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Lei Meng, Yijun He, Jinnian Chen, and Yumei Wu

1. Introduction The Special Sensor Microwave Imager (SSM/I) was first flown on the Defense Meteorological Satellite Program (DMSP) F8 satellite in June 1987 ( Hollinger et al. 1987 ). Since then, six SSM/I sensors have been launched successfully, and currently there are three sensors onboard the DMSP F13 – 15 . Our analysis is based on the observations by the SSM/I onboard the DMSP F14 between 1997 and 2002. The scan direction of the SSM/I on DMSP F14 is from left to right with a spatial

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Mei Han, Scott A. Braun, William S. Olson, P. Ola G. Persson, and Jian-Wen Bao

for evaluating model microphysical schemes ( Matsui et al. 2009 ; Li et al. 2009 ). In this paper, we use the observations from the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the MM5, version 3.7.4 (this version was released in October 2006 and has since been frozen; more information is available at

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Xiaolei Zou, Xiaoxu Tian, and Fuzhong Weng

1. Introduction The Advanced Microwave Scanning Radiometer (AMSR-E) is a conically scanning microwave imager on board the Earth Observing System (EOS) Aqua satellite. It was successfully launched into a polar orbit on 4 May 2002 with an equator-crossing time (ECT) at 1330 ( Kawanishi et al. 2003 ). The six low AMSR-E channels have frequencies at 6.925 (C band), 10.65 (X band), and 18.7 GHz (K band) with both horizontal and vertical polarization, and they are mainly used for retrieving the

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Mircea Grecu and William S. Olson

1. Introduction Precipitation estimation from satellite passive microwave radiometer data is a mathematically ill-posed problem. That is, the problem does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make the problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. For example, the algorithm used to estimate precipitation from observations provided by the

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Yonghwan Kwon, Zong-Liang Yang, Timothy J. Hoar, and Ally M. Toure

Hemisphere (e.g., Stewart et al. 2004 ). The climate and hydrological research communities are therefore invested in improving the estimation of spatial and temporal variation in snowpack. One approach to improving these estimates is the use of snow radiance data assimilation [hereafter, radiance assimilation (RA)] methods, in which microwave brightness temperature T B observations are directly assimilated into a land surface model (LSM). Previous studies have made significant progress in using this

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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

observations from Landsat]. However, these quantities are either too sensitive to nonsoil moisture factors (such as radar backscatter to surface roughness), or not directly related to the soil moisture content (e.g., LST, A , and VI). Consequently, SM estimates based on these finer-scale satellite observations are less reliable than the coarser-scale microwave radiometer observations, but with the trade-off of being higher spatial resolution. While the coarse-resolution SMAP radiometer observations may be

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H. Lievens, A. Al Bitar, N. E. C. Verhoest, F. Cabot, G. J. M. De Lannoy, M. Drusch, G. Dumedah, H.-J. Hendricks Franssen, Y. Kerr, S. K. Tomer, B. Martens, O. Merlin, M. Pan, M. J. van den Berg, H. Vereecken, J. P. Walker, E. F. Wood, and V. R. N. Pauwels

studies have used actual SMOS TB data ( De Lannoy et al. 2013 ; Montzka et al. 2013 ). This study proposes a method for optimizing a coupled land surface and radiative transfer model framework to decrease the amount of biases in the simulation of multiangular and multipolarization SMOS TB observations. Therefore, the Community Microwave Emission Modelling platform (CMEM; Holmes et al. 2008 ; Drusch et al. 2009 ; de Rosnay et al. 2009 ) is coupled to the Variable Infiltration Capacity model (VIC

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Mircea Grecu, William S. Olson, and Emmanouil N. Anagnostou

Introduction Past studies demonstrate various ways in which passive microwave information can contribute to the improvement of airborne and satelliteborne radar precipitation estimates. A straightforward option for including radiometer information in algorithms for precipitation estimation from airborne and spaceborne radar observations, such as those provided by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), is to estimate the path-integrated attenuation (PIA) at the

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Matthew D. Lebsock and Kentaroh Suzuki

the optical methods, errors in the microwave emissivity and atmospheric opacity models, and ambiguity in the partitioning of the observed microwave signal between cloud and precipitation ( Lebsock and Su 2014 ). In addition to the passive techniques, observations from the CloudSat ( Stephens et al. 2008 ) Cloud Profiling Radar ( Tanelli et al. 2008 ) have provided profiles of cloud liquid water content ( l c ) that when integrated through depth provide the W c ( Austin and Stephens 2001 ). The

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Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2 Kuligowski, R. J. , 2002 : A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor. , 3 , 112 – 130 . 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2 Kummerow, C. , and Giglio L. , 1995 : A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol. , 12 , 33 – 45 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Kummerow, C. , and Coauthors , 2001 : The evolution of the

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