Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper Stratosphere

2021 ◽  
Author(s):  
Mohar Chattopadhyay ◽  
Will McCarty ◽  
Isaac Moradi
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Vertical Temperature ◽  
Current Period ◽  
Microwave Sounding Unit ◽  
Fundamental Cause ◽  
Microwave Sounding ◽  
Modern Era ◽  
Assessing Sensitivity ◽  
Observing Systems

AbstractMicrowave temperature sounders provide key observations in data assimilation, both in the current and historical global observing systems, as they provide the largest amount of horizontal and vertical temperature information due to their insensitivity to clouds. In the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), microwave sounder radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) are assimilated beginning with NOAA-15 and continuing through the current period. The time series of observation minus background statistics for AMSU-A channels sensitive to upper stratosphere and lower mesosphere show variabilities due to changes in AMSU-A constellation in the early AMSU-A period. Noted discrepancies are seen at the onset and exit of AMSU-A observations on the NOAA-15, NOAA-16, NOAA-17, and NASA EOS Aqua satellites. This effort characterizes the sensitivity, both in terms of the observations and the MERRA-2 analysis. Furthermore, it explores the use of reprocessed and inter-calibrated datasets to evaluate whether these homogenized observations can reduce the disparity due to change in instrumental biases against the model background. The results indicate that the AMSU-A radiances used in MERRA-2 are the fundamental cause of this inter-platform sensitivity which can be mitigated by using reprocessed data. The results explore the importance of the reprocessing of the AMSU-A radiances as well as their inter-calibration.

scholarly journals Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

2014 ◽  
Vol 31 (10) ◽  
pp. 2206-2222 ◽  
Author(s):  
Xiaolei Zou ◽  
Fuzhong Weng ◽  
H. Yang
Keyword(s):  
Time Series ◽  
Atmospheric Temperature ◽  
Advanced Technology ◽  
Central Frequency ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cross Calibration ◽  
Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

scholarly journals Satellite and VIZ–Radiosonde Intercomparisons for Diagnosis of Nonclimatic Influences

2006 ◽  
Vol 23 (9) ◽  
pp. 1181-1194 ◽  
Author(s):  
John R. Christy ◽  
William B. Norris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Lower Troposphere ◽  
University Of Alabama ◽  
High Significance ◽  
Sensing Systems ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
The University

Abstract Radiosonde datasets of temperature often suffer from discontinuities due to changes in instrumentation, location, observing practices, and algorithms. To identify temporal discontinuities that affect the VIZ/Sippican family of radiosondes, the 1979–2004 time series of a composite of 31 VIZ stations are compared to composites of collocated values of layer temperatures from two microwave sounding unit datasets—the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS). Discontinuities in the radiosonde time series relative to the two satellite datasets were detected with high significance and with similar magnitudes; however, some instances occurred where only one satellite dataset differed from the radiosondes. For the products known as lower troposphere (LT; surface–300 hPa) and midtroposphere (MT; surface–75-hPa layer), significant discontinuities relative to both satellite datasets were found—two cases for LT and four for MT. These are likely associated with changes in the radiosonde system. Three apparent radiosonde discontinuities were also determined for the lower-stratospheric product (LS; 150–15 hPa). Because they cannot be definitely traced to changes in the radiosonde system, they could be the result of common errors in the satellite products. When adjustments are applied to the radiosondes based independently on each satellite dataset, 26-yr trends of UAH (RSS) are consistent with the radiosondes for LT, MT, and LS at the level of ±0.06, ±0.04, and ±0.07 (±0.12, ±0.10, and ±0.10) K decade−1. Also, simple statistical retrievals based on radiosonde-derived relationships of LT, MT, and LS indicate a higher level of consistency with UAH products than with those of RSS.

scholarly journals Estimation of Multivariate Observation-Error Statistics for AMSU-A Data

2011 ◽  
Vol 139 (12) ◽  
pp. 3765-3780 ◽  
Author(s):  
Vadim E. Gorin ◽  
Mikhail D. Tsyrulnikov
Keyword(s):  
Data Assimilation ◽  
Radiosonde Data ◽  
Observation Error ◽  
Error Statistics ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cross Correlations ◽  
Advanced Microwave Sounding Unit ◽  
Weakly Dependent ◽  
Forecast Background

Abstract Advanced Microwave Sounding Unit A (AMSU-A) observation-error covariances are objectively estimated by comparing satellite radiances with radiosonde data. Channels 6–8 are examined as being weakly dependent on the surface and on the stratosphere above the radiosonde top level. Significant horizontal, interchannel, temporal, and intersatellite correlations are found. Besides, cross correlations between satellite and forecast (background) errors (largely disregarded in practical data assimilation) proved to be far from zero. The directional isotropy hypothesis is found to be valid for satellite error correlations. Dependencies on the scan position, the season, and the satellite are also checked. Bootstrap simulations demonstrate that the estimated covariances are statistically significant. The estimated correlations are shown to be caused by the satellite errors in question and not by other (nonsatellite) factors.

