Victor Venema

Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Surrogate cloud fields generated with the iterative amplitude adapted Fourier transform algorithm

A new method of generating two-dimensional and three-dimensional cloud fields is presented, which share several important statistical properties with real measured cloud fields.Well-known algorithms such as the Fourier method and the Bounded Cascade method generate fields with a specified Fourier spectrum. The new iterative method allows for the specification of both the power spectrum and the amplitude distribution of the parameter of interest, e.g. the liquid water content or liquid water path. As such, the method is well suited to generate cloud fields based on measured data, and it is able to generate broken cloud fields. Important applications of such cloud fields are e.g. closure studies. The algorithm can be supplied with additional spatial constraints which can reduce the number of measured cases needed for such studies. In this study the suitability of the algorithm for radiative questions is evaluated by comparing the radiative properties of cloud fields from cloud resolving models of cumulus and stratocumulus with their surrogate fields at nadir, and for a solar zenith angle of 0◦ and 60◦. The cumulus surrogate clouds ended up to be identical to the large eddy simulation (LES) clouds on which they are based, except for translations and reflections. The root mean square differences of the stratocumulus transmittance and reflectance fields are less than 0.03% of the radiative budget. The radiances and mean actinic fluxes fit better than 2%. These results demonstrate that these LES clouds are well described from a radiative point of view, using only a power spectrum together with an amplitude distribution.

Ground-based remote sensing of stratocumulus properties during CLARA, 1996

A method is presented to obtain droplet concentration for water clouds from ground-based remote sensing observations. It relies on observations of cloud thickness, liquid water path, and optical extinction near the cloud base. The method was tested for two case studies (19 April 1996 and 4 September 1996) during the Clouds And Radiation experiment (CLARA). The CLARA experiment was designed to observe clouds using a variety of remote sensing instruments near the city of Delft in the western part of the Netherlands. The measurement of cloud thickness is dependent on the detection of cloud base by lidar and cloud top by radar. It is shown that during CLARA it was possible to detect cloud base with an uncertainty of less than 30 m using current lidar techniques. The agreement between in situ and remote sensing observations of droplet concentration was reasonable. An error analysis indicates that this method is most sensitive to uncertainties in liquid water path and the unknown effects of multiple scattering on lidar signal returns. When the liquid water path is very small the relative error of the liquid water path increases to unacceptable levels, so that the retrieval of droplet concentration becomes very difficult. The estimated uncertainty in the strength of multiple scattering can explain differences between observations and retrievals of droplet concentration on one day, but not the other.

Coherent Scattering of Microwaves by Particles: Evidence from Clouds and Smoke

Many radar measurements of the atmosphere can be explained in terms of two scattering mechanisms: incoherent scattering from particles, and coherent scattering from variations in the refractive index of the air, commonly called clear-air or Bragg scattering. Spatial variations in the liquid water content of clouds may also give a coherent contribution to the radar return, but it is commonly believed that this coherent scattering from the droplets is insignificant because variations in humidity have a much larger influence on the refractive index than equal variations in liquid water content. It is argued that the fluctuations in water vapor mixing ratio in clouds can be much smaller than those in liquid water mixing ratio. In this article an expression for the strength of the coherent scattering from particles will be derived for fluctuations caused by turbulent mixing with clean (i.e., particle-free) air, where it will be assumed that the particles follow the flow, that is, their inertia is neglected. It will be shown that the coherent contribution adds to the incoherent contribution, the latter always being present. The coherent particle scattering can be stronger than the incoherent scattering, especially at longer wavelengths and high particle concentrations. Recently published dual-frequency measurements of developing cumulus clouds and smoke show a correlation for which no explanation has been found in terms of incoherent particle scattering and coherent air scattering. Scatterplots of the reflectivity factors at both frequencies show a clustering of points in between the values that correspond to pure clear-air and pure incoherent scattering. Those differences in the radar reflectivity factors could be due to a mixture of Bragg scattering and incoherent particle scattering, but then no correlation is expected, because the origin of the scattering mechanism that dominates at each wavelength is different. However, coherent scattering from the particles can cause the radar reflectivities of dual-wavelength radar measurements to become correlated with each other. It may explain the slopes and the differences seen in the scatterplots of the radar reflectivities of cloud and smoke measurements, with reasonable values of the parameters involved. However, the correlation between the radar reflectivities is very tight near the cloud top and seems to be present in adiabatic cores as well. This is an indication that, apart from mixing with environmental air, the inertia of the droplets could also be important for the creation of small-scale fluctuations in droplet concentration.

