scholarly journals An Optimal Estimation Retrieval Algorithm for Microwave Humidity Sounding Channels with Minimal Scan Position Bias

2019 ◽  
Vol 36 (3) ◽  
pp. 409-425 ◽  
Author(s):  
Richard M. Schulte ◽  
Christian D. Kummerow
Keyword(s):  
Incidence Angle ◽  
Optimal Estimation ◽  
Precipitable Water ◽  
Forward Model ◽  
Retrieval Algorithm ◽  
Model Errors ◽  
Inversion Algorithm ◽  
Liquid Water Path ◽  
Small Satellites ◽  
Cloud Liquid Water Path

AbstractA flexible and physical optimal estimation-based inversion algorithm for retrieving atmospheric water vapor and cloud liquid water path from passive microwave radiometers over the global oceans is presented. The algorithm’s main strength lies in its ability to explicitly account for forward model errors that depend on the Earth incidence angle (EIA) at which a given radiometer measurement is made. Validation of total precipitable water (TPW) retrieved from Microwave Humidity Sounder (MHS) measurements against near-coincident estimates of TPW from SuomiNet GPS ground stations shows that retrieved TPW values agree closely with SuomiNet estimates, and somewhat better than values from the Microwave Integrated Retrieval System that are retrieved from the same MHS instruments. More importantly, it is found that the inclusion of appropriate forward model error assumptions, which are tailored to the EIA and sea surface temperature of the scene being considered, are able to almost entirely eliminate EIA-dependent biases in retrieved TPW. This result holds true across all satellites currently carrying an MHS instrument, despite the fact that only measurements from one satellite are used to estimate forward model errors. The consistency achieved by the retrieval algorithm across all view angles suggests that other inversion algorithms, particularly those for cross-track-scanning radiometers and potential future constellations of small satellites, would benefit from the inclusion of nuanced error assumptions that consider the effect of EIA.

scholarly journals A Passive Microwave Retrieval Algorithm with Minimal View-Angle Bias: Application to the TEMPEST-D CubeSat Mission

2020 ◽  
Vol 37 (2) ◽  
pp. 197-210
Author(s):  
Richard M. Schulte ◽  
Christian D. Kummerow ◽  
Wesley Berg ◽  
Steven C. Reising ◽  
Shannon T. Brown ◽  
...  
Keyword(s):  
Rapid Development ◽  
Passive Microwave ◽  
Forward Model ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Model Errors ◽  
Liquid Water Path ◽  
Water Path ◽  
The Tempest ◽  
Scan Pattern

AbstractThe rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite’s ground track.

Studies for the Odin sub-millimetre radiometer. II. Retrieval methodology

2002 ◽  
Vol 80 (4) ◽  
pp. 341-356 ◽  
Author(s):  
Ph. Baron ◽  
Ph. Ricaud ◽  
J de la Noë ◽  
J EP Eriksson ◽  
F Merino ◽  
...  
Keyword(s):  
Frequency Band ◽  
Estimation Method ◽  
High Sensitivity ◽  
Optimal Estimation ◽  
Forward Model ◽  
Model Errors ◽  
Target Species ◽  
Spectral Parameters ◽  
Chlorine Monoxide ◽  
Standard Code

This paper presents the first algorithm developed to retrieve atmospheric vertical profiles of trace gases from calibrated spectra measured by the sub-millimetre radiometer (SMR) onboard the Odin satellite. An estimation of atmospheric profiles is obtained by means of an inversion of the spectra using the Optimal Estimation Method. Great attention is paid to the study of the simultaneous retrieval of several species and nonlinearity effects. The measurement response is defined to give the altitude domain of a good retrieval. Main sources of measurement and forward model errors are characterized and separated into two categories: the fixed errors and the variable errors. We define a standard retrieval strategy that can be applied to theoretically investigate any frequency band of any observing Odin mode. For each frequency band, two categories of species are defined: the target species, i.e., the main species to be retrieved, and the interfering species, i.e., molecules emitting an interfering radiance in the observed band. The standard code is based upon an inversion of spectra using a linearized forward model and simultaneously estimates target species and interfering species. As an example, inversions of synthetic noise-free spectra of ozone and chlorine monoxide within an autocorrelator band ranging from 501.18 to 501.58 GHz are shown to behave as expected in the middle stratosphere and in the lower mesosphere. The error analysis shows retrieval limitations in the lower stratosphere that are mainly induced by the high sensitivity of the retrieval to parameters such as tangent height, accuracy in the vertical profile of the interfering species, and spectral parameters of both target lines and interfering lines. PACS Nos.: 42.68Ay, 07.07Df, 07.57Kp

scholarly journals Improved Retrieval of Cloud Liquid Water from CloudSat and MODIS

