Emmanouil N. Anagnostou

HYMEX , a 10-year Multidisciplinary Program on the mediterranean water cycle.

The Mediterranean countries are experiencing important challenges related to the water cycle, including water shortages and floods, extreme winds, and ice/snow storms, that impact critically the socioeconomic vitality in the area (causing damage to property, threatening lives, affecting the energy and transportation sectors, etc.). There are gaps in our understanding of the Mediterranean water cycle and its dynamics that include the variability of the Mediterranean Sea water budget and its feedback on the variability of the continental precipitation through air–sea interactions, the impact of precipitation variability on aquifer recharge, river discharge, and soil water content and vegetation characteristics specific to the Mediterranean basin and the mechanisms that control the location and intensity of heavy precipitating systems that often produce floods. The Hydrological Cycle in Mediterranean Experiment (HyMeX) program is a 10-yr concerted experimental effort at the international level that aims to advance the scientific knowledge of the water cycle variability in all compartments (land, sea, and atmosphere) and at various time and spatial scales. It also aims to improve the processes-based models needed for forecasting hydrometeorological extremes and the models of the regional climate system for predicting regional climate variability and evolution. Finally, it aims to assess the social and economic vulnerability to hydrometeorological natural hazards in the Mediterranean and the adaptation capacity of the territories and populations therein to provide support to policy makers to cope with water-related problems under the influence of climate change, by linking scientific outcomes with related policy requirements.

Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates

Abstract The most common rainfall measuring sensor for validation of radar-rainfall products is the rain gauge. However, the difference between area-rainfall and rain gauge point-rainfall estimates imposes additional noise in the radar–rain gauge difference statistics, which should not be interpreted as radar error. A methodology is proposed to quantify the radar-rainfall error variance by separating the variance of the rain gauge area-point rainfall difference from the variance of radar–rain gauge ratio. The error in this research is defined as the ratio of the “true” rainfall to the estimated mean-areal rainfall by radar and rain gauge. Both radar and rain gauge multiplicative errors are assumed to be stochastic variables, lognormally distributed, with zero covariance. The rain gauge area-point difference variance is quantified based on the areal-rainfall variance reduction factor evaluated in the logarithmic domain. The statistical method described here has two distinct characteristics: first, it propo...

The Use of TRMM Precipitation Radar Observations in Determining Ground Radar Calibration Biases

Since the successful launch of the Tropical Rainfall Measuring Mission (TRMM) satellite, measurements of a wide variety of precipitating systems have been obtained with unprecedented detail from the first space-based radar [precipitation radar (PR)]. In this research, a methodology is developed that matches coincident PR and ground-based volume scanning weather radar observations in a common earth parallel three-dimensional Cartesian grid. The data matching is performed in a way that minimizes uncertainties associated with the type of weather seen by the radars, grid resolution, and differences in radar sensitivities, sampling volumes, viewing angles, and radar frequencies. The authors present comparisons of reflectivity observations from the PR and several U.S. weather surveillance Doppler radars (WSR-88D) as well as research radars from the TRMM field campaigns in Kwajalein Atoll and the Large Biosphere Atmospheric (LBA) Experiment. Correlation values above 0.8 are determined between PR and ground radar matched data for levels above the zero isotherm. The reflectivity difference statistics derived from the matched data reveal radar systems with systematic differences ranging from 1 2t o27 dB. The authors argue that the main candidate for systematic differences exceeding 1 to 1.5 dB is the ground radar system calibration bias. To verify this argument, the authors used PR comparisons against well-calibrated ground-based systems, which showed systematic differences consistently less than 1.5 dB. Temporal analysis of the PR versus ground radar systematic differences reveals radar sites with up to 4.5dB bias changes within periods of two to six months. Similar evaluation of the PR systematic difference against stable ground radar systems shows bias fluctuations of less than 0.8 dB. It is also shown that bias adjustment derived from the methodology can have significant impact on the hydrologic applications of ground-based radar measurements. The proposed scheme can be a useful tool for the systematic monitoring of ground radar biases and the studying of its effect.

High-Resolution Rainfall Estimation from X-Band Polarimetric Radar Measurements

Abstract The paper presents a rainfall estimation technique based on algorithms that couple, along a radar ray, profiles of horizontal polarization reflectivity (ZH), differential reflectivity (ZDR), and differential propagation phase shift (ΦDP) from X-band polarimetric radar measurements. Based on in situ raindrop size distribution (DSD) data and using a three-parameter “normalized” gamma DSD model, relationships are derived that correct X-band reflectivity profiles for specific and differential attenuation, while simultaneously retrieving variations of the normalized intercept DSD parameter (Nw). The algorithm employs an iterative scheme to intrinsically account for raindrop oblateness variations from equilibrium condition. The study is facilitated from a field experiment conducted in the period October–November 2001 in Iowa City, Iowa, where observations from X-band dual-polarization mobile radar (XPOL) were collected simultaneously with high-resolution in situ disdrometer and rain-gauge rainfall meas...