A downscaling scheme for atmospheric variables to drive soil–vegetation–atmosphere transfer models

For driving soil–vegetation–transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called ‘deterministic’ part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400mhorizontal grid spacing).Anovel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Natural Three-Dimensional Predictor Domains for Statistical Precipitation Downscaling

The paper presents an approach for conditional airmass classification based on local precipitation rate distributions. The method seeks, within the potential region, three-dimensional atmospheric predictor domains with high impact on the local-scale phenomena. These predictor domains are derived by an algorithm consisting of a clustering method, namely, self-organizing maps, and a nonlinear optimization method, simulated annealing. The findings show that the resulting spatial structures can be attributed to well-known atmospheric processes. Since the optimized predictor domains probably contain relevant information for precipitation generation, these grid points may also be potential inputs for nonlinear downscaling methods. Based on this assumption, the potential of these optimized large-scale predictors for downscaling has been investigated by applying an artificial neural network as a nonparametric statistical downscaling model. Compared to preset local predictors, using the optimized predictors improves the accuracy of the downscaled time series, particularly in summer and autumn. However, optimizing predictors by a conditional classification does not guarantee that a predictor increases the explained variance of the downscaling model. To study the contribution of each predictor to the output variance, either individually or by interactions with other parameters, the sources of uncertainty have been estimated by global sensitivity analysis, which provides model-free sensitivity measures. It is shown that predictor interactions play an important part in the modeling process and should be taken into account in the predictor screening.

Fewer jumps, less memory: Homogenized temperature records and long memory

[1] Air temperature records are commonly subjected to inhomogeneities, e.g., sudden jumps caused by a relocation of the measurement station or by installing a new type of shelter. We study the effect of these inhomogeneities on the estimation of the Hurst exponent and show that they bias the estimates toward larger values. The Hurst exponent is a parameter to measure long-range dependence (LRD), which is a characteristic frequently used to describe the natural variability of temperature records. Analyzing a set of temperature time series before and after homogenization with respect to LRD, we find that the average Hurst exponent is clearly reduced for the homogenized series. To test whether (1) jumps cause this positive bias and (2) the homogenization does not artificially reduce the Hurst exponent estimates, we perform a simulation study. This test shows that inhomogeneities in the form of jumps bias the Hurst exponent estimation and that the homogenization procedure is able to remove this bias, leaving the Hurst exponent unchanged. This result holds for fractional autoregressive integrated moving average (FARIMA)-based as well as for detrended fluctuation analysis-based estimation. We conclude that the use of homogenized series is necessary to prevent misleading conclusions about the dependence structure and thus about subsequent analysis such as trend tests.

A call for new approaches to quantifying biases in observations of sea-surface temperature

AbstractGlobal surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of ...

Assimilation of radar and satellite data in mesoscale models: A physical initialization scheme

The quality of numerical precipitation prediction depends on the accuracy with which the model reproduces the true initial state of the atmosphere prior to the fo recast. Typically a numerical model needs a spin-up time of several hours until its hydrological cycle is establ ished. Assimilation of precipitation data can reduce the spin-up time significantly and consequently opens the po ssibility of nowcasting with Numerical Weather Prediction (NWP) models. We further enhanced the physical i nitialisation scheme (PIB, Physical Initialisation Bonn) by HAASE (2002) in order to improve quantitative precipitation nowc asting with a high-resolution NWP model. The assimilation scheme takes as input a radar bas ed precipitation product and a cloud top height field retrieved from satellite observations. During the assimilation window, PIB adjusts the vertical wind, humidity, cloud water, and cloud ice to force the model state towards the measurements. The most distinctive feature of the algorithm is the adjustment of th e vertical wind profile in the framework of a simple precipitation generation scheme. In this paper, we present an identical twin experiment, which reveals how the model variables are adjusted during the assimilation windo w, and which demonstrates the consistency of PIB with the physics of the NWP model. Three case studies with rea l m surements demonstrate that the scheme improves the forecast of the precipitation patterns, as wel l as the dynamics of the events. These improvements are found both during the assimilation window and for the firs t hours of the free forecast.

Downscaling near-surface atmospheric fields with multi-objective Genetic Programming

We present a new Genetic Programming based method to derive downscaling rules (i.e., functions or short programs) generating realistic high-resolution fields of atmospheric state variables near the surface given coarser-scale atmospheric information and high-resolution information on land surface properties. Such downscaling rules can be applied in coupled subsurface-land surface-atmosphere simulations or to generate high-resolution atmospheric input data for offline applications of land surface and subsurface models. Multiple features of the high-resolution fields, such as the spatial distribution of subgrid-scale variance, serve as objectives. The downscaling rules take an interpretable form and contain on average about 5 mathematical operations. The method is applied to downscale 10źm-temperature fields from 2.8źkm to 400źm grid resolution. A large part of the spatial variability is reproduced, also in stable nighttime situations, which generate very heterogeneous near-surface temperature fields in regions with distinct topography. A GP based method for the downscaling of coherent spatial fields is presented.Multiple characteristics of high-resolution fields are optimized simultaneously.Fine-scale structures are estimated from high-resolution land surface data.Spatial variability of near-surface temperature is well reproduced.