NOAA operational hydrological products derived from the advanced microwave sounding unit

Ferraro, Ralph; Weng, Fuzhong; Grody, Norman C.; Zhao, Limin; Meng, Huan; Kongoli, Cezar; Pellegrino, Paul; Qiu, Shuang; Dean, Charles

doi:10.1109/tgrs.2004.843249

Cited by 187 publications

(135 citation statements)

References 30 publications

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“…Thus, the liquid water path (LWP) product of NOAA-18 AMSU-A is used here to verify the precision of the cloud detection scheme. The LWP products from the operational Microwave Surface and Precipitation Products System (MSPPS) are often used in the cloud detection of AMSU-A (Weng and Grody, 1994;Ferraro et al, 2005). The LWP is only retrieved over the ocean (without sea ice) and varies from 0.01 to 2 kg m −2 .…”

Section: Cloud Detectionmentioning

confidence: 99%

Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

Liu

2016

Atmos. Meas. Tech.

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Abstract. FengYun-3C (FY-3C) is an operational polarorbiting satellite carrying the new-generation microwave sounding instruments in China. This paper describes the assimilation of the FY-3C Microwave Temperature Sounder-2 (MWTS-2) radiances in the Global and Regional Assimilation and PrEdiction System (GRAPES) of China Meteorological Administration. A quality control (QC) procedure for the assimilation of MWTS-2 radiance is proposed. Extensive monitoring before assimilation shows that MWTS-2 observations exhibit a clear striping pattern. A technique combining principal component analysis (PCA) and ensemble empirical mode decomposition (EEMD) is applied to the observations to remove the striping noise. Cloudy field-of-views (FOVs) are identified by applying the Visible and InfrarRed Radiometer (VIRR) cloud fraction threshold of 76 %. Other QC steps are conducted in the follow order: (i) coastal FOVs are removed, (ii) eight outmost FOVs are not used, (iii) channel 5 data over sea ice and land are not used, (iv) channel 6 observations are not used if the terrain altitudes are higher than 500 m, and (v) outliers with large differences between observations and model simulations are removed. Approximately 83, 75, 40, and 40 % of the observations are removed by the proposed QC for channels 5-8, respectively. After QC, the global biases and standard deviations are reduced significantly. The assimilation of the MWTS-2 radiances shows a positive impact when the control experiment assimilates only conventional observations. The experiments also show that the analysis and forecast errors are slightly reduced when the striping noise is removed from the observations. The quality control scheme of extracting the striping noise may contribute to the analysis and forecast accuracy.

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Section: Cloud Detectionmentioning

confidence: 99%

Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

Liu

2016

Atmos. Meas. Tech.

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“…[11] The AMSU-B and MHS data have been used for a variety of hydrological research applications such as retrieving humidity profiles, liquid and cloud ice water paths, for monitoring climate modes like monsoon, and assimilating into numerical weather prediction models [Karbou et al, 2005;Eymard et al, 2010;Ferraro et al, 2005;Liu and Weng, 2005;Andersson et al, 2007].…”

Section: Microwave Humidity Soundersmentioning

confidence: 99%

Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs)

et al. 2013

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[1] Simultaneous all angle collocations (SAACs) of microwave humidity sounders (AMSU-B and MHS) on-board polar orbiting satellites are used to estimate scan-dependent biases. This method has distinct advantages over previous methods, such as that the estimated scan-dependent biases are not influenced by diurnal differences between the edges of the scan and the biases can be estimated for both sides of the scan. We find the results are robust in the sense that biases estimated for one satellite pair can be reproduced by double differencing biases of these satellites with a third satellite. Channel 1 of these instruments shows the least bias for all satellites. Channel 2 has biases greater than 5 K, thus needs to be corrected. Channel 3 has biases of about 2 K and more and they are time varying for some of the satellites. Channel 4 has the largest bias which is about 15 K when the data are averaged for 5 years, but biases of individual months can be as large as 30 K. Channel 5 also has large and time varying biases for two of the AMSU-Bs. NOAA-15 (N15) channels are found to be affected the most, mainly due to radio frequency interference (RFI) from onboard data transmitters. Channel 4 of N15 shows the largest and time varying biases, so data of this channel should only be used with caution for climate applications. The two MHS instruments show the best agreement for all channels. Our estimates may be used to correct for scan-dependent biases of these instruments, or at least used as a guideline for excluding channels with large scan asymmetries from scientific analyses.

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“…However, because CPR does not scan, it only measures a 1.5 km-wide strip on the Earth surface by each satellite pass, which largely limits its utility for weather monitoring and climate data collection. Passive satellite sensors such as high-frequency (>80 GHz) microwave radiometers have also been used in detecting snowfall events [Liu and Curry, 1997;Chen and Staelin, 2003;Kongoli et al, 2003;Ferraro et al, 2005;Skofronick-Jackson et al, 2004;Noh et al, 2006Noh et al, , 2009. While most of these studies targeted moderate to heavy snowfall events under unfrozen or nonsnow-covered surfaces, the encouraging results from these studies offered a viable alternative to high-sensitivity space-borne radar for snowfall detection and retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…[4] Similar to the case of retrieving rainfall over land [Ferraro et al, 2005;Wang et al, 2009], the primary signature in the microwave spectrum for snowfall retrieval is believed to be the scattering by ice particles, which reduces the upwelling radiation emitted from surface and the lower atmosphere. The strength of the ice scattering signature has been examined using radiative transfer simulations by several investigators [e.g., Bennartz and Petty, 2001;Zhao and Weng, 2002;Bennartz and Bauer, 2003;Kneifel et al, 2010;Skofronick-Jackson and Johnson, 2011].…”

Section: Introductionmentioning

confidence: 99%

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

Liu

Seo

2013

JGR Atmospheres

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[1] It has been long believed that the dominant microwave signature of snowfall over land is the brightness temperature decrease caused by ice scattering. However, our analysis of multiyear satellite data revealed that on most of occasions, brightness temperatures are rather higher under snowfall than nonsnowfall conditions, likely due to the emission by cloud liquid water. This brightness temperature increase masks the scattering signature and complicates the snowfall detection problem. In this study, we propose a statistical method for snowfall detection, which is developed by using CloudSat radar to train high-frequency passive microwave observations. To capture the major variations of the brightness temperatures and reduce the dimensionality of independent variables, the detection algorithm is designed to use the information contained in the first three principal components resulted from Empirical Orthogonal Function (EOF) analysis, which capturẽ 99% of the total variances of brightness temperatures. Given a multichannel microwave observation, the algorithm first transforms the brightness temperature vector into EOF space and then retrieves a probability of snowfall by using the CloudSat radar-trained lookup table. Validation has been carried out by case studies and averaged horizontal snowfall fraction maps. The result indicated that the algorithm has clear skills in identifying snowfall areas even over mountainous regions.Citation: Liu, G., and E.-K. Seo (2013), Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach,

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NOAA operational hydrological products derived from the advanced microwave sounding unit

Cited by 187 publications

References 30 publications

Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

Direct assimilation of Chinese FY-3C Microwave Temperature Sounder-2 radiances in the global GRAPES system

Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs)

Detecting snowfall over land by satellite high‐frequency microwave observations: The lack of scattering signature and a statistical approach

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