Decadal and long-term sea level variability in the tropical Indo-Pacific Ocean

In this study, we analysed decadal and long-term steric sea level variations over 1966–2007 period in the Indo-Pacific sector, using an ocean general circulation model forced by reanalysis winds. The simulated steric sea level compares favourably with sea level from satellite altimetry and tide gauges at interannual and decadal timescales. The amplitude of decadal sea level variability (up to ~5 cm standard deviation) is typically nearly half of the interannual variations (up to ~10 cm) and two to three times larger than long-term sea level variations (up to 2 cm). Zonal wind stress varies at decadal timescales in the western Pacific and in the southern Indian Ocean, with coherent signals in ERA-40 (from which the model forcing is derived), NCEP, twentieth century and WASWind products. Contrary to the variability at interannual timescale, for which there is a tendency of El Niño and Indian Ocean Dipole events to co-occur, decadal wind stress variations are relatively independent in the two basins. In the Pacific, those wind stress variations drive Ekman pumping on either side of the equator, and induce low frequency sea level variations in the western Pacific through planetary wave propagation. The equatorial signal from the western Pacific travels southward to the west Australian coast through equatorial and coastal wave guides. In the Indian Ocean, decadal zonal wind stress variations induce sea level fluctuations in the eastern equatorial Indian Ocean and the Bay of Bengal, through equatorial and coastal wave-guides. Wind stress curl in the southern Indian Ocean drives decadal variability in the south-western Indian Ocean through planetary waves. Decadal sea level variations in the south–western Indian Ocean, in the eastern equatorial Indian Ocean and in the Bay of Bengal are weakly correlated to variability in the Pacific Ocean. Even though the wind variability is coherent among various wind products at decadal timescales, they show a large contrast in long-term wind stress changes, suggesting that long-term sea level changes from forced ocean models need to be interpreted with caution.     

Mechanisms of tropical Pacific interannual-to-decadal variability in the ARPEGE/ORCA global coupled model

Spatial and temporal structures of interannual-to-decadal variability in the tropical Pacific Ocean are investigated using results from a global atmosphere–ocean coupled general circulation model. The model produces quite realistic mean state characteristics, despite a sea surface temperature cold bias and a thermocline that is shallower than observations in the western Pacific. The periodicity and spatial patterns of the modelled El Niño Southern Oscillations (ENSO) compare well with those observed over the last 100 years, although the quasi-biennial timescale is dominant. Lag-regression analysis between the mean zonal wind stress and the 20°C isotherm depth suggests that the recently proposed recharge-oscillator paradigm is operating in the model. Decadal thermocline variability is characterized by enhanced variance over the western tropical South Pacific (~7°S). The associated subsurface temperature variability is primarily due to adiabatic displacements of the thermocline as a whole, arising from Ekman pumping anomalies located in the central Pacific, south of the equator. Related wind anomalies appear to be caused by SST anomalies in the eastern equatorial Pacific. This quasi-decadal variability has a timescale between 8 years and 20 years. The relationship between this decadal tropical mode and the low-frequency modulation of ENSO variance is also discussed. Results question the commonly accepted hypothesis that the low-frequency modulation of ENSO is due to decadal changes of the mean state characteristics.     

Spatial and temporal variability of summer rainfall over Ethiopia from observations and a regional climate model experiment

The spatial and temporal variability of rainfall over Ethiopia during the summer (JJAS) season is studied using observations (both station and satellite based) and model simulation data. The simulation dataset is generated using the fourth version of the International Center for Theoretical Physics Regional Climate Model (RegCM4) for the period 1989–2005. Ethiopia is first divided into 12 homogeneous regions using criteria including rotated empirical orthogonal function (REOF), spatial correlation, seasonal cycles, and topographical features. Spatially averaged observed and simulated rainfall time series are then generated and analyzed for each region. Standardized rainfall anomalies of the observations and the simulated data are highly correlated over the northern, western, northeastern, central, and southwestern regions, while a weak correlation is found over the border regions of the country. The dominant modes of rainfall variability are identified using REOF, while time–frequency variations of different dominant modes are described by wavelet analysis. The first leading patterns of rainfall and upper wind (averaged between 100 and 300 hPa) are highly correlated and exhibit similar features between simulation and observations over the northern, western, southwestern, and eastern regions of Ethiopia. The second loading pattern of rainfall and the first loading pattern of low-level wind (averaged between 850 and 1,000 hPa) exhibit a dipole structure across the southwestern and northeastern regions of the country. The dominant signals in the first rotated principal component (RPC) of rainfall and upper level wind fields show a period of 4–5 and 2–3 years, while the dominant signals in the second RPC show a period of 2–3 years at a 0.05 significance level. The correlations of significant RPCs across gauge, gridded, and model rainfall fields with that of low and upper level winds show the presence of a significant relationship (correlation exceeding ～0.6). Overall, the RegCM4 shows a good performance in simulating the spatial and temporal variability of precipitation over Ethiopia.     

