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Liang Liao and Robert Meneghini

the use of the DFR. On the other hand, this approach loses the capability of fully accounting for the DSD variations along the profile because a single adjustment factor is not able to account for variations in the DSD along the profile. In fact, the performance depends on a number of factors that include the model assumptions and the degree of uniformity of the DSD along the profiles. An evaluation of the algorithm performance is important in assessing the strengths and weaknesses of the

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Lijing Cheng, Hao Luo, Timothy Boyer, Rebecca Cowley, John Abraham, Viktor Gouretski, Franco Reseghetti, and Jiang Zhu

(those with collocated reference data). One problem with correction schemes is that there are no independent data used for evaluation. With the criteria for matching pairs used in the present work, the number of pairs has been greatly expanded (~20% of all XBT drops). Therefore, we designed a second test (named the “training/testing” test) where half of XBT–reference pairs in this study were used to train a correction scheme (training dataset) and the other half were used to test the performance of

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Wesley Berg, Stephen Bilanow, Ruiyao Chen, Saswati Datta, David Draper, Hamideh Ebrahimi, Spencer Farrar, W. Linwood Jones, Rachael Kroodsma, Darren McKague, Vivienne Payne, James Wang, Thomas Wilheit, and John Xun Yang

Radiation Measurement (ARM) sites. The observed profiles are used as input to a radiative transfer model to calculate single differences (i.e., observed minus simulated Tb) for the different radiometers based on overpasses of those sites. The single differences for the constellation sensors are then compared to those from GMI in order to evaluate calibration differences. The profiles used are from the ARM “merged sounding” product ( Troyan 2012 ) that uses a combination of observations from radiosonde

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H. Dong and X. Zou

1. Introduction Satellite microwave temperature sounders, humidity sounders, and imagers have provided complementary global observations of the global atmospheric and Earth surface variables for several decades. It is important to ensure the highest possible accuracy and precision of these observations before they are assimilated into numerical weather prediction (NWP) models. Although the Advanced Technology Microwave Sounder (ATMS) on board the Suomi–National Polar-Orbiting Partnership

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

of observed TBs. The inversion problem is therefore dramatically underdetermined, leading to an inherent retrieval uncertainty. Several evaluation studies of passive microwave retrievals have revealed heterogeneous performances depending on geographical areas, seasons, and types of precipitation systems ( Tang et al. 2014 ; Oliveira et al. 2015 ; Liu et al. 2017 ; Petković and Kummerow 2017 ; Guilloteau et al. 2017 ). The passive microwave retrieval over oceans generally outperforms the

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Rachael Kroodsma, Stephen Bilanow, and Darren McKague

other radiometer calibration issues, and in particular the GMI on-orbit analysis is used as a model for many of the TMI V8 updates. There are several modifications that are incorporated into TMI V8 to improve the calibration and quality of the data product. This paper discusses two of these modifications: the alignment of the instrument and feedhorns, and the along-scan temperature bias correction. Other corrections to TMI V8 include a hot load correction ( Alquaied et al. 2018a ), updated emissive

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Clément Guilloteau and Efi Foufoula-Georgiou

uncertainty. The use of nonlocal parameters in a neighbor-search algorithm does not require defining explicit relations between TB patterns and precipitation; it is simply assumed that similar TB patterns correspond to similar precipitation systems and geometries. In particular, with this approach, it is not necessary to explicitly correct for the parallax shift as is done in Guilloteau et al. (2018) . The retrieval performance is evaluated over 6 million randomly sampled DPR profiles and collocated GMI

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

evaluating numerical model output). Since a primary motivator for SIMBA development pertains to subfootprint-scale variabilities, the column grid total horizontal extent is typically set at 5, 10, 15, or 20 km (corresponding to the approximate size of GPM DPR and GMI pixels). However, the system can support larger column grids (as might be used, following the previous examples, for pixel-scale evaluations and/or numerical model output assessment). Prior to the ground-based scanning radar module gridding

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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

estimate the particle size more accurately than a single-frequency radar so that we can improve the estimates of rainfall rate and identify snow precipitation regions. In fact, by using the difference in the scattering and attenuation properties of liquid and solid water particles between Ku- and Ka-band electromagnetic (EM) waves, it is possible to estimate the mean diameter of precipitation particles once an appropriate particle size distribution (PSD) model is chosen. Since the mean particle size

Open access