Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000-13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network

Precipitation at Dumont d'Urville, Adélie Land, East Antarctica: the APRES3 field campaigns dataset

Compared to the other continents and lands, Antarctica suffers from a severe shortage of in situ observations of precipitation. APRES3 (Antarctic Precipitation, Remote Sensing from Surface and Space) is a program dedicated to improving the observation of Antarctic precipitation, both from the surface and from space, to assess climatologies and evaluate and ameliorate meteorological and climate models. A field measurement campaign was deployed at Dumont d'Urville station at the coast of Adélie Land in Antarctica, with an intensive observation period from November 2015 to February 2016 using X-band and K-band radars, a snow gauge, snowflake cameras and a disdrometer, followed by continuous radar monitoring through 2016 and beyond. Among other results, the observations show that a significant fraction of precipitation sublimates in a dry surface katabatic layer before it reaches and accumulates at the surface, a result derived from profiling radar measurements. While the bulk of the data analyses and scientific results are published in specialized journals, this paper provides a compact description of the dataset now archived in the PANGAEA data repository (https://www.pangaea.de, https://doi.org/10.1594/PANGAEA.883562) and made open to the scientific community to further its exploitation for Antarctic meteorology and climate research purposes.</p

Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars

In situ observations of snowfall over the Antarctic Ice Sheet are scarce. Currently, continent-wide assessments of snowfall are limited to information from the Cloud Profiling Radar on board the CloudSat satellite, which has not been evaluated up to now. In this study, snowfall derived from CloudSat is evaluated using three ground-based vertically profiling 24&thinsp;GHz precipitation radars (Micro Rain Radars: MRRs). Firstly, using the MRR long-term measurement records, an assessment of the uncertainty caused by the low temporal sampling rate of CloudSat (one revisit per 2.1 to 4.5 days) is performed. The 10–90th-percentile temporal sampling uncertainty in the snowfall climatology varies between 30&thinsp;% and 40&thinsp;% depending on the latitudinal location and revisit time of CloudSat. Secondly, an evaluation of the snowfall climatology indicates that the CloudSat product, derived at a resolution of 1∘ latitude by 2∘ longitude, is able to accurately represent the snowfall climatology at the three MRR sites (biases&thinsp;&lt;&thinsp;15&thinsp;%), outperforming ERA-Interim. For coarser and finer resolutions, the performance drops as a result of higher omission errors by CloudSat. Moreover, the CloudSat product does not perform well in simulating individual snowfall events. Since the difference between the MRRs and the CloudSat climatology are limited and the temporal uncertainty is lower than current Climate Model Intercomparison Project Phase 5 (CMIP5) snowfall variability, our results imply that the CloudSat product is valuable for climate model evaluation purposes.</p

EOP-Campagne de mesures hydrologiques

International audienceThe hydrological field campaigns performed during the HyMeX EOP (2011–2014), in the framework of the Cévennes-Vivarais Mediterranean Hydrometeorological Observatory and the ANR FloodScale project, aimed to improve our understanding of the active hydrological processes triggering flash floods, as well as our skill in modelling such floods. The campaigns were based on a multi-scale observation strategy: 1) plot or hillslope experimentations brought observations about the sub-surface flow and the bedrock permeability, the soil moisture variability in time and space, for several land uses; 2) nested small catchments

Variability of rain drop size distribution and its effect on the Z-R relationship : a case study for intense Mediterranean rainfall

International audienceThis paper presents an analysis of the variability of rain drop size distributions in intense Mediterranean rainfall and its impact on the reflectivity — rain-rate conversion. Two concurrent approaches for estimating the Z–R relationship from DSD measurements are reviewed: (1) non-linear regression techniques on the scattergraphs of the (Z, R) pairs derived for each DSD spectra; (2) use of a DSD model fitting based on a scaling law formulation. The two approaches are implemented over a DSD dataset of 75 h of Mediterranean rain collected with a ground-based optical DSD sensor. As a result of the heterogeneity of the rain processes, the seasonal Z–R relationship coefficients obtained are very diverse and strongly dependent on the fitting methodology. A consistency test of the seasonal Z–R relationships is proposed to assess the most reliable estimation procedures in terms of rainfall estimation. Using the DSD-derived rain-rate time series as a reference, it is shown that the regression techniques are significantly better than the DSD modelling approach. The same consistency test shows that event-fitted Z–R relationships do not systematically improve the test scores, supporting the idea that the intra-event DSD variability is dominant. This finding is confirmed with an in-depth analysis of one rain event, showing evidence of rainfall organisation into several phases each one presenting very stable scale and shape DSD parameters over several hours, and abrupt transitions from one phase to the next. A rain-typing algorithm applied to the 3D reflectivity data observed concomitantly with an operational S-band radar is consistently able to recognise the two most intense phases of the rain event as convective