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Marielle Gosset

Abstract

An analysis of the effects of nonuniform beam filling on the measurement of the propagation phase shift for X-band radar is presented. An analytical expression is derived for the measured differential phase shift. It is shown that the phase measurement is weighted by the reflectivities and the attenuation within the sampling volume and that this may lead to negative values in the retrieved specific differential phase shift K DP. These effects are compared with those due to the influence of the backscattering phase shift δ. Both effects are quantified using numerical simulations. A very simple rain model with one cell is used to analyze and understand the problem. Comparison of X and S bands shows that the effects of azimuthal gradients are stronger at X band. The problem is also illustrated with radar measurements carried out with an X-band coherent polarimetric radar. For practical purposes, the problem appears to be small for beamwidths of less than 1° and the cell sizes considered here, or if the differential phase shift is evaluated over a long path. This study illustrates some of the practical problems that may be encountered using rain measurement algorithms based on K DP at X band, even though the high values of K DP at X band compared to longer wavelengths make its use attractive for rain estimation, especially for lower rain rates.

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Marielle Gosset and Isztar Zawadzki

Abstract

Most attenuation correction algorithms are based on the assumption of an average, range-independent power-law relationship, k = aZ b, between attenuation k and reflectivity Z. This paper analyzes how nonuniform beam filling (NUBF) of the radar beam can modify the value of the coefficients a and b with distance.

This analytical study shows that there are two mechanisms in which NUBF affects the apparent attenuation. One is the global averaging performed by the radar beam within the sampling volume itself. This tends to increase the apparent attenuation. The other mechanism is the gradual building of an angular weighting function due to the accumulated attenuation by a nonuniform rain field between the radar and the sampling volume. This can cause a serious decrease in the apparent specific and path-integrated attenuations.

The practical consequences of these analytical results and their quantitative effects on rain retrieval with an X-band ground-based radar are then analyzed by simulations. A radar simulator “scanning” on a real, high-resolution reflectivity field is used to study the scatter in the relationship between the specific attenuation and Z. It is shown that in a typical horizontal rain structure, the two mechanisms responsible for overestimation or underestimation of the specific attenuation tend to compensate each other with distance. The resulting effect of NUBF from the point of view of ground-based radar is quite neutral.

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Marielle Gosset and Henri Sauvageot

Abstract

A dual-wavelength method to differentiate supercooled water from ice and to measure mass content in each phase in cold stratiform clouds is proposed and discussed. The differential attenuation A d, whose direct measurement is available with dual-wavelength radar, is a linear function of the liquid water content M w (the contribution from ice hydrometeors is negligible in comparison). Measuring both A d and the radar reflectivity factor Z e leads to a system of two equations expressed as functions of M w and M I (ice water content); its solution provides the values of both M w and M I between any two ranges along the radar beam, and as a consequence the distribution pattern of those two parameters within the cloud. Simulations of the method on two idealized cloud structures with various spatial distributions of M w and M I are shown. From a comparative study, the wavelength couple of 3.2 cm and 0.86 cm has been selected as the most suitable one, either for ground-based midrange cloud observations or for an airborne radar.

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Clément Guilloteau, Rémy Roca, and Marielle Gosset

Abstract

Validation studies have assessed the accuracy of satellite-based precipitation estimates at coarse scale (1° and 1 day or coarser) in the tropics, but little is known about their ability to capture the finescale variability of precipitation. Rain detection masks derived from four multisatellite passive sensor products [Tropical Amount of Precipitation with an Estimate of Errors (TAPEER), PERSIANN-CCS, CMORPH, and GSMaP] are evaluated against ground radar data in Burkina Faso. The multiscale evaluation is performed down to 2.8 km and 15 min through discrete wavelet transform. The comparison of wavelet coefficients allows identification of the scales for which the precipitation fraction (fraction of space and time that is rainy) derived from satellite observations is consistent with the reference. The wavelet-based spectral analysis indicates that the energy distribution associated with the rain/no rain variability throughout spatial and temporal scales in satellite products agrees well with radar-based precipitation fields. The wavelet coefficients characterizing very finescale variations (finer than 40 km and 2 h) of satellite and ground radar masks are poorly correlated. Coarse spatial and temporal scales are essentially responsible for the agreement between satellite and radar masks. Consequently, the spectral energy of the difference between the two masks is concentrated in fine scales. Satellite-derived multiyear mean diurnal cycles of rain occurrence are in good agreement with gauge data in Benin and Niger. Spectral analysis and diurnal cycle computation are also performed in the West Africa region using the TRMM Precipitation Radar. The results at the regional scale are consistent with the results obtained over the ground radar and gauge sites.

