Decorrelation Length Research Articles

Effect of wall cooling on boundary-layer-induced pressure fluctuations at Mach 6

Direct numerical simulations of turbulent boundary layers with a nominal free-stream Mach number of $6$ and a Reynolds number of $Re_{\unicode[STIX]{x1D70F}}\approx 450$ are conducted at a wall-to-recovery temperature ratio of $T_{w}/T_{r}=0.25$ and compared with a previous database for $T_{w}/T_{r}=0.76$ in order to investigate pressure fluctuations and their dependence on wall temperature. The wall-temperature dependence of widely used velocity and temperature scaling laws for high-speed turbulent boundary layers is consistent with previous studies. The near-wall pressure-fluctuation intensities are dramatically modified by wall-temperature conditions. At different wall temperatures, the variation of pressure-fluctuation intensities as a function of wall-normal distance is dramatically modified in the near-wall region but remains almost intact away from the wall. Wall cooling also has a strong effect on the frequency spectrum of wall-pressure fluctuations, resulting in a higher dominant frequency and a sharper spectrum peak with a faster roll-off at both the high- and low-frequency ends. The effect of wall cooling on the free-stream noise spectrum can be largely accounted for by the associated changes in boundary-layer velocity and length scales. The pressure structures within the boundary layer and in the free stream evolve less rapidly as the wall temperature decreases, resulting in an increase in the decorrelation length of coherent pressure structures for the colder-wall case. The pressure structures propagate with similar speeds for both wall temperatures. Due to wall cooling, the generated pressure disturbances undergo less refraction before they are radiated to the free stream, resulting in a slightly steeper radiation wave front in the free stream. Acoustic sources are largely concentrated in the near-wall region; wall cooling most significantly influences the nonlinear (slow) component of the acoustic source term by enhancing dilatational fluctuations in the viscous sublayer while damping vortical fluctuations in the buffer and log layers.

Efficiently estimating some common geostatistical models by ‘energy–variance matching’ or its randomized ‘conditional–mean’ versions

We consider the problem of fitting an isotropic zero-mean stationary Gaussian field model to (possibly noisy) observations, when the model belongs to the Matérn family with known regularity index ν>0, or to the spherical family. For estimating the correlation range (also called “decorrelation length”) and the variance of the field, two simple estimating functions based on the so-called “conditional Gaussian Gibbs-energy mean” (CGEM) and the empirical variance (EV) were recently introduced. This article presents an extensive Monte Carlo simulation study for problems with around a thousand observations and settings including large, moderate, and even “small”, correlation ranges. The known observation sites are either on a 2D grid (including a case of “very non-uniform” grid spacings) or randomly uniformly distributed on a simple 2D region. Some experiments for a 256×256 grid with missing values are also analyzed.This study empirically demonstrates that, for all the (possibly random) uniform designs, the statistical efficiency of CGEM-EV compared to exact maximum likelihood (ML) is globally very satisfactory (except a degradation for the very extremal ranges in some contexts) provided the signal-to-noise ratio (SNR) is strong enough and ν is not too large, this SNR restriction being alleviated as ν decreases. For the “very non-uniform” design, a simple weighting of EV restores this efficiency. In the less favorable cases, the statistical loss remains in fact acceptable: e.g. for the largest considered index (ν=3/2) and a “not strong enough” SNR, it may happen (in fact only for large ranges) that CGEM-EV almost doubles the mean squared error for the range parameter or for the widely used combination of the two parameters known as microergodic-parameter. Furthermore an important conclusion for computational efficiency is that the use of the natural fast randomized-trace version of CGEM-EV does not significantly degrade this statistical efficiency.

