMACHY-IMLM). Both SCIAMACHY WVC products are compared with collocated water vapour amounts determined from daily relative humidity radiosonde measurements obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) radiosonde network. The SCIAMACHY-ESA WVC product is compared with radiosonde-derived WVC amounts for an 18-month period from February 2010 to mid-August 2011, and the SCIAMACHY-IMLM WVC amounts are compared with radiosonde WVC amounts for the two individual years of 2004 and 2009. In addition the WVC amounts from SCIAMACHY-ESA and SCIAMACHY-IMLM are also compared with each other for a 1-month period for June 2009. The AMC-DOAS method used to retrieve SCIAMACHY-ESA WVC is able to correct for water vapour present below the clouds and can be used during cloudy conditions over both land and ocean surfaces. Results indicate a good agreement between the WVC amounts of SCIAMACHY-ESA and that of radiosondes, with a mean difference of −0.32 g cm−2 for all collocated cases. Overall the SCIAMACHY-ESA WVC amounts are smaller than the radiosonde WVC amounts, especially over oceans. For cloudy conditions the WVC bias has a clear dependence on the cloud top height and increases with increasing cloud top heights larger than approximately 2 km. A likely cause for this could be the different vertical profile shapes of water vapour and O2 leading to different relative changes in their optical thickness, which makes the air mass factor (AMF) correction method used in the algorithm less suitable for high clouds. The SCIAMACHY-IMLM product's water vapour measurements are best used over land surfaces during cloud-free conditions, and in these cases a good agreement is found when compared to radiosonde WVC amounts, with a mean difference of 0.08 g cm−2. It is shown that over ocean surfaces during cloudy conditions the partial SCIAMACHY-IMLM water vapour column above the cloud can be well estimated by using the simultaneously retrieved methane column to calculate the cloud top height. Comparing the two satellite WVC products with each other indicates that SCIAMACHY-ESA consistently measures higher WVC amounts than those of SCIAMACHY-IMLM. Furthermore, the importance of the choice of cloud product is highlighted, as intercomparisons between the two SCIAMACHY WVC products indicate that using different cloud products to screen water vapour data for cloud-free conditions influences the data selection and may ultimately lead to a variation in results. In the last section of the paper, various options for filtering the two SCIAMACHY WVC data sets are discussed and best selection criteria suggested.

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Error analysis for CO and CH<sub>4</sub> total column retrievals from SCIAMACHY 2.3 μm spectra

Atmospheric Chemistry and Physics ◽

10.5194/acp-8-3999-2008 ◽

2008 ◽

Vol 8 (14) ◽

pp. 3999-4017 ◽

Cited By ~ 51

Author(s):

A. M. S. Gloudemans ◽

H. Schrijver ◽

O. P. Hasekamp ◽

I. Aben

Keyword(s):

Sensitivity Analysis ◽

Likelihood Method ◽

Retrieval Algorithm ◽

Polarization Sensitivity ◽

Line Intensities ◽

Aerosol Scattering ◽

Detailed Assessment ◽

Instrument Noise ◽

Iterative Maximum Likelihood ◽

The Impact

Abstract. A detailed sensitivity analysis of the Iterative Maximum Likelihood Method (IMLM) algorithm and its application to the SCIAMACHY 2.3 μm spectra is presented. The sensitivity analysis includes a detailed assessment of the impact of aerosols in the 2.3 μm range. Results show that near strong aerosol sources mineral dust and biomass aerosols can have an effect of ~7–10% on the CH4 total columns retrieved from this wavelength range if aerosol scattering is neglected in the retrieval algorithm. Similar but somewhat larger effects are found for CO, but due to the larger variability of CO these errors are less important. Away from strong sources much smaller effects of a few percent are found. Using CH4 as a proxy for CO and/or including aerosol information in the retrieval algorithm significantly reduces these errors for both CO and CH4. Spectroscopic uncertainties are mostly negligible except for uncertainties in the CH4 intrinsic line intensities, which can be important. Application of the IMLM algorithm to the SCIAMACHY 2.3 μm spectra shows that the quality of the retrieved CO and CH4 total columns is good, except for a bias for large instrument-noise errors which is partly due to remaining calibration issues. Polarization sensitivity of the SCIAMACHY instrument has a negligible effect on the retrieved CO and CH4 total columns. The H2O total columns, which have to be retrieved simultaneously with CO and CH4 due to overlapping absorption lines, agree well with H2O total columns from ECMWF data. This ensures that the fit to the H2O absorptions is of sufficient quality not to hamper the retrieved CO and CH4 total columns from SCIAMACHY spectra.

