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Xuelei Feng, Feiqin Xie, Chi O. Ao, and Richard A. Anthes

atmospheric research. Numerous studies have demonstrated the high quality of RO data in the upper troposphere and lower stratosphere from GPS/Meteorology (MET) ( Rocken et al. 1997 ; Kursinski et al. 1997 ; Feng and Herman 1999 ; Tsuda et al. 2000 ) and CHAMP ( Wickert et al. 2001 ). However, these earlier RO missions, which were equipped with phase-locked loop tracking receivers, encountered significant signal tracking challenges in the presence of large moisture variations in the lower troposphere

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David Themens and Frédéric Fabry

upper bound at roughly 300 hPa. This choice of the RAP coordinate system for our analysis allows us to avoid potential correlation artifacts that would result from interpolating to another coordinate system. The covariances, which will determine how information spreads from measurement locations to surrounding areas, are derived from a monthly climatology assembled from a 500 km × 500 km region near Montreal by computing, for each height, the variance of the temperature and moisture fields at all

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G. Reverdin, S. Morisset, J. Boutin, N. Martin, M. Sena-Martins, F. Gaillard, P. Blouch, J. Rolland, J. Font, J. Salvador, P. Fernández, and D. Stammer

also identified in the tropics near-surface stratification related to local rainfall. Satellite-based L-band radiometry can be used to monitor salinity in a layer on the order of 1 cm ( Ulaby et al. 1986 ). This observation is now provided using the European Space Agency’s Soil Moisture and Ocean Salinity ( SMOS ) satellite ( Font et al. 2010 ) and Aquarius/Satelite de Aplicaciones Cientificas-D ( SAC-D ; Lagerloef et al. 2010 , S70–S71) satellite missions. After applying geophysical

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David A. Faysash and Eric A. Smith

imagery represent suitable data for assimilating into the biosphere model for improving model performance over a diurnal cycle, particularly for processes related to soil moisture. The Department of Energy’s Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM CART) site has been selected as the main study area for the retrieval and assimilation experiments, since this region is well instrumented. The ARM CART domain, encompassing an area of roughly 760 km × 320 km within Kansas

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Jens Reichardt

applies to an excitation wavelength of 354.7 nm, which is used for the water spectrometer described in this article. This paper presents the first lidar measurements of calibrated Raman backscatter-coefficient spectra S con of liquid-water, mixed-phase, and ice clouds. The measurements were performed with a water spectrometer that was added to the high-performance Raman Lidar for Atmospheric Moisture Sensing (RAMSES) of the German Meteorological Service, located at the Richard Aßmann Observatory in

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It has come to the attention of AMS that the paper previously referenced as “Penc, R. S., 2001: Moisture analysis of a type I cloud-topped boundary layer from Doppler radar and rawinsonde observations. J. Atmos. Oceanic Technol. , 18, 1941–1958 ” was drawn substantially from the masters thesis of Major J. P. Dreher. AMS has determined that Major Dreher merits coauthorship of this paper. The correct reference for this paper is as follows: Penc, R. S. , and Dreher J. P. , 2001 : Moisture

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Simon P. de Szoeke


A small integrated oceanographic thermometer with a nominal response time of 1 s was affixed to a floating hose “sea snake” towed near the bow of a research vessel. The sensor measured the near-surface ocean temperature accurately and in agreement with other platforms. The effect of conduction and evaporation is modeled for a sensor impulsively alternated between water and air. Large thermal mass makes most sea snake thermometers insensitive to temperature impulses. The smaller 1-s thermometer cooled by evaporation, but the sensor never reached the wet-bulb temperature. The cooling was less than 6% of the (~2.7°C) difference between the ocean temperature and the wet-bulb temperature in 99% of 2-s−1 samples. Filtering outliers, such as with a median, effectively removes the evaporative cooling effect from 1- or 10-min average temperatures.

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Alemu Tadesse and Emmanouil N. Anagnostou


This paper presents development of a statistical procedure for estimation of ensemble rainfall fields from a combination of ground radar observations and in situ rain gauge measurements. The uncertainty framework characterizes radar-rainfall estimation algorithm limitation accounting for rain gauge sampling uncertainty. The procedure is applied on a multicomponent rainfall estimation algorithm, which utilizes a rain-path attenuation correction technique, a power-law reflectivity-to-rainfall (ZR) relationship, and a parameter to differentiate between convective (C) and stratiform (S) regimes in the ZR conversion. Uncertainty is explicitly accounted for by evaluating the algorithm’s parameter set posterior probability density function (known as parameters’ equifinality) on the basis of the Generalized Likelihood Uncertainty Estimation (GLUE) framework. The study is facilitated by NASA’s C-band Doppler radar [named the Tropical Ocean Global Atmosphere (TOGA)] observations and four dense rain gauge clusters available from the Tropical Rainfall Measuring Mission (TRMM)-Large-Scale Biosphere–Atmosphere (LBA) experiment, conducted between January and February of 1999 in Southwest Amazon. Statistics are proposed for jointly evaluating the wideness of radar retrieval uncertainty limits [uncertainty ratio (UR)] and the percentage of observations that fall within those error bounds [exceedance ratio (ER)]. Results show that the parameter range selected in GLUE could characterize the radar-rainfall estimation uncertainty. Combined assessment of UR and ER for a varying range of parameters’ equifinality provides an objective basis for comparing rain retrieval algorithms and determining uncertainty bounds. Ensemble radar-rainfall fields derived on the basis of this procedure can be used to statistically assess satellite rain retrieval algorithms and derive ensemble hydrologic predictions driven by radar-rainfall input (e.g., runoff and soil moisture).

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Authors Laurence C. Breaker et al. have reported an error in Fig. 7 of their paper “Preliminary Results from Long-Term Measurements of Atmospheric Moisture in the Marine Boundary Layer in the Gulf of Mexico,” which appeared on pages 661–676 of the June issue of the Journal of Atmospheric and Oceanic Technology, Vol. 15, No. 3 . The corrected version of the figure appears below. Fig. 7. Survival percentages as a function of hull type. The number of instruments used in the calculations for

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E. Robert Kursinski and Thomas Gebhardt

to what was anticipated (e.g., Kursinski et al. 2000 ). While the reasons have not been entirely clear, the present work and its applications suggest some of the causes and how to improve the impact (E. R. Kursinski et al. 2014, unpublished manuscript). The focus of the work presented here is a new deconvolution method designed to estimate and remove zero-mean random errors in histograms of water vapor derived from GPS RO. Such histograms reveal the distribution of atmospheric moisture for the

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