Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar–Radiometer Retrievals

Abstract Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation–brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation–brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR)–TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm's database, a generally positive impact on latent heating estimation from passive microwave observations is expected.

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The GPM Operational Radiometer Algorithm - Changes for 2021

10.5194/egusphere-egu2020-1277 ◽

2020 ◽

Author(s):

Christian Kummerow ◽

Paula Brown

Keyword(s):

A Priori ◽

Microwave Radiometer ◽

The United States ◽

Passive Microwave ◽

Dual Frequency ◽

Brightness Temperatures ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Combined Algorithm

<p>The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States.&#160; GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar&#8217;s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.&#160; In May of 2017, GPM released Version 5 of its precipitation products starting with GMI and continuing with the constellation of radiometers.&#160; The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.&#160; Since this release, the Combined Algorithm hydrometeor profiles have shown good consistency with surface observations and computed brightness temperatures agree reasonably well with GMI observations in precipitating regions.&#160; The same is true for MIRS profiles in non-precipitating regions.&#160; Version 7 of the GPROF code will therefore make use of these operational products to construct it's a-priori databases.&#160; This will allow continuous improvements in the a-priori database as these operational products are reprocessed with newer versions, while allowing the user community to better focus on the algorithm&#8217;s error covariance matrix and its validation. &#160;Results from early versions of this algorithm will be presented.&#160; In addition to creating an a-priori database that can be more directly updated with improvement to the raining and non-raining scenes, GPROF is also undertaking steps to improve the orographic representation of snow and a Neural Network based Convective/Stratiform classification of precipitation that will both help improve instantaneous correlations with in-situ observations.</p>

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INFLUENCE OF REGULARITY OF SATELLITE MICROWAVE RADIOMETER MEASUREMENTS ON THE ACCURACY OF BRIGHTNESS TEMPERATURE REPRODUCTION IN AREAS OF THE TROPICAL CYCLONES

Meteorologiya i Gidrologiya ◽

10.52002/0130-2906-2021-4-78-85 ◽

2021 ◽

pp. 78-85

Author(s):

А. G. Grankov ◽

◽

А. А. Milshin ◽

Keyword(s):

Brightness Temperature ◽

Tropical Cyclones ◽

Microwave Radiometer ◽

Measurement Data ◽

Hurricane Wilma ◽

Brightness Temperatures ◽

Atmosphere System ◽

Ocean Atmosphere ◽

The Caribbean ◽

Satellite Microwave

An accuracy of reproduction of daily variations in the ocean–atmosphere system brightness temperature in the areas of development and movement of tropical hurricanes in the Caribbean Sea and Gulf of Mexico is analyzed. The analysis is based on the data of single and group satellite microwave radiometer measurements. The results are obtained using archival measurement data of SSM/I radiometers from the F11, F13, F14, and F15 DMSP satellites during the period of existence of tropical hurricanes Bret and Wilma. An example is given to demonstrate the use of daily brightness temperatures obtained from DMSP satellites for monitoring the development and propagation of hurricane Wilma.

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Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

Annals of Glaciology ◽

10.3189/s0260305500012726 ◽

1993 ◽

Vol 17 ◽

pp. 131-136 ◽

Cited By ~ 8

Author(s):

Kenneth C. Jezek ◽

Carolyn J. Merry ◽

Don J. Cavalieri

Keyword(s):

Brightness Temperature ◽

Microwave Radiometer ◽

Correlated Data ◽

Data Sets ◽

Microwave Brightness Temperature ◽

Brightness Temperatures ◽

Scatter Plots ◽

Microwave Imager ◽

Highly Correlated ◽

The Antarctic

Spaceborne data are becoming sufficiently extensive spatially and sufficiently lengthy over time to provide important gauges of global change. There is a potentially long record of microwave brightness temperature from NASA's Scanning Multichannel Microwave Radiometer (SMMR), followed by the Navy's Special Sensor Microwave Imager (SSM/I). Thus it is natural to combine data from successive satellite programs into a single, long record. To do this, we compare brightness temperature data collected during the brief overlap period (7 July-20 August 1987) of SMMR and SSM/I. Only data collected over the Antarctic ice sheet are used to limit spatial and temporal complications associated with the open ocean and sea ice. Linear regressions are computed from scatter plots of complementary pairs of channels from each sensor revealing highly correlated data sets, supporting the argument that there are important relative calibration differences between the two instruments. The calibration scheme was applied to a set of average monthly brightness temperatures for a sector of East Antarctica.

