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  • Author or Editor: David A. Marks x
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David S. Henderson, Christian D. Kummerow, and David A. Marks

Abstract

Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.

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Ali Tokay, Leo Pio D’Adderio, David A. Marks, Jason L. Pippitt, David B. Wolff, and Walter A. Petersen

Abstract

The ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter D mass and normalized intercept parameter N W—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both D mass and N W are obtained from radar-measured reflectivity Z H and differential reflectivity Z DR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of Z H and Z DR and derived D mass and N W with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for Z H, Z DR, D mass, and log(N W) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL Z HZ DR pairs were well outside the envelope of 2DVD-estimated Z HZ DR pairs. The exclusion of these pairs improved the comparisons noticeably.

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Witold F. Krajewski, Mark L. Morrissey, James A. Smith, and David T. Rexroth

Abstract

A Monte Carlo simulation study is conducted to investigate the performance of the area-threshold method of estimating mean areas rainfall. The study uses a stochastic space-time model of rainfall as the true rainfall-field generator. Simple schemes of simulating radar observations of the simulated rainfall fields are employed. The schemes address both random and systematic components of the radar rainfall-estimation process. The results of the area-threshold method are compared to the results based on conventional averaging of radar-estimated point rainfall observations. The results demonstrate that when the exponent parameter in the ZR relationship has small uncertainty (about ±10%), the conventional method works better than the area-threshold method. When the errors are higher (±20%), the area-threshold method with optimum threshold in the 5–10 mm h−1 range performs best. For even higher errors in the ZR relationship, the area-threshold method with a low threshold provides the best performance.

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Annakaisa von Lerber, Dmitri Moisseev, David A. Marks, Walter Petersen, Ari-Matti Harri, and V. Chandrasekar

Abstract

Currently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying event-specific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from 0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A).

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Paul M. Della-Marta, Mark A. Liniger, Christof Appenzeller, David N. Bresch, Pamela Köllner-Heck, and Veruska Muccione

Abstract

Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310–396 yr in length. Extended winter (October–April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high threshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatologies require the application of calibration techniques to result in meaningful comparisons. Using a combined dynamical–statistical sampling technique, the largest influence on ERA-40 return period (RP) uncertainties is the sampling variability associated with only 45 seasons of storms. However, both maximum likelihood (ML) and L-moments (LM) methods of fitting a generalized Pareto distribution result in biased parameters and biased RP at sample sizes typically obtained from 45 seasons of reanalysis data. The authors correct the bias in the ML and LM methods and find that the ML-based ERA-40 climatology overestimates the RP of windstorms with RPs between 10 and 300 yr and underestimates the RP of windstorms with RPs greater than 300 yr. A 50-yr event in ERA-40 is approximately a 40-yr event after bias correction. Biases in the LM method result in higher RPs after bias correction although they are small when compared with those of the ML method. The climatologies are linked to the Swiss Reinsurance Company (Swiss Re) European windstorm loss model. New estimates of the risk of loss are compared with those from historical and stochastically generated windstorm fields used by Swiss Re. The resulting loss-frequency relationship matches well with the two independently modeled estimates and clearly demonstrates the added value by using alternative data and methods, as proposed in this study, to estimate the RP of high RP losses.

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Claire Pettersen, Mark S. Kulie, Larry F. Bliven, Aronne J. Merrelli, Walter A. Petersen, Timothy J. Wagner, David B. Wolff, and Norman B. Wood

Abstract

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.

Open access
Ricardo C. Muñoz, Laurence Armi, José A. Rutllant, Mark Falvey, C. David Whiteman, René Garreaud, Andrés Arriagada, Federico Flores, and Nicolás Donoso

Abstract

Raco is the local name given to a strong (gusts up to 17 m s−1), warm, and dry down-valley wind observed at the exit of the Maipo River Canyon in central Chile. Its climatology is documented based on eight years of surface measurements near the canyon exit together with a more complete characterization of its structure during an intensive observational period (IOP) carried out in July 2018. Raco winds occur in the cold season under well-defined synoptic conditions, beginning abruptly at any time during the night, reaching maximum hourly averages around 10 m s−1, and terminating around noon with the onset of afternoon westerly up-valley winds. About 25% of the days in May–August have more than six raco hours between 0100 and 1200 LT, and raco episodes last typically 1–2 days. The sudden appearance of raco winds at the surface can be accompanied by conspicuous warming (up to 10°C) and drying (up to 3 g kg−1). Raco winds are associated with a strong along-canyon pressure gradient, a regional pressure fall, and clear skies. During the IOP, radiosondes launched from both extremes of the canyon exit corridor showed a nocturnal easterly jet at 700 m AGL that occasionally descended rapidly to the surface, producing the raco. Transects along the canyon performed with a mobile ceilometer revealed a sharp frontlike feature between the cold pool over the Santiago Valley and the raco-affected conditions in the Maipo Canyon. Possible factors producing the easterly jet aloft and its occasional descent toward the surface are discussed, and a gap-wind mechanism is postulated to be at work.

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Evan A. Kalina, Sergey Y. Matrosov, Joseph J. Cione, Frank D. Marks, Jothiram Vivekanandan, Robert A. Black, John C. Hubbert, Michael M. Bell, David E. Kingsmill, and Allen B. White

Abstract

Dual-polarization scanning radar measurements, air temperature soundings, and a polarimetric radar-based particle identification scheme are used to generate maps and probability density functions (PDFs) of the ice water path (IWP) in Hurricanes Arthur (2014) and Irene (2011) at landfall. The IWP is separated into the contribution from small ice (i.e., ice crystals), termed small-particle IWP, and large ice (i.e., graupel and snow), termed large-particle IWP. Vertically profiling radar data from Hurricane Arthur suggest that the small ice particles detected by the scanning radar have fall velocities mostly greater than 0.25 m s−1 and that the particle identification scheme is capable of distinguishing between small and large ice particles in a mean sense. The IWP maps and PDFs reveal that the total and large-particle IWPs range up to 10 kg m−2, with the largest values confined to intense convective precipitation within the rainbands and eyewall. Small-particle IWP remains mostly <4 kg m−2, with the largest small-particle IWP values collocated with maxima in the total IWP. PDFs of the small-to-total IWP ratio have shapes that depend on the precipitation type (i.e., intense convective, stratiform, or weak-echo precipitation). The IWP ratio distribution is narrowest (broadest) in intense convective (weak echo) precipitation and peaks at a ratio of about 0.1 (0.3).

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