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  • IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission x
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Haonan Chen, V. Chandrasekar, and Renzo Bechini

DPR and passive radiometer on board GPM extend the observation range attained by TRMM from tropics to most of the globe and provide accurate measurement of rainfall, snowfall, and other precipitation types. Through improved measurements of precipitation, the GPM mission is helping to advance our understanding of Earth’s water and energy cycle, as well as climate changes. As an indispensable part of the GPM mission, ground validation helps to develop the radar and radiometer retrieval algorithms by

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Merhala Thurai, Kumar Vijay Mishra, V. N. Bringi, and Witold F. Krajewski

al. 2014 ) have conducted similar comparative analyses with rain gauges for dual-polarimetric tower-mounted X-band radars. In particular, Vieux and Imgarten (2012) studied the scale dependence of hydrological uncertainties by comparing rainfall estimates of tower-mounted Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band radars with a dense rain gauge network. In the case of mobile X-band radars, Schneebeli and Berne (2012) devised rain-rate algorithms but did not

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Kumar Vijay Mishra, Witold F. Krajewski, Radoslaw Goska, Daniel Ceynar, Bong-Chul Seo, Anton Kruger, James J. Niemeier, Miguel B. Galvez, Merhala Thurai, V. N. Bringi, Leonid Tolstoy, Paul A. Kucera, Walter A. Petersen, Jacopo Grazioli, and Andrew L. Pazmany

features of the Iowa XPOL radars and their scientific objectives in section 2 and compare them with existing X-band radars. Section 3 gives a brief overview of the field campaign and the deployment of Iowa XPOL radars. Section 4 provides a performance analysis of the raw data and meteorological products, including the range-oversampling capabilities of the Iowa XPOL radars. We introduce the attenuation-correction algorithm for XPOL-4 in section 5 . Section 6 demonstrates the consistency of

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

year, this requires fitting three parameters (a generalized sinusoid has four, but in this case, the period is already known) to define its vertical shift, its horizontal shift, and its amplitude. These were fit via a genetic algorithm, maximizing the correlation of the β series (using the various sinusoidal-shaped η series generated via the genetic algorithm) with the empirically measured values of θ . The chosen historical window is 200–250 h at sensors in this watershed. Next, the three

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Bong-Chul Seo, Brenda Dolan, Witold F. Krajewski, Steven A. Rutledge, and Walter Petersen

of single-polarization (SP) QPE. A number of radar-rainfall (RR) datasets acquired through IFloodS were extensively evaluated with respect to a scale that is often used for hydrologic applications ( Seo et al. 2014 ). Seo et al. (2014) reveal that the Colorado State University (CSU)-DP estimates generated using the CSU Hydrometeor Identification Rainfall Optimization (CSU-HIDRO; Cifelli et al. 2011 ) algorithm statistically performs better than radar-only DP estimates ( Istok et al. 2009

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Felipe Quintero, Witold F. Krajewski, Ricardo Mantilla, Scott Small, and Bong-Chul Seo

Abstract

Rainfall maps that are derived from satellite observations provide hydrologists with an unprecedented opportunity to forecast floods globally. However, the limitations of using these precipitation estimates with respect to producing reliable flood forecasts at multiple scales are not well understood. To address the scientific and practical question of applicability of space-based rainfall products for global flood forecasting, a data evaluation framework is developed that allows tracking the rainfall effects in space and time across scales in the river network. This provides insights on the effects of rainfall product resolution and uncertainty. Obtaining such insights is not possible when the hydrologic evaluation is based on discharge observations from single gauges. The proposed framework also explores the ability of hydrologic model structure to answer questions pertaining to the utility of space-based rainfall observations for flood forecasting. To illustrate the framework, hydrometeorological data collected during the Iowa Flood Studies (IFloodS) campaign in Iowa are used to perform a hydrologic simulation using two different rainfall–runoff model structures and three rainfall products, two of which are radar based [stage IV and Iowa Flood Center (IFC)] and one satellite based [TMPA–Research Version (RV)]. This allows for exploring the differences in rainfall estimates at several spatial and temporal scales and provides improved understanding of how these differences affect flood predictions at multiple basin scales. The framework allows for exploring the differences in peak flow estimation due to nonlinearities in the hydrologic model structure and determining how these differences behave with an increase in the upstream area through the drainage network. The framework provides an alternative evaluation of precipitation estimates, based on the diagnostics of hydrological model results.

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Bong-Chul Seo, Witold F. Krajewski, Felipe Quintero, Mohamed ElSaadani, Radoslaw Goska, Luciana K. Cunha, Brenda Dolan, David B. Wolff, James A. Smith, Steven A. Rutledge, and Walter A. Petersen

.S. National Weather Service operational and Colorado State University experimental blended precipitation processing algorithms). We also compare these radar-only products with rain-gauge-corrected RR estimates (Stage IV and Q2-Corrected products). We explore the algorithm-dependent features (e.g., SP versus DP) among the RR estimates based on the comprehensive analyses of product intercomparison. The uncertainty for different temporal and spatial resolution products is also characterized using ground

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Luciana K. Cunha, James A. Smith, Witold F. Krajewski, Mary Lynn Baeck, and Bong-Chul Seo

; Tabary et al. 2011 ), and improvements are not always consistent ( Cunha et al. 2013 ). The current lack of consensus and consistency of DP algorithms is partially due to the fact that the DP technology has only been recently implemented in operational practice and is still being advanced and tested. Similar to the development of SP technology, many years of research will be required until we learn how to optimally use the measurements obtained by DP radars. The NEXRAD program completed the DP

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Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Kuolin Hsu, and Amir AghaKouchak

-SMA, unlike other distributed models with fixed values for subdomains or the entire domain, an advanced algorithm was designed to derive a priori parameters from soil and land use data. BreZo is a hydraulic model that solves the shallow-water equations using a Godunov-type finite volume method with an unstructured grid of triangular cells. A detailed description of the model can be seen in Begnudelli and Sanders (2006) . One of the primary advances of the model is that it was designed for working with an

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Andrea Thorstensen, Phu Nguyen, Kuolin Hsu, and Soroosh Sorooshian

). The heart of the model is the Sacramento Soil Moisture Accounting (SAC-SMA) with Heat Transfer Component (SAC-HT). In SAC-HT, unlike other distributed models with fixed values for subdomains or the entire domain, an advanced algorithm was designed to derive a priori parameters from soil and land-use data. Recent enhancements to the basic SAC-SMA model include the use of Noah LSM–based physics to estimate a physically meaningful soil moisture profile as well as evapotranspiration from the soil

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