Effects of cloud condensate vertical alignment on radiative transfer calculations in deep convective regions

Effects of cloud condensate vertical alignment on radiative transfer process were investigated using cloud resolving model explicit simulations, which provide a surrogate for subgrid cloud geometry. Diagnostic results showed that the decorrelation length Lcw varies in the vertical dimension, with larger Lcw occurring in convective clouds and smaller Lcw in cirrus clouds. A new parameterization of Lcw is proposed that takes into account such varying features and gives rise to improvements in simulations of cloud radiative forcing (CRF) and radiative heating, i.e., the peak of bias is respectively reduced by 8Wm−2 for SWCF and 2Wm−2 for LWCF in comparison with Lcw=1km.The role of Lcw in modulating CRFs is twofold. On the one hand, larger Lcw tends to increase the standard deviation of optical depth στ, as dense and tenuous parts of the clouds would be increasingly aligned in the vertical dimension, thereby broadening the probability distribution. On the other hand, larger στ causes a decrease in the solar albedo and thermal emissivity, as implied in their convex functions on τ. As a result, increasing (decreasing) Lcwleads to decreased (increased) CRFs, as revealed by comparisons among Lcw=0, Lcw=1km andLcw=∞. It also affects the vertical structure of radiative flux and thus influences the radiative heating. A better representation of στ in the vertical dimension yields an improved simulation of radiative heating. Although the importance of vertical alignment of cloud condensate is found to be less than that of cloud cover in regards to their impacts on CRFs, it still has enough of an effect on modulating the cloud radiative transfer process.

Mechanisms of bottom boundary fluxes in a numerical model of the Shetland shelf

Across-slope bottom boundary layer (BBL) fluxes on the shelf-edge connect this region to deeper waters. Two proposed ways in which across-slope BBL fluxes can occur, in regions that have a slope current aligned to the bathymetry, are the frictional veering of bottom currents termed the ‘Ekman drain’ and through local wind-forced downwelling (wind-driven surface Ekman flow with an associated bottom flow). We investigate the variability, magnitude and spatial scale of BBL fluxes on the Shetland shelf, which has a prominent slope current, using a high-resolution (∼2 km) configuration of the MITgcm model. Fluxes are analysed in the BBL at the shelf break near the 200 m isobath and are found to have a seasonal variability with high/low volume transport in winter/summer respectively. By using a multivariate regression approach, we find that the locally wind-driven Ekman transport plays no explicit role in explaining daily bottom fluxes. We can better explain the variability of the across-slope BBL flux as a linear function of the speed and across-slope component of the interior flow, corresponding to an Ekman plus mean-flow flux. We estimate that the mean-flow is a greater contributor than the Ekman flux to the BBL flux. The spatial heterogeneity of the BBL fluxes can be attributed to the mean-flow, which has a much shorter decorrelation length compared to the Ekman flux. We conclude that both the speed and direction of the interior current determines the daily BBL flux. The wind does not explicitly contribute through local downwelling, but may influence the interior current and therefore implicitly the BBL fluxes on longer timescales.

Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

AbstractThe spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 × 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-μm reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.

Bayesian estimation of the observation‐error covariance matrix in ensemble‐based filters

We develop a Bayesian technique for estimating the parameters in the observation‐noise covariance matrix Rt for ensemble data assimilation. We design a posterior distribution by using the ensemble‐approximated likelihood and a Wishart prior distribution and present an iterative algorithm for parameter estimation. The temporal smoothness of Rt can be controlled by an adequate choice of two parameters of the prior distribution, the covariance matrix S and the number of degrees of freedom ν. The ν parameter can be estimated by maximizing the marginal likelihood. The present formalism can handle cases in which the number of data points or data positions varies with time, the former of which is exemplified in the experiments. We present an application to a coupled atmosphere–ocean model under each of the following assumptions: Rt is a scalar multiple of a fixed matrix (Rt=αtΣ, where αt is the scalar parameter and Σ is the fixed matrix), Rt is diagonal, Rt has fixed eigenvectors or Rt has no specific structure. We verify that the proposed algorithm works well and that only a limited number of iterations are necessary. When Rt has one of the structures mentioned above, by assuming S to be the previous estimate we obtain a Bayesian estimate of Rt that varies smoothly in time compared with the maximum‐likelihood estimate. When Rt has no specific structure, we need to regularize S to maintain the positive‐definiteness. Through twin experiments, we find that the best estimate of Rt is, in general, obtained by a combination of structure‐free Rt and tapered S using decorrelation lengths of half the size of the model ocean basin. From experiments using real observations, we find that the estimates of the structured Rt lead to overfitting of the data compared with the structure‐free Rt.