North Atlantic atmospheric circulation and surface wind in the Northeast of the Iberian Peninsula: uncertainty and long term downscaled variability

The variability and predictability of the surface wind field at the regional scale is explored over a complex terrain region in the northeastern Iberian Peninsula by means of a downscaling technique based on Canonical Correlation Analysis. More than a decade of observations (1992–2005) allows for calibrating and validating a statistical method that elicits the main associations between the large scale atmospheric circulation over the North Atlantic and Mediterranean areas and the regional wind field. In an initial step the downscaling model is designed by selecting parameter values from practise. To a large extent, the variability of the wind at monthly timescales is found to be governed by the large scale circulation modulated by the particular orographic features of the area. The sensitivity of the downscaling methodology to the selection of the model parameter values is explored, in a second step, by performing a systematic sampling of the parameters space, avoiding a heuristic selection. This provides a metric for the uncertainty associated with the various possible model configurations. The uncertainties associated with the model configuration are considerably dependent on the spatial variability of the wind. While the sampling of the parameters space in the model set up moderately impact estimations during the calibration period, the regional wind variability is very sensitive to the parameters selection at longer timescales. This fact illustrates that downscaling exercises based on a single configuration of parameters should be interpreted with extreme caution. The downscaling model is used to extend the estimations several centuries to the past using long datasets of sea level pressure, thereby illustrating the large temporal variability of the regional wind field from interannual to multicentennial timescales. The analysis does not evidence long term trends throughout the twentieth century, however anomalous episodes of high/low wind speeds are identified.     

How Well do Existing Indices Measure the Strength of the East Asian Winter Monsoon？

Defining the intensity of the East Asian winter monsoon (EAWM) with a simple index has been a difficult task. This paper elaborates on the meanings of 18 existing EAWM strength indices and classifies them into four categories: low level wind indices, upper zonal wind shear indices, east-west pressure contrast indices, and East Asian trough indices. The temporal/spatial performance and prediction potential of these indices are then analyzed for the 1957--2001 period. It reveals that on the decadal timescale, most indices except the east--west pressure contrast indices can well capture the continuous weakening of the EAWM around 1986. On the interannual timescale, the low level wind indices and East Asian trough indices have the best predictability based on knowledge of the El Nino-Southern Oscillation and Arctic Oscillation, respectively. All the 18 existing indices can well describe the EAWM-related circulation, precipitation, and lower tropospheric air temperature anomalies. However, the variations of surface air temperature over large areas of central China cannot be well captured by most indices, which is possibly related to topographic effects. The results of this study may provide a possible reference for future studies of the EAWM.     

南海夏季风活动的年际和年代际特征

利用NCEP风场资料和候平均向外长波辐射（OLR）资料分析了南海区域低层风场与对流活动的关系,在此基础上,采用南海中南部的纬向风平均值来定义南海夏季风的爆发,确定了长序列（1949～1998）的南海夏季风爆发日期和强度指数,并研究南海夏季风活动的年际和年代际变化特征。结果表明：南海夏季风爆发日期和强度指数呈显著的反相关;50年来的气候趋势是,爆发日期逐渐偏晚,强度指数逐渐减弱。二者都存在着明显的年际和年代际变化,它们在不同阶段上的波动是各种时间尺度振荡叠加的结果,而年代际尺度具有非常重要的作用。东印度洋海温异常在南海夏季风爆发前后,均与南海夏季风强度指数呈显著的反相关。东太平洋海温异常在南海夏季风爆发之前,与强度指数反相关,而爆发之后,与强度指数正相关。这体现了南海夏季风活动与ENSO事件的密切关系。     

