scholarly journals A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

2019 ◽  
Vol 12 (7) ◽  
pp. 3943-3961 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapor ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information ◽  
Water Vapor Mixing Ratio

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

scholarly journals A practical method to remove a priori information from lidar optimal estimation method retrievals

2018 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapour ◽  
Grid Point ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information

Abstract. Lidar retrievals of atmospheric temperature and water vapour mixing ratio profiles using the Optimal Estimation Method (OEM) typically use a retrieval grid whose number of points is larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their a priori, which can affect the results in the higher altitudes of the temperature and water vapour profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval’s averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by transforming the retrieval from a fine grid to a coarser grid such that the averaging kernel is close to unity at each grid point. In this case, setting the a priori term in the OEM retrieval equation to zero minimizes the effect of the a priori for the coarse grid retrieval. We demonstrate the improvements gained by this technique for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

scholarly journals Ozone profile smoothness as a priori information in the inversion of limb measurements

2004 ◽  
Vol 22 (10) ◽  
pp. 3411-3420 ◽  
Author(s):  
V. F. Sofieva ◽  
J. Tamminen ◽  
H. Haario ◽  
E. Kyrölä ◽  
M. Lehtinen
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
A Priori Information ◽  
Ozone Profile ◽  
Stellar Occultation ◽  
Atmospheric Profiles ◽  
Priori Information ◽  
Typical Measurement

Abstract. In this work we discuss inclusion of a priori information about the smoothness of atmospheric profiles in inversion algorithms. The smoothness requirement can be formulated in the form of Tikhonov-type regularization, where the smoothness of atmospheric profiles is considered as a constraint or in the form of Bayesian optimal estimation (maximum a posteriori method, MAP), where the smoothness of profiles can be included as a priori information. We develop further two recently proposed retrieval methods. One of them - Tikhonov-type regularization according to the target resolution - develops the classical Tikhonov regularization. The second method - maximum a posteriori method with smoothness a priori - effectively combines the ideas of the classical MAP method and Tikhonov-type regularization. We discuss a grid-independent formulation for the proposed inversion methods, thus isolating the choice of calculation grid from the question of how strong the smoothing should be. The discussed approaches are applied to the problem of ozone profile retrieval from stellar occultation measurements by the GOMOS instrument on board the Envisat satellite. Realistic simulations for the typical measurement conditions with smoothness a priori information created from 10-years analysis of ozone sounding at Sodankylä and analysis of the total retrieval error illustrate the advantages of the proposed methods. The proposed methods are equally applicable to other profile retrieval problems from remote sensing measurements.

scholarly journals Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

2016 ◽  
Vol 9 (10) ◽  
pp. 5249-5263 ◽  
Author(s):  
Biyan Chen ◽  
Zhizhao Liu
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
A Priori ◽  
Atmospheric Water Vapor ◽  
Source Water ◽  
A Priori Information ◽  
Atmospheric Water ◽  
Source Data ◽  
Modeling Accuracy ◽  
Priori Information

Abstract. Acquiring accurate atmospheric water vapor spatial information remains one of the most challenging tasks in meteorology. The tomographic technique is a powerful tool for modeling atmospheric water vapor and monitoring the water vapor spatial and temporal distribution/variation information. This paper presents a study on the monitoring of water vapor variations using tomographic techniques based on multi-source water vapor data, including GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sun photometer and synoptic station measurements. An extensive investigation has been carried out using multi-source data collected from May to October 2013 in Hong Kong. With the use of radiosonde observed profiles, five different vertical a priori information schemes were designed and examined. Analysis results revealed that the best vertical constraint is to employ the average radiosonde profiles over the 3 days prior to the tomographic time and that the assimilation of multi-source data can increase the tomography modeling accuracy. Based on the best vertical a priori information scheme, comparisons of slant wet delay (SWD) measurements between GPS data and multi-observational tomography showed that the root mean square error (RMSE) of their differences is 10.85 mm. Multi-observational tomography achieved an accuracy of 7.13 mm km−1 when compared with radiosonde wet refractivity observations. The vertical layer tomographic modeling accuracy was also assessed using radiosonde water vapor profiles. An accuracy of 11.44 mm km−1 at the lowest layer (0–0.4 km) and an RMSE of 3.30 mm km−1 at the uppermost layer (7.5–8.5 km) were yielded. At last, a test of the tomographic modeling in a torrential storm occurring on 21–22 May 2013 in Hong Kong demonstrated that the tomographic modeling is very robust, even during severe precipitation conditions.

