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Liang Liao and Robert Meneghini

Abstract

A physical evaluation of the rain profiling retrieval algorithms for the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors’ parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals.

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Minda Le and V. Chandrasekar

Abstract

Extensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed.

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Xinxuan Zhang and Emmanouil N. Anagnostou

Abstract

The study evaluated a numerical weather model (WRF)-based satellite precipitation adjustment technique with 81 heavy precipitation events that occurred in three tropical mountainous regions (Colombia, Peru, and Taiwan). The technique was applied on two widely used near-real-time global satellite precipitation products—the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Global Satellite Mapping of Precipitation project (GSMaP)—for each precipitation event. The WRF-adjusted satellite products along with the near-real-time and gauge-adjusted satellite products as well as the WRF simulation were evaluated by independent gauge networks at daily scale and event total scale. Results show that the near-real-time precipitation products exhibited severe underestimation relative to the gauge observations over the three tropical mountainous regions. The underestimation tended to be larger for higher rainfall accumulations. The WRF-based satellite adjustment provided considerable improvements to the near-real-time CMORPH and GSMaP products. Moreover, error metrics show that WRF-adjusted satellite products outperformed the gauge-adjusted counterparts for most of the events. The effectiveness of WRF-based satellite adjustment varied with events of different physical processes. Thus, the technique applied on satellite precipitation estimates of these events may exhibit inconsistencies in the bias correction.

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

Abstract

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

Abstract

The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.

Open access
Liang Liao and Robert Meneghini

Abstract

To overcome a deficiency in the standard Ku- and Ka-band dual-wavelength radar technique, a modified version of the method is introduced. The deficiency arises from ambiguities in the estimate of the mass-weighted diameter D m of the raindrop size distribution (DSD) derived from the differential frequency ratio (DFR), defined as the difference between the radar reflectivity factors (dB) at Ku and Ka band Z KuZ Ka. In particular, for DFR values less than zero, there are two possible solutions of D m, leading to ambiguities in the retrieved DSD parameters. It is shown that the double solutions to D m are effectively eliminated if the DFR is modified from Z KuZ Ka to Z KuγZ Ka (dB), where γ is a constant with a value less than 0.8. An optimal radar algorithm that uses the modified DFR for the retrieval of rain and D m profiles is described. The validity and accuracy of the algorithm are tested by applying it to radar profiles that are generated from measured DSD data. Comparisons of the rain rates and D m estimated from the modified DFR algorithm to the same hydrometeor quantities computed directly from the DSD spectra (or the truth) indicate that the modified DFR-based profiling retrievals perform fairly well and are superior in accuracy and robustness to retrievals using the standard DFR.

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

Abstract

Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.

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Daniel J. Cecil and Themis Chronis

Abstract

Coefficients are derived for computing the polarization-corrected temperature (PCT) for 10-, 19-, 37- and 89-GHz (and similar) frequencies, with applicability to satellites in the Global Precipitation Measurement mission constellation and their predecessors. PCTs for 10- and 19-GHz frequencies have been nonexistent or seldom used in the past; developing those is the main goal of this study. For 37 and 89 GHz, other formulations of PCT have already become well established. We consider those frequencies here in order to test whether the large sample sizes that are readily available now would point to different formulations of PCT. The purpose of the PCT is to reduce the effects of surface emissivity differences in a scene and draw attention to ice scattering signals related to precipitation. In particular, our intention is to develop a PCT formula that minimizes the differences between land and water surfaces, so that signatures resulting from deep convection are not easily confused with water surfaces. The new formulations of PCT for 10- and 19-GHz measurements hold promise for identifying and investigating intense convection. Four examples are shown from relevant cases. The PCT for each frequency is effective at drawing attention to the most intense convection, and removing ambiguous signals that are related to underlying land or water surfaces. For 37 and 89 GHz, the older formulations of PCT from the literature yield generally similar values as ours, with the differences mainly being a few kelvins over oceans. An optimal formulation of PCT can depend on location and season; results are presented here separated by latitude and month.

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

Abstract

Instantaneous surface rain rate estimates from the Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) and combined DPR and multifrequency microwave imager (CMB) version-5 products are compared to those from the Met Office Radarnet 4 system’s Great Britain and Ireland (GBI) radar composite product. The spaceborne and ground-based rainfall products are collocated spatially and temporally and compared at 5- and 25-km resolutions over GBI during a 3-yr period (from May 2014 to April 2017). The comparison results are evaluated as a function of both the intensity and variability of precipitation within the DPR field of view and are stratified spatially and seasonally. CMB and DPR products underestimate rain rates with respect to the Radarnet product by 21% and 31%, respectively, when considering 25-km resolution data taken within 75 km of a ground-based radar. Large variability in the discrepancies between spaceborne and ground-based rain rate estimates is the result of limitations of both systems and random errors in the collocation of their measurements. The Radarnet retrieval is affected by issues with measuring the vertical extent of precipitation at far ranges, while the GPM system struggles in properly quantifying orographic precipitation. Part of the underestimation by the GPM products appears to be a consequence of an erroneous DPR clutter identification in the presence of low freezing levels. Both products are susceptible to seasonal variations in performance and decreases in precision with increased levels of heterogeneity within the instruments’ field of view.

Open access
Kamil Mroz, Alessandro Battaglia, Timothy J. Lang, Simone Tanelli, and Gian Franco Sacco

Abstract

A statistical analysis of simultaneous observations of more than 800 hailstorms over the continental United States performed by the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) and the ground-based Next Generation Weather Radar (NEXRAD) network has been carried out. Several distinctive features of DPR measurements of hail-bearing columns, potentially exploitable by hydrometeor classification algorithms, are identified. In particular, the height and the strength of the Ka-band reflectivity peak show a strong relationship with the hail shaft area within the instrument field of view (FOV). Signatures of multiple scattering (MS) at the Ka band are observed for a range of rimed particles, including but not exclusively for hail. MS amplifies uncertainty in the effective Ka reflectivity estimate and has a negative impact on the accuracy of dual-frequency rainfall retrievals at the ground. The hydrometeor composition of convective cells presents a large inhomogeneity within the DPR FOV. Strong nonuniform beamfilling (NUBF) introduces large ambiguities in the attenuation correction at Ku and Ka bands, which additionally hamper quantitative retrievals. The effective detection of profiles affected by MS is a very challenging task, since the inhomogeneity within the DPR FOV may result in measurements that look remarkably like MS signatures. The shape of the DPR reflectivity profiles is the result of the complex interplay between the scattering properties of the different hydrometeors, NUBF, and MS effects, which significantly reduces the ability of the DPR system to detect hail at the ground.

Open access