Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves. 相似文献
A regional reanalysis product—China Ocean Reanalysis(CORA)—has been developed for the China's seas and the adjacent areas. In this study, the intraseasonal variabilities(ISVs) in CORA are assessed by comparing with observations and two other reanalysis products(ECCO2 and SODA). CORA shows a better performance in capturing the intraseasonal sea surface temperatures(SSTs) and the intraseasonal sea surface heights(SSHs) than ECCO2 and SODA do, probably due to its high resolution, stronger response to the intraseasonal forcing in the atmosphere(especially the Madden-Julian Oscillation), and more available regional data for assimilation. But at the subsurface, the ISVs in CORA are likely to be weaker than reality, which is probably attributed to rare observational data for assimilation and weak diapycnal eddy diffusivity in the CORA model. According to the comparison results, CORA is a good choice for the study related to variabilities at the surface, but cares have to be taken for the study focusing on the subsurface processes. 相似文献
Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used. 相似文献
By using the hourly data from surface meteorological stations in China, the 3-hour precipitation data from
CRA-Interim (Chinese Reanalysis-Interim), ERA5 (ECMWF Reanalysis 5) and JRA-55 (Japanese Reanalysis-55) are
compared, both on the spatial-temporal distributions and on bias with observation precipitation in China. The results
show that: (1) The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual
average precipitation in China. The simulation of topographic forced precipitation in complex terrain by CRA-interim is
more detailed, while CRA-interim has larger negative bias in central and East China, and larger positive bias in
southwest China. (2) In terms of seasonal precipitation, the three sets of reanalysis datasets overestimate the precipitation
in the heavy rainfall zone of spring and summer, especially in southwest China. CRA interim’s location of the rain belt
in the First Rainy Season in South China is west by south, the summer precipitation has positive bias in southwest and
South China. (3) All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of
drought and flood, but the overall the CRA-Interim generally shows negative bias, while the ERA5 and JRA-55 exhibit
positive bias. (4) For the diurnal variation of precipitation in summer, all the reanalysis datasets perform better in
simulating the daytime precipitation than in the night, and bias of CRA-interim is less in southeast and northeast than
elsewhere. (5) ERA5 generally performs the best on the evaluation of quantitative precipitation forecast, the JRA-55 is
the next, followed by the CRA-Interim. CRA-Interim has higher missing rate and lower threat score for heavy rains;
however, at the level of downpour, the CRA-Interim performs slightly better. 相似文献
The role of Arctic clouds in the recent rapid Arctic warming has attracted much attention. However, Arctic cloud water paths(CWPs) from reanalysis datasets have not been well evaluated. This study evaluated the CWPs as well as LWPs(cloud liquid water paths) and IWPs(cloud ice water paths) from five reanalysis datasets(MERRA-2,MERRA, ERA-Interim, JRA-55, and ERA5) against the COSP(Cloud Feedback Model Intercomparison Project Observations Simulator Package) output for MODIS from the MERRA-2 CSP(COSP satellite simulator) collection(defined as M2 Modis in short). Averaged over 1980–2015 and over the Arctic region(north of 60°N), the mean CWPs of these five datasets range from 49.5 g/m~2(MERRA) to 82.7 g/m~2(ERA-Interim), much smaller than that from M2 Modis(140.0 g/m~2). However, the spatial distributions of CWPs, show similar patterns among these reanalyses, with relatively small values over Greenland and large values over the North Atlantic. Consistent with M2 Modis, these reanalyses show larger LWPs than IWPs, except for ERA-Interim. However, MERRA-2 and MERRA underestimate the ratio of IWPs to CWPs over the entire Arctic, while ERA-Interim and JRA-55 overestimate this ratio. ERA5 shows the best performance in terms of the ratio of IWPs to CWPs. All datasets exhibit larger CWPs and LWPs in summer than in winter. For M2 Modis, IWPs hold seasonal variation similar with LWPs over the land but opposite over the ocean. Following the Arctic warming, the trends in LWPs and IWPs during 1980~2015 show that LWPs increase and IWPs decrease across all datasets, although not statistically significant. Correlation analysis suggests that all datasets have similar interannual variability. The study further found that the inclusion of re-evaporation processes increases the humidity in the atmosphere over the land and that a more realistic liquid/ice phase can be obtained by independently treating the liquid and ice water contents. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
Summer precipitation products from the 45-Year European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis (ERA-40), and NCEP-Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2), and Climatic Research Unit (CRU) TS 2.1 dataset are compared with the corresponding observations over China in order to understand the quality and utility of the reanalysis datasets for the period 1979–2001. The results reveal that although the magnitude and location of the rainfall belts differ among the reanalysis, CRU, and station data over South and West China, the spatial distributions show good agreement over most areas of China. In comparison with the observations in most areas of China, CRU best matches the observed summer precipitation, while ERA-40 reports less precipitation and NCEP-2 reports more precipitation than the observations. With regard to the amplitude of the interannual variations, CRU is better than either of the reanalyses in representing the corresponding observations. The amplitude in NCEP-2 is stronger but that of ERA-40 is weaker than the observations in most study domains. NCEP-2 has a more obvious interannual variability than ERA-40 or CRU in most areas of East China. Through an Empirical orthogonal function (EOF) analysis, the main features of the rainfall belts produced by CRU agree better with the observations than with those produced by the reanalyses in the Yangtze-Huaihe River valley. In East of China, particularly in the Yangtze-Huaihe River valley, CRU can reveal the quasi-biennial oscillation of summer precipitation represented by the observations, but the signal of ERA-40 is comparatively weak and not very obvious, whereas that of NCEP-2 is also weak before 1990 but very strong after 1990. The results also suggest that the magnitude of the precipitation difference between ERA-40 and the observations is smaller than that between NCEP-2 and the observations, but the variations represented by NCEP-2 are more reasonable than those given by ERA-40 in most areas of East China to some extent. 相似文献