Biological and physical controls on O2/Ar, Ar and pCO2 variability at the Western Antarctic Peninsula and in the Drake Passage

Using simultaneous sub-kilometer resolution underway measurements of surface O2/Ar, total O2 and pCO2 from annual austral summer surveys in 2012, 2013 and 2014, we explore the impacts of biological and physical processes on the O2 and pCO2 system spatial and interannual variability at the Western Antarctic Peninsula (WAP). In the WAP, mean O2/Ar supersaturation was (7.6±9.1)% and mean pCO2 supersaturation was (−28±22)%. We see substantial spatial variability in O2 and pCO2 including sub-mesoscale/mesoscale variability with decorrelation length scales of ~4.5km, consistent with the regional Rossby radius. This variability is embedded within onshore–offshore gradients. O2 in the LTER grid region is driven primarily by biological processes as seen by the median ratio of the magnitude of biological oxygen (O2/Ar) to physical oxygen (Ar) supersaturation anomalies (%) relative to atmospheric equilibrium (2.6), however physical processes have a more pronounced influence in the southern onshore region of the grid where we see active sea-ice melting. Total O2 measurements should be interpreted with caution in regions of significant sea-ice formation and melt and glacial meltwater input. pCO2 undersaturation predominantly reflects biological processes in the LTER grid. In contrast we compare these results to the Drake Passage where gas supersaturations vary by smaller magnitudes and decorrelate at length scales of ~12km, in line with latitudinal changes in the regional Rossby radius. Here biological processes induce smaller O2/Ar supersaturations (mean (0.14±1.3)%) and pCO2 undersaturations (mean (−2.8±3.9)%) than in the WAP, and pressure changes, bubble and gas exchange fluxes drive stable Ar supersaturations.

Optimising the deployment of renewable resources for the Australian NEM (National Electricity Market) and the effect of atmospheric length scales

This study sheds new light on the variability of wind power across the Australian NEM (National Electricity Market) and in doing so gives an insight on the potential network configuration for a high RE (Renewable Electricity) future. We present idealised cost-minimised simulations for the NEM utilising onshore wind, large-scale solar, pumped hydro and OCGT (open cycle gas turbines) technologies. A model using gridded meteorological data from the regional ACCESS-R (Australian Community Climate and Earth-System Simulator) simulates wind and solar technology output along with generation from OCGT to meet demand in the NEM for the period 2010–2011. A cost for connecting each location to the nearest major load centre is introduced and a base scenario created from an initial connection cost of $1 M/km. A sensitivity study reveals that a cost of $8 M/km results in the contraction of all renewable resources to four major wind installations. Compared to the base scenario the four major wind locations share much of the variability in renewable energy output, demonstrating that the NEM region has four distinct wind regimes. Separated by 1,400 km these four wind installations provide an optimisation-based decorrelation length for the NEM. This information is particularly useful for long-term planners of large-scale energy infrastructure.

Flexible polyelectrolyte chain in a strong electrolyte solution: Insight into equilibrium properties and force-extension behavior from mesoscale simulation.

