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Junhong Wang

Abstract

The dropsonde humidity data have not been fully utilized due to lack of knowledge on performance of the dropsonde humidity sensor. This study evaluates the performance of the dropsonde humidity sensor using data collected from two field experiments, the Dynamics and Chemistry of Marine Stratocumulus Phase II: Entrainment Studies (DYCOMS-II) and the International H2O Project (IHOP)_2002. During DYCOMS-II, 63 dropsondes were dropped above marine stratocumulus clouds. It provides a unique opportunity to evaluate the performance of the dropsonde humidity sensor within clouds. Relative humidity (RH) inside clouds did not reach 100% all the time, but the maximum RH reached 100% for 28% of soundings and was within the sensor accuracy range (94%–100%). This suggests that the dropsonde humidity sensor displays no systematic dry bias near saturation. The dropsonde humidity sensor experienced large time-lag errors when it descended from a dry environment above clouds into clouds. The mean estimated time constant of the sensor is 5 s at 15°C, which is much larger than 0.5 s at 20°C given by the manufacturer. The humidity sensor still reported near-saturation RH after it exited clouds because of water on the sensor. The approximately coincident dropsonde and aircraft temperature data during DYCOMS-II show that the dropsonde underestimates temperatures inside and below clouds by averages of 0.21° and 0.93°C, respectively. Seventy-one pairs of dropsonde and radiosonde soundings during IHOP_2002 were launched within a half hour and 50 km and sampled the same air mass based on the visual examination. The comparisons show that the dropsonde and radiosonde RH data agree with each other within ±2% RH, suggesting no systematic dry bias in dropsonde humidity data. However, dropsonde-measured temperature is consistently colder than that by radiosonde by ∼0.4°C.

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Junhong Wang and Liangying Zhang

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A global, 10-yr (February 1997–April 2006), 2-hourly dataset of atmospheric precipitable water (PW) was produced from ground-based global positioning system (GPS) measurements of zenith tropospheric delay (ZTD) at approximately 350 International Global Navigation Satellite Systems (GNSS) Service (IGS) ground stations. A total of 130 pairs of radiosonde and GPS stations are found within a 50-km distance and 100-m elevation of each other. At these stations, 14 types of radiosondes are launched and the following 3 types of humidity sensors are used: capacitive polymer, carbon hygristor, and goldbeater’s skin. The PW comparison between radiosonde and GPS data reveals three types of systematic errors in the global radiosonde PW data: measurement biases of the 14 radiosonde types along with their characteristics, long-term temporal inhomogeneity, and diurnal sampling errors of once- and twice-daily radiosonde data. The capacitive polymer generally shows mean dry bias of −1.19 mm (−6.8%). However, the carbon hygristor and goldbeater’s skin hygrometers have mean moist biases of 1.01 mm (3.4%) and 0.76 mm (5.4%), respectively. The protective shield over the humidity sensor boom introduced in late 2000 reduces the PW dry bias from 6.1% and 2.6% in 2000 to 3.9% and −1.14% (wet bias) in 2001 for the Vaisala RS80A and RS80H, respectively. The dry bias in Vaisala radiosondes has larger magnitudes during the day than at night, especially for RS90 and RS92, with a day–night difference of 5%–7%. The time series of monthly mean PW differences between the radiosonde and GPS are able to detect significant changes associated with known radiosonde type changes. Such changes would have a significant impact on the long-term trend estimate. Diurnal sampling errors of twice-daily radiosonde data are generally within 2%, but can be as much as 10%–15% for the once-daily soundings. In conclusion, this study demonstrates that the global GPS PW data are useful for identifying and quantifying several kinds of systematic errors in global radiosonde PW data. Several recommendations are made for future needs of global radiosonde and GPS networks and data.