Multiseason evaluation of the MM5, COAMPS and WRF over southeast United States

Three models, MM5, COAMPS, and WRF, have been applied for the warm season in 2003 and the cool season in 2003?C2004 to evaluate their performances. All models run over the same domain area covering the north Gulf Mexico and southeastern United States (US) region with the same spatial resolution of 27?km. It was found that the temporal variations of the mean error distribution and strength at 24 and 36?h were rather weak for surface temperature, sea level pressure, and surface wind speed for all models. A warm bias in surface temperature forecasts dominated over land during the warm season, whereas a cool bias existed during the cool season. The MM5 and WRF produced negative biases of sea level pressure during the warm season and positive biases during the cool season while the COAMPS yielded a similar distribution of sea level pressure biases during both seasons. During both seasons, similar surface wind speed biases produced by each model included a high wind speed forecast over most areas by MM5 while the COAMPS and WRF yielded weak surface winds over the western Plains and stronger surface winds over the eastern Plains. Root-mean-squared errors revealed that the forecast of surface temperature, sea level pressure, and surface wind speed were degraded with the increase of forecast time. For rainfall evaluation, it was found that the MM5 underpredicted seasonal precipitation while the COAMPS and WRF overpredicted. The bias scores revealed that the MM5 yielded an underprediction of the coverage of precipitation areas, especially for heavier rainfall events. The MM5 presented the lower threat score at lighter rainfall events compared to the COAMPS and WRF. For moderate and heavier thresholds, all models lacked forecast accuracy. The WRF accuracy in predicting precipitation was heavily dependent upon the performance of the selected cumulus parameterization scheme. Use of the Grell?CDevenyi and Bette?CMiller?CJanjic schemes helps suppress precipitation overprediction.     

Diurnal and seasonal variations of wind farm impacts on land surface temperature over western Texas

This paper analyzes seasonal and diurnal variations of MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data at ~1.1 km for the period of 2003–2011 over a region in West-Central Texas, where four of the world’s largest wind farms are located. Seasonal anomalies are created from MODIS Terra (~10:30 a.m. and 10:30 p.m. local solar time) and Aqua (~1:30 a.m. and 1:30 p.m. local solar time) LSTs, and their spatiotemporal variability is analyzed by comparing the LST changes between wind farm pixels (WFPs) and nearby non wind farm pixels (NNWFPs) using different methods under different quality controls. Our analyses show consistently that there is a warming effect of 0.31–0.70 °C at nighttime for the nine-year period during which data was collected over WFPs relative to NNWFPs, in all seasons for both Terra and Aqua measurements, while the changes at daytime are much noisier. The nighttime warming effect is much larger in summer than winter and at ~10:30 p.m. than ~1:30 a.m. and hence the largest warming effect is observed at ~10:30 p.m. in summer. The spatial pattern and magnitude of this warming effect couple very well with the geographic distribution of wind turbines and such coupling is stronger at nighttime than daytime and in summer than winter. Together, these results suggest that the warming effect observed in MODIS over wind farms are very likely attributable to the development of wind farms. This inference is consistent with the increasing number of operational wind turbines with time during the study period, the diurnal and seasonal variations in the frequency of wind speed and direction distribution, and the changes in near-surface atmospheric boundary layer (ABL) conditions due to wind farm operations. The nocturnal ABL is typically stable and much thinner than the daytime ABL and hence the turbine enhanced vertical mixing produces a stronger nighttime effect. The stronger wind speed and the higher frequency of the wind speed within the optimal power generation range in summer than winter and at nighttime than daytime likely drives wind turbines to generate more electricity and turbulence and consequently results in the strongest warming effect at nighttime in summer. Similarly, the stronger wind speed and the higher frequency of optimal wind speed at ~10:30 p.m. than that at ~1:30 a.m. might help explain, to some extent, why the nighttime LST warming effect is slightly larger at ~10:30 p.m. than ~1:30 a.m. The nighttime warming effect seen in spring and fall are smaller than that in summer and can be explained similarly.     

