Statistical-dynamical analysis of wind observations usinga variational approach

Summary  In this paper, we present a variational analysis of wind and pressure data which takes into account statistical characteristics of the data and a linear model describing the dynamical relations between the analysed variables. The variational approach is used to adjust the given data to the dynamical model and it will be shown that this adjustment process can be controlled in an uncomplicated, comprehensible, and reproducible way by very few parameters only. The dependence of the analysis results on these parameters is investigated in theory and this theoretical conclusions are tested by an application. It is studied how effectively this method can be used to correct erroneous data. We apply the analysis to the COADS (Comprehensive Ocean-Atmosphere Data Set) wind and pressure data of the Januaries 1951–1993 over the North Atlantic and introduce the Ekman balance as a weak dynamical constraint. This specific data set is chosen because several previous investigations suggested that there is a spurious trend in the COADS wind speed of ∼ 1 ms⁻¹ since the mid-century. The results show that the control parameters can be effectively used to shift the wind field continuously between the identity with the input data and the exact consistency with the dynamical model. But it has to be admitted that the Ekman balance is inadequate in the tropics and that it overestimates the magnitude of the horizontal vector wind although this dynamical model is more suitable than the frequently used geostrophic balance. Received January 20, 2000 Revised April 10, 2000     

Observed spatiotemporal characteristics of drought on various time scales over the Czech Republic

This paper analyses the observed spatiotemporal characteristics of drought in the Czech Republic during the growing season (April to September) as quantified using the Standardised Precipitation Evapotranspiration Index (SPEI) on various time scales. The SPEI was calculated for various lags (1, 3, 6, 12, and 24 months) from monthly records of mean temperature and precipitation totals using a dense network of 184 climatological stations for the period 1961–2010. The characteristics of drought were analysed in terms of the temporal evolution of the SPEI, the frequency distribution and duration of drought at the country level, and for three regions delimited by station altitude. The driest and the wettest years during the growing season were identified. The frequency distribution of the SPEI values for seven drought category classes (in per cent) indicates that normal moisture conditions represent approximately 65 % of the total SPEI values for all time scales in all three regions, whereas moderate drought and moderate wet conditions are almost equally distributed around 10.5 %. Differences in extremely dry conditions (5 %) compared with extremely wet conditions (1.5 %) were observed with increasing SPEI time scales. The results of the non-parametric Mann–Kendall trend test applied to the SPEI series indicate prevailing negative trends (drought) at the majority of the stations. The percentage of stations displaying a significant negative trend for the 90, 95, 99, and 99.9 % confidence levels is approximately 40 %. An Empirical Orthogonal Functions (EOF) analysis was used to identify the principal patterns of variability of the SPEI during the growing season that accounted for the highest amount of statistical variance. The variance explained by the leading EOF range 66 to 56 %, whereas for EOF2 and EOF3, the value is between 7 and 11 % and between 4 and 7 %, respectively, for the SPEI is calculated for 1- to 24-month lags.     

风电场风速降尺度预报方法对比分析

使用中尺度数值天气预报业务模式9 km和3 km分辨率的模式输出产品,分别应用小尺度模式CALMET模式和双线性插值(BLI)方法将预报风速进行降尺度处理,并对比预报风速和风塔观测资料。结果表明：WRF模式9 km分辨率的模式输出经过CALMET模式降尺度以后得到的风速预报效果比3 km分辨率的模式输出略好。同时,由于中尺度数值预报模式分辨率本身较高,使用BLI也可以得到较好的风速预报。将风速分为0 m·s^-1≤风速<5 m·s^-1,5 m·s^-1≤风速<10 m·s^-1和风速≥10 m·s^-1共3个等级,检验3个风速等级的预报偏差百分比得出,CALMET模式和BLI方法对10 m·s^-1以上的大风的预报效果相对较差;如何对大风预报进行订正对风速预报准确率的提高具有重要的意义。     

Regional dynamical downscaling with CCLM over East Asia

Inspired by the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the hindcast (1971–2000) and projection (2021–2050) simulations based on a resolution of $0.44^\circ$ over the East Asia domain are performed with the regional climate model COSMO-CLM (CCLM). The simulations are driven by ERA-40 reanalysis data and output of the global climate model ECHAM5. This is the first time that the CCLM is adapted and evaluated for the East Asia Monsoon region; the setup is considered a starting point for further improvements in this region by the CCLM community. The evaluation results show that the CCLM is able to reasonably capture the climate features in this region, especially the monsoon dynamics on small scales. However, total precipitation in the northern part of the domain, over the Tibetan Plateau, and over east Indonesia has a pronounced wet bias. The projected climate change under the A1B scenario indicates an overall annual surface temperature increase of 1–2 K, but no significant precipitation changes.     

