scholarly journals Eliminating the influence of attitude on brightness temperatures measurement for polarimetric microwave radiometer

2012 ◽  
Vol 61 (1) ◽  
pp. 018401
Author(s):  
Lu Wen ◽  
Yan Wei ◽  
Wang Rui ◽  
Wang Ying-Qiang
Keyword(s):  
Microwave Radiometer ◽  
Brightness Temperatures ◽  
Polarimetric Microwave Radiometer

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

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.

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

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
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.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

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

2019 ◽  
Vol 36 (3) ◽  
pp. 473-489 ◽  
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.

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

1993 ◽  
Vol 17 ◽  
pp. 131-136 ◽  
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.

Characterization of Precipitating Clouds by Ground-Based Measurements with the Triple-Frequency Polarized Microwave Radiometer ADMIRARI

2010 ◽  
Vol 49 (3) ◽  
pp. 394-414 ◽  
Author(s):  
Alessandro Battaglia ◽  
Pablo Saavedra ◽  
Thomas Rose ◽  
Clemens Simmer
Keyword(s):  
Microwave Radiometer ◽  
Three Dimensional ◽  
Axial Ratio ◽  
Retrieval Algorithm ◽  
Nonspherical Particles ◽  
Brightness Temperatures ◽  
Triple Frequency ◽  
Raindrop Size Distribution ◽  
Retrieval Scheme

Abstract A groundbreaking new-concept multiwavelength dual-polarized Advanced Microwave Radiometer for Rain Identification (ADMIRARI) has been built and continuously operated in two field campaigns: the Convective and Orographically Induced Precipitation Study (COPS) and the European Integrated Project on Aerosol Cloud Climate Air Quality Interactions (EUCAARI). The radiometer has 6 channels working in horizontal and vertical polarization at 10.65, 21.0, and 36.5 GHz, and it is completely steerable both in azimuth and in elevation. The instrument is suited to be operated in rainy conditions and is intended for retrieving simultaneously water vapor, rain, and cloud liquid water paths. To this goal the authors implemented a Bayesian retrieval scheme based on many state realizations simulated by the Goddard Cumulus Ensemble model that build up a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations, which account for the presence of nonspherical particles in preferential orientation, simulate the downwelling brightness temperatures and establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. Particular attention is devoted to the sensitivity of the ADMIRARI signal to 3D effects, raindrop size distribution, and axial ratio parameterizations. The polarization and multifrequency signals represent key information to separate the effects introduced by non-Rayleigh scatterers and to separate rainwater (r-LWP) from the cloud water component (c-LWP). Long-term observations demonstrate that observed brightness temperatures and polarization differences can be well interpreted and reproduced by the simulated ones for all three channels simultaneously. Rough estimates of r-LWP derived from collocated observations with a micro rain radar confirm the rain/no rain separation and the variability trend of r-LWP provided by the radiometer-based retrieval algorithm. With this work the authors demonstrate the potential of ADMIRARI to retrieve information about the rain/cloud partitioning for midlatitude precipitation systems; future studies with this instrument will provide crucial information on rain efficiency of clouds for cloud modelers that might lead toward a better characterization of rain processes.

scholarly journals Long term Observations minus Background monitoring of ground-based microwave radiometer network. Part 1: Brightness Temperatures

2017 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
Ulrich Löhnert ◽  
Olivier Caumont ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Scale Model ◽  
Radiative Transfer Model ◽  
Line Center ◽  
High Temporal Resolution ◽  
Transfer Model ◽  
Observation Operator ◽  
Brightness Temperatures

Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g., variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation and root-mean-square) for water vapor channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line center (~ 2–2.5 K) towards the high-frequency wing (~ 0.8–1.3 K). Statistics for zenith and lower elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (

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