Macromolecules with ionizable groups are ubiquitous in biological and synthetic systems. Due to the complex interaction between chain and electrostatic decorrelation lengths, both equilibrium properties and micro-mechanical response of dilute solutions of polyelectrolytes (PEs) are more complex than their neutral counterparts. In this work, the bead-rod micromechanical description of a chain is used to perform hi-fidelity Brownian dynamics simulation of dilute PE solutions to ascertain the self-similar equilibrium behavior of PE chains with various linear charge densities, scaling of the Kuhn step length (lE) with salt concentration cs and the force-extension behavior of the PE chain. In accord with earlier theoretical predictions, our results indicate that for a chain with n Kuhn segments, lE ∼ cs (-0.5) as linear charge density approaches 1/n. Moreover, the constant force ensemble simulation results accurately predict the initial non-linear force-extension region of PE chain recently measured via single chain experiments. Finally, inspired by Cohen's extraction of Warner's force law from the inverse Langevin force law, a novel numerical scheme is developed to extract a new elastic force law for real chains from our discrete set of force-extension data similar to Padè expansion, which accurately depicts the initial non-linear region where the total Kuhn length is less than the thermal screening length.

Dual state-parameter simultaneous estimation using localised ensemble Kalman filter and application in environmental model

Parameters in a hydrological model play a pivotal role for prediction. Good estimates of the parameters and state variables enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is realised based on the localised ensemble Kalman filter (EnKF) for estimation of both parameters and state variables of a groundwater flow model. The hydraulic head field (state variable) and the distribution of heterogeneous hydraulic conductivity (parameter) are simultaneously estimated through limited groundwater level observations and the localisation method. The localisation method is used to map updated weights and correlation distances from measured grid point to the grid point to be updated. The horizontal de-correlation length is determined by geostatistical method with a value L = 25 m. The analysed results indicate that the distribution of hydraulic conductivity estimated by the localised EnKF approach could well match the real field only after five data assimilation steps with few 25 observation wells. Meanwhile, the simulation of hydraulic conductivity is significantly improved by the localised EnKF approach.

Cloud overlapping parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM with McICA scheme

Vertical decorrelation length (Lcf) as used to determine overlap of cloudy layers in GCMs was obtained from CloudSat/CALIPSO measurements, made between 2007 and 2010, and analyzed in terms of monthly means. Global distributions of Lcf were produced for several cross-sectional lengths. Results show that: Lcf over the tropical convective regions typically exceeds 2km and shift meridionally with season; the smallest Lcf (<1km) tends to occur in regions dominated by marine stratiform clouds; Lcf for mid-to-high latitude continents of the Northern Hemisphere (NH) ranges from 5–6km during winter to 2–3km during summer; and there are marked differences between continental and oceanic values of Lcf in the mid-latitudes of the NH.These monthly-gridded, observationally-based values of Lcf data were then used by the Monte Carlo Independent Column Approximation (McICA) radiation routines within the Beijing Climate Center's GCM (BCC_AGCM2.0.1). Additionally, the GCM was run with two other descriptions of Lcf: one varied with latitude only, and the other was simply 2km everywhere all the time. It is shown that using the observationally-based Lcf in the GCM led to local and seasonal changes in total cloud fraction and shortwave (longwave) cloud radiative effects that serve mostly to reduce model biases. This indicates that usage of Lcf that vary according to location and time has the potential to improve climate simulations.

Overlap statistics of shallow boundary layer clouds: Comparing ground‐based observations with large‐eddy simulations

AbstractHigh‐resolution ground‐based measurements are used to assess the realism of fine‐scale numerical simulations of shallow cumulus cloud fields. The overlap statistics of cumuli as produced by large‐eddy simulations (LES) are confronted with Cloudnet data sets at the Jülich Observatory for Cloud Evolution. The Cloudnet pixel is small enough to detect cumuliform cloud overlap. Cloud fraction masks are derived for five different cases, using gridded time‐height data sets at various temporal and vertical resolutions. The overlap ratio (R), i.e., the ratio between cloud fraction by volume and by area, is studied as a function of the vertical resolution. Good agreement is found between R derived from observations and simulations. An inverse linear function is found to best describe the observed overlap behavior, confirming previous LES results. Simulated and observed decorrelation lengths are smaller (∼300 m) than previously reported (&gt;1 km). A similar diurnal variation in the overlap efficiency is found in observations and simulations.