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Aiguo Dai and Junhong Wang

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Global surface pressure data from 1976 to 1997 from over 7500 land stations and the Comprehensive Ocean–Atmosphere Data Set have been analyzed using harmonic and zonal harmonic methods. It is found that the diurnal pressure oscillation (S 1) is comparable to the semidiurnal pressure oscillation (S 2) in magnitude over much of the globe except for the low-latitude open oceans, where S 2 is about twice as strong as S 1. Over many land areas, such as the western United States, the Tibetan Plateau, and eastern Africa, S 1 is even stronger than S 2. This is in contrast to the conventional notion that S 2 predominates over much of the globe. The highest amplitudes (∼1.3 mb) of S 1 are found over northern South America and eastern Africa close to the equator. Here S 1 is also strong (∼1.1 mb) over high terrain such as the Rockies and the Tibetan Plateau. The largest amplitudes of S 2 (∼1.0–1.3 mb) are in the Tropics over South America, the eastern and western Pacific, and the Indian Ocean. Here S 1 peaks around 0600–0800 LST at low latitudes and around 1000–1200 LST over most of midlatitudes, while S 2 peaks around 1000 and 2200 LST over low- and midlatitudes. Here S 1 is much stronger over the land than over the ocean and its amplitude distribution is strongly influenced by landmasses, while the land–sea differences of S 2 are small. The spatial variations of S 1 correlate significantly with spatial variations in the diurnal temperature range at the surface, suggesting that sensible heating from the ground is a major forcing for S 1. Although S 2 is much more homogeneous zonally than S 1, there are considerable zonal variations in the amplitude of S 2, which cannot be explained by zonal variations in ozone and water vapor. Other forcings such as those through clouds’ reflection and absorption of solar radiation and latent heating in convective precipitation are needed to explain the observed regional and zonal variations in S 2. The migrating tides S11 and S22 predominate over other zonal wave components. However, the nonmigrating tides are substantially stronger than previously reported. The amplitudes of both the migrating and nonmigrating tides decrease rapidly poleward with a slower pace at middle and high latitudes.

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Junhong Wang and William B. Rossow

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A method is described to use rawinsonde data to estimate cloud vertical structure, including cloud-top and cloud-base heights, cloud-layer thickness, and the characteristics of multilayered clouds. Cloud-layer base and top locations are identified based on three criteria: maximum relative humidity in a cloud of at least 87%, minimum relative humidity of at least 84%, and relative humidity jumps exceeding 3% at cloud-layer top and base, where relative humidity is with respect to liquid water at temperatures greater than or equal to 0°C and with respect to ice at temperatures less than 0°C. The analysis method is tested at 30 ocean sites by comparing with cloud properties derived from other independent data sources. Comparison of layer-cloud frequencies of occurrence with surface observations shows that rawinsonde observations (RAOBS) usually detect the same number of cloud layers for low and middle clouds as the surface observers, but disagree more for high-level clouds. There is good agreement between the seasonal variations of RAOBS-determined top pressure of the highest cloud and that from the International Satellite Cloud Climate Project (ISCCP) data. RAOBS-determined top pressures of low and middle clouds agree better with ISCCP, but RAOBS often fail to detect very high and thin clouds. The frequency of multilayered clouds is qualitatively consistent with that estimated from surface observations. In cloudy soundings at these ocean sites, multilayered clouds occur 56% of the time and are predominately two layered. Multilayered clouds are most frequent (≈70%) in the Tropics (10°S–10°N) and least frequent at subtropical eastern Pacific stations. The frequency of multilayered clouds is higher in summer than in winter at low-latitude stations (30°S–30°N), but the opposite variation appears at the two subtropical stations. The frequency distributions of cloud top, cloud base, and cloud-layer thickness and cloud occurrence as a function of height are also presented. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer.

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William B. Rossow, Yuanchong Zhang, and Junhong Wang

