Horizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1-3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

A novel uncertainty quantification method is used to evaluate the impact of uncertainties of parameters within the icing model in the modeling chain for icing-related wind power production loss forecasts. As a first step, uncertain parameters in the icing model were identified from the literature and personal communications. These parameters are the median volume diameter of the hydrometeors, the sticking efficiency for snow and graupel, the Nusselt number, the shedding factor, and the wind erosion factor. The sensitivity of these parameters on icing-related wind power production losses is examined. An icing model ensemble representing the estimated parameter uncertainties is designed using so-called deterministic sampling and is run for two periods over a total of 29 weeks. Deterministic sampling allows an exact representation of the uncertainty and, in future applications, further calibration of these parameters. Also, the number of required ensemble members is reduced drastically relative to the commonly used random-sampling method, thus enabling faster delivery and a more flexible system. The results from random and deterministic sampling are compared and agree very well, confirming the usefulness of deterministic sampling. The ensemble mean of the nine-member icing model ensemble generated with deterministic sampling is shown to improve the forecast skill relative to one single forecast for the winter periods. In addition, the ensemble spread provides valuable information as compared with a single forecast in terms of forecasting uncertainty. However, addressing uncertainties in the icing model alone underestimates the forecast uncertainty, thus stressing the need for a fully probabilistic approach in the modeling chain for wind power forecasts in a cold climate.

The Satellite Application Facility on Climate Monitoring (CM-SAF) is aiming to retrieve satellite-derived geophysical parameters suitable for climate monitoring. CM-SAF started routine operations in early 2007 and provides a climatology of parameters describing the global energy and water cycle on a regional scale and partially on a global scale. Here, the authors focus on the performance of cloud detection methods applied to measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation geostationary spacecraft. The retrieved cloud mask is the basis for calculating the cloud fractional coverage (CFC) but is also mandatory for retrieving other geophysical parameters. Therefore, the quality of the cloud detection directly influences climate monitoring of many other parameters derived from spaceborne sensors. CM-SAF products and results of an alternative cloud coverage retrieval provided by the Institut fur Weltraumwissenschaften of the Freie Universitat in Berlin, Germany (FUB), were validated against synoptic measurements. Furthermore, and on the basis of case studies, an initial comparison was performed of CM-SAF results with results derived from the Moderate Resolution Imaging Spectrometer (MODIS) and from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Results show that the CFC from CM-SAF and FUB agrees well with synoptic data and MODIS data over midlatitudes but is underestimated over the tropics and overestimated toward the edges of the visible Earth disk.

The benefits of potential electric power transfers between the Pacific Northwest (PNW) and California ( CA) are evaluated using a linked set of hydrologic, reservoir, and power demand simulation models for the Columbia River and the Sacramento-San Joaquin reservoir systems. The models provide a framework for evaluating climate-related variations and long-range predictability of regional electric power demand, hydropower production, and the benefits of potential electric power transfers between the PNW and CA. The period of analysis is 1917-2002. The study results show that hydropower production and regional electric power demands in the PNW and CA are out of phase seasonally but that hydropower productions in the PNW and CA have strongly covaried on an annual basis in recent decades. Winter electric power demand and spring and annual hydropower production in the PNW are related to both El Nino-Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) through variations in winter climate. Summer power demand in CA is related primarily to variations in the PDO in spring. Hydropower production in CA, despite recent covariation with the PNW, is not strongly related to ENSO variability overall. Primarily because of strong variations in supply in the PNW, potential hydropower transfers between the PNW and CA in spring and summer are shown to be correlated to ENSO and PDO, and the conditional probability distributions of these transfers are therefore predictable with long lead times. Such electric power transfers are estimated to have potential average annual benefits of $136 and $79 million for CA and the PNW, respectively, at the year-2000 regional demand level. These benefits are on average 11%-27% larger during cold ENSO/PDO events and are 16%-30% lower during warm ENSO/PDO events. Power transfers from the PNW to CA and hydropower production in CA are comparable in magnitude, on average.