Impacts of cloud overlap assumptions on radiative budgets and heating fields in convective regions

Impacts of cloud overlap assumptions on radiative budgets and heating fields are explored with the aid of a cloud-resolving model (CRM), which provided cloud geometry as well as cloud micro and macro properties. Large-scale forcing data to drive the CRM are from TRMM Kwajalein Experiment and the Global Atmospheric Research Program's Atlantic Tropical Experiment field campaigns during which abundant convective systems were observed. The investigated overlap assumptions include those that were traditional and widely used in the past and the one that was recently addressed by Hogan and Illingworth (2000), in which the vertically projected cloud fraction is expressed by a linear combination of maximum and random overlap, with the weighting coefficient depending on the so-called decorrelation length Lcf. Results show that both shortwave and longwave cloud radiative forcings (SWCF/LWCF) are significantly underestimated under maximum (MO) and maximum-random (MRO) overlap assumptions, whereas remarkably overestimated under the random overlap (RO) assumption in comparison with that using CRM inherent cloud geometry. These biases can reach as high as 100 Wm−2 for SWCF and 60 Wm−2 for LWCF. By its very nature, the general overlap (GenO) assumption exhibits an encouraging performance on both SWCF and LWCF simulations, with the biases almost reduced by 3-fold compared with traditional overlap assumptions. The superiority of GenO assumption is also manifested in the simulation of shortwave and longwave radiative heating fields, which are either significantly overestimated or underestimated under traditional overlap assumptions. The study also pointed out the deficiency of constant assumption on Lcf in GenO assumption. Further examinations indicate that the CRM diagnostic Lcf varies among different cloud types and tends to be stratified in the vertical. The new parameterization that takes into account variation of Lcf in the vertical well reproduces such a relationship and leads to better simulations on radiative heating fields. It is therefore desirable to specify or parameterize Lcf in terms of cloud categories rather than constantly specified if to further improve the model performance.

An Interpretation of Cloud Overlap Statistics

Abstract Observational studies have shown that the vertical overlap of cloudy layers separated by clear sky can exceed that of the random overlap assumption, suggesting a tendency toward minimum overlap. In addition, the rate of decorrelation of vertically continuous clouds with increasing layer separation is sensitive to the horizontal scale of the cloud scenes used. The authors give a heuristic argument that these phenomena result from data truncation, where overcast or single cloud layers are removed from the analysis. This occurs more frequently as the cloud sampling scale falls progressively below the typical cloud system scale. The postulate is supported by sampling artificial cyclic and subsequently more realistic fractal cloud scenes at various length scales. The fractal clouds indicate that the degree of minimal overlap diagnosed in previous studies for discontinuous clouds could result from sampling randomly overlapped clouds at spatial scales that are 30%–80% of the cloud system scale. Removing scenes with cloud cover exceeding 50% from the analysis reduces the impact of data truncation, with discontinuous clouds not minimally overlapped and the decorrelation of continuous clouds less sensitive to the sampling scale. Using CloudSat–CALIPSO data, a decorrelation length scale of approximately 4.0 km is found. In light of these results, the previously documented dependence of overlap decorrelation length scale on latitude is not entirely a physical phenomenon but can be reinterpreted as resulting from sampling cloud systems that increase significantly in size from the tropics to midlatitudes using a fixed sampling scale.

