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Chaohua Dong, Jun Yang, Wenjian Zhang, Zhongdong Yang, Naimeng Lu, Jinming Shi, Peng Zhang, Yujie Liu, and Bin Cai

FengYun-3A (FY-3A), the first satellite in the second generation of the Chinese polar-orbiting meteorological satellites, was launched at Taiyuan, China, launching center on 27 May 2008. Equipped with both sounding and imaging payloads, enabling more powerful observations than the first generation of the FY-1 series, FY-3A carries 11 instruments. Two of them are the same as those on FY-1C/D, while the others, whose spectral bands cover violet, visible, near-infrared, infrared, and microwave spectral regions, are all newly developed. FY-3A instruments can be used to detect and study weather, clouds, radiation, climate, atmosphere, land, ocean, and other environmental features. FY-3A check out took about 5 months following its launch; FY-3A has been operational since January 2009. The plan for the future FY-3 series is to operate two polar-orbiting spacecraft—one in the morning and the other in the afternoon orbit—with different payloads for each spacecraft. This orbit configuration will be further coordinated with the World Meteorological Organization (WMO). One low-inclination orbit spacecraft is under consideration for radar and passive microwave precipitation measurement missions. Details are under discussion and yet to be determined. An overview of the first launch, FY-3A (the second generation of the Chinese meteorological satellites), and its imaging and sounding capabilities and potential applications are given in this paper.

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Fanglin Yang, Hua-Lu Pan, Steven K. Krueger, Shrinivas Moorthi, and Stephen J. Lord

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This study evaluates the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) against observations made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program at the southern Great Plains site for the years 2001–04. The spatial and temporal scales of the observations are examined to search for an optimum approach for comparing grid-mean model forecasts with single-point observations. A single-column model (SCM) based upon the GFS was also used to aid in understanding certain forecast errors. The investigation is focused on the surface energy fluxes and clouds. Results show that the overall performance of the GFS model has been improving, although certain forecast errors remain. The model overestimated the daily maximum latent heat flux by 76 W m−2 and the daily maximum surface downward solar flux by 44 W m−2, and underestimated the daily maximum sensible heat flux by 44 W m−2. The model’s surface energy balance was reached by a cancellation of errors. For clouds, the GFS was able to capture the observed evolutions of cloud systems during major synoptic events. However, on average, the model largely underestimated cloud fraction in the lower and midtroposphere, especially for daytime nonprecipitating low clouds because shallow convection in the GFS does not produce clouds. Analyses of surface radiative fluxes revealed that the diurnal cycle of the model’s surface downward longwave flux (SDLW) was not in phase with that of the ARM-observed SDLW. SCM experiments showed that this error was caused by an inaccurate scaling factor, which was a function of ground skin temperature and was used to adjust the SDLW at each model time step to that computed by the model’s longwave radiative transfer routine once every 3 h. A method has been proposed to correct this error in the operational forecast model. It was also noticed that the SDLW biases changed from mostly negative in 2003 to slightly positive in 2004. This change was traced back to errors in the near-surface air temperature. In addition, the SDLW simulated with the newly implemented Rapid Radiative Transfer Model longwave routine in the GFS is usually 5–10 W m−2 larger than that simulated with the previous routine. The forecasts of surface downward shortwave flux (SDSW) were relatively accurate under clear-sky conditions. The errors in SDSW were primarily caused by inaccurate forecasts of cloud properties. Results from this study can be used as guidance for the further development of the GFS.

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Ning Lu, Kevin E. Trenberth, Jun Qin, Kun Yang, and Ling Yao

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Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

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Hong-Hai Zhang, Gui-Peng Yang, Chun-Ying Liu, and Lu-Ping Su

Abstract

The total suspended particulate (TSP) samples over the Yellow Sea and the East China Sea were collected to determine the major compositions of water-soluble ionic species during two cruises in autumn 2007. The aerosol compositions exhibited an obvious regional variation between the two cruises, with higher concentrations (except Na+ and Mg2+) over the northern Yellow Sea during the first cruise. The concentrations of the secondary ions [non–sea salt sulfate (nss-), , and ] were 11 ± 4.9, 3.1 ± 2.1, and 3.7 ± 2.6 μg m−3, respectively, which together contributed over 72% of the total determined ion concentrations. Significant correlations between these secondary ions were found within each sampling period, while nss-K+ and nss-Ca2+ showed strong correlation with each other. The calculated results of equivalent concentrations of anions (nss- and ) and cations ( and Ca2+) showed that the acidic species were mostly neutralized with the alkaline species over the study areas. The mass ratio of nss-/ was 1.4 during the investigation period. In addition, the concentrations of MSA were 0.011 ± 0.044 and 0.0081 ± 0.0047 μg m−3 during the two cruises, respectively. Based on the measured MSA, nss-, and their ratios, the relative biogenic sulfur contribution to the total nss- was estimated to be only 2.0% during the two cruises, further suggesting the major contribution of anthropogenic source to sulfur budget over the marginal seas of China.