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s^?1, considerably smaller than the result (1.29 m s^?1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.     

Impact of cold air surges on rainfall variability in the Sahel and wet African tropics: a multi-scale analysis

Satellite-derived rainfall estimates and the ERA-Interim reanalysis are used to better understand cold air surge/precipitation interactions and to identify the implications for rainfall variability in the Sahel and tropical Africa on synoptic to seasonal timescales. At the synoptic timescale, cold air surges are associated with cold conditions over the eastern Sahara throughout the year due to the eastward passage of surface low pressure systems over the Mediterranean and the subsequent ridging over northern Africa. Rainfall decreases over central and eastern Africa approximately 4–5 days after the cold air first arrives in northeastern Africa. These precipitation anomalies persist for 4 or more days. At the seasonal timescale, a significant relationship between eastern Saharan low-level temperatures and rainfall in the Sahel and tropical Africa is identified, with colder conditions associated with reduced convection on the northern flank of the primary convergence zone, and vice versa. During boreal winter, the anomalous rainfall occurs over tropical Africa (0°N–8°N). During the summer, rainfall anomalies associated with cold air surges occur over the Sahel (10°N–16°N). These relationships are mediated by anomalous anticyclonic flow over northwestern Africa and western Europe. The analysis shows that cold air surges are significantly associated with summertime cooling over the Sahara, but less so during the winter.     

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.     

Downscaling over Vietnam using the stretched-grid CCAM: verification of the mean and interannual variability of rainfall

Rainfall over Vietnam is highly variable from north to south, due to the interaction of the monsoonal winds with the terrain. There is high rainfall from April to September, and little rainfall from October to March (except along the central Vietnam coast). In order to study the ability of the Commonwealth Scientific and Industrial Research Organisation stretched-grid Conformal Cubic Atmospheric Model (CCAM) to capture the climatic and interannual variability of rainfall, downscaled simulations at approximately 20 km horizontal resolution over the region were produced for the period 1979–2001. A scale-selective digital filter was used to force the winds, temperature and sea-level pressure from the ERA-Interim reanalysis for length scales greater than about 700 km. For wind and temperature, the forcing is applied for pressure-sigma levels above about 0.9. ERA-Interim sea surface temperatures were used over the oceans. The simulations were primarily validated against the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of the Water Resources rainfall dataset and station observations using standard statistical methods. It was found that CCAM reproduces well the amount and spatial variability of rainfall, with an area-averaged bias for the entire study domain of less than 1 mm day^?1; CCAM is also able to capture the rainfall pattern under different El Niño Southern Oscillation phases reasonably well for the dry season. For interannual variability, the simulation generally performed better for North and Central Vietnam than for South Vietnam, where rainfall variability was overestimated.     

Quasi-periodic, global oscillations in sea level pressure on intraseasonal timescales

The sea level pressure (SLP) variability in 30–60 day intraseasonal timescales is investigated using 25 years of reanalysis data addressing two issues. The first concerns the non-zero zonal mean component of SLP near the equator and its meridional connections, and the second concerns the fast eastward propagation (EP) speed of SLP compared to that of zonal wind. It is shown that the entire globe resonates with high amplitude wave activity during some periods which may last for few to several months, followed by lull periods of varying duration. SLP variations in the tropical belt are highly coherent from 25°S to 25°N, uncorrelated with variations in mid latitudes and again significantly correlated but with opposite phase around 60°S and 65°N. Near the equator (8°S–8°N), the zonal mean contributes significantly to the total variance in SLP, and after its removal, SLP shows a dominant zonal wavenumber one structure having a periodicity of 40 days and EP speeds comparable to that of zonal winds in the Indian Ocean. SLP from many of the atmospheric and coupled general circulation models show similar behaviour in the meridional direction although their propagation characteristics in the tropical belt differ widely.     