2016 ◽  
Vol 55 (8) ◽  
pp. 1831-1844 ◽  
Author(s):  
Jussi Leinonen ◽  
Matthew D. Lebsock ◽  
Graeme L. Stephens ◽  
Kentaroh Suzuki
Keyword(s):  
Liquid Water ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Radar Signal ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Cloud Optical Depth ◽  
Cloud Water Content ◽  
Most Significant Change

AbstractA revised version of the CloudSat–MODIS cloud liquid water retrieval algorithm is presented. The new algorithm, which combines measurements of radar reflectivity and cloud optical depth, addresses issues discovered in the current CloudSat–MODIS cloud water content (CWC) product. This current product is shown to be underconstrained by observations and to be too dependent on prior information incorporated into the Bayesian optimal-estimation algorithm. The most significant change made to the algorithm in this study was decreasing the number of independent variables to allow the observations to constrain the retrieved values better. The retrieval was also reformulated for improved compliance with the mathematical assumptions of the optimal-estimation algorithm. To validate the accuracy of the revised algorithm, the path-integrated attenuation (PIA) of the CloudSat radar signal was computed from the algorithm results. These modeled values were compared with independent measurements of the PIA that were obtained using a surface reference technique. This comparison shows that the cloud liquid water retrieved by the algorithm is close to being unbiased. The revised algorithm was also found to be an improvement over the current CloudSat CWC product and, to a lesser degree, the MODIS-derived cloud liquid water path.

scholarly journals The Atmospheric Radiation Measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

2013 ◽  
Vol 6 (2) ◽  
pp. 3723-3763 ◽  
Author(s):  
M. P. Cadeddu ◽  
J. C. Liljegren ◽  
D. D. Turner
Keyword(s):  
Scientific Community ◽  
Early Stage ◽  
Precipitable Water ◽  
Atmospheric Radiation ◽  
Radiation Measurement ◽  
Liquid Water Path ◽  
Atmospheric Radiation Measurement ◽  
Cloud Liquid Water Path ◽  
Microwave Radiometers ◽  
Microwave Instrumentation

Abstract. The Climate Research Facility of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program operates a network of ground-based microwave radiometers. Data and retrievals from these instruments have been available to the scientific community for almost 20 yr. In the past five years the network has been expanded to include a total of 22 microwave radiometers deployed in various locations around the world. The new instruments cover a frequency range between 22 and 197 GHz and are consistently and automatically calibrated. The latest addition to the network is a new generation of three-channel radiometers currently in the early stage of deployment at all ARM sites. The network has been specifically designed to achieve increased accuracy in the retrieval of precipitable water vapor (PWV) and cloud liquid water path (LWP) with the long-term goal of providing the scientific community with reliable, calibrated radiometric data and retrievals of important geophysical quantities with well-characterized uncertainties. The radiometers provide high-quality, continuous datasets that can be utilized in a wealth of applications and scientific studies. This paper presents an overview of the microwave instrumentation, calibration procedures, data, and retrievals that are available for download from the ARM data archive.

scholarly journals The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals

2013 ◽  
Vol 6 (9) ◽  
pp. 2359-2372 ◽  
Author(s):  
M. P. Cadeddu ◽  
J. C. Liljegren ◽  
D. D. Turner
Keyword(s):  
Scientific Community ◽  
Early Stage ◽  
Precipitable Water ◽  
Atmospheric Radiation ◽  
Radiation Measurement ◽  
Liquid Water Path ◽  
Atmospheric Radiation Measurement ◽  
Cloud Liquid Water Path ◽  
Microwave Radiometers ◽  
Microwave Instrumentation

Abstract. The Climate Research Facility of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program operates a network of ground-based microwave radiometers. Data and retrievals from these instruments have been available to the scientific community for almost 20 yr. In the past five years the network has expanded to include a total of 22 microwave radiometers deployed in various locations around the world. The new instruments cover a frequency range between 22 and 197 GHz and are consistently and automatically calibrated. The latest addition to the network is a new generation of three-channel radiometers, currently in the early stage of deployment at all ARM sites. The network has been specifically designed to achieve increased accuracy in the retrieval of precipitable water vapor (PWV) and cloud liquid water path (LWP) with the long-term goal of providing the scientific community with reliable, calibrated radiometric data and retrievals of important geophysical quantities with well-characterized uncertainties. The radiometers provide high-quality, continuous datasets that can be utilized in a wealth of applications and scientific studies. This paper presents an overview of the microwave instrumentation, calibration procedures, data, and retrievals that are available for download from the ARM data archive.