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To diagnose how cloud processes feed back on weather- and climate-scale variations of the atmosphere requires determining the changes that clouds produce in the atmospheric diabatic heating by radiation and precipitation at the same scales of variation. In particular, not only the magnitude of these changes must be quantified but also their correlation with atmospheric temperature variations; hence, the space–time resolution of the cloud perturbations must be sufficient to account for the majority of these variations. Although extensive new global cloud and radiative flux datasets have recently become available, the vertical profiles of clouds and consequent radiative flux divergence have not been systematically measured covering weather-scale variations from about 100 km, 3 h up to climate-scale variations of 10 000 km, decadal inclusive. By combining the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and an analysis of radiosonde humidity profiles, a statistical model has been developed that associates each cloud type, recognizable from satellite measurements, with a particular cloud vertical structure. Application of this model to the ISCCP cloud layer amounts produces estimates of low-level cloud amounts and average cloud-base pressures that are quantitatively closer to observations based on surface weather observations, capturing the variations with latitude and season and land and ocean (results are less good in the polar regions). The main advantage of this statistical model is that the correlations of cloud vertical structure with meteorology are qualitatively similar to “classical” information relating cloud properties to weather. These results can be evaluated and improved with the advent of satellites that can directly probe cloud vertical structures over the globe, providing statistics with changing meteorological conditions.

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Biswadev Roy, Jeffrey B. Halverson, and Junhong Wang

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Hundreds of Vaisala sondes with an RS80-H Humicap thin-film capacitor humidity sensor were launched during the Tropical Rainfall Measuring Mission (TRMM) field campaigns (1999) Large Scale Biosphere–Atmosphere (LBA) experiment held in Brazil and the Kwajalein Experiment (KWAJEX) held in the Republic of the Marshall Islands. Six humidity error correction algorithms were used primarily for applying system-bias correction to RS80-H humidity data. All TRMM field campaign Vaisala humidity soundings were corrected for dry bias using this algorithm. An overall improvement of 3% RH for daytime and 5% RH for nighttime soundings was achieved. Sonde age was ascertained using respective serial numbers (in this case the range is 0.06–2.07 yr) and used in the algorithm for calculation of sensor aging error and chemical contamination errors. Chemical contamination error is also found to be a dominant error source. Daytime sensor-arm heating for the first 50 s of the sonde launch is found to bear a cosine variation with sonde age. Surface reference temperature and sonde registered surface temperature are both used for calculating surface saturation vapor pressure, which in turn is used for sensor-arm-heating error estimation during the first 50 s. Site-mean CAPE values are found to increase significantly after correction. It is suggested that sonde surface temperature error must also be corrected for sonde age while using the present RS80-H correction algorithm. An age–height plot of the differences between the uncorrected and corrected specific humidity value for all Vaisala soundings shows an age-dependent increase (approximately 3.4 g kg−1 for 2-yr-old sondes). Variation of specific humidity difference was not found to be very significant for the upper levels when the sensor is less than 1.25 yr old.

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Junhong Wang, Aiguo Dai, and Carl Mears

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This study analyzes trends in precipitable water (PW) over land and ocean from 1988 to 2011, the PW–surface temperature T s relationship, and their diurnal asymmetry using homogenized radiosonde data, microwave satellite observations, and ground-based global positioning system (GPS) measurements. It is found that positive PW trends predominate over the globe, with larger magnitudes over ocean than over land. The PW trend is correlated with surface warming spatially over ocean with a pattern correlation coefficient of 0.51. The PW percentage increase rate normalized by T s expressed as is larger and closer to the rate implied by the Clausius–Clapeyron (C–C) equation over ocean than over land. The 2-hourly GPS PW data show that the PW trend from 1995 to 2011 is larger at night than during daytime. Nighttime PW monthly anomalies correlate positively and significantly with nighttime minimum temperature T min at all stations, but this is not true for daytime PW and maximum temperature T max. The ratio of relative PW changes with T min () is larger and closer to the C–C equation’s implied value of ~7% K−1 than . This suggests that the relationship between PW and T s at night is a better indicator of the water vapor feedback than that during daytime, when clouds and other factors also influence T s.

