Evaluation of Cloud Microphysical Schemes for a Warm Frontal Snowband during the GPM Cold Season Precipitation Experiment (GCPEx)

Detailed observations from the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx) of an intense warm frontal band on 18 February 2012 were used to evaluate several bulk microphysical parameterizations within the NASA-Unified Weather Research and Forecasting (NU-WRF) Model. These included the Predicted Particle Properties (P3), Morrison (MORR), Stony Brook University (SBU), and Goddard four-class ice (4ICE) microphysics schemes. All schemes were able to predict the snowband, but the simulated intensities varied because of various assumptions in these schemes. The saturation adjustment scheme within MORR promoted excessive amounts of cloud water evaporational cooling in the warm sector, which contributed to a decrease in midlevel instability approaching the frontal band and thus a weaker band. In contrast, the explicit calculation of cloud water condensation/evaporation in the P3 scheme produced limited amounts of evaporational cooling, which allowed for greater midlevel instability to support band development. The P3 and SBU schemes produced moderate rime/graupel mass within the band that was confirmed by observations, while the MORR and 4ICE schemes drastically underpredicted the graupel mass. The high-density, fast-falling rimed particles in P3 underwent weak sublimation and melting, which helped promote a stronger horizontal temperature gradient and greater low-level instability along the frontal band compared to the other schemes. Overall, the schemes that use specified thresholds for converting between the predefined ice-phase categories of cloud ice, snow, and graupel had the most unrepresentative hydrometeor types. These results highlight the advantage of predicting ice particle properties and explicitly calculating cloud water condensation/evaporation in the P3 scheme.

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Structure and Evolution of a Warm Frontal Precipitation Band during the GPM Cold Season Precipitation Experiment (GCPEx)

Monthly Weather Review ◽

10.1175/mwr-d-16-0072.1 ◽

2017 ◽

Vol 145 (2) ◽

pp. 473-493 ◽

Cited By ~ 7

Author(s):

Brian A. Colle ◽

Aaron R. Naeger ◽

Andrew Molthan

Keyword(s):

Wrf Model ◽

Cold Season ◽

Early Maturity ◽

Low Level ◽

The North ◽

Warm Front ◽

Precipitation Measurement ◽

Precipitation Experiment ◽

Precipitation Band ◽

Global Precipitation

This paper describes the evolution of an intense precipitation band associated with a relatively weak warm front observed during the Global Precipitation Measurement (GPM) Mission Cold Season Precipitation Experiment (GCPEx) over southern Ontario, Canada, on 18 February 2012. The warm frontal precipitation band went through genesis, maturity, and decay over a 5–6-h period. The Weather Research and Forecasting (WRF) Model nested down to 1-km grid spacing was able to realistically predict the precipitation band evolution, albeit somewhat weaker and slightly farther south than observed. Band genesis began in an area of precipitation with embedded convection to the north of the warm front in a region of weak frontogenetical forcing at low levels and a weakly positive to slightly negative moist potential vorticity (MPV*) from 900 to 650 hPa. A midlevel dry intrusion helped reduce the midlevel stability, while the precipitation band intensified as the low-level frontogenesis intensified in a sloping layer with the warm front. Aggregates of unrimed snow occurred within the band during early maturity, while more supercooled water and graupel occurred as the upward motion increased because of the frontogenetical circulation. As the low-level cyclone moved east, the low-level deformation decreased and the column stabilized for vertical and slantwise ascent, and the warm frontal band weakened. A WRF experiment turning off latent heating resulted in limited precipitation band development and a weaker warm front, while turning off latent cooling only intensified the frontal precipitation band as additional midlevel instability compensated for the small decrease in frontogenetical forcing.

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Hydrometeorological Assessment of Satellite and Model Precipitation Products over Taiwan

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0130.1 ◽

2021 ◽

Author(s):

Pin-Lun Li ◽

Chia-Jeng Chen ◽

Liao-Fan Lin

Keyword(s):

Extreme Event ◽

Wrf Model ◽

Warm Season ◽

Flow Simulation ◽

Cold Season ◽

Regional Variations ◽

Precipitation Measurement ◽

Precipitation Estimation ◽

Global Precipitation ◽

Model Precipitation

AbstractSatellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multi-satellitE Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation_near-real-time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulation: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.

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Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): For Measurement’s Sake, Let It Snow

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-13-00262.1 ◽

2015 ◽

Vol 96 (10) ◽

pp. 1719-1741 ◽

Cited By ~ 48

Author(s):

Gail Skofronick-Jackson ◽

David Hudak ◽

Walter Petersen ◽

Stephen W. Nesbitt ◽

V. Chandrasekar ◽

...

Keyword(s):

Precipitation Amount ◽

Cold Season ◽

Dual Frequency ◽

Field Campaign ◽

Precipitation Measurement ◽

Precipitation Experiment ◽

Research Aircraft ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract As a component of Earth’s hydrologic cycle, and especially at higher latitudes, falling snow creates snowpack accumulation that in turn provides a large proportion of the freshwater resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011/12 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite and radiometers on constellation member satellites. Multiparameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude, and in situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites that in turn were taking in situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx field campaign is described and three illustrative cases detailed.