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A procedure for meaningful unsupervised clustering and its application for solvent classification

Open Chemistry ◽

10.2478/s11532-014-0514-6 ◽

2014 ◽

Vol 12 (5) ◽

pp. 594-603 ◽

Cited By ~ 2

Author(s):

Yaroslava Pushkarova ◽

Yuriy Kholin

Keyword(s):

Neural Networks ◽

A Priori ◽

Initial Distribution ◽

C60 Fullerene ◽

Classification Problems ◽

Probabilistic Networks ◽

Reliable Classification ◽

Priori Information ◽

Number Of Classes

AbstractArtificial neural networks have proven to be a powerful tool for solving classification problems. Some difficulties still need to be overcome for their successful application to chemical data. The use of supervised neural networks implies the initial distribution of patterns between the pre-determined classes, while attribution of objects to the classes may be uncertain. Unsupervised neural networks are free from this problem, but do not always reveal the real structure of data. Classification algorithms which do not require a priori information about the distribution of patterns between the pre-determined classes and provide meaningful results are of special interest. This paper presents an approach based on the combination of Kohonen and probabilistic networks which enables the determination of the number of classes and the reliable classification of objects. This is illustrated for a set of 76 solvents based on nine characteristics. The resulting classification is chemically interpretable. The approach proved to be also applicable in a different field, namely in examining the solubility of C60 fullerene. The solvents belonging to the same group demonstrate similar abilities to dissolve C60. This makes it possible to estimate the solubility of fullerenes in solvents for which there are no experimental data

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Passive Microwave Melt Onset Retrieval Based on a Variable Threshold: Assessment in the Canadian Arctic Archipelago

Remote Sensing ◽

10.3390/rs11111304 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1304 ◽

Cited By ~ 2

Author(s):

Stephen Marshall ◽

K. Andrea Scott ◽

Randall K. Scharien

Keyword(s):

Brightness Temperature ◽

Spatial Resolution ◽

Microwave Radiometer ◽

Canadian Arctic ◽

Surface Air Temperature ◽

Remote Sensing Data ◽

Passive Microwave ◽

Canadian Arctic Archipelago ◽

Variable Threshold ◽

Resolution Data

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

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A 1D-Var Approach to Retrieve Clear-Sky Wet Tropospheric Correction from Current and Future Altimetry Missions

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0133.1 ◽

2019 ◽

Vol 36 (3) ◽

pp. 473-489 ◽

Cited By ~ 1

Author(s):

Laura Hermozo ◽

Laurence Eymard ◽

Fatima Karbou ◽

Bruno Picard ◽

Mickael Pardé

Keyword(s):

Data Assimilation ◽

Microwave Radiometer ◽

Passive Microwave ◽

Simulation Experiments ◽

Low Frequencies ◽

Clear Sky ◽

Brightness Temperatures ◽

Atmospheric Stratification ◽

Atmospheric Path ◽

Occultation Data

AbstractStatistical methods are usually used to provide estimations of the wet tropospheric correction (WTC), necessary to correct altimetry measurements for atmospheric path delays, using brightness temperatures measured at two or three low frequencies from a passive microwave radiometer on board the altimeter mission. Despite their overall accuracy over oceanic surfaces, uncertainties still remain in specific regions of complex atmospheric stratification. Thus, there is still a need to improve the methods currently used by taking into account the frequency-dependent information content of the observations and the atmospheric and surface variations in the surroundings of the observations. In this article we focus on the assimilation of relevant passive microwave observations to retrieve the WTC over ocean using different altimeter mission contexts (current and future, providing brightness temperature measurements at higher frequencies in addition to classical low frequencies). Data assimilation is performed using a one-dimensional variational data assimilation (1D-Var) method. The behavior of the 1D-Var is evaluated by verifying its physical consistency when using pseudo- and real observations. Several observing-system simulation experiments are run and their results are analyzed to evaluate global and regional WTC retrievals. Comparisons of 1D-Var-based TWC retrieval and reference products from classical WTC retrieval algorithms or radio-occultation data are also performed to assess the 1D-Var performances.