Generalizing Cloud Overlap Treatment to Include the Effect of Wind Shear

Abstract Six months of CloudSat and CALIPSO observations have been divided into over 8 million cloud scenes and collocated with ECMWF wind analyses to identify an empirical relationship between cloud overlap and wind shear for use in atmospheric models. For vertically continuous cloudy layers, cloud decorrelates from maximum toward random overlap as the layer separation distance increases, and the authors demonstrate a systematic impact of wind shear on the resulting decorrelation length scale. As expected, cloud decorrelates over smaller distances as wind shear increases. A simple, empirical linear fit parameterization is suggested that is straightforward to add to existing radiation schemes, although it is shown that the parameters are quite sensitive to the processing details of the cloud mask data and also to the fitting method used. The wind shear–overlap dependency is implemented in the radiation scheme of the ECMWF Integrated Forecast System. It has a similar-magnitude impact on the radiative budget as that of switching from a fixed decorrelation length scale to the latitude-dependent length scale presently used in the operational model, altering the zonal-mean, top-of-atmosphere, equator-to-midlatitude gradient of shortwave radiation by approximately 2 W m−2.

Probabilistic tail dependence of intense precipitation on spatiotemporal scale in observations, reanalyses, and GCMs

Daily precipitation variability as observed from weather stations is heavy tailed at most locations around the world. It is thought that diversity in precipitation-causing weather events is fundamental in producing heavy-tailed distributions, and it arises from theory that at least one of the precipitation types contributing to a heavy-tailed climatological record must also be heavy-tailed. Precipitation is a multi-scale phenomenon with a rich spatial structure and short decorrelation length and timescales; the spatiotemporal scale at which precipitation is observed is thus an important factor when considering its statistics and extremes. In this study, we examine the spatiotemporal scaling behavior of intense precipitation from point-scale to large grid cells and from 1 day to 4 weeks over the entire globe. We go on to validate the current generation of historically-forced climate models and reanalyses against observational data at consistent spatial scales. Our results demonstrate that the prevalence and magnitude of heavy tails in observations decrease when moving to lower spatiotemporal resolutions, as is consistent with stochastic theory. Reanalyses and climate models generally reproduce large, synoptic scale distribution classifications, but struggle to reproduce the statistics in regions that are strongly affected by mesoscale phenomena. We discuss these results in relation to physically consistent atmospheric regimes. We conclude with a global view of precipitation distribution type at daily resolution as calculated from the best-performing reanalysis, the Climate Forecast System Reanalysis.

Along‐isopycnal variability of spice in the North Pacific

AbstractTwo hydrographic surveys in the Gulf of Alaska and the North Pacific subtropical gyre are presented. Both surveys are roughly perpendicular to lateral temperature gradients, and were collected in the summer when there was a shallow mixed layer and a seasonal thermocline. Isopycnal displacements and horizontal velocities are dominated by internal waves. Spice anomalies along isopycnals are examined to diagnose lateral stirring mechanisms. The spectra of spice anomaly gradients along near‐surface isopycnals roughly follow power laws of (variance spectra power laws of ), and in most cases, the spectra become redder at depth. The near‐surface spectra are possibly consistent with the predictions of quasi‐geostrophic turbulence theory (when surface buoyancy effects are accounted for), but the spectra at depth are inconsistent with any quasi‐geostrophic theory. Probability distributions of spice gradients exhibit a large peak at low gradients and long tails for large gradients, symptomatic of fronts. Vertical coherence of the spice signal falls off with a decorrelation depth scale that has a maximum of about 80 m at 100 km wavelengths and depends on horizontal wavelength with a power law of approximately . Lateral decorrelation length scales are 20–40 km, close to the baroclinic Rossby radius. Lateral stirring occurs over large scales, with average lateral displacements of about 200 km in the upper 75 m, decreasing to 100 km at greater depths. The depth variation of the statistics indicates that time history of tracer stirring on each isopycnal is important, or that there are unconsidered depth‐dependent stirring mechanisms.

