Hydrologic applications of weather radar

Journal of Hydrology 531 (2015) 231–233

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Editorial

Hydrologic applications of weather radar

1. Introduction By providing high-resolution quantitative precipitation information (QPI), weather radars have revolutionized hydrology in the last two decades. With the aid of GIS technology, radar-based quantitative precipitation estimates (QPE) have enabled routine high-resolution hydrologic modeling in many parts of the world. Given the ever-increasing need for higher-resolution hydrologic and water resources information for a wide range of applications, one may expect that the use of weather radar will only grow. Despite the tremendous progress, a number of significant scientific, technological and engineering challenges remain to realize its potential. New challenges are also emerging as new areas of applications are discovered, explored and pursued. The purpose of this special issue is to provide the readership with some of the latest advances, lessons learned, experiences gained, and science issues and challenges related to hydrologic applications of weather radar. The special issue features 20 contributions on various topics which reflect the increasing diversity as well as the areas of focus in radar hydrology today. The contributions may be grouped as follows:

 Radar QPE (Kwon et al.; Hall et al.; Chen and Chandrasekar; Seo and Krajewski; Sandford).  Multi-radar and multisensor precipitation analysis (Fortin et al.; Kim et al.; Rafieeinasab et al.a).  Hydrologic modeling (Emmanuel et al.; Zoccatelli et al.; Dyer et al.).  Urban hydrologic and hydraulic applications (Rafieeinasab et al.b; Ochoa-Rodriguez et al.; Wang et al.).  Precipitation frequency analysis (Marra and Morin; Eldardiry et al.).  Hydrometeorological process studies (Campos and Wang; Wilson and Barros).  Precipitation nowcasting, forecasting (Yu et al.).  Hydrometeorological applications (Lo Conti et al.).

2. Recent progress and advances Perhaps the most significant change that has taken place in hydrologic applications of weather radar is that it is now considered an essential tool for operational hydrology and water resources management in many parts of the world. In some countries, the water sector has appropriated weather radar exclusively http://dx.doi.org/10.1016/j.jhydrol.2015.11.010 0022-1694/Ó 2015 Published by Elsevier B.V.

for hydrologic applications (Kwon et al.). One of the early drivers for radar hydrology was distributed hydrologic modeling to allow simulation of streamflow, soil moisture and other hydrologic variables at any location within the catchment modeled. Today, with the aid of GIS technology, distributed modeling is routinely practiced in semi-distributed or gridded form (Reed et al., 2004). Among the recent scientific and technological advances in weather radar, by far the most important is polarimetry. Polarimetric radar has led to improved QPE owing to improved data quality (Tabary et al., 2011) and the availability of rain rate retrieval techniques that are immune to attenuation and partial beam blockage (Bringi et al., 2001; Diederich et al., 2015). With the strong appeal of lower cost, polarimetric X-band radar is enjoying resurgence (Le Conti et al., this issue) particularly in urban applications where high-resolution sensing is essential (Chen and Chandrasekar, 2015). Polarimetry has also advanced hydrometeor classification (Lim et al., 2013) and understanding of hydrometeorological processes associated with heavy-to-extreme precipitation (Ryzhkov et al., 2011) which are critical to quantitative precipitation forecasting (QPF) and, in particular, microphysical assimilation of radar data into numerical weather prediction (NWP) models (Jung et al., 2005). 3. Challenges and needs Despite the very considerable progress made during the last two decades, the users cannot yet, a priori, be fully confident of radar QPI. While polarimetric radar has already made a large positive impact, concerted research efforts are needed to fully benefit from it, including advancing understanding of hydrometeorological processes, establishing fundamental relationships between the microphysical processes and the various polarimetry-derived variables, and improving understanding, characterization and correction of errors that are important to QPE and QPF (see, e.g., Sandford). For shorter-wavelength radars, signal extinction from attenuation in heavy rainfall or hail is a daunting challenge. Compositing with other radar data from similar and/or longerwavelength systems is necessary to address the above (Zhang et al., 2011; Rafieeinasab et al.a). A large contributing factor to the success of radar hydrology has been multisensor QPE which merges radar QPE with rain gauge data and satellite QPE as necessary. For example, the Advanced Hydrologic Prediction Service (AHPS) precipitation analysis (http://water.weather.gov/precip/), which mosaics the Multisensor Precipitation Estimator (MPE) products (Seo et al., 2010) from the

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Editorial / Journal of Hydrology 531 (2015) 231–233