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Maxime Turko, Marielle Gosset, Modeste Kacou, Christophe Bouvier, Nanee Chahinian, Aaron Boone, and Matias Alcoba

Abstract

Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost-effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou, Burkina Faso, where a hydrological model has been set up. The analysis is based on numerical simulations, using high-resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: (i) the precision of the raw information provided by the operator and (ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or of short length (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

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Rémy Roca, Philippe Chambon, Isabelle Jobard, Pierre-Emmanuel Kirstetter, Marielle Gosset, and Jean Claude Bergès

Abstract

Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed using dedicated techniques that come down to an estimation of the sampling errors associated to these measurements. The results show that the new generation of combined infrared–microwave (IR–MW) satellite products is describing the rain variability similarly to ground measurements. At the 10-day scale, all products reveal high regional and seasonal skills. The day-to-day comparison indicates that some products perform better than others, whereas all of them exhibit high skills when the spectral band of African easterly waves is considered. The seasonal variability of the diurnal scale as well as its relative daily importance is only captured by some products. Plans for future extensive intercomparison exercises are briefly discussed.

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Clement Guilloteau, Marielle Gosset, Cecile Vignolles, Matias Alcoba, Yves M. Tourre, and Jean-Pierre Lacaux

Abstract

Spatiotemporal rainfall variability is a key parameter controlling the dynamics of mosquitoes/vector-borne diseases such as malaria, Rift Valley fever (RVF), or dengue. Impacts from rainfall heterogeneity at small scales (i.e., 1–10 km) on the risk of epidemics (i.e., host bite rate or number of bites per host and per night) must be thoroughly evaluated. A model with hydrological and entomological components for risk prediction of the RVF zoonosis is proposed. The model predicts the production of two mosquito species within a 45 km × 45 km area in the Ferlo region, Senegal. The three necessary steps include 1) best rainfall estimation on a small scale, 2) adequate forcing of a simple hydrological model leading to pond dynamics (ponds are the primary larvae breeding grounds), and 3) best estimate of mosquito life cycles obtained from the coupled entomological model. The sensitivity of the model to the spatiotemporal heterogeneity of rainfall is first tested using high-resolution rain fields from a weather radar. The need for high-resolution rain data is thus demonstrated. Several high-resolution satellite rainfall products are evaluated in the region of interest using a dense rain gauge network. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42, version 6 (TMPA-3B42V6), and 3B42 in real time (TMPA-3B42RT); Global Satellite Mapping of Precipitation (GSMaP) in near–real time (GSMaP-NRT) and Moving Vector with Kalman version (GSMaP-MVK); African Rainfall Estimation Algorithm, version 2.0 (RFE 2.0); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) are tested and finally corrected using a probability matching method. The corrected products are then used as forcing to the coupled model over the 2003–10 period. The predicted number and size of ponds and their dynamics are greatly improved compared to the model forced only by a single gauge. A more realistic spatiotemporal distribution of the host bite rate of the RVF vectors is thus expected.

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Odin Marc, Romulo A. Jucá Oliveira, Marielle Gosset, Robert Emberson, and Jean-Philippe Malet

Abstract

Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R *, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R * and landsliding varies from case to case and across products, and we often retrieved R * > 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding.

Significance Statement

Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.

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Olivier Caumont, Véronique Ducrocq, Guy Delrieu, Marielle Gosset, Jean-Pierre Pinty, Jacques Parent du Châtelet, Hervé Andrieu, Yvon Lemaître, and Georges Scialom

Abstract

A full radar simulator for high-resolution (1–5 km) nonhydrostatic models has been developed within the research nonhydrostatic mesoscale atmospheric (Meso-NH) model. This simulator is made up of building blocks, each of which describes a particular physical process (scattering, beam bending, etc.). For each of these blocks, several formulations have been implemented. For instance, the radar simulator offers the possibility to choose among Rayleigh, Rayleigh–Gans, Mie, or T-matrix scattering methods, and beam bending can be derived from an effective earth radius or can depend on the vertical gradient of the refractive index of air. Moreover, the radar simulator is fully consistent with the microphysical parameterizations used by the atmospheric numerical model.

Sensitivity experiments were carried out using different configurations for the simulator. They permitted the specification of an observation operator for assimilation of radar reflectivities by high-resolution nonhydrostatic numerical weather prediction systems, as well as for their validation. A study of the flash flood of 8–9 September 2002 in southeastern France, which was well documented with volumetric data from an S-band radar, serves to illustrate the capabilities of the radar simulator as a validation tool for a mesoscale model.

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Marielle Gosset, Harald Kunstmann, François Zougmore, Frederic Cazenave, Hidde Leijnse, Remko Uijlenhoet, Christian Chwala, Felix Keis, Ali Doumounia, Barry Boubacar, Modeste Kacou, Pinhas Alpert, Hagit Messer, Jörg Rieckermann, and Joost Hoedjes
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