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  • Author or Editor: Jing Zhang x
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Xiaoqing Peng
,
Tingjun Zhang
,
Yijing Liu
, and
Jing Luo

Abstract

Freezing/thawing indices are useful for assessments of climate change, surface and subsurface hydrology, energy balance, moisture balance, carbon exchange, ecosystem diversity and productivity. Current freezing/thawing indices are inadequate to meet these requirements. We use 16 Coupled Model Intercomparison Project phase 5 (CMIP5) models available for 1850–2005, three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) during 2006–2100, and Climatic Research Unit gridded observations for 1901–2014, to assess the performance of freezing/thawing indices derived from CMIP5 models during 1901–2005. We also analyzed past spatial patterns of freezing/thawing indices and projected these over three RCPs. Results show that CMIP5 models can reproduce the spatial pattern of freezing/thawing indices in the Northern Hemisphere but that the thawing index slightly underestimated observations and the freezing index slightly overestimated them. The thawing index agreed slightly better with observations than did the freezing index. There is significant spatial variability in the freezing/thawing indices, ranging from 0° to 10 000°C day. Over the entire Northern Hemisphere, the time series of the area-averaged thawing index derived from CMIP5 output increased significantly at about 1.14°C day yr−1 during 1850–2005, 1.51°C day yr−1 for RCP2.6, 5.32°C day yr−1 for RCP4.5, and 13.85°C day yr−1 for RCP8.5 during 2006–2100. The area-averaged freezing index decreased significantly at −1.39°C day yr−1 during 1850–2004, −1.2°C day yr−1 for RCP2.6, −4.3°C day yr−1 for RCP4.5, and −9.8°C day yr−1 for RCP8.5 during 2006–2100. The greatest decreases in the freezing index are projected to occur at high latitudes and high altitudes, where the magnitude of the decreasing rate of the freezing index is far greater than that of the increasing rate of the thawing index.

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Jingzhuo Wang
,
Jing Chen
,
Hanbin Zhang
,
Ruoyun Ma
, and
Fajing Chen

Abstract

To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.

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Suhua Liu
,
Hongbo Su
,
Jing Tian
,
Renhua Zhang
,
Weizhen Wang
, and
Yueru Wu

Abstract

Surface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods—the temperature–vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)—have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1°C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

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Yu Wang
,
Hong-Qing Wang
,
Lei Han
,
Yin-Jing Lin
, and
Yan Zhang

Abstract

This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

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Peter A. Bieniek
,
Uma S. Bhatt
,
John E. Walsh
,
T. Scott Rupp
,
Jing Zhang
,
Jeremy R. Krieger
, and
Rick Lader

Abstract

The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) has been downscaled using a regional model covering Alaska at 20-km spatial and hourly temporal resolution for 1979–2013. Stakeholders can utilize these enhanced-resolution data to investigate climate- and weather-related phenomena in Alaska. Temperature and precipitation are analyzed and compared among ERA-Interim, WRF Model downscaling, and in situ observations. Relative to ERA-Interim, the downscaling is shown to improve the spatial representation of temperature and precipitation around Alaska’s complex terrain. Improvements include increased winter and decreased summer higher-elevation downscaled seasonal average temperatures. Precipitation is also enhanced over higher elevations in all seasons relative to the reanalysis. These spatial distributions of temperature and precipitation are consistent with the few available gridded observational datasets that account for topography. The downscaled precipitation generally exceeds observationally derived estimates in all seasons over mainland Alaska, and it is less than observations in the southeast. Temperature biases tended to be more mixed, and the downscaling reduces absolute bias at higher elevations, especially in winter. Careful selection of data for local site analysis from the downscaling can help to reduce these biases, especially those due to inconsistencies in elevation. Improved meteorological station coverage at higher elevations will be necessary to better evaluate gridded downscaled products in Alaska because biases vary and may even change sign with elevation.

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Qiong Wu
,
Hong-Qing Wang
,
Yi-Zhou Zhuang
,
Yin-Jing Lin
,
Yan Zhang
, and
Sai-Sai Ding

Abstract

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0 and 10.8 μm (BTD12.0–10.8), and the BT difference between 6.7 and 10.8 μm (BTD6.7–10.8). Correlations among these IR indicators were investigated using MTSAT-1R images for summer 2007 over East Asia. Temporal, spatial, and numerical frequency distributions were used to represent the correlations. The results showed that large BTD12.0–10.8 values can be observed in the growth of cumulus congestus and associated with the boundary of different terrain where convection was more likely to generate and develop. The results also showed that numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, the shapes of the HT2Ds varied with latitude and the type of underlying surface. After carefully analyzing the correlations among the IR indicators, a conceptual model of the convection life cycle was constructed according to these HT2Ds. A new cloud convection index (CCI) was defined with the combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model. The preliminary test results demonstrated that CCI could effectively identify convective clouds. CCI value and its time trend could reflect the growth or decline of convective clouds.

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