Search

Many segmentation or change‐point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non‐climatic changes. One drawback is that it is difficult to decide whether the detected change‐point is due to the candidate series or to the reference. A so‐called attribution procedure is typically applied in a post‐processing step for each detected change‐point. This article describes a new statistical method for the attribution of change‐points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change‐point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change‐point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross‐validation and the best one was applied to a real data set (49 main stations with 114 change‐points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change‐points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections. [ABSTRACT FROM AUTHOR]

The deployment of special instrumentation for the Hydrological Cycle in the Mediterranean Experiment (HyMeX) provides a valuable opportunity to investigate the spatio‐temporal variability of atmospheric water vapour across scales in relationship with the occurrence of Heavy Precipitation Systems (HPSs) in the north Western Mediterranean (WMed) during the Intensive Observation Period (IOP12), which is the focus of this investigation. High‐resolution convection‐permitting COSMO simulations complement the observational network and allow the calculation of on‐line trajectories. In addition to the presence of a favourable large‐scale situation and low‐level convergence, atmospheric moisture changes resulting in conditionally unstable air are identified as responsible for convective initiation (CI). All HPSs within the north‐WMed form in periods/areas of maximum integrated water vapour (IWV; 35–45 kg/m2) after an increase of about 10–20 kg/m2. The most intense events receive moisture from different sources simultaneously and show a sudden increase of about 10 kg/m2 between 6 and 12 h prior to the event, whereas in the less intense events the increase is larger, about 20 kg/m2, over a period of at least 24–36 h. Changes in the lower (∼900 hPa) and mid‐troposphere (∼700 hPa) control the evolution of the atmospheric moisture and the instability increase prior to CI. Spatial inhomogeneities in the lower boundary layer determine the timing and location of deep convection, whereas enhanced moisture in the mid‐troposphere favours intensification. Moister and deeper boundary layers, with updraughts reaching up to 2 km are identified in those pre‐convective environments leading to HPS, whereas dry, shallow boundary layers are found everywhere else. The build‐up time and vertical distribution of the moisture changes are found to be crucial for the evolution and severity of the HPSs rather than the amount of total column atmospheric moisture. This article investigates the spatio‐temporal variability of atmospheric water vapour across scales in relation to the occurrence of heavy precipitation systems in the northwestern Mediterranean. Synergies between different instruments, Global Positioning System (GPS) stations, radiosondes, airborne and ground‐based lidars and space‐borne observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), among others, are used. High‐resolution convection‐permitting simulations complement the observational network. Daily mean spatial distribution of total column integrated water vapour (IWV) from MODIS (background) and GPS (circles) observations. Simulated COSMO‐7km and COSMO‐2.8km 950 hPa winds at 1200 UTC are superimposed. [ABSTRACT FROM AUTHOR]

Comprehensive measurements were carried out on the mountainous island of Corsica during the Hydrological cycle in the Mediterranean Experiment (HyMeX) field campaign in 2012. Using data from an operational GPS network, radiosondes and microwave radiometers, we studied the spatio-temporal variability of integrated water vapour (IWV) and convection-related parameters in the pre-convective environment over the island. The following spatial differences in the IWV evolution occurred frequently. In the interior of the island the IWV strongly increased around noon, while on the upstream coast only small variations were observed. We showed that a transport of water vapour with thermally driven circulations and the formation of elevated humidity layers due to advective venting caused the diurnal IWV variation over and downstream of the mountains. Spatial inhomogeneities in the pre-convective atmospheric conditions evolved and the knowledge of these turned out to be crucial to understanding the timing and location of isolated deep convection over the island. [ABSTRACT FROM AUTHOR]

The Hydrological cycle in the Mediterranean eXperiment ( HyMeX) was held in autumn 2012 during which part of the observational effort was established on Menorca Island to characterize the upwind marine low-level flow. The ground-based Water-vapour Raman Lidar ( WALI), the airborne water-vapour lidar LEANDRE 2 and boundary-layer pressurized balloons were implemented during the first Special Observing Period and contributed to characterize water vapour variability in the vicinity of the Balearic Islands, together with the existing network of Global Positioning System receivers. Furthermore, analyses from regional and global numerical weather prediction ( NWP) models ( AROME-WMED, the ECMWF/ IFS NWP system and the Weather Research and Forecasting ( WRF) model) were also available over large domains encompassing part of or the entire western Mediterranean basin. We assess the consistency of water vapour mixing ratio ( WVMR) profiles and integrated water vapour contents ( IWVC) derived from the different datasets by comparing them to a common reference, the ground-based lidar WALI. We use consistency indicators such as root-mean-square errors, biases and correlations. Comparison between WVMR profiles from ground-based and airborne lidars (ground-based lidar and boundary-layer pressurized balloons) leads to a root-mean-square error lower than 1.6 g kg−1 (1.3 g kg−1) when the closest possible air masses are sampled. We observed a good agreement between the vertical WVMR profiles derived from WALI and the numerical models with correlations higher than 0.7 and root-mean-square errors lower than 2 g kg−1. Regarding IWVCs, the models exhibit biases less than 2 kg m−2, root-mean-square errors lower than 2.3 g kg−1 and correlations higher than 0.86 when compared to WALI. Finally, AROME-WMED 48 h forecasts were compared with WALI data composited over eleven 48 h periods. The quality of the forecast does not visibly degrade within the 48 h period from the initial analyses. [ABSTRACT FROM AUTHOR]