TY - GEN
AU - D. Bouniol
AU - A. Protat
AU - J. Delanoë
AU - J. Pelon
AU - D.P. Donovan
AU - J.-M. Piriou
AU - F. Bouyssel
AU - A.M. Tompkins
AU - D. Wilson
AU - Y. Morille
AU - M. Haeffelin
AU - E.J. O'Connor
AU - Robin Hogan
AU - A.J. Illingworth
AB - This paper assesses the ability of four operational weather forecast models (ECMWF, ARPEGE, RACMO and Met Office) to generate a cloud at the right location and time (i.e. the cloud occurrence) using a two year time series of observations collected by profiling ground-based active sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, NL, Chilbolton, UK, Palaiseau, F.). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. It is shown that all models tend to overestimate the occurrence of high-level clouds, while for lower levels the errors are contrasting. The data set is then divided into seasons to evaluate the potential of the models to generate different cloud situations. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as between the seasons of a given year. Overall, the model biases observed using the whole data set are still found at seasonal scale, but the models generally manage to capture the seasonal variation in cloud occurrence well. This study demonstrates the usefulness of long time series in order to monitor and guide changes to a given parametrisation.
BT - ECMWF Technical Memoranda
DA - 12/2007
DO - 10.21957/wv8w1sryh
LA - eng
M1 - 526
N2 - This paper assesses the ability of four operational weather forecast models (ECMWF, ARPEGE, RACMO and Met Office) to generate a cloud at the right location and time (i.e. the cloud occurrence) using a two year time series of observations collected by profiling ground-based active sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, NL, Chilbolton, UK, Palaiseau, F.). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. It is shown that all models tend to overestimate the occurrence of high-level clouds, while for lower levels the errors are contrasting. The data set is then divided into seasons to evaluate the potential of the models to generate different cloud situations. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as between the seasons of a given year. Overall, the model biases observed using the whole data set are still found at seasonal scale, but the models generally manage to capture the seasonal variation in cloud occurrence well. This study demonstrates the usefulness of long time series in order to monitor and guide changes to a given parametrisation.
PB - ECMWF
PY - 2007
EP - 16
T2 - ECMWF Technical Memoranda
TI - Evaluating cloud occurrence in the ECMWF integrated forecast system and three other operational forecasts models using long-term ground-based radar and lidar measurements.
UR - https://www.ecmwf.int/node/8331
ER -