scholarly journals A Study of the NOAA Near-Nadir AMSU-A Brightness Temperatures over Antarctica

2010 ◽  
Vol 27 (6) ◽  
pp. 995-1004 ◽  
Author(s):  
Tsan Mo
Keyword(s):  
Time Series ◽  
Test Site ◽  
Good Test ◽  
Brightness Temperatures ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Diurnal Effect ◽  
Natural Test ◽  
Microwave Radiometers ◽  
The Antarctic

Abstract Daily mean brightness temperatures over Antarctica derived from measurements by three Advanced Microwave Sounding Unit-A (AMSU-A) radiometers on board NOAA-18, NOAA-19, and MetOp-A satellites are studied. To demonstrate the characteristics of the data over this test site, time series of daily averages of the brightness temperatures are constructed. These time series provide a useful pattern of annual variation of the AMSU-A measurements for intercalibration of microwave radiometers on board multiple satellites. To investigate the diurnal effect on the measurements, the time series of daily averaged brightness temperatures are constructed separately for the ascending and descending passes. Results show that there are little diurnal differences in measurements during the Antarctic winter months from each satellite. Therefore these measurements provide a practical approach to obtain relative channel biases of intersatellite data. The monthly averages of the measurements over July 2009 are employed to obtain the relative channel biases because it is the coldest month in Antarctica. The resultant channel biases for the three satellites are within the range of ±0.1 K for channels 1–5 and ±0.3 K for channels 6–15. This is strong evidence that Antarctica is a potentially good test site for intercalibration of microwave radiometers on board multiple satellites. The small relative biases at channels 1–5 indicate that Antarctica is a very stable test site that is particularly useful for intercalibration of measurements from the window channels. The establishment of a natural test site for calibration references is important for calibration and validation of spaceborne microwave instruments.

Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations

2012 ◽  
Vol 25 (23) ◽  
pp. 8108-8131 ◽  
Author(s):  
Leopold Haimberger ◽  
Christina Tavolato ◽  
Stefan Sperka
Keyword(s):  
Time Series ◽  
Size Estimation ◽  
Climate Data ◽  
Weather Forecasts ◽  
Temperature Trends ◽  
Radiosonde Observation ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Cost

Abstract This article describes progress in the homogenization of global radiosonde temperatures with updated versions of the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) and Radiosonde Innovation Composite Homogenization (RICH) software packages. These are automated methods to homogenize the global radiosonde temperature dataset back to 1958. The break dates are determined from analysis of time series of differences between radiosonde temperatures (obs) and background forecasts (bg) of climate data assimilation systems used for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the ongoing interim ECMWF Re-Analysis (ERA-Interim). RAOBCORE uses the obs−bg time series also for estimating the break sizes. RICH determines the break sizes either by comparing the observations of a tested time series with observations of neighboring radiosonde time series (RICH-obs) or by comparing their background departures (RICH-τ). Consequently RAOBCORE results may be influenced by inhomogeneities in the bg, whereas break size estimation with RICH-obs is independent of the bg. The adjustment quality of RICH-obs, on the other hand, may suffer from large interpolation errors at remote stations. RICH-τ is a compromise that substantially reduces interpolation errors at the cost of slight dependence on the bg. Adjustment uncertainty is estimated by comparing the three methods and also by varying parameters in RICH. The adjusted radiosonde time series are compared with recent temperature datasets based on (Advanced) Microwave Sounding Unit [(A)MSU] radiances. The overall spatiotemporal consistency of the homogenized dataset has improved compared to earlier versions, particularly in the presatellite era. Vertical profiles of temperature trends are more consistent with satellite data as well.

scholarly journals Validation of merged MSU4 and AMSU9 temperature climate records with a new 2002–2012 vertically resolved temperature record

2015 ◽  
Vol 8 (1) ◽  
pp. 235-267 ◽  
Author(s):  
A. A. Penckwitt ◽  
G. E. Bodeker ◽  
P. Stoll ◽  
J. Lewis ◽  
T. von Clarmann ◽  
...  
Keyword(s):  
Time Series ◽  
Radio Occultation ◽  
Temperature Data ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
Microwave Sounding Unit ◽  
Source Data ◽  
Microwave Sounding ◽  
Temperature Time