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components

A regression-based downscaling technique was applied to monthly mean surface wind observations from stations throughout western Canada as well as from buoys in the Northeast Pacific Ocean over the period 1979–2006. A predictor set was developed from principal component analysis of the three wind components at 500?hPa and mean sea-level pressure taken from the NCEP Reanalysis II. Building on the results of a companion paper, Curry et al. (Clim Dyn 2011, doi:10.1007/s00382-011-1173-3), the downscaling was applied to both wind speed and wind components, in an effort to evaluate the utility of each type of predictand. Cross-validated prediction skill varied strongly with season, with autumn and summer displaying the highest and lowest skill, respectively. In most cases wind components were predicted with better skill than wind speeds. The predictive ability of wind components was found to be strongly related to their orientation. Wind components with the best predictions were often oriented along topographically significant features such as constricted valleys, mountain ranges or ocean channels. This influence of directionality on predictive ability is most prominent during autumn and winter at inland sites with complex topography. Stations in regions with relatively flat terrain (where topographic steering is minimal) exhibit inter-station consistencies including region-wide seasonal shifts in the direction of the best predicted wind component. The conclusion that wind components can be skillfully predicted only over a limited range of directions at most stations limits the scope of statistically downscaled wind speed predictions. It seems likely that such limitations apply to other regions of complex terrain as well.     

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed

Surface wind speed is a key climatic variable of interest in many applications, including assessments of storm-related infrastructure damage and feasibility studies of wind power generation. In this work and a companion paper (van der Kamp et al. 2011), the relationship between local surface wind and large-scale climate variables was studied using multiple regression analysis. The analysis was performed using monthly mean station data from British Columbia, Canada and large-scale climate variables (predictors) from the NCEP-2 reanalysis over the period 1979–2006. Two regression-based methodologies were compared. The first relates the annual cycle of station wind speed to that of the large-scale predictors at the closest grid box to the station. It is shown that the relatively high correlation coefficients obtained with this method are attributable to the dominant influence of region-wide seasonality, and thus contain minimal information about local wind behaviour at the stations. The second method uses interannually varying data for individual months, aggregated into seasons, and is demonstrated to contain intrinsically local information about the surface winds. The dependence of local wind speed upon large-scale predictors over a much larger region surrounding the station was also explored, resulting in 2D maps of spatial correlations. The cross-validated explained variance using the interannual method was highest in autumn and winter, ranging from 30 to 70% at about a dozen stations in the region. Reasons for the limited predictive skill of the regressions and directions for future progress are reviewed.     

非均匀地表通量的一种双参量统计-动力参数化方案

本文提出了一种非均匀地表通量的双参量统计-动力参数化方案,并根据该方案,以遥感资料反演的地表温度和土壤湿度为参量,对长江三角洲地区的日区域平均蒸发散通量进行了数值试验。     

Evaluation of a WRF dynamical downscaling simulation over California

This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.     

A high-resolution ocean-atmosphere coupled downscaling of the present climate over California

A fully coupled regional ocean-atmosphere model system that consists of the regional spectral model and the regional ocean modeling system for atmosphere and ocean components, respectively, is applied to downscale the present climate (1985–1994) over California from a global simulation of the Community Climate System Model 3.0 (CCSM3). The horizontal resolution of the regional coupled modeling system is 10 km, while that of the CCSM3 is at a spectral truncation of T85 (approximately 1.4°). The effects of the coupling along the California coast in the boreal summer and winter are highlighted. Evaluation of the sea surface temperature (SST) and 2-m air temperature climatology shows that alleviation of the warm bias along the California coast in the global model output is clear in the regional coupled model run. The 10-m wind is also improved by reducing the northwesterly winds along the coast. The higher resolution coupling effect on the temperature and specific humidity is the largest near the surface, while the significant impact on the wind magnitude appears at a height of approximately 850-hPa heights. The frequency of the Catalina Eddy and its duration are increased by more than 60 % in the coupled downscaling, which is attributed to enhanced offshore sea-breeze. Our study indicates that coupling is vital to regional climate downscaling of mesoscale phenomena over coastal areas.     

大小兴安岭林区风场动力降尺度对比研究

对2017年春季黑龙江省大、小兴安岭林区的6个代表站点10 m风场进行降尺度分析,并结合观测数据对比分析了WRF模式和CALMET降尺度模式的10 m风速、风向预报结果。结果表明：两模式逐小时风速预报与观测的相关系数为0.5-0.7,且随着风速的增加,模式的预报准确率逐渐提高,夜间的风速预报偏差较大,进入白天后,偏差明显减小。WRF模式对风速变化趋势的预报效果优于CALMET模式,与观测的风速相关性更高,而CALMET模式对较大风速的预报效果优于WRF模式。在风向预报方面,WRF和CALMET的风向模拟与观测风向均有较好的一致性,模式预报准确率较高的两个风向也刚好对应各站的盛行风向。同时,本文用回归方法对日平均风速进行订正发现,订正后各站的日平均风速预报准确率平均提高了50%,具有较好的业务应用价值。     