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Climatology of Passive Microwave Brightness Temperatures in Tropical Cyclones and their Relations to Storm Intensities as Seen by FY-3B/MWRI

Remote Sensing ◽

10.3390/rs12010147 ◽

2020 ◽

Vol 12 (1) ◽

pp. 147

Author(s):

Bo Qian ◽

Haiyan Jiang ◽

Fuzhong Weng ◽

Ying Wu

Keyword(s):

Brightness Temperature ◽

Tropical Cyclones ◽

Inner Core ◽

Surface Wind ◽

Radial Distance ◽

Estimation Algorithm ◽

Passive Microwave ◽

Intensity Estimation ◽

Brightness Temperatures ◽

Dependent Samples

A new database, the tropical cyclones passive microwave brightness temperature (TCsBT) database including 6273 overpasses of 503 tropical cyclones (TC) was established from 6-year (2011–2016) Fengyun-3B (FY-3B) Microwave Radiation Imager (MWRI) Level-1 brightness temperature (TB) data and TC best-track data. An algorithm to estimate the TC intensity is developed using MWRI TB’s from the database. The relationship between microwave TB and the maximum sustained surface wind (Vmax) of TCs is derived from the TCsBT database. A high correlation coefficient between MWRI channel TB and Vmax is found at the radial distance 50–100 km near the TC inner core. Brightness temperatures at 10.65, 18.70, 23.8, and 36.5 GHz increase but 89 GHz TB’s and polarization corrected TB at 36.5 GHz (PCT36.50) and PCT89 decrease with increasing TC intensity. The TCsBT database is further separated into the 5063 dependent samples (2010–2015) for the development of the TC intensity estimation algorithm and 1210 independent samples (2016) for algorithm verification. The stepwise regression method is used to select the optimal combination of storm intensity estimation variables from 12 candidate variables and four parameters (10.65h, 23.80v, 89.00v and PCT36.50) were selected for multiple regression models development. Among the four predictors, PCT36.50 contributes the most in estimating TC intensity. In addition, the errors are lower for estimating 6-h and 12-h future Vmax than estimating the current Vmax.

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Impacts of A Priori Databases Using Six WRF Microphysics Schemes on Passive Microwave Rainfall Retrievals

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-12-00261.1 ◽

2013 ◽

Vol 30 (10) ◽

pp. 2367-2381 ◽

Cited By ~ 18

Author(s):

Ju-Hye Kim ◽

Dong-Bin Shin ◽

Christian Kummerow

Keyword(s):

A Priori ◽

Wrf Model ◽

Tropical Rainfall Measuring Mission ◽

Cloud Microphysics ◽

Passive Microwave ◽

A Priori Information ◽

Physically Based ◽

Double Moment ◽

Priori Information ◽

The Individual

Abstract Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

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Comparison of SMMR and SSM/I passive microwave data collected over Antarctica

Annals of Glaciology ◽

10.1017/s0260305500012726 ◽

1993 ◽

Vol 17 ◽

pp. 131-136 ◽

Cited By ~ 32

Author(s):

Kenneth C. Jezek ◽

Carolyn J. Merry ◽

Don J. Cavalieri

Keyword(s):

Brightness Temperature ◽

Microwave Radiometer ◽

Correlated Data ◽

Data Sets ◽

Microwave Brightness Temperature ◽

Brightness Temperatures ◽

Scatter Plots ◽

Microwave Imager ◽

Highly Correlated ◽

The Antarctic

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Retrieval of Canopy component temperatures through Bayesian inversion of directional thermal measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-3007-2009 ◽

2009 ◽

Vol 6 (2) ◽

pp. 3007-3040 ◽

Cited By ~ 6

Author(s):

J. Timmermans ◽

W. Verhoef ◽

C. van der Tol ◽

Z. Su

Keyword(s):

Sensitivity Analysis ◽

Surface Temperature ◽

A Priori ◽

Inversion Method ◽

Success Rates ◽

Brightness Temperatures ◽

Ill Posed ◽

Priori Information ◽

The Individual ◽

High Level

Abstract. In remote sensing evapotranspiration is estimated using a single surface temperature. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (ℜspectra≈0.3, ℜgonio≈0.3, and ℜAATSR≈0.5), and no improvement using mono-angular sensors (ℜ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K), but for low LAI values the measurement setup provides extra disturbances in the directional brightness temperatures, RMSEyoung maize=2.85 K, RMSEmature maize=2.85 K. As these disturbances, were only present for two crops and can be eliminated using masked thermal images the method is considered successful.

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