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Jian Lu, Gang Chen, L. Ruby Leung, D. Alex Burrows, Qing Yang, Koichi Sakaguchi, and Samson Hagos

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Systematic sensitivity of the jet position and intensity to horizontal model resolution is identified in several aquaplanet AGCMs, with the coarser resolution producing a more equatorward eddy-driven jet and a stronger upper-tropospheric jet intensity. As the resolution of the models increases to 50 km or finer, the jet position and intensity show signs of convergence within each model group. The mechanism for this convergence behavior is investigated using a hybrid Eulerian–Lagrangian finite-amplitude wave activity budget developed for the upper-tropospheric absolute vorticity. The results suggest that the poleward shift of the eddy-driven jet with higher resolution can be attributed to the smaller effective diffusivity of the model in the midlatitudes that allows more wave activity to survive the dissipation and to reach the subtropical critical latitude for wave breaking. The enhanced subtropical wave breaking and associated irreversible vorticity mixing act to maintain a more poleward peak of the vorticity gradient, and thus a more poleward jet. Being overdissipative, the coarse-resolution AGCMs misrepresent the nuanced nonlinear aspect of the midlatitude eddy–mean flow interaction, giving rise to the equatorward bias of the eddy-driven jet. In accordance with the asymptotic behavior of effective diffusivity of Batchelor turbulence in the large Peclet number limit, the upper-tropospheric effective diffusivity of the aquaplanet AGCMs displays signs of convergence in the midlatitude toward a value of approximately 107 m2 s−1 for the ∇2 diffusion. This provides a dynamical underpinning for the convergence of the jet stream observed in these AGCMs at high resolution.

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Zhihua He, Long Yang, Fuqiang Tian, Guangheng Ni, Aizhong Hou, and Hui Lu

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The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

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Jing Sun, Yingying Chen, Kun Yang, Hui Lu, Long Zhao, and Donghai Zheng

Abstract

In the central-eastern Tibetan Plateau (TP) there is abundant organic matter in topsoils, which plays a crucial role in determining soil hydraulic properties that need to be properly described in land surface models. Limited soil parameterizations consider the impacts of soil organic matter (SOM), but they still show poor performance in the TP. A dedicated field campaign is therefore conducted by taking undisturbed soil samples in the central TP to obtain in-situ soil hydraulic parameters and to advance SOM parameterizations. The observed findings are twofold. 1) The SOM pore-size distribution parameter, derived from measured soil water retention curves, has been demonstrated to be much underestimated in previous studies. 2) SOM saturated hydraulic conductivity is overestimated. Accordingly, a new soil hydraulic parameterization is established by modifying a commonly used one based on observations, which is then evaluated by incorporating it into Noah-MP. Compared with the original ones, the new parameterization significantly improves surface soil liquid water simulations at stations with high surface SOM content, especially in the warm season. A further application with the revised Noah-MP indicates that SOM can enhance sensible heat flux but decrease evaporation and subsurface soil temperature in the warm season, and tends to have a much weak effect in the cold season. This study provides insights into the role of SOM in modulating soil state and surface energy budget. Note that, however, there are many other factors at play and the new parameterization is not necessarily applicable beyond the TP.

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Samson Hagos, L. Ruby Leung, Qing Yang, Chun Zhao, and Jian Lu

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This study examines the sensitivity of atmospheric river (AR) frequency simulated by a global model with different grid resolutions and dynamical cores. Analysis is performed on aquaplanet simulations using version 4 of the Community Atmosphere Model (CAM4) at 240-, 120-, 60-, and 30-km model resolutions, each with the Model for Prediction Across Scales (MPAS) and High-Order Methods Modeling Environment (HOMME) dynamical cores. The frequency of AR events decreases with model resolution and the HOMME dynamical core produces more AR events than MPAS. Comparing the frequencies determined using absolute and percentile thresholds of large-scale conditions used to define an AR, model sensitivity is found to be related to the overall sensitivity of subtropical westerlies, atmospheric precipitable water content and profile, and to a lesser extent extratropical Rossby wave activity to model resolution and dynamical core. Real-world simulations using MPAS at 120- and 30-km grid resolutions also exhibit a decrease of AR frequency with increasing resolution over the southern east Pacific, but the difference is smaller over the northern east Pacific. This interhemispheric difference is related to the enhancement of convection in the tropics with increased resolution. This anomalous convection sets off Rossby wave patterns that weaken the subtropical westerlies over the southern east Pacific but has relatively little effect on those over the northern east Pacific. In comparison to the NCEP-2 reanalysis, MPAS real-world simulations are found to underestimate AR frequencies at both resolutions likely because of their climatologically drier subtropics and poleward-shifted jets. This study highlights the important links between model climatology of large-scale conditions and extremes.

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Jun Yang, Weitao Lyu, Ying Ma, Yijun Zhang, Qingyong Li, Wen Yao, and Tianshu Lu

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The macroscopic characteristics of clouds in the Tibetan Plateau are crucial to understanding the local climatic conditions and their impact on the global climate and water vapor cycle. In this study, the variations of cloud cover and cloud types are analyzed by using total-sky images of two consecutive years in Shigatse, Tibetan Plateau. The results show that the cloud cover in Shigatse presents a distinct seasonal difference that is characterized by low cloud cover in autumn and winter and high cloud cover in summer and spring. July is the month with the largest cloud coverage, and its average cloud cover exceeds 75%. The probability of clouds in the sky is the lowest in November, with an average cloud cover of less than 20%. The diurnal variations of cloud cover in different months also have considerable differences. Specifically, cloud cover is higher in the afternoon than that in the morning in most months, whereas the cloud cover throughout the day varies little from July to September. The dominant cloud types in different months are also not the same. The proportion of clear sky is large in autumn and winter. Stratiform cloud occupies the highest percentage in March, April, July, and August. The probability of emergence of cirrus is highest in May and June. The Shigatse region has clear rainy and dry seasons, and correlation analysis between precipitation and clouds shows that the largest cumulative precipitation, the highest cloud cover, and the highest proportion of stratiform clouds occur simultaneously in July.

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Robert Lund, Xiaolan L. Wang, Qi Qi Lu, Jaxk Reeves, Colin Gallagher, and Yang Feng

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Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Classical changepoint tests based on sums of squared errors are modified to take into account series autocorrelations and periodicities. The methods are applied in the analyses of two climate series.

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