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Sensitivity of Simulated GMI Brightness Temperatures to Variations in Particle Size Distributions in a Severe Hailstorm

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0031.1 ◽

2019 ◽

Vol 58 (9) ◽

pp. 1905-1930 ◽

Cited By ~ 1

Author(s):

Kenneth D. Leppert II ◽

Daniel J. Cecil

Keyword(s):

Particle Size ◽

Particle Diameter ◽

Radar Reflectivity ◽

Brightness Temperatures ◽

Precipitation Measurement ◽

Distribution Parameters ◽

High Concentrations ◽

Microwave Imager ◽

Cloud Ice ◽

Global Precipitation

AbstractGlobal Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter Do of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing Do for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing Do of cloud ice, channels at ≥89.0 GHz are most sensitive to changing Do of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail Do. Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.

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Evaluating Simulated Microphysics during OLYMPEX Using GPM Satellite Observations

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-18-0271.1 ◽

2019 ◽

Vol 76 (4) ◽

pp. 1093-1105 ◽

Cited By ~ 2

Author(s):

Robert Conrick ◽

Clifford F. Mass

Keyword(s):

Rain Rate ◽

Vertical Mixing ◽

Wrf Model ◽

Cloud Water ◽

Mixing Ratio ◽

Dual Frequency ◽

Cloud Water Content ◽

Microwave Imager ◽

Rain Rates ◽

Global Precipitation

Abstract This study evaluates moist physics in the Weather Research and Forecasting (WRF) Model using observations collected during the Olympic Mountains Experiment (OLYMPEX) field campaign by the Global Precipitation Measurement (GPM) satellite, including data from the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) instruments. Even though WRF using Thompson et al. microphysics was able to realistically simulate water vapor concentrations approaching the barrier, there was underprediction of cloud water content and rain rates offshore and over western slopes of terrain. We showed that underprediction of rain rate occurred when cloud water was underpredicted, establishing a connection between cloud water and rain-rate deficits. Evaluations of vertical hydrometeor mixing ratio profiles indicated that WRF produced too little cloud water and rainwater content, particularly below 2.5 km, with excessive snow above this altitude. Simulated mixing ratio profiles were less influenced by coastal proximity or midlatitude storm sector than were GMI profiles. Evaluations of different synoptic storm sectors suggested that postfrontal storm sectors were simulated most realistically, while warm sectors had the largest errors. DPR observations confirm the underprediction of rain rates noted by GMI, with no dependence on whether rain occurs over land or water. Finally, WRF underpredicted radar reflectivity below 2 km and overpredicted above 2 km, consistent with GMI vertical mixing ratio profiles.

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Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

Remote Sensing ◽

10.3390/rs13122264 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2264

Author(s):

F. Joseph Turk ◽

Sarah E. Ringerud ◽

Andrea Camplani ◽

Daniele Casella ◽

Randy J. Chase ◽

...

Keyword(s):

Heavy Precipitation ◽

Cold Season ◽

Dual Frequency ◽

Light Rain ◽

Triple Frequency ◽

W Band ◽

Precipitation Measurement ◽

Precipitation Structure ◽

Global Precipitation Measurement ◽

Global Precipitation

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

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Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency using Space-borne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Monthly Weather Review ◽

10.1175/mwr-d-21-0066.1 ◽

2021 ◽

Author(s):

Yasutaka Ikuta ◽

Masaki Satoh ◽

Masahiro Sawada ◽

Hiroshi Kusabiraki ◽

Takuji Kubota

Keyword(s):

Cloud Microphysics ◽

Japan Meteorological Agency ◽

Dual Frequency ◽

Microphysics Scheme ◽

Precipitation Measurement ◽

Single Column ◽

Microwave Imager ◽

Cloud Ice ◽

Global Precipitation Measurement ◽

Global Precipitation

AbstractIn this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

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PERBANDINGAN SEBARAN CURAH HUJAN HASIL OBSERVASI SATELIT GPM DENGAN MODEL CUACA WRF (Studi Kasus: Siklon Tropis Cempaka Dan Dahlia, 27 Nov – 3 Des 2017)

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.998 ◽

2019 ◽

Vol 3 ◽

pp. 1063

Author(s):

Fatkhuroyan Fatkhuroyan

Keyword(s):

Weather Research And Forecasting ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Japan Aerospace Exploration Agency ◽

Weather Research

Satelit GPM (Global Precipitation Measurement) merupakan proyek kerjasama antara NASA (National Aeronautics and Space Administration) dan JAXA (Japan Aerospace Exploration Agency) serta lembaga internasional lainnya untuk membuat satelit generasi terbaru dalam rangka pengamatan curah hujan di bumi sejak 2014. Model Cuaca WRF (Weather Research and Forecasting) merupakan model cuaca numerik yang telah dipakai oleh BMKG (Badan Meteorologi Klimatologi dan Geofisika) untuk pelayan prediksi cuaca harian kepada masyarakat. Pada tanggal 27 November – 3 Desember 2017 telah terjadi bencana alam siklon tropis Cempaka dan Dahlia di samudra Hindia sebelah selatan pulau Jawa. Tujuan Penelitian ialah untuk mengetahui sebaran akumulasi curah hujan antara observasi satelit GPM dan model cuaca WRF, serta keakuratan model WRF terhadap observasi satelit GPM saat terjadinya bencana alam tersebut. Metode yang dipakai ialah dengan melakukan analisa meteorologi pertumbuhan terjadinya siklon tropis tersebut hingga terjadinya hujan sangat lebat secara temporal maupun spasial. Dari hasil analisa disimpulkan bahwa satelit GPM memiliki luasan sebaran curah hujan yang lebih kecil daripada sebaran hujan model cuaca WRF pada saat siklon tropis Cempaka dan Dahlia. Bias akumulasi sebaran hujan model cuaca WRF juga cukup bagus terhadap satelit GPM sehingga dapat dilakukan antisipasi dampak hujan lebat yang terjadi.

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