Satellite rainfall estimates: new perspectives for meteorology and climate from the EURAINSAT project

Satellite meteorology is facing a crucial period of its history since recent missions have revealed instrumental for quantitative rainfall measurements from space and newly conceived missions are at hand. International partnership is rapidly developing and research projects keep the community focused on rapidly developing research and operational issues. A perspective is given through the structure of EURAINSAT, a project of the 5th Framework Programme of the European Commission. Its key objective is the development of algorithms for rapidly-updated satellite rainfall estimations at the geostationary scale. The project is fostering international research on satellite rainfall estimations building a bridge between Europe and the U.S. for present and future missions

Exploitation of cloud top characterization from three-channel IR measurements in a physical PMW rain retrieval algorithm

Rainfall intensity estimates by passive microwave (PMW) measurements from space perform generally better over the sea surface with respect to land, due to the problems in separating true rain signatures from those produced by surfaces having similar spectral behaviour (e.g. snow, ice, desert and semiarid grounds). The screening procedure aimed at recognizing the various surface types and delimit precipitation is based on tests that rely on PMW measurements only and global thresholds. The shortcoming is that the approach tries to discard spurious precipitating features (often detected over the land-sea border) thus leading to no-rain conservative tests and thresholds. The TRMM mission, with its long record of simultaneous data from the Visible and Infrared Radiometer System (VIRS), the TRMM Microwave Imager (TMI) and rain profiles from the Precipitation Radar (PR) allows for unambiguous testing of the usefulness of cloud top characterization in rain detection. </p><p style=&quot;line-height: 20px;&quot;> An intense precipitation event over the North Africa is analysed exploiting a night microphysical RGB scheme applied to VIRS measurements to classify and characterize the components of the observed scenario and to discriminate the various types of clouds. This classification is compared to the rain intensity maps derived from TMI by means of the Goddard profiling algorithm and to the near-surface rain intensities derived from PR. The comparison allows to quantify the difference between the two rain retrievals and to assess the usefulness of RGB analysis in identifying areas of precipitation

Assessment and tuning of the behaviour of a microphysical characterisation scheme

International audienceThe correct classification of prevailing bulk hydrometeor type within a radar resolution volume is a challenge task even if a full set of polarimetric radar observables is available. Indeed scattering and propagation effects from the variety of hydrometeors present interact each others and sometimes, if not often, tend to obscure the characteristic signature of each weather radar target type. This consideration is enforced when the atmospheric volume is sampled with a wavelength where both Mie scattering effects and attenuation start to become relevant. In this paper, we utilize the hydrometeor classification scheme developed at the National Severe Storms Laboratory (USA). Briefly, the scheme uses a fuzzy logic approach to combine different polarimetric variables and environmental temperature in order to determine the most likely type of prevalent hydrometeor in the radar volume. This means that the resulting classification is based on two characteristics: the volume polarimetric responses and the thermal value. The relative balance between these two is managed through the coefficients in the fuzzy scheme. We have observed that these parameters are crucial in order to get "physical reasonable result", independently from the meteorological character of the event investigated. Our work is based on a reduced set of polarimetric variables (Z and ZDR) as input. Data used in this study were collected by a C-band radar over weather events ranging from convective to stratiform

Warm season precipitation climatology: first European results

International audienceTo date very low scores are associated to quantitative precipitation forecasts (QPF) of warm season precipitation, a fact mostly due to the little knowledge of the mechanisms driving these phenomena. The study aims to produce a five-year climatology (1999?2003) of warm season precipitation systems (MJJA) over Europe using Meteosat IR brightness temperatures as a contribution to a global study launched by the World Weather Research Programme (WWRP). Cold cloud persistence, span and duration of weather systems were determined to derive the zonal propagation speed and daily cycles

Recent results from the Arctic Radiation and Turbulence Interaction STudy (ARTIST) project

Ground-based measurements were conducted at Ny-˚Alesund in the Svalbard Islands in the framework of the research project ARTIST (Arctic Radiation and Turbulence Interaction STudy) funded by the European Communities. Key objectivesof the campaign were: 1) provide all participantswith ground reference data as input to models describing the development of the atmospheric boundary level, 2) compute the surface roughness length in order to characterise the surface of the site, 3) parameterise the surface energy exchanges, calculating the surface radiation flux, the sensible and latent heat fluxes, and 4) obtain the surface energy balance during both clear and cloudy sky conditions. The cloud radiative forcing has been also estimated. Final results of the analysis of the data set are presented

Quantitative precipitation estimation over antarctica using different ze-sr relationships based on snowfall classification combining ground observations

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods

Satellite radiometric remote sensing of rainfall fields: multi-sensor retrieval techniques at geostationary scale

International audienceThe Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org