scholarly journals Cross-comparison of cloud liquid water path derived from observations by two space-borne and one ground-based instrument in northern Europe

2019 ◽  
Vol 12 (11) ◽  
pp. 5927-5946 ◽  
Author(s):  
Vladimir S. Kostsov ◽  
Anke Kniffka ◽  
Martin Stengel ◽  
Dmitry V. Ionov
Keyword(s):  
Liquid Water ◽  
Spatial Inhomogeneity ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Simple Estimate ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Geographical Regions ◽  
Temporal And Spatial ◽  
Cloud Liquid Water Path

Abstract. Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms using different observation methods and processing algorithms. Validation of LWP retrievals is a complicated task since a cloud cover is characterised by strong temporal and spatial variability while remote sensing methods have different temporal and spatial resolutions. An attempt has been made to compare and analyse the collocated LWP data delivered by two satellite instruments SEVIRI and AVHRR together with the data derived from microwave observations by the ground-based radiometer RPG-HATPRO. The geographical region of interest is the vicinity of St. Petersburg, Russia, where the RPG-HATPRO radiometer is operating. The study is focused on two problems. The first one is the so-called scale difference problem, which originates from dissimilar spatial resolutions of measurements. The second problem refers to the land–sea LWP gradient. The radiometric site is located 2.5 km from the coastline where the effects of the LWP gradient are pronounced. A good agreement of data obtained at the microwave radiometer location by all three instruments (HATPRO, SEVIRI, and AVHRR) during warm and cold seasons is demonstrated (the largest correlation coefficient 0.93 was detected for HATPRO and AVHRR datasets). The analysis showed no bias of the SEVIRI results with respect to HATPRO data and a large positive bias (0.013–0.017 kg m−2) of the AVHRR results for both warm and cold seasons. The analysis of LWP maps plotted on the basis of the SEVIRI and AVHRR measurements over land and water surfaces in the vicinity of St. Petersburg revealed the unexpectedly high LWP values delivered by AVHRR during the cold season over the Neva River bay and over the Saimaa Lake and the abnormal land–sea LWP gradient in these areas. For the detailed evaluation of atmospheric state and ice cover in the considered geographical regions during the periods of ground-based and satellite measurements, reanalysis data were used. It is shown that the most probable reason for the observed artefacts in the AVHRR measurements over water and ice surfaces is the coarse resolution of the land–sea and snow–ice masks used by the AVHRR retrieval algorithm. The influence of a cloud field inhomogeneity on the agreement between the satellite and the ground-based data is studied. For this purpose, the simple estimate of the LWP temporal variability is used as a measure of the spatial inhomogeneity. It has been demonstrated that both instruments are equally sensitive to the inhomogeneity of a cloud field despite the fact that they have different spatial resolutions.

The Remote Sensing of Clouds and Precipitation from Space: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3742-3765 ◽  
Author(s):  
Graeme L. Stephens ◽  
Christian D. Kummerow
Keyword(s):  
Forward Model ◽  
Model Parameters ◽  
Liquid Water Path ◽  
Error Characterization ◽  
Other Information ◽  
Future Data ◽  
Cloud Liquid Water Path ◽  
Main Components ◽  
Definition Of ◽  
Passive Measurements

