Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique

AbstractCollocated active and passive remote sensing measurements collected at U.S. Department of Energy Atmospheric Radiation Measurement Program sites enable simultaneous retrieval of cloud and precipitation properties and air motion. Previous studies indicate the parameters of a bimodal cloud particle size distribution can be effectively constrained using a combination of passive microwave radiometer and radar observations; however, aspects of the particle size distribution and particle shape are typically assumed to be known. In addition, many retrievals assume the observation and retrieval error statistics have Gaussian distributions and use least squares minimization techniques to find a solution. In truth, the retrieval error characteristics are largely unknown. Markov chain Monte Carlo (MCMC) methods can be used to produce a robust estimate of the probability distribution of a retrieved quantity that is nonlinearly related to the measurements and that has non-Gaussian error statistics. In this work, an MCMC algorithm is used to explore the error characteristics of cloud property retrievals from surface-based W-band radar and low-frequency microwave radiometer observations for a case of orographic snowfall. In this particular case, it is found that a combination of passive microwave radiometer measurements with radar reflectivity and Doppler velocity is sufficient to constrain the liquid and ice particle size distributions, but only if the width parameter of the assumed gamma particle size distribution and mass–dimensional relationships are specified. If the width parameter and mass–dimensional relationships are allowed to vary realistically, a unique retrieval of the liquid and ice particle size distribution for this orographic snowfall case is rendered far more problematic.

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Estimation Errors of Component Spectra Estimated by Means of the Concentration-Spectrum Correlation: Part II

Applied Spectroscopy ◽

10.1366/0003702894202300 ◽

1989 ◽

Vol 43 (5) ◽

pp. 855-860 ◽

Cited By ~ 2

Author(s):

Jun Uozumi ◽

Toshimitsu Asakura

Keyword(s):

Theoretical Analysis ◽

Computer Simulations ◽

Estimation Error ◽

Correlation Method ◽

Statistical Technique ◽

Weighting Factor ◽

Estimation Errors ◽

True Discrimination ◽

Spectrum Correlation ◽

Nonparametric Statistical

Estimation errors accompanying component spectra calculated by means of the concentration-spectrum correlation method are investigated by theoretical analysis and computer simulations. Discussion is concentrated on a modified version of the method, which operates under the constraint that the sum of all the component concentrations in a sample is unity. In an agreement similar to that for the basic method, which was treated in an earlier paper [Appl. Spectrosc. 43, 74 (1989)], the estimation error consists of a superposition of other component spectra, each multiplied by a weighting factor. In this case, however, the weighting factor is a function of five sample statistics: the averages and the standard deviations of the concentrations of both the objective and the interfering components, and the correlation coefficient of these two components. It is shown again that the nonparametric statistical technique called a bootstrap is useful as a tool of false-true discrimination of the peaks in the estimated spectra.

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CO2 spatial distribution over Mexican urban centers from satellite observations

10.5194/egusphere-egu21-12572 ◽

2021 ◽

Author(s):

Mixtli Campos-Pineda ◽

Noémie Taquet ◽

Wolfgang Stremme ◽

Alejandro Bezanilla ◽

Thomas Lauvaux ◽

...

Keyword(s):

Spatial Distribution ◽

Urban Areas ◽

Complex Terrain ◽

Active Volcano ◽

Pressure Data ◽

Urban Centers ◽

The Public ◽

Using Data ◽

Areas Of Interest ◽

Level 2

The Mexico City Metropolitan Area (MCMA), located in proximity to an active volcano, is the largest urban center in North America and there is great interest to better characterize carbon emissions of this and other major urban centers in the country. NASA&#8217;s Orbiting Carbon Observatory (OCO-3) was installed in the International Space (ISS) in 2019. The inclusion of a Pointing Mirror Assembly (PMA) in this third iteration allows for a new mode of data collection that samples an area of ~80 x 80 km in approximately 2 minutes. This mode is used to collect map-like data, called Snapshot Area Maps (SAMs), over areas of interest (e.g. volcanos or urban areas). The OCO-3 module has collected SAMs over the MCMA (and the Popocat&#233;petl volcano) throughout 2020, and also of the metropolitan areas of Guadalajara and Monterrey throughout the second half of 2020.Using data from the public release of OCO-3 Level 2 (L2) &#8220;Lite EarlyR&#8221; product, available at the Goddard Earth Sciences Data and Information Services Center (GES DISC), we have built maps of the spatial distribution of xCO2 for these regions. Data is filtered according to the reported quality flag in the data product, compared with ground-based FTIR measurements of column xCO2 over the MCMA region and averaged with an oversampling method. Surface pressure data with the averaged xCO2 is used to calculate the concentrations within the mixed layer (xCO2ML) in order to compensate for the effects of the complex terrain.&#160; This product is also used&#160; for comparison with CO spatial distributions obtained from TROPOMI data products and a simple xCOML/xCO2ML ratio is obtained and mapped for the three urban centers. This work showcases the utility of SAMs in cooperation with ground-based measurements to produce detailed descriptions of the spatial distribution of CO2 for a wide variety of applications, as well as the importance of frequent soundings over important emission sources around the world.

