Validating IASI Temperature and Moisture Sounding Retrievals over East Asia Using Radiosonde Observations

Kwon, Eun-Han; Sohn, Byung-Ju; Smith, William L.; Li, Jun

doi:10.1175/jtech-d-11-00078.1

Cited by 30 publications

(38 citation statements)

References 25 publications

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“…To examine the moistness dependency, the error statistics of the UM analysis and ERA-I reanalysis are examined as a function of the total precipitable water (TPW) between the surface and 100 hPa. Similar analyses were taken for the temperature and moisture retrievals from satellite-based hyperspectral sounder measurements [23,24].…”

Section: Moistness-depending Statisticsmentioning

confidence: 97%

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

et al. 2016

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Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January-December 2012. The motivation was to examine the overall performance of data assimilation outputs. The difference statistics of the collocated model outputs versus the radiosonde observations indicated a good agreement for the temperature, amongst datasets, while less agreement was found for the relative humidity. A comparison of the UM outputs from the UKMO and KMA revealed that they are similar to each other. The introduction of the new version of UM into the KMA in May 2012 resulted in an improved analysis performance, particularly for the moisture field. On the other hand, ECMWF reanalysis data showed slightly reduced performance for relative humidity compared with the UM, with a significant humid bias in the upper troposphere. ECMWF reanalysis temperature fields showed nearly the same performance as the two UM analyses. The root mean square differences (RMSDs) of the relative humidity for the three models were larger for more humid conditions, suggesting that humidity forecasts are less reliable under these conditions.

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Section: Moistness-depending Statisticsmentioning

confidence: 97%

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

et al. 2016

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“…Radiosonde is a useful source for retrieval validation , and is widely used (Tobin, et al, 2006;Kwon, et al, 2012). In this paper, only the FY-3C VASS data within 1h of the radiosonde and 1o latitude/longitude (expect for the polar region) are used in retrieval validation.…”

Section: Comparison To Radiosondementioning

confidence: 99%

Retrieval of atmospheric and surface parameters from VASS/FY-3C data under non-precipitation condition

et al. 2014

SPIE Proceedings

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The Vertical Atmospheric Sounding Suits (VASS) onboard FY-3C satellite includes The Infrared Atmospheric Sounder (IRAS), Microwave Temperature Sounder (MWTS) and Microwave Temperature and moisture sounder (MWTHS-II). The IRAS is similar to that onboard FY-3A/B, while the MWTS-II/MWTHS-II is more sophisticated than their precursors. MWTS has 13 channels mainly at the window region and 57 O2 absorption band, and MWTHS has 15 channels mainly at the 118 O2 absorption band and the 183 H2O absorption band. A package has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the VASS measurements, which is remap to IRAS Field of view. The algorithm that retrieves these parameters contains four steps: 1) cloud and precipitation detection, 2) bias adjustment for VASS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. The package does not process precipitation FOV, and for non-precipitation cloud FOV, the measurements from middle to low channels of IRAS are excluded. Till now all instruments are under orbit examination stage, and the primary results show that temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 2.0 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700-300 mb, 3-km layers from 300-30 mb, and 5-km layers from 30-1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 2-km layers from the surface to nearly 300 mb.

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“…However, the retrieval accuracy of the moisture profile during the winter over the land has not been analyzed fully. According to a study on the retrieval accuracy of sounding products of the Infrared Atmospheric Sounding Interferometer over East Asia [37], the derived TPW shows higher RMSE over land under dry conditions, which corresponds to the winter condition in East Asia. This is because the transmittance in the water vapor channels increases when there is little amount of moisture in the atmosphere and then the outgoing radiance is influenced by the surface.…”

Section: Discussionmentioning

confidence: 99%

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

2017

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Abstract:In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.

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Validating IASI Temperature and Moisture Sounding Retrievals over East Asia Using Radiosonde Observations

Cited by 30 publications

References 25 publications

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Retrieval of atmospheric and surface parameters from VASS/FY-3C data under non-precipitation condition

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

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