Abstract. A new database of monthly mean zonal mean (5° zones) temperature time series spanning 17 pressure levels from 300 to 7 hPa and extending from 2002 to 2012 was created by merging monthly mean time series from two satellite-based mid-infrared spectrometers (ACE-FTS and MIPAS), a microwave sounder (SMR), and from three satellite-based radio occultation experiments (GRACE, CHAMP, and TSX). The primary intended use of this new temperature data set is to validate the merging of the Microwave Sounding Unit channel 4 (MSU4), and Advanced Microwave Sounding Unit channel 9 (AMSU9) temperature time series conducted in previous studies. The six source data sets were merged by removing offsets and trends between the different measurement series. Weighted means were calculated of the six source data sets where the weights were a function of the uncertainty on the original monthly mean data. This new temperature data set of the upper troposphere and stratosphere has been validated by comparing it to RATPAC-A, COSMIC radio occultation data as well as the NCEPCFSR reanalyses. Differences in all three cases were typically < 2 K in the upper troposphere and lower stratosphere, but could reach up to 5 K in the mid-stratosphere. The data across the 17 pressure levels have then been vertically integrated, using the MSU4/AMSU9 weighting function, to provide a deep vertical layer temperature proxy of the merged MSU4+AMSU9 series. Differences between this vertically integrated data set and two different versions of the MSU4+AMSU9 data set – one from Remote Sensing Systems and one from the University of Alabama at Huntsville – were examined for discontinuities. No statistically significant discontinuities were found in either of those two data sets suggesting that the transition from the MSU4+AMSU9 data to AMSU9 data only does not introduce any discontinuities in the MSU4+AMSU9 climate data records that might compromise their use in temperature trend analyses.

scholarly journals Impacts of Multiple Radiance Data Assimilation on the Simulation of Typhoon Chan-Hom

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 957 ◽  
Author(s):  
Dongmei Xu ◽  
Aiqing Shu ◽  
Feifei Shen ◽  
Jinzhong Min ◽  
Hong Li ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Skill Score ◽  
Atmospheric Infrared Sounder ◽  
Sea Level Pressure ◽  
Steering Flow ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
System Data ◽  
Radiance Data

With the module of assimilating AMSU-A (Advanced Microwave Sounding Unit-A) and AIRS (Atmospheric Infrared Sounder) data in the WRFDA (Weather Research and Forecasting Model Data Assimilation) system, the impacts of joint assimilation of the radiance observations from two satellites on the simulation of typhoon Chan-hom (2015) are addressed. For comparison, experiments with the assimilation of solely GTS (Global Telecommunications System) data, AMSU-A data, or AIRS data are also performed. The results show that, compared to other experiments, the analysis field after assimilating multiple radiance data is closer to the observation. The simulated steering flow in its forecast field is conductive to the northeast twist of the typhoon. In addition, the simulated rainband and the FSS (fraction skill score) calculated from the experiment with assimilating multiple radiance data are better. In the deterministic forecast, better performance is obtained from the simulation with multiple radiance data in the forecast of track, MSLP (minimum sea level pressure), and MSW (maximum surface wind).

scholarly journals A Four-Season Impact Study of Rawinsonde, GOES, and POES Data in the Eta Data Assimilation System. Part II: Contribution of the Components

2005 ◽  
Vol 20 (2) ◽  
pp. 178-198 ◽  
Author(s):  
Tom H. Zapotocny ◽  
W. Paul Menzel ◽  
James A. Jung ◽  
James P. Nelson
Keyword(s):  
Data Assimilation ◽  
Precipitable Water ◽  
Remotely Sensed ◽  
Data Types ◽  
Model Domain ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Time Periods ◽  
Microwave Imager ◽  
The Impact

Abstract The impact of in situ rawinsonde observations (raob), remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar-Orbiting Operational Environmental Satellite (POES) observations routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. This work examines the contribution of nine individual components of the total observing system. The nine data types examined include rawinsonde mass and wind observations, GOES mass and wind observations, POES observations from the Microwave Sounding Unit (MSU), the Advanced Microwave Sounding Unit (AMSU-A and AMSU-B), the High Resolution Infrared Radiation Sounder (HIRS), and column total precipitable water and low-level wind observations from the Special Sensor Microwave Imager (SSM/I). The results are relevant for users of the Eta Model trying to compare/contrast the overall forecast impact of traditional, largely land-based rawinsonde observations against remotely sensed satellite observations, which are available domainwide. The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. Throughout these periods, a November 2001 32-km version of the EDAS is run 10 times at both 0000 and 1200 UTC. The 10 runs include a control run, which utilizes all data types routinely used in the EDAS, and 9 experimental runs in which one of the component data types noted above is denied. Differences between the experimental and control runs are then accumulated over the 15-day periods and analyzed to demonstrate the 00-h sensitivity and 24-h forecast impact of these individual data types in the EDAS. The diagnostics are computed over the entire horizontal model domain and a subsection covering the continental United States (CONUS) and adjacent coastal waters on isobaric surfaces extending into the lower stratosphere. The 24-h forecast impact results show that a positive forecast impact is achieved from most of the nine component data sources during all four time periods. HIRS, MSU, and SSM/I wind observations yield only a slight positive forecast impact to all fields. Rawinsonde and GOES wind observations have the largest positive forecast impact for temperature over both the entire model domain and the extended CONUS. The same data types also provide the largest forecast impact to the u component of the wind, while GOES wind observations provide the largest forecast impact to moisture.

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