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Downloaded 09/30/21 10:18 AM UTC 88 JOURNAL of HYDROMETEOROLOGY VOLUME 19 Differences Is Evaporation JANUARY 2018 M A R T I N A I T I S E T A L . 87 A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations STEVEN M. MARTINAITIS Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma HEATHER M. GRAMS NOAA/NWS/Radar Operations Center, Norman, Oklahoma CARRIE LANGSTON Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma JIAN ZHANG AND KENNETH HOWARD NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma (Manuscript received 23 May 2017, in final form 7 September 2017) ABSTRACT Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the pre- cipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large- scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as ‘‘false zero’’ observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s. 1. Introduction lack the spatial distribution needed to accurately repre- sent precipitation, especially with convection (e.g., High spatiotemporal resolution surface precipitation Goodrich et al. 1995). Radar-derived quantitative pre- values are vital to multiple hydrometeorological appli- cipitation estimation (QPE) provides better spatial cations, including flood prediction and water resource coverage and resolution than precipitation gauges. management. Other applications, such as providing real- Radar-based QPE is determined at beam level and occurs time information related to wildfire suppression, require at varying altitudes above the ground; therefore, it is knowing whether precipitation is even occurring at the important to understand that radar-derived precipitation surface. Precipitation gauge measurements can provide rates and accumulations do not necessarily equate to the real-time surface accumulations, but gauge networks actual precipitation measured at the surface. Microphysical and environmental processes can alter Corresponding author: Steven M. Martinaitis, steven.martinaitis@ precipitation rates between the elevated radar beam noaa.gov level and the surface. A primary contributor to these DOI: 10.1175/JHM-D-17-0093.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/30/21 10:18 AM UTC 88 JOURNAL OF HYDROMETEOROLOGY VOLUME 19 differences is evaporation. Analyses of radar observa- Heymsfield and Donner (1990) suggested a separate tions describe the depletion of small hydrometeors and evaporation parameterization of snow, since snow the evolution of the drop size distribution (DSD) of was shown to evaporate faster than rain. Gregory (1995) precipitation through evaporation (Levin et al. 1991; derived an evaporation correction methodology ap- Penide et al. 2013). Neglecting evaporative effects has plicable for both rain and snow precipitation rates in been shown to create significant uncertainties in surface large-scale numerical models through a number of rainfall retrievals (Comstock et al. 2004) and a sub- simplifications and assumptions regarding microphysical sequent overestimation of surface rain rates (Gori and properties. The results were comparable to previous Joss 1980; Hu and Srivastava 1995; Li and Srivastava studies using more complex evaporative calculations 2001); thus, the need exists for a real-time evaporative (e.g., Clough and Franks 1991) and various DSDs; correction of radar-derived precipitation rates. moreover, the simplifications were designed for in- Radar observations have been used to indirectly es- creased computational efficiency to run within a large- timate evaporation through fixed relationships between scale modeling framework. reflectivity Z and either rainwater content lost (Leary This paper discusses the applicability of implementa- and Houze 1979) or rainfall rates (Rosenfeld and Mintz tion of an evaporation correction scheme for real-time 1988); however, Li and Srivastava (2001) noted that mosaicked radar-based precipitation rate fields within utilizing fixed relationships resulted in significant errors the high spatiotemporal resolution Multi-Radar Multi- with estimating evaporation and precipitation rates. Sensor (MRMS) system (Zhang et al. 2016). The evap- Numerous studies examined changes in polarimetric oration correction scheme was designed around the variables to modify precipitation in models through work of Gregory (1995) and applied to the 3D MRMS microphysical parameterizations. Li and Srivastava grid space through a computationally efficient method- (2001) evaluated a single raindrop using Z and differ- ology. The objectives of applying an evaporation cor- ential reflectivity ZDR to modify the DSD and rain rate rection algorithm to radar-derived precipitation are to through evaporation in a steady-state thermodynamic reduce the overestimation of QPE in environments de- environmental profile. Kumjian and Ryzhkov (2010) fined by subsaturated atmospheric profiles, remove false investigated the vertical changes of polarimetric vari- surface precipitation (i.e., precipitation detected at the ables and its impacts on liquid precipitation rates , radar beam level but not occurring at the surface), and 2 10 mm h 1 within a one-dimensional rainshaft model. improve the quality control (QC) of automated hourly Xie et al. (2016) also examined the vertical change in gauge observations. The computing time of the evapo- Z and ZDR with respect to evaporation, evaporation- rative correction scheme is also evaluated to ensure it related cooling rates below the melting layer, and DSD has minimal impact on real-time data latency within the evolution through validation of DSD observations and operational MRMS system. microphysical simulations with radar observations in- dicating evaporation. 2. Evaporation correction equations The inclusion of polarimetric variables resulted in The fundamental theory of evaporation for a water improved evaporative correction of precipitation and droplet or ice particle is generally framed as the rate of mitigated uncertainties in surface-based QPE; however, mass diffusion with respect to time (e.g., Rogers and some limitations with utilizing polarimetric datasets Yau 1996; Li and Srivastava 2001; Kumjian and exist. Challenges arise when approximating changes in Ryzhkov 2010), ZDR values for solid precipitation sampled above the melting layer that transition to liquid precipitation prior dm 5 2pDf D (T, p)[r 2 r (T )], (1) to reaching the surface. Polarimetric fields like ZDR can dt y y y ys w be noisy and require proper calibration. Furthermore, an evaporation correction scheme based on polarimetric where m is the mass of a water droplet or ice particle data would also preclude its use in areas covered by with diameter D, fy is the ventilation coefficient for va- radars without dual-polarization technology. por diffusivity, Dy is the molecular diffusion coefficient, Other model parameterizing of evaporation defined ry is the vapor density of the ambient environment, and the evaporation coefficient to be dependent upon pre- rys is the saturation vapor density, which is the equiva- cipitation rates but treated the evaporation of rain and lent of the vapor density at the water droplet or ice particle snow as the same (Schlesinger et al. 1988; Sundqvist surface. The quantity ry 2 rys is the vapor density deficit 1988; Feingold 1993). Microphysical modeling tech- as a function of wet-bulb (or ice bulb) temperature Tw of niques have identified evaporative differences between the air through which the hydrometeor falls. The direct use rain and snow (e.g., Clough and Franks 1991), and of Eq. (1) is computationally inefficient for real-time use. Unauthenticated | Downloaded 09/30/21 10:18 AM UTC JANUARY 2018 M A R T I N A I T I S E T A L . 89 TABLE 1. The seven different precipitation types classified for the MRMS SPT product. Listed are the R–Z relationship, the reflectivity 2 cap (dBZ), and rate cap (mm h 1) for each precipitation type. 2 MRMS SPT classification R–Z relationship Reflectivity cap (dBZ) Rate cap (mm h 1) Cool stratiform rain R 5 0.0365Z0.625 50 48.6 Warm stratiform rain R 5 0.1155Z0.5 50 48.6 Tropical stratiform rain R 5 0.010Z0.833 50 103.8 Convective rain R 5 0.017Z0.714 53
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