scholarly journals Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 1: Method and synthetic examples

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R805-R814 ◽  
Author(s):  
Zhen Xing ◽  
Alfredo Mazzotti
Keyword(s):  
Genetic Algorithm ◽  
Rayleigh Wave ◽  
A Priori ◽  
Computing Time ◽  
A Priori Information ◽  
Fine Grid ◽  
Near Surface ◽  
Reference Models ◽  
Full Waveform ◽  
Priori Information

When reliable a priori information is not available, it is difficult to correctly predict near-surface S-wave velocity models from Rayleigh waves through existing techniques, especially in the case of complex geology. To tackle this issue, we have developed a new method: two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI). Adopting a two-grid parameterization of the model, the genetic algorithm inverts for unknown velocities and densities at the nodes of a coarse grid, whereas the forward modeling is performed on a fine grid to avoid numerical dispersion. A bilinear interpolation brings the coarse-grid results into the fine-grid models. The coarse inversion grid allows for a significant reduction in the computing time required by the genetic algorithm to converge. With a coarser grid, there are fewer unknowns and less required computing time, at the expense of the model resolution. To further increase efficiency, our inversion code can perform the optimization using an offset-marching strategy and/or a frequency-marching strategy that can make use of different kinds of objective functions and allows for parallel computing. We illustrate the effect of our inversion method using three synthetic examples with rather complex near-surface models. Although no a priori information was used in all three tests, the long-wavelength structures of the reference models were fairly predicted, and satisfactory matches between “observed” and predicted data were achieved. The fair predictions of the reference models suggest that the final models estimated by our genetic-algorithm FWI, which we call macromodels, would be suitable inputs to gradient-based Rayleigh-wave FWI for further refinement. We also explored other issues related to the practical use of the method in different work and explored applications of the method to field data.

scholarly journals Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

2016 ◽  
Vol 9 (3) ◽  
pp. 909-928 ◽  
Author(s):  
Daniel Fisher ◽  
Caroline A. Poulsen ◽  
Gareth E. Thomas ◽  
Jan-Peter Muller
Keyword(s):  
A Priori ◽  
Single Layer ◽  
Optimal Estimation ◽  
Open Water ◽  
A Priori Information ◽  
Cloud Parameters ◽  
Cloud Properties ◽  
Priori Information ◽  
Cloud Parameter ◽  
The Impact

Abstract. In this paper we evaluate the impact on the cloud parameter retrievals of the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm following the inclusion of stereo-derived cloud top heights as a priori information. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use such independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The Along-Track Scanning Radiometer (AATSR) instrument has two views and three thermal channels, so it is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact of the stereo a priori information on the microphysical cloud properties of cloud optical thickness (COT) and effective radius (RE) was evaluated and generally found to be very small for single-layer clouds conditions over open water (mean RE differences of 2.2 (±5.9) microns and mean COD differences of 0.5 (±1.8) for single-layer ice clouds over open water at elevations of above 9 km, which are most strongly affected by the inclusion of the a priori).

scholarly journals Retrieval of temperature and water vapor profiles from radio occultation refractivity and bending angle measurements using an Optimal Estimation approach: a simulation study

2005 ◽  
Vol 5 (6) ◽  
pp. 1665-1677 ◽  
Author(s):  
A. von Engeln ◽  
G. Nedoluha
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
A Priori ◽  
Estimation Method ◽  
Lower Troposphere ◽  
Optimal Estimation ◽  
Pronounced Difference ◽  
Bending Angle ◽  
Advantages And Disadvantages ◽  
The Impact