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Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service

10.5194/egusphere-egu2020-18636 ◽

2020 ◽

Author(s):

Giulia Panegrossi ◽

Paolo Sanò ◽

Leonardo Bagaglini ◽

Daniele Casella ◽

Elsa Cattani ◽

...

Keyword(s):

Neural Network ◽

Climate Change ◽

Satellite Observations ◽

Passive Microwave ◽

Monthly Precipitation ◽

Climate Data ◽

Data Record ◽

Level 3 ◽

Climate Data Record ◽

Level 2

Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1&#176;x1&#176; spatial grid in order to improve spatiotemporal satellite coverage of the globe.The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; San&#242; et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).Here, we present the status of the Level 2 product&#8217;s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product&#8217;s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.ReferencesAnderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003San&#242;, P., et al., 2015, DOI: 10.5194/amt-8-837-2015

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The ESA MIPAS/Envisat level2-v8 dataset: 10 years of measurements retrieved with ORM v8.22

Atmospheric Measurement Techniques ◽

10.5194/amt-14-7975-2021 ◽

2021 ◽

Vol 14 (12) ◽

pp. 7975-7998

Author(s):

Bianca Maria Dinelli ◽

Piera Raspollini ◽

Marco Gai ◽

Luca Sgheri ◽

Marco Ridolfi ◽

...

Keyword(s):

Degrees Of Freedom ◽

Michelson Interferometer ◽

European Space Agency ◽

Vertical Resolution ◽

Scientific Prototype ◽

Retrieval Error ◽

Trace Species ◽

Vertical Range ◽

Vertical Grid ◽

Level 2

Abstract. The observations acquired during the full mission of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, aboard the European Space Agency Environmental Satellite (Envisat), have been analysed with version 8.22 of the Optimised Retrieval Model (ORM), originally developed as the scientific prototype of the ESA level-2 processor for MIPAS observations. The results of the analyses have been included into the MIPAS level-2 version 8 (level2-v8) database containing atmospheric fields of pressure, temperature, and volume mixing ratio (VMR) of MIPAS main targets H2O, O3, HNO3, CH4, N2O, and NO2, along with the minor gases CFC-11, ClONO2, N2O5, CFC-12, COF2, CCl4, CF4, HCFC-22, C2H2, CH3Cl, COCl2, C2H6, OCS, and HDO. The database covers all the measurements acquired by MIPAS in the nominal measurement mode of the full resolution (FR) part of the mission (from July 2002 to March 2004) and all the observation modes of the optimised resolution (OR) part (from January 2005 to April 2012). The number of species included in the MIPAS level2-v8 dataset makes it of particular importance for the studies of stratospheric chemistry. The database is considered by ESA the final release of the MIPAS level-2 products. The ORM algorithm is operated at the vertical grid coincident to the tangent altitudes of the observations or to a subset of them, spanning (in the nominal mode) the altitude range from 6 to 68 km in the FR phase and from 6 to 70 km in the OR period. In the latitude domain, FR profiles are spaced by about 4.7∘, while the OR profiles are spaced by about 3.7∘. For each retrieved species, the auxiliary data and the retrieval choices are described. Each product is characterised in terms of the retrieval error, spatial resolution, and “useful” vertical range in both phases of the MIPAS mission. These depend on the characteristics of the measurements (spectral and vertical resolution of the measurements), the retrieval choices (number of spectral points included in the analyses, number of altitudes included in the vertical retrieval grid), and the information content of the measurements for each trace species. For temperature, water vapour, ozone, and nitric acid, the number of degrees of freedom is significantly larger in the OR phase than in the FR one, mainly due to the finer vertical measurement grid. In the FR phase, some trace species are characterised by a smaller retrieval error with respect to the OR phase, mainly due to the larger number of spectral points used in the analyses, along with the reduced vertical resolution. The way of handling possible caveats (negative VMR, vertical grid representation) is discussed. The quality of the retrieved profiles is assessed through four criteria, two providing information on the successful convergence of the retrieval iterations, one on the capability of the retrieval to reproduce the measurements, and one on the presence of outliers. An easy way to identify and filter the problematic profiles with the information contained in the output files is provided. MIPAS level2-v8 data are available to the scientific community through the ESA portal (https://doi.org/10.5270/EN1-c8hgqx4).

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The ESA MIPAS/ENVISAT Level2-v8 dataset: 10 years of measurements retrieved with ORM v8.22

10.5194/amt-2021-215 ◽

2021 ◽

Author(s):

Bianca Maria Dinelli ◽

Piera Raspollini ◽

Marco Gai ◽

Luca Sgheri ◽

Marco Ridolfi ◽

...