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Tianbao Zhao, Aiguo Dai, and Junhong Wang

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Radiosonde humidity data provide the longest record for assessing changes in atmospheric water vapor, but they often contain large discontinuities because of changes in instrumentation and observational practices. In this study, the variations and trends in tropospheric humidity (up to 300 hPa) over China are analyzed using a newly homogenized radiosonde dataset. It is shown that the homogenization removes the large shifts in the original records of dewpoint depression (DPD) resulting from sonde changes in recent years in China, and it improves the DPD’s correlation with precipitation and the spatial coherence of the DPD trend from 1970 to 2008. The homogenized DPD data, together with homogenized temperature, are used to compute the precipitable water (PW), whose correlation with the PW from ground-based global positioning system (GPS) measurements at three collocated stations is also improved after the homogenization. During 1970–2008 when the record is relatively complete, tropospheric specific humidity after the homogenization shows upward trends, with surface–300-hPa PW increasing by about 2%–5% decade−1 over most of China and by more than 5% decade−1 over northern China in winter. The PW variations and changes are highly correlated with those in lower–midtropospheric mean temperature (r = 0.83), with a dPW/dT slope of ~7.6% K−1, which is slightly higher than the 7% K−1 implied by Clausius–Clapeyron equation with a constant relative humidity (RH). The radiosonde data show only small variations and weak trends in tropospheric RH over China. An empirical orthogonal function (EOF) analysis of the PW reveals several types of variability over China, with the first EOF (31.4% variance) representing an upward PW trend over most of China (mainly since 1987). The second EOF (12.0% variance) shows a dipole pattern between Southeast and Northwest China and it is associated with a similar dipole pattern in atmospheric vertical motion. This mode exhibits mostly multiyear variations that are significantly correlated with Pacific decadal oscillation (PDO) and ENSO indices.

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Junhong Wang, William B. Rossow, and Yuanchong Zhang

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A global cloud vertical structure (CVS) climatic dataset is created by applying an analysis method to a 20-yr collection of twice-daily rawinsonde humidity profiles to estimate the height of cloud layers. The CVS dataset gives the vertical distribution of cloud layers for single and multilayered clouds, as well as the top and base heights and layer thicknesses of each layer, together with the original rawinsonde profiles of temperature, humidity, and winds. The average values are cloud-top height = 4.0 km above mean sea level (MSL), cloud-base height = 2.4 km MSL, cloud-layer thickness = 1.6 km, and separation distance between consecutive layers = 2.2 km. Multilayered clouds occur 42% of the time and are predominately two-layered. The lowest layer of multilayered cloud systems is usually located in the atmospheric boundary layer (below 2-km height MSL). Clouds over the ocean occur more frequently at lower levels and are more often formed in multiple layers than over land. Latitudinal variations of CVS also show maxima and minima that correspond to the locations of the intertropical convergence zone, the summer monsoons, the subtropical subsidence zones, and the midlatitude storm zones. Multilayered clouds exist most frequently in the Tropics and least frequently in the subtropics; there are more multilayered clouds in summer than in winter. Cloud layers are thicker in winter than in summer at mid- and high latitudes, but are thinner in winter in Southeast Asia.

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Jennifer Fowler, Junhong Wang, Deborah Ross, Thomas Colligan, and Jaxen Godfrey

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The 21 August 2017 total solar eclipse was the first total eclipse on the mainland of the United States since 1979. The Atmospheric Responses of 2017 Total Solar Eclipse (ARTSE2017) project was created to observe the response of the atmosphere to the shadow of the moon. During the eclipse, 10 sites launched radiosondes in a very rapid, serial weather balloon deployment along the totality path, and high-resolution mesoscale meteorological network (mesonet) data were collected in three states. Here, we focus on the results obtained from the radiosonde field campaign in Fort Laramie, Wyoming, and the New York State Mesonet (NYSM). In Fort Laramie, 36 people from 13 institutions flew 19 radiosondes and launched 5 large balloons carrying video payloads before, during, and after the eclipse while continuously recording surface weather data. Preliminary analysis of the radiosonde data provided inconclusive evidence of eclipse-driven gravity waves but showed that the short duration of darkness during totality was enough to alter boundary layer (BL) height, the lowest layer of the atmosphere, substantially. The statewide impact of the partial eclipse in New York State (NYS) was observed for solar radiation, surface temperature, surface wind, and surface-layer lapse rate using NYSM data. Importantly, the radiosonde and mesonet data collected during the eclipse will be available for public access. ARTSE2017 also focused on education, including students from all demographics (undergraduate and K–12) and the general public. Finally, we summarize goals accomplished from leveraging resources for education, research, and workforce development on undergraduate students from a variety of fields.

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