Abstract. The Optimal Estimation Method is used to retrieve temperature and water vapor profiles from simulated radio occultation measurements in order to assess how different retrieval schemes may affect the assimilation of this data. High resolution ECMWF global fields are used by a state-of-the-art radio occultation simulator to provide quasi-realistic bending angle and refractivity profiles. Both types of profiles are used in the retrieval process to assess their advantages and disadvantages. The impact of the GPS measurement is expressed as an improvement over the a priori knowledge (taken from a 24h old analysis). Large improvements are found for temperature in the upper troposphere and lower stratosphere. Only very small improvements are found in the lower troposphere, where water vapor is present. Water vapor improvements are only significant between about 1 km to 7 km. No pronounced difference is found between retrievals based upon bending angles or refractivity. Results are compared to idealized retrievals, where the atmosphere is spherically symmetric and instrument noise is not included. Comparing idealized to quasi-realistic calculations shows that the main impact of a ray tracing algorithm can be expected for low latitude water vapor, where the horizontal variability is high. We also address the effect of altitude correlations in the temperature and water vapor. Overall, we find that water vapor and temperature retrievals using bending angle profiles are more CPU intensive than refractivity profiles, but that they do not provide significantly better results.

scholarly journals Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

2015 ◽  
Vol 8 (5) ◽  
pp. 5283-5327
Author(s):  
D. Fisher ◽  
C. A. Poulsen ◽  
G. E. Thomas ◽  
J.-P. Muller
Keyword(s):  
Cloud Model ◽  
A Priori ◽  
Single Layer ◽  
Optimal Estimation ◽  
A Priori Information ◽  
Cloud Parameters ◽  
Microphysical Properties ◽  
Priori Information ◽  
The Impact ◽  
High Clouds

Abstract. In this paper we evaluate the retrievals of cloud top height when stereo derived heights are combined with the radiometric cloud top heights retrieved from the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The AATSR instrument has two views and three thermal channels so is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact on the microphysical properties of the cloud such as optical depth and effective radius was evaluated and found to be very small with the biggest differences occurring over bright land surfaces and for high clouds. Overall the cost of the retrievals increased indicating a poorer radiative fit of radiances to the cloud model, which currently uses a single layer cloud model. Best results and improved fit to the radiances may be obtained in the future if a multi-layer model is used.

Joint interpretation and uncertainty analysis of petrophysical properties in unconventional shale reservoirs

2015 ◽  
Vol 3 (1) ◽  
pp. SA33-SA49 ◽  
Author(s):  
Qinshan Yang ◽  
Carlos Torres-Verdín
Keyword(s):  
Joint Inversion ◽  
A Priori ◽  
Estimation Method ◽  
Monte Carlo Algorithm ◽  
Posteriori Probability ◽  
Inversion Method ◽  
A Posteriori ◽  
A Priori Information ◽  
Core Data ◽  
Priori Information

Interpretation of hydrocarbon-bearing shale is subject to great uncertainty because of pervasive heterogeneity, thin beds, and incomplete and uncertain knowledge of saturation-porosity-resistivity models. We developed a stochastic joint-inversion method specifically developed to address the quantitative petrophysical interpretation of hydrocarbon-bearing shale. The method was based on the rapid and interactive numerical simulation of resistivity and nuclear logs. Instead of property values themselves, the estimation method delivered the a posteriori probability of each property. The Markov-chain Monte Carlo algorithm was used to sample the model space to quantify the a posteriori distribution of formation properties. Additionally, the new interpretation method allows the use of fit-for-purpose statistical correlations between water saturation, salt concentration, porosity, and electrical resistivity to implement uncertain, non-Archie resistivity models derived from core data, including those affected by total organic carbon (TOC). In the case of underdetermined estimation problems, i.e., when the number of measurements was lower than the number of unknowns, the use of a priori information enabled plausible results within prespecified petrophysical and compositional bounds. The developed stochastic interpretation technique was successfully verified with data acquired in the Barnett and Haynesville Shales. Core data (including X-ray diffraction data) were combined into a priori information for interpretation of nuclear and resistivity logs. Results consisted of mineral concentrations, TOC, and porosity together with their uncertainty. Eighty percent of the core data was located within the 95% credible interval of estimated mineral/fluid concentrations.

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