Keyword(s):

Michelson Interferometer ◽

European Space Agency ◽

Vertical Resolution ◽

Scientific Prototype ◽

Data Set ◽

Retrieval Error ◽

Trace Species ◽

Vertical Range ◽

Vertical Grid ◽

Level 2

Abstract. The observations acquired during the full mission of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, on board the European Space Agency ENVISAT satellite, have been analysed with version 8.22 of the Optimised Retrieval Model (ORM), originally developed as the scientific prototype of the ESA level 2 processor for MIPAS observations. The results of the analyses have been included into the MIPAS level 2 version 8 (level2-v8) database containing atmospheric fields of pressure, temperature and volume mixing ratio of MIPAS main targets H2O, O3, HNO3, CH4, N2O, and NO2, along with the minor gases CFC-11, ClONO2, N2O5, CFC-12, COF2, CCl4, CF4, HCFC-22, C2H2, CH3Cl, COCl2, C2H6, OCS, HDO. The database covers all the measurements acquired by MIPAS in the nominal measurement mode of the Full Resolution (FR) part of the mission (from July 2002 to March 2004) and all the observation modes of the Optimised Resolution (OR) part (from January 2005 to April 2012). The number of species included in the MIPAS level2-v8 data-set makes it of particular importance for the studies of stratospheric chemistry. The database is considered by ESA the final release of the MIPAS level 2 products. The ORM algorithm is operated at the vertical grid coincident to the tangent altitudes of the observations or to a subset of them, spanning (in the nominal mode) the altitude range from 6 to 68 km in the FR phase and from 6 to 70 km in the OR period. In the latitude domain, FR profiles are spaced by about 4.7 degrees while the OR profiles are spaced by about 3.7 degrees. For each retrieved species the auxiliary data and the retrieval choices are described. Each product is characterised in terms of the retrieval error, spatial resolution, and 'useful' vertical range in both phases of the MIPAS mission. These depend on the characteristics of the measurements (spectral and vertical resolution of the measurements), on the retrieval choices (number of spectral points included in the analyses, number of altitudes included in the vertical retrieval grid), and on the information content of the measurements for each trace species. For temperature, water vapour, ozone and nitric acid the number of degrees of freedom is significantly larger in the OR phase than in the FR one, mainly due to the finer vertical measurement grid. In the FR phase some trace species are characterised by a smaller retrieval error with respect to the OR phase, mainly due to the larger number of spectral points used in the analyses, along with the reduced vertical resolution. The way of handling possible caveats (negative VMR, vertical grid representation) is discussed. The quality of the retrieved profiles is assessed through four criteria, two providing information on the successful convergence of the retrieval iterations, one on the capability of the retrieval to reproduce the measurements, and one on the presence of outliers. An easy way to identify and filter the problematic profiles with the information contained in the output files is provided. MIPAS level2-v8 data are available to the scientific community through the ESA portal https://earth.esa.int/eogateway/.

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The Level 2 research product algorithms for the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES)

Atmospheric Measurement Techniques ◽

10.5194/amt-4-2105-2011 ◽

2011 ◽

Vol 4 (10) ◽

pp. 2105-2124 ◽

Cited By ~ 42

Author(s):

P. Baron ◽

J. Urban ◽

H. Sagawa ◽

J. Möller ◽

D. P. Murtagh ◽

...

Keyword(s):

Error Analysis ◽

Pressure Profile ◽

Calibration Procedure ◽

Submillimeter Wave ◽

Trace Gas ◽

Line Center ◽

Large Temperature ◽

High Signal ◽

Retrieval Error ◽

Level 2

Abstract. This paper describes the algorithms of the level-2 research (L2r) processing chain developed for the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES). The chain has been developed in parallel to the operational chain for conducting researches on calibration and retrieval algorithms. L2r chain products are available to the scientific community. The objective of version 2 is the retrieval of the vertical distribution of trace gases in the altitude range of 18–90 km. A theoretical error analysis is conducted to estimate the retrieval feasibility of key parameters of the processing: line-of-sight elevation tangent altitudes (or angles), temperature and ozone profiles. While pointing information is often retrieved from molecular oxygen lines, there is no oxygen line in the SMILES spectra, so the strong ozone line at 625.371 GHz has been chosen. The pointing parameters and the ozone profiles are retrieved from the line wings which are measured with high signal to noise ratio, whereas the temperature profile is retrieved from the optically thick line center. The main systematic component of the retrieval error was found to be the neglect of the non-linearity of the radiometric gain in the calibration procedure. This causes a temperature retrieval error of 5–10 K. Because of these large temperature errors, it is not possible to construct a reliable hydrostatic pressure profile. However, as a consequence of the retrieval of pointing parameters, pressure induced errors are significantly reduced if the retrieved trace gas profiles are represented on pressure levels instead of geometric altitude levels. Further, various setups of trace gas retrievals have been tested. The error analysis for the retrieved HOCl profile demonstrates that best results for inverting weak lines can be obtained by using narrow spectral windows.

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On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0293.1 ◽

2020 ◽

Vol 21 (6) ◽

pp. 1367-1381 ◽

Cited By ~ 3

Author(s):

Shruti A. Upadhyaya ◽

Pierre-Emmanuel Kirstetter ◽

Jonathan J. Gourley ◽

Robert J. Kuligowski

Keyword(s):

Complex Terrain ◽

Passive Microwave ◽

Calibration Data ◽

Estimation Errors ◽

Budget Analysis ◽

Stratiform Precipitation ◽

Precipitation Estimation ◽

Ground Validation ◽

Detection Score ◽

Systematic And Random Error

ABSTRACTThe launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

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