Large‐eddy simulation of small‐scale surface effects on the convective boundary‐layer structure

Abstract

The effects of small‐scale surface inhomogeneities on the turbulence structure in the convective boundary layer are investigated using a high‐resolution large‐eddy simulation model. Surface heat flux variations are sinusoidal and two‐dimensional, dividing the total domain into a checkerboard‐like pattern of surface hot spots with a 500‐m wavelength in the x and y directions, or 1/4 of the domain size. The selected wind speeds were 1 and 4 m s^‐l, respectively. As a comparison, a simulation of the turbulence structure was performed over a homogeneous surface.

When the wind speed is light, surface heat flux variations influence the horizontally averaged turbulence statistics, including the higher moments despite the small characteristic length of the surface perturbation. Stronger mean wind speeds weaken the effects of inhomogeneous surface conditions on the turbulence structure in the convective boundary layer.

Results from conditional sampling show that when the mean wind speed is small, weak mean circulations occur, with updraft branches above the high heat flux regions and down‐draft branches above the low heat flux regions. The inhomogeneous surface induces significant differences in the turbulence statistics between the high and low heat flux regions. However, the effect of the surface perturbations weaken rapidly when the mean wind speed increases. This research has implications in the explanation of the large‐scale variability commonly encountered in aircraft observations of atmospheric turbulence.     

Validation of ECMWF and NCEP–NCAR Reanalysis Data in Antarctica

The European Center for Medium-Range Weather Forecast (ECMWF) Re-Analysis (ERA-40) and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) ECMWF (ERA-40) and NCEP-NCAR reanalysis data were compared with Antarctic station observations, including surface-layer and upper-layer atmospheric observations, on intraseasonal and interannual timescales. At the interannual timescale, atmospheric pressure at different height levels in the ERA-40 data are in better agre...     

Seasonal scale variability of the East Asian winter monsoon and the development of a two-dimensional monsoon index

This study investigates the seasonal scale variability of the East Asian winter monsoon (EAWM), which is distinguished from the seasonal cycle with temporal variation throughout winter. Winters lasting 120 days (Nov. 17–Mar. 16) for a period of 64 years from the NCEP daily reanalysis data set are used to study the seasonal scale variability of the EAWM. Cyclostationary empirical orthogonal function (CSEOF) analysis is adopted to decompose the variability of the EAWM. The second CSEOF mode of 850-hPa temperature exhibits a seasonal scale variation, the physical mechanism of which is explained in terms of physically consistent variations of temperature, geopotential height, sea level pressure, wind, and surface heat fluxes. The seasonal-scale EAWM exhibits a weak subseasonal and a strong interannual variability and has gradually weakened during the 64 years. In a weak EAWM phase, the land-sea contrast of sea level pressure declines in East Asia. Consistent with this change, low-level winds decrease and warm thermal advection increases over the eastern part of mid-latitude East Asia. Latent and sensible heat fluxes are reduced significantly over the marginal seas in East Asia. However, during a strong EAWM phase, the physical conditions in East Asia reverse. A large fraction of the variability of the EAWM is explained by the seasonal cycle and the seasonal scale variation. A two-dimensional EAWM index was developed to explain these two distinct components of the EAWM variability. The new index appears to be suitable for measuring both the subseasonal and the interannual variability of the EAWM.     

High-resolution sea wind hindcasts over the Mediterranean area

The goal of this study is to develop a high-resolution atmospheric hindcast over the Mediterranean area using the WRF-ARW model, focusing on offshore surface wind fields. In order to choose the most adequate model configuration, the study provides details on the calibration of the experimental saet-up through a sensitivity test considering the October–December 2001 period (the 2001 super-storm event in the West Mediterranean). A daily forecast outperforms the spectral technique of previous products and the boundary data from ERA-Interim reanalysis produces the most accurate estimates in terms of wind variability and hour-to-hour correspondence. According to the sensitivity test, two data sets of wind hindcast are produced: the SeaWind I (30-km horizontal resolution for a period of 60 years) and the SeaWind II (15-km horizontal resolution for 20 years). The validation of the resulting surface winds is undertaken considering two offshore observational datasets. On the one hand, hourly surface buoy stations are used to validate wind time series at specific locations; on the other hand, wind altimeter satellite observations are considered for spatial validation in the whole Mediterranean Sea. The results obtained from this validation process show a very good agreement with observations for the southern Europe region. Finally, SeaWind I and II are used to characterize offshore wind fields in the Mediterranean Sea. The statistical structure of sea surface wind is analyzed and the agreement with Weibull probability distribution is discussed. In addition, wind persistence and extreme wind speed (50 year return period) are characterized and relevant areas of wind power generation are described by estimating wind energy quantities.     

