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George Duffy, Greg Mcfarquhar, Stephen W. Nesbitt, and Ralf Bennartz

Abstract

The retrieval of the mass-weighted mean diameter (D m) is a fundamental component of spaceborne precipitation retrievals. The Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) satellite is the first satellite to use dual-wavelength ratio measurements—the quotient of radar reflectivity factors (Z) measured at Ku and Ka wavelengths—to retrieve D m. While it is established that DWR, being theoretically insensitive to changes in ice crystal mass and concentration, can provide a superior retrieval of D m compared to Z-based retrievals, the benefits of this retrieval have yet to be directly observed or quantified. In this study, DWR–D m and ZD m relationships are empirically generated from collocated airborne radar and in situ cloud particle probe measurements. Data are collected during nine intensive observation periods (IOPs) from three experiments representing different locations and times of year. Across IOPs with varying ice crystal concentrations, cloud temperatures, and storm types, ZD m relationships vary considerably while the DWR–D m relationship remains consistent. This study confirms that a DWR–D m relationship can provide a more accurate and consistent D m retrieval than a ZD m relationship, quantified by a reduced overall RMSE (0.19 and 0.25 mm, respectively) and a reduced range of biases between experiments (0.11 and 0.32 mm, respectively).

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Ali Tokay, Annakaisa von Lerber, Claire Pettersen, Mark S. Kulie, Dmitri N. Moisseev, and David B. Wolff

Abstract

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017-18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 hours. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.

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Manikandan Rajagopal, Edward Zipser, George Huffman, James Russell, and Jackson Tan

Abstract

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG) is a global precipitation product that uses precipitation retrievals from the virtual constellation of satellites with passive microwave (PMW) sensors, as available. In the absence of PMW observations, IMERG uses a Kalman filter scheme to morph precipitation from one PMW observation to the next. In this study, an analysis of convective systems observed during the Convective Process Experiment (CPEX) suggests that IMERG precipitation depends more strongly on the availability of PMW observations than previously suspected. Following this evidence, we explore systematic biases in IMERG through bulk statistics.

In two CPEX case studies, cloud photographs, pilot’s radar, and infrared imagery suggest that IMERG represents the spatial extent of precipitation relatively well when there is a PMW observation but sometimes produces spurious precipitation areas in the absence of PMW observations. Also, considering an observed convective system as a precipitation object in IMERG, the maximum rain rate peaked during PMW overpasses, with lower values between them. Bulk statistics reveal that these biases occur throughout IMERG Version 06. We find that locations and times without PMW observations have a higher frequency of light precipitation rates and a lower frequency of heavy precipitation rates due to retrieval artifacts. These results reveal deficiencies in the IMERG Kalman Filter scheme, which have led to the development of the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN; described in a companion paper) that will be applied in the next version of IMERG.

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Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

Abstract

Applications like drought monitoring and forecasting can profit from the global and near-real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multisatellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a 5-yr period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data are divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data are included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can become accurate over the middle and high latitudes.

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

Abstract

This paper describes a new Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) developed for snow-cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (ATMS) is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent of the cloud cover. The algorithm shows very good performance for cold temperatures (2-m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.

Open access
Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

Abstract

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States during the period from November 2014 to September 2020. The analysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPM Combined Radar Radiometer Algorithm (CORRA), the CloudSat Snow Profile product (2C-SNOW-PROFILE), and two passive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI (SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the products analyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of the snow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in the GMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall rates by a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h−1) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of the remote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by the confounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers.

Open access
Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

Abstract

Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

Abstract

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter Dml and the liquid equivalent normalized intercept parameter Nwl. Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for Dml, Nwl, and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for Dml, Nwl, and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

Abstract

Two power-law relations linking equivalent radar reflectivity factor Z e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N 0 of the PSD calculated from concurrent disdrometer measurements. If N 0 is used to adjust the prefactor of the Z e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Z e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z e –S relationships from literature reveal larger differences.

Open access
Jackson Tan, George J. Huffman, David T. Bolvin, Eric J. Nelkin, and Manikandan Rajagopal

Abstract

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

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