On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

Abstract. Cities are increasingly vulnerable to floods generated by intense rainfall, because of urbanisation of flood-prone areas and ongoing urban densification. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction in cities. In this paper, a detailed study of the sensitivity of urban hydrodynamic response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar were used as input into a detailed hydrodynamic sewer model for an urban catchment in the city of Rotterdam, the Netherlands. The aim was to characterise how the effect of space and time aggregation on rainfall structure affects hydrodynamic modelling of urban catchments, for resolutions ranging from 100 to 2000 m and from 1 to 10 min. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm characteristics and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show that for rainfall resolution lower than half the catchment size, rainfall volumes mean and standard deviations decrease as a result of smoothing of rainfall gradients. Moreover, deviations in maximum water depths, from 10 to 30% depending on the storm, occurred for rainfall resolution close to storm size, as a result of rainfall aggregation. Model results also showed that modelled runoff peaks are more sensitive to rainfall resolution than maximum in-sewer water depths as flow routing has a damping effect on in-sewer water level variations. Temporal resolution aggregation of rainfall inputs led to increase in de-correlation lengths and resulted in time shift in modelled flow peaks by several minutes. Sensitivity to temporal resolution of rainfall inputs was low compared to spatial resolution, for the storms analysed in this study.

Spatial power fluctuation correlations in urban rooftop photovoltaic systems

AbstractIn this paper, we investigate the spatial dependence of variations in power output of small residential solar photovoltaic (PV) systems in a densely populated urban area (≈100km2) in and around Utrecht, the Netherlands. Research into the geo‐statistical behavior of this kind of randomly spaced collection of PV systems is complementary to other studies in the field of compact regularly spaced MW‐scale PV plants. Fluctuations in power output between PV systems are correlated up to a certain decorrelation length. Decorrelation is reached (within 1 − e−3≈95%) in an exponential model and the spatial scale ranges from 100 m to approx. 15 km, with a mean value 0.34(±0.2), 2.6(±0.3), and 5.0(±0.5) km for measurement time step of the time series of respectively 1, 5, and 15 min. These length scales are typical for an urban environment and is important for reduction of variability in aggregated output variability of PV systems. Furthermore, the distance‐independent variability still itself was found to be strictly linearly dependent on daily mean variability values. This is a good validation of the decorrelation of inter‐system ramp rate correlation over distances longer than the characteristic decorrelation length. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Application and evaluation of a new radiation code under McICA scheme in BCC_AGCM2.0.1

Abstract. This research incorporates the correlated k distribution BCC-RAD radiation model into the climate model BCC_AGCM2.0.1 and examines the change in climate simulation by implementation of the new radiation algorithm. It is shown that both clear-sky radiation fluxes and cloud radiative forcings (CRFs) are improved. The modeled atmospheric temperature and specific humidity are also improved due to changes in radiative heating rates, which most likely stem from the revised treatment of gaseous absorption. Subgrid cloud variability, including vertical overlap of fractional clouds and horizontal inhomogeneity in cloud condensate, is addressed by using the Monte Carlo Independent Column Approximation (McICA) method. In McICA, a cloud-type-dependent function for cloud fraction decorrelation length, which gives zonal mean results very close to the observations of CloudSat/CALIPSO, is developed. Compared to utilizing a globally constant decorrelation length, the maximum changes in seasonal CRFs by the new scheme can be as large as 10 and 20 W m−2 for longwave (LW) and shortwave (SW) CRFs, respectively, mostly located in the tropics. The inclusion of an observation-based horizontal inhomogeneity of cloud condensate has also a significant impact on CRFs, with global means of ~ 1.5 W m−2 and ~ 3.7 Wm−2 for LW and SW CRFs at the top of atmosphere (TOA), respectively. Generally, incorporating McICA and horizontal inhomogeneity of cloud condensate in the BCC-RAD model reduces global mean TOA and surface SW and LW flux biases in BCC_AGCM2.0.1. These results demonstrate the feasibility of the new model configuration to be used in BCC_AGCM2.0.1 for climate simulations, and also indicate that more detailed real-world information on cloud structures should be obtained to constrain cloud settings in McICA in the future.