Dynamical downscaling of temperature variability over Tunisia: evaluation a 21-year-long simulation performed with the WRF model.

8 pagesInternational audienceThis study evaluates the capabilities of the Weather Research and Forecasting (WRF) model to reproduce the space-time variability of near-surface air temperature over Tunisia. Downscaling is based on two nested domains with a first domain covering the Mediterranean Basin and forced by 21 years of ERA-Interim reanalysis (1991-2011), and a second domain (12 km spatial resolution) centered on Tunisia. Analyses and comparisons are focused on daily average (Tavg), minimum (Tmin) and maximum (Tmax) near-surface air temperatures and are carried out at the annual and seasonal timescales. WRF results are assessed against various climatological products (ERA-Interim, EOBS and a local network of 18 surface weather stations). The model correctly reproduces the spatial patterns of temperature being significantly superimposed with local topographic features. However, it broadly tends to underestimate temperatures especially in winter. Temporal variability of temperature is also properly reproduced by the model although systematic cold biases mostly concerning Tmax, reproduced throughout the whole simulation period, and prevailing during the winter months. Comparisons also suggest that the WRF errors are not rooted in the driving model but could be probably linked to deficiencies in the model parameterizations of diurnal/nocturnal physical processes that largely impact Tmax / Tmin

Weather Forecasting for Weather Derivatives

We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.Risk management; hedging; insurance; seasonality; temperature; financial derivatives

Weather that is not visible from earth - High-resolution television photography

Weather forecasting by use of meteorological satellite television cloud cove

Predicting Inflation: Professional Experts Versus No-Change Forecasts

We compare forecasts of United States inflation from the Survey of Professional Forecasters (SPF) to predictions made by simple statistical techniques. In nowcasting, economic expertise is persuasive. When projecting beyond the current quarter, novel yet simplistic probabilistic no-change forecasts are equally competitive. We further interpret surveys as ensembles of forecasts, and show that they can be used similarly to the ways in which ensemble prediction systems have transformed weather forecasting. Then we borrow another idea from weather forecasting, in that we apply statistical techniques to postprocess the SPF forecast, based on experience from the recent past. The foregoing conclusions remain unchanged after survey postprocessing

Preconditioned iterative methods for implicit equations

This paper was written for The European Centre for Medium Range Weather Forecasting Workshop on Developments in Numerical Methods for Very High Resolution Global Models, June 2000