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Yingying Chen, Kun Yang, Degang Zhou, Jun Qin, and Xiaofeng Guo

Abstract

Daytime land surface temperatures in arid and semiarid regions are typically not well simulated in current land surface models (LSMs). This study first evaluates the importance of parameterizing the thermal roughness length (z 0h) to model the surface temperature (T sfc) and turbulent sensible heat flux (H) in arid regions. Six schemes for z 0h are implemented into the Noah LSM, revealing the high sensitivity of the simulations to its parameterization. Comparisons are then performed between the original Noah LSM and a revised version with a novel z 0h scheme against observations at four arid or semiarid sites, including one in Arizona and three in western China. The land they cover is sparse grass or bare soil. The results indicate that the original Noah LSM significantly underestimates T sfc and overestimates H in the daytime, whereas the revised model can simulate well both T sfc and H simultaneously. The improved version benefits from the successful modeling of the diurnal variation of z 0h, which the original model cannot produce.

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Jun Yang, Zhiqing Zhang, Caiying Wei, Feng Lu, and Qiang Guo

Abstract

China is developing a new generation of geostationary meteorological satellites called Fengyun-4 (FY-4), which is planned for launch beginning in 2016. Following upon the current FY-2 satellite series, FY-4 will carry four new instruments: the Advanced Geosynchronous Radiation Imager (AGRI), the Geosynchronous Interferometric Infrared Sounder (GIIRS), the Lightning Mapping Imager (LMI), and the Space Environment Package (SEP). The first satellite of the FY-4 series launched on 11 December 2016 is experimental, and the following four or more satellites will be operational.

The main objectives of the FY-4 series are to monitor rapidly changing weather systems and to improve warning and forecasting capabilities. The FY-4 measurements are aimed at accomplishing 1) high temporal and spatial resolution imaging in 14 spectral bands from the visible, near-infrared, and infrared (IR) spectral regions; 2) lightning imaging; and 3) high-spectral-resolution IR sounding observations over China and adjacent regions. FY-4 will also enhance the space weather monitoring and warning with SEP. Current products from FY-2 will be improved by FY-4, and a number of new products will also be introduced. FY-4’s sounding and imaging data will be used to improve applications in a wide range of ocean, land, and atmosphere monitoring plus forecasting extreme weather (especially typhoons and thunderstorms); overall, FY-4 will contribute to more accurate understanding and forecasting of China’s weather, climate, environment, and natural disasters. This new generation of Chinese geostationary weather satellites is being developed in parallel with the new generation of geostationary meteorological satellite systems from the international community of satellite providers and is intended to be an important contribution to the global observing system.

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Kun Yang, Xiaofeng Guo, Jie He, Jun Qin, and Toshio Koike

Abstract

Atmospheric heating over the Tibetan Plateau (TP) enhances the Asian summer monsoon. This study presents a state-of-the-art estimate of the heating components and their total over the TP, with the aid of high-accuracy experimental data, an updated land surface model, and carefully selected satellite data.

The new estimate differs from previous estimates in three aspects: 1) different seasonality—the new estimation shows the maximum total heat source occurs in July (the mature period of the monsoon), rather than in the previously reported month of May or June (around the onset of the monsoon), because previous studies greatly overestimated radiative cooling during the monsoon season [June–August (JJA)]; 2) different regional pattern—the eastern TP exhibits stronger heating than the western TP in summer, whereas previous studies gave either an opposite spatial pattern because of overestimated sensible heat flux over the western TP or an overall weaker heat source because of overestimated radiative cooling; and 3) different trend—sensible heat, radiative convergence, and the total heat source have decreased since the 1980s, but their weakening trends were overestimated in a recent study. These biases in previous studies are due to fairly empirical methods and data that were not evaluated against experimental data.

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Kun Yang, Jun Qin, Xiaofeng Guo, Degang Zhou, and Yaoming Ma

Abstract

To clarify the thermal forcing of the Tibetan Plateau, long-term coarse-temporal-resolution data from the China Meteorological Administration have been widely used to estimate surface sensible heat flux by bulk methods in many previous studies; however, these estimates have seldom been evaluated against observations. This study at first evaluates three widely used bulk schemes against Tibet instrumental flux data. The evaluation shows that large uncertainties exist in the heat flux estimated by these schemes; in particular, upward heat fluxes in winter may be significantly underestimated, because diurnal variations of atmospheric stability were not taken into account. To improve the estimate, a new method is developed to disaggregate coarse-resolution meteorological data to hourly according to statistical relationships derived from high-resolution experimental data, and then sensible heat flux is estimated from the hourly data by a well-validated flux scheme. Evaluations against heat flux observations in summer and against net radiation observations in winter indicate that the new method performs much better than previous schemes, and therefore it provides a robust basis for quantifying the Tibetan surface energy budget.

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Jingzhuo Wang, Jing Chen, Jun Du, Yutao Zhang, Yu Xia, and Guo Deng

Abstract

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

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Jun Ge, Weidong Guo, Andrew J. Pitman, Martin G. De Kauwe, Xuelong Chen, and Congbin Fu

Abstract

China is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid amplifying climate-related warming.

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Guoyu Ren, Yaqing Zhou, Ziying Chu, Jiangxing Zhou, Aiying Zhang, Jun Guo, and Xuefeng Liu

Abstract

A dataset of 282 meteorological stations including all of the ordinary and national basic/reference surface stations of north China is used to analyze the urbanization effect on surface air temperature trends. These stations are classified into rural, small city, medium city, large city, and metropolis based on the updated information of total population and specific station locations. The significance of urban warming effects on regional average temperature trends is estimated using monthly mean temperature series of the station group datasets, which undergo inhomogeneity adjustment. The authors found that the largest effect of urbanization on annual mean surface air temperature trends occurs for the large-city station group, with the urban warming being 0.16°C (10 yr)−1, and the effect is the smallest for the small-city station group with urban warming being only 0.07°C (10 yr)−1. A similar assessment is made for the dataset of national basic/reference stations, which has been widely used in regional climate change analyses in China. The results indicate that the regional average annual mean temperature series, as calculated using the data from the national basic/reference stations, is significantly impacted by urban warming, and the trend of urban warming is estimated to be 0.11°C (10 yr)−1. The contribution of urban warming to total annual mean surface air temperature change as estimated with the national basic/reference station dataset reaches 37.9%. It is therefore obvious that, in the current regional average surface air temperature series in north China, or probably in the country as a whole, there still remain large effects from urban warming. The urban warming bias for the regional average temperature anomaly series is corrected. After that, the increasing rate of the regional annual mean temperature is brought down from 0.29°C (10 yr)−1 to 0.18°C (10 yr)−1, and the total change in temperature approaches 0.72°C for the period analyzed.

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Jun Li, Zhaoli Wang, Xushu Wu, Chong-Yu Xu, Shenglian Guo, and Xiaohong Chen

Abstract

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings.

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Yihong Duan, Jiandong Gong, Jun Du, Martin Charron, Jing Chen, Guo Deng, Geoff DiMego, Masahiro Hara, Masaru Kunii, Xiaoli Li, Yinglin Li, Kazuo Saito, Hiromu Seko, Yong Wang, and Christoph Wittmann

The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.

The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.

The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).

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