On the use of real‐time radar rainfall estimates for flood prediction in mountainous basins

This paper investigates the effect of systematic mean-field and range-dependent radar rainfall errors on the accuracy of runoff simulation in mountainous basins. Statistical analysis of radar rainfall and runoff simulation error is performed on six flood events for two medium size watersheds in northern Italy, located at 38 and 60 km basin-to-radar distances, respectively. We show significant range-related rainfall biases, which are due to the high elevation radar scans used to minimize the interception of the radar beam with the topography. These biases are corrected by converting radar reflectivity measurements at a given altitude into their equivalent surface values, using real-time identification of the mean vertical reflectivity profile. The mean-field bias is adjusted using a multiplicative factor determined based on real-time radar-rain gauge comparisons. The impact of the above radar rainfall biases, and the improvements obtained from the proposed corrections, on areal-rainfall estimation and runoff simulation are evaluated. It is shown that radar rainfall biases magnify through the rainfall-runoff transformation and preclude the accurate simulation of runoff, particularly for the distant basin. The combined correction procedure results in significant reduction of the hydrologic prediction error, especially at the distant basin.

Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates

This paper presents an in-depth investigation of the error properties of two high-resolution global-scale satellite rain retrievals verified against rainfall fields derived from a moderate-resolution rain-gauge network (25-30-km intergage distances) covering a region in the midwestern U.S. (Oklahoma Mesonet). Evaluated satellite retrievals include the NASA Tropical Rainfall Measuring Mission multisatellite precipitation analysis and the National Oceanic and Atmospheric Administration Climate Prediction Center morphing technique. The two satellite products are contrasted against a rain-gauge-adjusted radar rainfall product from the WSR-88D network in continental U.S. This paper presents an error characterization of the Mesonet rainfall fields based on an independent small-scale, but very dense (100-m intergage distances), rain-gauge network (named Micronet). The Mesonet error analysis, although significantly lower than the corresponding error statistics derived for the satellite and radar products, demonstrates the need to benchmark reference data sources prior to their quantitative use in validating remote sensing retrievals. In terms of the remote sensing rainfall products, this paper provides quantitative comparisons between the two satellite estimates and the most definitive rain-gauge-adjusted radar rainfall estimates at corresponding spatial and temporal resolutions (25 km and 3 hourly). Error quantification presented herein includes zero- (rain detection probability and false alarm), first- (bias ratio), and second-order (root mean square error and correlation) statistics as well as an evaluation of the spatial structure of error at warm and cold seasons of 2004 and 2006.

Error Analysis of Satellite Precipitation Products in Mountainous Basins

Accuratequantitative precipitationestimationover mountainous basins is of great importancebecause of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42V7), and in near‐real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and PrecipitationEstimationfromRemotelySensedImagery UsingArtificialNeural Networks(PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003‐10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May‐August) and cold (September‐December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

Real-Time Radar Rainfall Estimation. Part I: Algorithm Formulation

Abstract A multicomponent radar-based algorithm for real-time precipitation estimation is developed. The algorithm emphasizes the combined use of weather radar observations and in situ rain gauge rainfall measurements. The temporal and spatial scales of interest are hourly to storm-total accumulations for areas of 4 km2 to approximately 16 km2. The processing steps include beam–height-effect correction, vertical integration, convective–stratiform classification, conversion from radar observables to rainfall rate, range-effect correction, and transformation of the estimated rainfall rates from polar coordinates to a Cartesian grid. Additionally, the algorithm applies advection correction to the gridded rainfall rates to minimize the temporal sampling effect and, subsequently, aggregates the corrected rainfall rates to 1-hourly, 3-hourly, and storm-total accumulations. The system applies different parameter values for convective and stratiform regimes. The calibration of the system is formulated as a global...

Improving Radar-Based Estimation of Rainfall over Complex Terrain

Abstract This paper investigates a multicomponent radar-based rainfall estimation algorithm that includes optimum parameter estimation and error correction schemes associated with radar operation over mountainous terrain. Algorithm preprocessing steps include correction for terrain blocking, adjustment for rain attenuation, and interpolation of reflectivity data from polar radar coordinates to a three-level (1, 2, and 3 km) vertically integrated Cartesian grid. The error correction schemes investigated herein include a simple but efficient approach to correct for the vertical variation of reflectivity and a stochastic filtering approach for mean-field radar-rainfall bias adjustment. The primary algorithm parameters are estimated through a global optimization scheme. Eight major flood-inducing storm events observed coincidentally by a C-band weather radar and 39 rain gauge stations over an alpine region of northeast Italy are used. We describe sensitivity analysis of the parameter values obtained from glob...

Overland Precipitation Estimation from TRMM Passive Microwave Observations

Procedures for passive microwave precipitation estimation over land are investigated based on a large database of Tropical Rainfall Measuring Mission (TRMM) observations. The procedures include components for rain area delineation, convective/stratiform (C/S) rain classification, and estimation of vertically integrated water content or surface rainfall rate. The investigated algorithms include neural network schemes for both the rain area and C/S classification and statistical algorithms for precipitation estimation. The coincident active and passive microwave observations from TRMM, with the active (TRMM precipitation radar) observations providing the reference values for the various precipitation parameters, are used for algorithm calibration and validation. The calibration and validation are based on 1 yr of data over the continental United States and a repetitive sampling strategy that make the results statistically significant. Good agreement is demonstrated with TRMM precipitation radar observations in rain delineation, and it is shown that C/S classification can considerably improve precipitation estimation. It is also shown that better performance may be achieved in estimating vertically integrated hydrometeor contents as compared with rainfall rates.