近千年东亚季风变化统计动力反演与驱动机制研究

利用观测数据和非线性统计-动力学方法,构建了东亚季风变化的动力方程。量化了单因子强迫及各因子间相互作用在东亚季风演化中的相对贡献率,为东亚季风驱动机制研究提供了量化参考。研究发现:（1）过去千年东亚季风是多种因子共同作用下的复杂非线性动力系统。有些因子以起驱动作用为主,则有些以反馈调节作用为主,因子间交互作用与东亚季风演化存在耦合效应机制。（2）季风的驱动力主要来源于副热带太平洋海表温度、青藏高原动力热力强迫、CO₂和N₂O交叉项、太阳辐射和N₂O交叉项、CO₂与CH₄交叉项等的耦合作用机制;调节作用主要是石笋δ18O指代的地理位置、单因子CO₂浓度、太阳辐射变化、CH₄与N₂O交叉项、太阳辐射与ENSO交叉项等的耦合作用机制。温室气体（CO₂、CH₄与N₂O）浓度对东亚季风演化的驱动与调节作用贡献较大。（3）通过动力反演机制推论副热带太平洋和热带西太平洋对东亚季风均有驱动作用,但主要驱动力来自副热带太平洋,即驱动东亚季风变化的主源地在副热带太平洋海区,次源地在热带西太平洋海区。（4）由海-陆温差对季风演变贡献大小推测石笋δ18O指代的也主要是夏季风信息。      

On the generation of synthetic wind roses in orographically structured terrain (research note)

Summary A strategy was developed to overcome the difficulty of unknown forcing for the modelling of fields of wind roses with meso- and microscale models. It was applied to a site with heavy orographically structured terrain. The supra-scale winds which act as forcing for the model were calculated from a measured distribution of surface winds by an optimized inversion technique. The method is applicable to all types of mesoscale models.With 3 Figures     

Meteorological singularities in the Czech Republic in the period 1961–2002

Summary Based on the mean daily values of air temperature, air pressure, and precipitation totals at 13 climatological stations within the territory of the Czech Republic in the period 1961–2002, a statistical analysis of “meteorological singularities” (i.e., calendar-dependent deviations from the mean annual variation for selected meteorological elements) was performed. At the 13 stations analysed, a total of 45 meteorological singularities (37 singularities in air temperature, 35 in air pressure, and 30 in precipitation) were found. The singularities detected correlate well with cases traditionally recognised in the Czech Republic as well as with the results of analyses performed for Germany. Despite the considerable variability of singularities in time and space, most of them are found across the entire territory of the Czech Republic and can be observed for the most part in all three elements processed. The majority of the singularities detected may be explained on the basis of circulation mechanisms, by relating them to a significantly higher occurrence of certain groups of synoptic situations characterised by anomalous temperature or precipitation effects. Cases of “competition” between singularities, when different singularities may occur on the same calendar day, were found.     

Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.     

Precipitation,temperature and wind in Norway: dynamical downscaling of ERA40

A novel downscaling approach of the ERA40 (ECMWF 40-years reanalysis) data set has been taken and results for comparison with observations in Norway are shown. The method applies a nudging technique in a stretched global model, focused in the Norwegian Sea (67°N, 5°W). The effective resolution is three times the one of the ERA40, equivalent to about 30 km grid spacing in the area of focus. Longer waves (<T42) in the downscaled solution are nudged towards the ERA40 solution, and thus the large-scale circulation is similar in the two data sets. The shorter waves are free to evolve, and produce high intensities of winds and precipitation. The comparison to observations incorporate numerous station data points of (1) precipitation (#357), (2) temperature (#98) and (3) wind (#10), and for the period 1961–1990, the downscaled data set shows large improvements over ERA40. The daily precipitation shows considerable reduction in bias (from 50 to 11%), and twofold reduction at the 99.9 percentile (from −59 to −29%). The daily temperature showed a bias reduction of about a degree in most areas, and relative large RMSE reduction (from 7.5 to 5.0°C except winter). The wind comparison showed a slight improvement in bias, and significant improvements in RMSE.     

Use of a soil moisture network for drought monitoring in the Czech Republic

Since 2000, the network of stations that make up the Czech Hydrometeorological Institute (CHMI) has measured the soil moisture content at the 0- to 0.9-m layer using sensors placed within the natural soil profile under closely cropped grass cover. Using information from 8?years of continuous observation at seven stations throughout the Czech Republic, we verified the usefulness of the Soil Moisture Index (SMI). The SMI is a potentially useful index for calculating the water deficit in the Czech Republic and Central Europe. During this period, a statistically significant decrease in moisture content was detected, and negative SMI values predominated. There were frequent occurrences of flash drought, defined as a very rapid decline in soil moisture during a 3-week period. The CHMI can use SMI values below ?3 in the Integrated Warning Service System. The routine calculation of SMI values can alert agricultural producers to the development of flash drought conditions and provide them with information regarding the effectiveness of recent rainfall events. An increase in soil moisture, in contrast, could serve as a warning sign for hydrology because it creates the preconditions for flooding. The complex study of soil humidity regimes is becoming more significant in connection with current global climatic change warnings in hydrological cycles.