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  • Author or Editor: Clément Guilloteau x
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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.

Full access
Clément Guilloteau
,
Efi Foufoula-Georgiou
, and
Christian D. Kummerow

Abstract

The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)—the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the “effective resolution” of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed.

Open access
Clement Guilloteau
,
Efi Foufoula-Georgiou
,
Pierre Kirstetter
,
Jackson Tan
, and
George J. Huffman

Abstract

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatiotemporal scale. We propose a spectral error model that accounts for the neighboring space–time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency–wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space–time linear filtering term. The random errors are represented as a nonstationary additive noise. The spectral error model is applied to the IMERG multisatellite precipitation product, and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge–radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass (attenuating the fine-scale variability). While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from different sources shall not be expected to automatically have statistically independent errors.

Significance Statement

Satellite precipitation products are nowadays widely used for climate and environmental research, water management, risk analysis, and decision support at the local, regional, and global scales. For all these applications, knowledge about the accuracy of the products is critical for their usability. However, products are not systematically provided with a quantitative measure of the uncertainty associated with each individual estimate. Various parametric error models have been proposed for uncertainty quantification, mostly assuming that the uncertainty is only a function of the precipitation intensity at the pixel and time of interest. By projecting satellite precipitation fields and their retrieval errors into the Fourier frequency–wavenumber domain, we show that we can explicitly take into account the neighboring space–time multiscale dynamics of precipitation and compute a scale-dependent uncertainty.

Open access
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.

Full access
Clement Guilloteau
,
Efi Foufoula-Georgiou
,
Pierre Kirstetter
,
Jackson Tan
, and
George J. Huffman

Abstract

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Open access
F. Joseph Turk
,
Sarah E. Ringerud
,
Yalei You
,
Andrea Camplani
,
Daniele Casella
,
Giulia Panegrossi
,
Paolo Sanò
,
Ardeshir Ebtehaj
,
Clement Guilloteau
,
Nobuyuki Utsumi
,
Catherine Prigent
, and
Christa Peters-Lidard

Abstract

A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

Free access