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Proposition and Evaluation of a Novel Method Based on Videogrammetry to Measure Three-Dimensional Rib Motion during Breathing

Journal of Applied Biomechanics ◽

10.1123/jab.25.3.247 ◽

2009 ◽

Vol 25 (3) ◽

pp. 247-252 ◽

Cited By ~ 7

Author(s):

Karine Jacon Sarro ◽

Amanda Piaia Silvatti ◽

Andrea Aliverti ◽

Ricardo M. L. Barros

Keyword(s):

Magnetic Resonance ◽

Relative Motion ◽

Kinematic Analysis ◽

Healthy Subjects ◽

Three Dimensional ◽

Motion Artifact ◽

Mean Value ◽

Breathing Cycle ◽

Angular Variation ◽

Novel Method

A novel method based on kinematical analysis is proposed to describe the three-dimensional motion of the ribs during breathing. The three-dimensional coordinates of markers on the ribs and vertebrae were used to calculate the orientation of the ribs as a function of time. A test measured the relative motion between the markers and the ribs using magnetic resonance and the results revealed that the skin motion artifact found for the ribs (absolute mean value 3.9 mm) would induce maximum errors of 4° on rib motion calculation. The method identified a signal coherent with the breathing cycle for the angles of the ribs around the mediolateral axis and was also able to show differences between healthy nonathletes and swimmers, which presented greater angular variation of the ribs (p < .05). In conclusion, this study has shown the reliability of using three-dimensional kinematic analysis to evaluate the movement of the ribs during breathing as well as its potential to identify differences in the behavior of the rib motion in trained swimmers and untrained healthy subjects.

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SCIAMACHY CO over land and oceans: 2003–2007 interannual variability

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-3799-2009 ◽

2009 ◽

Vol 9 (11) ◽

pp. 3799-3813 ◽

Cited By ~ 40

Author(s):

A. M. S. Gloudemans ◽

A. T. J. de Laat ◽

H. Schrijver ◽

I. Aben ◽

J. F. Meirink ◽

...

Keyword(s):

Interannual Variability ◽

Biomass Burning ◽

Accurate Determination ◽

Likelihood Method ◽

Retrieval Algorithm ◽

Data Set ◽

Noise Error ◽

A Minor ◽

Iterative Maximum Likelihood ◽

Good Agreement

Abstract. We present a new method to obtain accurate SCIAMACHY CO columns over clouded ocean scenes. Based on an improved version of the Iterative Maximum Likelihood Method (IMLM) retrieval algorithm, we now have retrieved five years of data over both land and clouded ocean scenes between 2003 and 2007. The ocean-cloud method uses the CH4 columns retrieved simultaneously with the CO columns to determine the cloud top height. The CH4 cloud top height is in good agreement with the FRESCO+ cloud top height determined from UV-VIS oxygen-A band measurements, providing confidence that the CH4 cloud top height is a good diagnostic of the cloud top height over (partially) clouded ocean scenes. The CO measurements over clouded ocean scenes have been compared with collocated modeled CO columns over the same clouds and agree well. Using clouded ocean scenes quadruples the number of useful CO measurements compared to land-only measurements. The five-year CO data set over land and clouded ocean scenes presented here is based on an improved version of the IMLM algorithm which includes a more accurate determination of the random instrument-noise error for CO. This leads to a smaller spread in the differences between single CO measurements and the corresponding model values. The new version, IMLM version 7.4, also uses updated spectroscopic parameters for H2O and CH4 but this has only a minor impact on the retrieved CO columns. The five-year data set shows significant interannual variability over land and over clouded ocean scenes. Three examples are highlighted: the Asian outflow of pollution over the northern Pacific, the biomass-burning outflow over the Indian Ocean originating from Indonesia, and biomass burning in Brazil. In general there is good agreement between observed and modeled seasonal cycles and interannual variability.

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