Abstract This paper presents a critical review of a number of popular methods that have been developed to retrieve cloud and precipitation properties from satellite radiance measurements. The emphasis of the paper is on the retrieval uncertainties associated with these methods, as these shape future data assimilation applications, either in the form of direct radiance assimilation or assimilation of retrieved geophysical data, or even in the use of retrieved information as a source of model error characterization. It is demonstrated throughout the paper how cloud and precipitation observing systems developed around seemingly simple concepts are in fact very complex and largely underconstrained, which explains, in part, why assigning realistic errors to these properties has been so elusive in the past. Two primary sources of error that define the observing system are highlighted throughout: (i) the first source is errors associated with the identification of cloudy scenes from clear scenes and the identification of precipitation in cloudy scenes from nonprecipitating cloudy scenes. The problems of discriminating of cloud clear and cloud precipitation are illustrated using examples drawn from microwave cloud liquid water path and precipitation retrievals. (ii) The second source is errors introduced by the forward model and its related parameters. The forward model generally contains two main components: a model of the atmosphere and the cloud and precipitation structures imbedded in that atmosphere and a forward model of the radiative transfer that produces the synthetic measurement that is ultimately compared to the measurement. The vast majority of methods developed for deriving cloud and precipitation information from satellite measurements is highly sensitive to these model parameters, which merely reflects the underconstrained nature of the problem and the need for other information in deriving solutions. The cloud and precipitation retrieval examples presented in this paper are most often constructed around very unrealistic atmosphere models typically composed of just a few layers. The consequence is that the retrievals become too sensitive to the unobserved parameters of those layers and the atmosphere above and below. Clearly a better definition of the atmospheric state, and the vertical structure of clouds and precipitation, are needed to improve the information extracted from satellite observations, and it is for this reason that the combination of active and passive measurements offers much hope for improving cloud and precipitation retrievals.

Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI)

2014 ◽  
Vol 53 (3) ◽  
pp. 752-771 ◽  
Author(s):  
D. D. Turner ◽  
U. Löhnert
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Electromagnetic Spectrum ◽  
Slight Reduction ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Mean Square Errors

AbstractThe Atmospheric Emitted Radiance Interferometer (AERI) observes spectrally resolved downwelling radiance emitted by the atmosphere in the infrared portion of the electromagnetic spectrum. Profiles of temperature and water vapor, and cloud liquid water path and effective radius for a single liquid cloud layer, are retrieved using an optimal estimation–based physical retrieval algorithm from AERI-observed radiance data. This algorithm provides a full error covariance matrix for the solution, and both the degrees of freedom for signal and the Shannon information content. The algorithm is evaluated with both synthetic and real AERI observations. The AERI is shown to have approximately 85% and 70% of its information in the lowest 2 km of the atmosphere for temperature and water vapor profiles, respectively. In clear-sky situations, the mean bias errors with respect to the radiosonde profiles are less than 0.2 K and 0.3 g kg−1 for heights below 2 km for temperature and water vapor mixing ratio, respectively; the maximum root-mean-square errors are less than 1 K and 0.8 g kg−1. The errors in the retrieved profiles in cloudy situations are larger, due in part to the scattering contribution to the downwelling radiance that was not accounted for in the forward model. Scattering is largest in one of the spectral regions used in the retrieval, however, and removing this spectral region results in a slight reduction of the information content but a considerable improvement in the accuracy of the retrieved thermodynamic profiles.

On the Consequences of Resampling Microwave Radiometer Observations for Use in Retrieval Algorithms

2009 ◽  
Vol 48 (9) ◽  
pp. 1981-1993 ◽  
Author(s):  
Anita D. Rapp ◽  
M. Lebsock ◽  
C. Kummerow
Keyword(s):  
Wind Speed ◽  
Liquid Water ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Time Averaging ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Brightness Temperatures ◽  
Cloud Liquid Water Path

Abstract How to deal with the different spatial resolutions of multifrequency satellite microwave radiometer measurements is a common problem in retrievals of cloud properties and rainfall. Data convolution and deconvolution is a common approach to resampling the measurements to a single resolution. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements are resampled to the resolution of the 19-GHz field of view for use in a multifrequency optimal estimation retrieval algorithm of cloud liquid water path, total precipitable water, and wind speed. Resampling the TMI measurements is found to have a strong influence on retrievals of cloud liquid water path and a slight influence on wind speed. Beam-filling effects in the resampled brightness temperatures are shown to be responsible for the large differences between the retrievals using the TMI native resolution and resampled brightness temperatures. Synthetic retrievals are performed to test the sensitivity of the retrieved parameters to beam-filling effects in the resampling of each of the different channels. Beam-filling effects due to the convolution of the 85-GHz channels are shown to be the largest contributor to differences in retrieved cloud liquid water path. Differences in retrieved wind speeds are found to be a combination of effects from deconvolving the 10-GHz brightness temperatures and compensation effects due to the lower liquid water path being retrieved by the high-frequency channels. The influence of beam-filling effects on daily and monthly averages of cloud liquid water path is also explored. Results show that space–time averaging of cloud liquid water path cannot fully compensate for the beam-filling effects and should be considered when using cloud liquid water path data for validation or in climate studies.

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