12 River Forecast Centers (RFC) over the conterminous US, is one of the most widely used National Weather Service (NWS) products. Much additional research is needed to seamlessly integrate radar QPE products from different radar systems of different wavelengths, rain gauge data, satellite QPE and NWP model output (Fortin et al., this issue; Zhang et al., 2011), and to provide uncertainty estimates and verification information of the resulting products (Villarini et al., 2009). Despite advances in multisensor estimation, bias remains a large issue which impairs the use of radar-based QPI for quantitative hydrologic applications. Additional attention to reducing conditional bias is also necessary to improve the accuracy of multisensor QPE for heavy-to-extreme events (Habib et al., 2011; Seo et al., 2014). When weather radar first became popular in hydrologic applications, a considerable interest existed in radar-based QPF for flood forecasting and emergency management. The interest has waned somewhat in recent years probably because good results did not come as easily as many thought possible. With polarimetric radar and its ability to differentiate hydrometeor types, one may expect a renewed interest in short-term QPF based on assimilation of polarimetric data into NWP models and other approaches. It is widely recognized, however, that numerical modeling of the microphysical processes must be improved first before significant and consistent improvement in QPF skill may be realized through data assimilation (DA). In the meantime, the combined use with NWP output of radar data via simpler nowcasting-type models that account for motion only has gained interest (Turner et al., 2004; Bowler et al., 2006). Given that improvement via DA is expected to occur over time, this simpler approach of combining nowcasting and NWP output deserves more attention. A straightforward and promising use of polarimetric radar data for early detection of damaging precipitation is to identify polarimetric signatures, such as the vertical profile of differential reflectivity (Chandrasekar et al., 2013). For example, detection of the first echo (Nakakita et al. 2013) may provide a useful increase in lead time particularly if exists above the freezing level. While distributed hydrologic modeling is commonly practiced today, we are yet to develop clear understanding and highresolution picture of how different catchments respond to different patterns of precipitation and how different hydrologic processes may be at work under different conditions. Case studies indicate that we do not yet fully understand the catchment dynamics at small scales and the filtering effects of catchments at larger scales for streamflow response (Segond et al., 2007). Radar hydrology is critical to improving our understanding of the basin dynamics as it provides the only practical means to capture the spatiotemporal variability of precipitation (Emmanuel et al., 2012; Zoccatelli et al.). An insufficient level of understanding of the hydrologic processes also exists in urban areas where land cover consists of both impervious and pervious areas in varying proportions, the weather–vegetation–soil moisture dynamics are not well understood, and the hydrologic processes are further complicated due to man-made structures (Ochoa-Rodriguez et al.; Rafieeinasab et al.). Radar hydrology and meteorology have created important weather radar databases (Xie et al., 2005; Tabary et al., 2012). Spurred by the success of the Operational Program for Exchange of Weather Radar, or OPERA (http://www.eumetnet.eu/ opera092013/a_start.html, Huuskonen et al., 2014), of the European National Meteorological Services Network (EUMETNET), the World Meteorological Organization (WMO) has identified the global exchange of weather radar data as a high priority and has initiated efforts toward radar data exchange on the global scale (Michelson et al., 2013). These datasets open doors to real-time DA into NWP and other models as well as to new research such as spatiotemporal analysis of precipitation over long periods to

discover and derive statistical properties of precipitation, estimation of extreme values (Marra and Morin, this issue, Eldardiry et al.) and areal reduction factors, characterization of precipitation events according to meteorological conditions, statistical modeling of precipitation events, and climatologically-based radar precipitation analysis (Hou et al., 2014). Such databases can also support hydrologic analysis, water budget studies, calibration of hydrologic models, estimation of aquifer recharge (Dyer et al.), and studies of surface water–groundwater interactions. Despite recent advances in distribution of radar-based precipitation information to the user community, the use of radar products for practical applications by novice users, such as consulting engineers in small firms, remains a challenge. To address the situation, it is necessary to develop, provide and support tools and resources for easy access to and processing of the data for specific drainage basins and for easy extraction of the necessary information at the desired temporal resolution without having to write computer programs or to carry out additional data processing. Weather radar today is not a push-button or turn-key device but requires significant understanding and know-how. It is expected that the above situation will continue into the foreseeable future. For the user-base to expand, it is important that new and prospective users can be easily trained or are able to selftrain. To that end, coordinated efforts are necessary to develop, make available and support training materials and resources at different levels of knowledge-base and understanding which will accelerate the pace and breadth of hydrologic applications of weather radar. 4. Future and further steps Radar hydrology remains an emergent field of research which is getting close to maturity. While the future is very bright, we believe that the following community efforts are necessary to advance science and engineering of radar hydrology, and the technology that drives it: (1) remain an active hydrologic subdiscipline organized around the core scientific communities, (2) actively engage the related communities in hydrometeorology, meteorology and electrical engineering to synergistically leverage advances in other disciplines, (3) be very open-minded about all hydrologic and water resources applications that can benefit from radar-based QPI via different means, (4) promote and develop interdisciplinary collaborations to push the scientific and technological limits, and to demonstrate value, (5) develop and support tools and resources to train users and to help develop young scientists and engineers at various levels, and (6) avoid duplication of efforts by sharing expertise and knowhow, and by promoting and developing shareable community tools and resources. Acknowledgements The first guest editor would like to thank Alexander Ryzhkov of the National Severe Storms Laboratory for very helpful discussions. References Bowler, N.E., Pierce, C.E., Seed, A.W., 2006. STEPS: a probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Q. J. R. Meteorol. Soc. 132, 2127–2155. Bringi, V.N., Keenan, T.D., Chandrasekar, V., 2001. Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: a selfconsistent method with constraints. IEEE Trans. Geosci. Remote Sens. 39 (9). Chandrasekar, V., Keränen, R., Lim, S., Moisseev, D., 2013. Recent advances in classification of observations from dual polarization weather radars. Atmos. Res. 119, 97–111. Diederich, M., Troemel, S., Ryzhkov, A., Zhang, P., Simmer, C., 2015. Use of specific attenuation for rainfall measurements at X-band radar wavelengths. Part II:

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Dong-Jun Seo Department of Civil Engineering, The University of Texas at Arlington, Box 19308, Rm 248E Nedderman Hall, 416 Yates St, Arlington, TX 76019-0308, United States E-mail address: [email protected] URL: http://www.uta.edu/ce/research/hwrl/index.php Emad Habib Department of Civil Engineering, University of Louisiana at Lafayette, P.O. Box 42291, Lafayette, LA 70504, United States E-mail address: [email protected] URL: https://sites.google.com/site/emadhabib2013/ Hervé Andrieu PRES LUNAM, IFSTTAR, Département GERS and IRSTV, FR CNRS 2488, Bouguenais, France E-mail address: [email protected] URLs: http://www.irstv.fr, http://www.ifsttar.fr/ Efrat Morin Department of Geography & Program of Hydrology and Water Resources, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel E-mail address: [email protected] URL: http://www.hydrometeorology-lab.huji.ac.il