Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

Susskind, Joel; Blaisdell, John; Iredell, Lena; Keita, Fricky

doi:10.1109/tgrs.2010.2070508

Cited by 147 publications

(153 citation statements)

References 15 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…AIRS measures thermal emission in the 4.7 µm absorption band, as does MOPITT (McMillan et al, 2005Warner et al, 2007). However, unlike MOPITT and other instruments in this comparison, AIRS possesses a cloud clearing capability (Susskind et al, 2003(Susskind et al, , 2010 that enables it to retrieve partly cloudy scenes and thus achieve 70% effective daily coverage. Version 5 CO retrieval is described by and .…”

Section: Airsmentioning

confidence: 99%

Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

et al. 2010

View full text Add to dashboard Cite

Abstract. We combine CO column measurements from the MOPITT, AIRS, SCIAMACHY, and TES satellite instruments in a full-year (May 2004-April 2005 global inversion of CO sources at 4 • ×5 • spatial resolution and monthly temporal resolution. The inversion uses the GEOS-Chem chemical transport model (CTM) and its adjoint applied to MO-PITT, AIRS, and SCIAMACHY. Observations from TES, surface sites (NOAA/GMD), and aircraft (MOZAIC) are used for evaluation of the a posteriori solution. Using GEOSChem as a common intercomparison platform shows global consistency between the different satellite datasets and with the in situ data. Differences can be largely explained by different averaging kernels and a priori information. The global CO emission from combustion as constrained in the inversion is 1350 Tg a −1 . This is much higher than current bottomup emission inventories. A large fraction of the correction results from a seasonal underestimate of CO sources at northern mid-latitudes in winter and suggests a larger-thanexpected CO source from vehicle cold starts and residential heating. Implementing this seasonal variation of emissions solves the long-standing problem of models underestimating CO in the northern extratropics in winter-spring. A posCorrespondence to: M. Kopacz (mkopacz@princeton.edu) teriori emissions also indicate a general underestimation of biomass burning in the GFED2 inventory. However, the tropical biomass burning constraints are not quantitatively consistent across the different datasets.

show abstract

Section: Airsmentioning

confidence: 99%

Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Of these instruments, IASI is the only one that continuously covers the full spectral range. AIRS, IASI and CrIS retrievals are described in, for example, Susskind et al (2011), August et al (2014), and Gambacorta et al (2012). Examples of evaluation results for water vapour products from ATOVS and hyperspectral instruments can be found in, for example, Bedka et al (2010), Reale et al (2012) and Divakarla et al (2014).…”

Section: Introductionmentioning

confidence: 99%

The CM SAF ATOVS data record: overview of methodology and evaluation of total column water and profiles of tropospheric humidity

Courcoux

Schröder

2015

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Recently, the reprocessed Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and temperature data record was released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). ATOVS observations from infrared and microwave sounders onboard the National Oceanic and Atmospheric Agency (NOAA)-15-19 satellites and EUMETSAT's Meteorological Operational (Metop-A) satellite have been consistently reprocessed to generate 13 years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) of global water vapour and temperature daily and monthly means with a spatial resolution of 90 km × 90 km. The data set is referenced under the following digital object identifier (DOI): doi:10.5676/EUM_SAF_CM/WVT_ATOVS/V001. After preprocessing, a maximum likelihood solution scheme was applied to the observations to simultaneously infer temperature and water vapour profiles. In a postprocessing step, an objective interpolation method (Kriging) was applied to allow for gap filling. The product suite includes total precipitable water vapour (TPW), layer-integrated precipitable water vapour (LPW) and layer mean temperature for five tropospheric layers between the surface and 200 hPa, as well as specific humidity and temperature at six tropospheric levels between 1000 and 200 hPa. To our knowledge, this is the first time that the ATOVS record (1998-now) has been consistently reprocessed (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) to retrieve water vapour.TPW and LPW products were compared to corresponding products from the Global Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde observations and from the Atmospheric Infrared Sounder (AIRS) version 5 satellite data record. TPW shows a good agreement with the GUAN radiosonde data: average bias and root mean square error (RMSE) are −0.2 and 3.3 kg m −2 , respectively. For LPW, the maximum absolute (relative) bias and RMSE values decrease (increase) strongly with height. The maximum bias and RMSE are found at the lowest layer and are −0.7 and 2.5 kg m −2 , respectively. While the RMSE relative to AIRS is generally smaller, the TPW bias relative to AIRS is larger, with dominant contributions from precipitating areas. The consistently reprocessed ATOVS data record exhibits improved quality and stability relative to the operational CM SAF products when compared to the TPW from GUAN radiosonde data over the period 2004-2011. Finally, it became evident that the change in the number of satellites used for the retrieval combined with the use of the Kriging leads to breakpoints in the ATOVS data record; therefore, a variability analysis of the data record is not recommended for the time period from

show abstract

“…Water vapor along with temperature affects various atmospheric processes, particularly cloud formation, initiation of convective storms (Trenberth et al, 2005) and tropical cyclones (Needs, 2009;Ali, 2009). Therefore, accurate information about their spatial and temporal distribution as well as variation in the lower troposphere is essential for the initialization of numerical weather prediction models, which in turn improves the forecast of various weather events (NRC, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Ground-based microwave radiometers have been designed, fabricated and used to sense water vapor and temperature profiles from ground to 10 km altitude (Iturbide-Sanchez et al, 2007;Solheim, et al, 1998). Satellite-based instruments like Advanced Microwave Sounding Units (AMSU-A and B) on board NOAA-15 (Susskind et al, 2011;Rosenkranz, 2001) (Rao, et al, 2013) have been used to retrieve humidity and temperature profiles in addition to a range of other parameters. The AMSU-A and B channels operate close to the 22.235, 60 and 183 GHz absorption lines as well as at the 89 GHz window frequency.…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

Panda

Sahoo

Pandithurai

2017

Geosci. Instrum. Method. Data Syst.

View full text Add to dashboard Cite

Abstract. Ground-based microwave measurements performed at water vapor and oxygen absorption line frequencies are widely used for remote sensing of tropospheric water vapor density and temperature profiles, respectively. Recent work has shown that Bayesian optimal estimation can be used for improving accuracy of radiometer retrieved water vapor and temperature profiles. This paper focuses on using Bayesian optimal estimation along with time series of independent frequency measurements at K-and V-bands. The measurements are used along with statistically significant but short background data sets to retrieve and sense temporal variations and gradients in water vapor and temperature profiles.

show abstract

Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

Cited by 147 publications

References 15 publications

Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

The CM SAF ATOVS data record: overview of methodology and evaluation of total column water and profiles of tropospheric humidity

Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

Contact Info

Product

Resources

About