Thierry Castel

Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE) by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Meteo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1) PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2) both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3) PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE). This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

Sensitivity of the C-band SRTM DEM Vertical Accuracy to Terrain Characteristics and Spatial Resolution

This work reports the results of a careful regional analysis of the SRTM DEM (Shuttle Radar Topography Mission – Digital Elevation Model) vertical accuracy as a function of both topography and Land-Use/Land Cover (LULC). Absolute vertical errors appear LULC-dependent, with some values greater than the stated accuracy of the SRTM dataset, mostly over forested areas. The results show that the structure of the errors is well modeled by a cosine power n of the local incidence angle (θloc). SRTM quality is further assessed using slope and topographical similarity indexes. The results show a lower relative accuracy on slope with a R2 = 0.5 and a moderate agreement (Kappa ≈ 0.4) between SRTM- and IGN-derived slopes. The application of a simple cosine squared correction on the 90 m SRTM dataset shows only a slight improvement of the relative accuracy despite a 7 m decrease of the mean absolute elevation error. The accuracy is strongly improved (R2 = 0.93 and Kappa = 0.75) for data resampled at a 150 m to 500 m horizontal resolution. These results support the idea that for regional application purposes the topographic correction as well as the spatial resampling of the SRTM dataset are needed.

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

This 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.