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Basivi Radhakrishna, Frédéric Fabry, John J. Braun, and Teresa Van Hove

Abstract

The variation of precipitable water vapor (PW) over the continental United States is examined at various time scales using spatial maps of a column-averaged mixing ratio (CAMR) that is derived from integrated column PW from both observations and reanalysis data. CAMR spatial maps are generated utilizing PW measurements obtained from a network of ground-based global positioning system (GPS) receivers and the North American Regional Reanalysis (NARR) over a time span of 4 yr (February 2009–January 2013). The effect of topography on PW is mitigated by vertically averaging the mixing ratio instead of integrating the absolute humidity. An ordinary kriging interpolation technique is used to generate spatial maps of CAMR. The observed and predicted PW derived by GPS and NARR correlate well with each other at annual and monthly scales. When focusing on its diurnal cycle, moisture peaks in the late afternoon over the Great Plains and late night over the Rockies. It is also found that atmospheric moisture within NARR generally increases in the second half of the UTC day and is adjusted significantly lower when external observations, such as radiosondes, are assimilated into the analysis system. These adjustments in the analysis introduce nonphysical offsets that are not present within the GPS-derived moisture fields. At meso-β and meso-α scales, GPS PW fields can be used as a precursor to forecast convection up to 3 h prior to initiation. As stated previously, the correlation between GPS and NARR is high (>0.98) at monthly and seasonal time scales, but there is poor correlation at time scales less than a day. This indicates that the water budget within NARR is not in proper balance over these short-term time scales. Over the continental United States, daily cycles of PW and precipitation are coupled differently in different areas.

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Yuei-An Liou, Yu-Tun Teng, Teresa Van Hove, and James C. Liljegren

Abstract

The sensing of precipitable water (PW) using the Global Positioning System (GPS) in the near Tropics is investigated. GPS data acquired from the Central Weather Bureau’s Taipei weather station in Banchao (Taipei), Taiwan, and each of nine International GPS Service (IGS) stations were utilized to determine independently the PW at the Taipei site from 18 to 24 March 1998. Baselines between Taipei and the other nine stations range from 676 to 3009 km. The PW determined from GPS observations for the nine baseline cases are compared with measurements by a dual-channel water vapor radiometer (WVR) and radiosondes at the Taipei site. Although previous results from other locations show that the variability in the rms difference between GPS- and WVR-observed PW ranges from 1 to 2 mm, a variability of 2.2 mm is found. The increase is consistent with scaling of the variability with the total water vapor burden (PW). In addition, accurate absolute PW estimates from GPS data for baseline lengths between 1500 and 3000 km were obtained. Previously, 500 and 2000 km have been recommended in the literature as the minimum baseline length needed for accurate absolute PW estimation. An exception occurs when GPS data acquired in Guam, one of the nine IGS stations, were utilized. This result is a possible further indication that the rms difference between GPS- and WVR-measured PW is dependent on the total water vapor burden, because both Taipei and Guam are located in more humid regions than the other stations.

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Christian Rocken, Teresa Van Hove, James Johnson, Fred Solheim, Randolph Ware, Mike Bevis, Steve Chiswell, and Steve Businger

Abstract

Atmospheric water vapor was measured with six Global Positioning System (GPS) receivers for 1 month at sites in Colorado, Kansas, and Oklahoma. During the time of the experiment from 7 May to 2 June 1993, the area experienced severe weather. The experiment, called “GPS/STORM,” used GPS signals to sense water vapor and tested the accuracy of the method for meteorological applications. Zenith wet delay and precipitable water (PW) were estimated, relative to Platteville, Colorado, every 30 min at five sites. At three of these five sites the authors compared GPS estimates of PW to water vapor radiometer (WVR) measurements. GPS and WVR estimates agree to 1–2 mm rms. For GPS/STORM site spacing of 500–900 km, high-accuracy GPS satellite orbits are required to estimate 1–2-mm-level PW. Broadcast orbits do not have sufficient accuracy. It is possible, however, to estimate orbit improvements simultaneously with PW. Therefore, it is feasible that future meteorological GPS networks provide near-real-time high-resolution PW for weather forecasting.

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Paul E. Ciesielski, Hungjui Yu, Richard H. Johnson, Kunio Yoneyama, Masaki Katsumata, Charles N. Long, Junhong Wang, Scot M. Loehrer, Kathryn Young, Steven F. Williams, William Brown, John Braun, and Teresa Van Hove

Abstract

The upper-air sounding network for Dynamics of the Madden–Julian Oscillation (DYNAMO) has provided an unprecedented set of observations for studying the MJO over the Indian Ocean, where coupling of this oscillation with deep convection first occurs. With 72 rawinsonde sites and dropsonde data from 13 aircraft missions, the sounding network covers the tropics from eastern Africa to the western Pacific. In total nearly 26 000 soundings were collected from this network during the experiment’s 6-month extended observing period (from October 2011 to March 2012). Slightly more than half of the soundings, collected from 33 sites, are at high vertical resolution. Rigorous post–field phase processing of the sonde data included several levels of quality checks and a variety of corrections that address a number of issues (e.g., daytime dry bias, baseline surface data errors, ship deck heating effects, and artificial dry spikes in slow-ascent soundings).

Because of the importance of an accurate description of the moisture field in meeting the scientific goals of the experiment, particular attention is given to humidity correction and its validation. The humidity corrections, though small relative to some previous field campaigns, produced high-fidelity moisture analyses in which sonde precipitable water compared well with independent estimates. An assessment of operational model moisture analyses using corrected sonde data shows an overall good agreement with the exception at upper levels, where model moisture and clouds are more abundant than the sonde data would indicate.

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