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Eui-Seok Chung, Brian J. Soden, and Viju O. John

homogenized upper-tropospheric water vapor dataset with a long-term stability. The rectification and intersatellite calibration of SSM/T-2 measurements are deferred because of the lack of calibration information in the 2000s. In addition, the AMSU-B and MHS 183.31 ± 3 GHz channel observations will be analyzed to produce a continuous dataset of midtropospheric water vapor suitable for climate studies. 2. Satellite-based microwave radiometer observations Satellite-based radiance measurements of the 183-GHz

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Lidia Cucurull and Richard A. Anthes

system that already is tuned to many different observational systems ( English et al. 2013 ). Since the expected end of the lifetime of Suomi-NPP is 2016, and the launch of the first JPSS satellite has been delayed from 2016 to at least early 2017, a gap or significant reduction in the U.S. microwave satellite data stream is possible. However, because there are other MW observations besides the ones on the NOAA satellites, as well as a number of infrared (IR) sensors on various satellites and radio

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Matthias Drusch, Thomas Holmes, Patricia de Rosnay, and Gianpaolo Balsamo

1. Introduction Satellite-borne passive microwave observations at L-band will become routinely available for the first time through the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission (SMOS) foreseen in 2009. The sensitivity of L-band measurements to soil moisture has been thoroughly analyzed (e.g., Ulaby et al. 1986 ), and the applicability of soil moisture retrievals has been demonstrated over the previous decades (e.g., Jackson et al. 1999 ). In recent years, data

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Y. Hu and X. Zou

appears ragged or is partially obscured or if the TC center is totally covered by a central dense overcast cloud field ( Wimmers and Velden 2010 ). Microwave observations from polar-orbiting satellites can penetrate clouds except for heavy precipitation and can well reveal convective organizations and eyewall structures, beneficial for locating TC centers. The ARCHER was applied to 37- and 85–92-GHz microwave observations from imagers, e.g., the Special Sensor Microwave Imager, the Special Sensor

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Isaac Moradi, K. Franklin Evans, Will McCarty, Marangelly Cordero-Fuentes, Ronald Gelaro, and Robert A. Black

issues ( Bauer et al. 2010 ). As expected, excluding cloud contaminated observations causes a significant lack of satellite data in the rainbands of tropical cyclones. Measurements from infrared instruments are restricted in the presence of convective clouds and thus do not provide much information on the state of the atmosphere. However, microwave measurements are less sensitive to clouds and are capable of providing information even in the presence of deep convective clouds such as in the case of

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Anita D. Rapp, M. Lebsock, and C. Kummerow

to a common resolution, shows that inhomogeneity effects are still very large. In this paper, we examine the consequences of data convolution and deconvolution on an optimal estimation (OE) retrieval algorithm that uses microwave radiometer measurements to retrieve cloud LWP, wind speed, and total precipitable water (TPW). Results show that data resampling has a substantial effect on the retrieved parameters when compared with retrievals performed on microwave radiometer observations at their

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

infrared (IR) images from low-Earth-orbiting (LEO) or geostationary (GEO) satellites provide regular observations of clouds from which estimates of precipitation may be generated. However, although precipitation originates from clouds, not all clouds produce precipitation. More importantly, the relationship between the cloud-top properties and the precipitation reaching the surface is indirect. Passive microwave (PM) radiometers allow a more direct measure of precipitation to be made since these

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Fatima Karbou, Florence Rabier, and Catherine Prigent

on this type of surface ( English 2008 ). Studies focusing on the modeling of snow and sea ice emissivity were conducted within the framework of Concordiasi, to improve the use of microwave remote sensing observations in numerical weather prediction (NWP). Guedj et al. (2010) studied the impact of the reflection assumptions on the emissivity: specular, Lambertian, or using different specularity parameters following previous studies about the role of surface approximations on the emissivities

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Ulrich Löhnert, D. D. Turner, and S. Crewell

identified when the correlation matrix was plotted (not shown). Like the microwave spectrum, the infrared spectrum also contains information on the vertical profile of temperature and humidity. Smith et al. (1999) and Feltz et al. (1998) used spectral observations from 612–713 and 2223–2260 cm −1 (i.e., measurements from the 15- and 4.3- μ m CO 2 bands, respectively) for temperature profiling, and observations from 538–588 and 1250–1350 cm −1 (i.e., measurements from the wings of the rotational

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Min-Jeong Kim, Jianjun Jin, Amal El Akkraoui, Will McCarty, Ricardo Todling, Wei Gu, and Ronald Gelaro

use of observations from the GPM Microwave Imager (GMI), a conically scanning microwave radiometer with 13 channels and a swath width of 885 km and fields of view ranging from 5 to 25 km, depending on frequency. The radiance data used are the GMI level 1 1C-R, version 4 data product that coregisters the pixels of the GMI low-frequency and high-frequency channels using a nearest-neighbor approach. More details about GPM and its instruments can be found in Hou et al. (2014) and Skofronick

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