Stochastically-forced multidecadal variability in the North Atlantic: a model study

Observations show a multidecadal signal in the North Atlantic ocean, but the underlying mechanism and cause of its timescale remain unknown. Previous studies have suggested that it may be driven by the North Atlantic Oscillation (NAO), which is the dominant pattern of winter atmospheric variability. To further address this issue, the global ocean general circulation model, Nucleus for European Modelling of the Ocean (NEMO), is driven using a 2,000 years long white noise forcing associated with the NAO. Focusing on key ocean circulation patterns, we show that the Atlantic Meridional Overturning Circulation (AMOC) and Sub-polar gyre (SPG) strength both have enhanced power at low frequencies but no dominant timescale, and thus provide no evidence for a oscillatory ocean-only mode of variability. Instead, both indices respond linearly to the NAO forcing, but with different response times. The variability of the AMOC at 30°N is strongly enhanced on timescales longer than 90 years, while that of the SPG strength starts increasing at 15 years. The different response characteristics are confirmed by constructing simple statistical models that show AMOC and SPG variability can be related to the NAO variability of the previous 53 and 10 winters, respectively. Alternatively, the AMOC and the SPG strength can be reconstructed with Auto-regressive (AR) models of order seven and five, respectively. Both statistical models reconstruct interannual and multidecadal AMOC variability well, while on the other hand, the AR(5) reconstruction of the SPG strength only captures multidecadal variability. Using these methods to reconstruct ocean variables can be useful for prediction and model intercomparision.     

Using the spectral scaling exponent for validation of quantitative precipitation forecasts

This study evaluates the spectral scaling of a heavy rainfall event and assesses the performance of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model in terms of the multiscale variability of rainfall in the temporal spectral domain. The event occurred over southern Malay Peninsula on 18 December 2006 and was simulated at high resolutions. 10, 5 and 1?min aggregate rainfall data from rain gauge stations in Singapore and simulated rainfall sampled at different evaluation points on 0.9, 0.3 and 0.1?km grids were utilized. The simulated and observed rain rates were compared via Fourier and wavelet analyses. A scaling regime was noted in the observed rainfall spectra in the timescales between 60?min and 2?min. The scaling exponent obtained from the observed spectra has a value of about 2, which may be indicative of the physics of turbulence and raindrop coalescence and might suggest the predominance of a characteristic raindrop size. At 0.9?km resolution, the model rainfall spectra showed similar scaling to the observed down to about 10?min, below which a fall-off in variance was noted as compared to observations. Higher spatial resolution of up to 0.1?km was crucial to improve the ability of the model to resolve the shorter timescale variability. We suggest that the evaluation of dynamical models in the spectral domain is a crucial step in the validation of quantitative precipitation forecasts and assessing the minimal grid resolution necessary to capture rainfall variability for certain short timescales may be important for hydrological predictions.     

海南岛沿海近地面风时空分布特征的观测分析

利用2012年海南岛沿海6个常规气象站、2个海岛站的逐时风向、风速资料,分别对全年以及不同季节内近地面风速大小、风速日变化以及风向频率分布等进行了统计分析.结果表明:2012年全年海南岛沿海近地面风速约在1.8~5.7 m/s之间,其中三亚站风速最大,冬季高达6.5 m/s,大部分站点夏季风速最弱,最大风速出现在春、冬季;海南岛南部沿海风速大于北部,东部大于西部;各站24 h风速基本呈现白天大、夜晚小的典型特征,由于所处地形、植被独特,三亚部分季节风速呈现相反的日变化特征;全年各站基本存在两个盛行风向,大部分站点近地面风向与南海季风的风向变化较为一致,夏季以南风、西南风为主,冬季以北风、东北风为主;各季沿海近地面风向南北部差异较大,东西部差异较小,随着季节转变,南部沿海盛行风转向最明显,东西部次之,北部则不明显.