Assessment of GPM high‐frequency microwave measurements with radiative transfer simulation under snowfall conditions

Yin, Mengtao; Liu, Guosheng

doi:10.1002/qj.3515

Cited by 10 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the ground, weather radars are typically used to estimate precipitation but are also extensively used for research purposes. Furthermore, Doppler radars have demonstrated capabilities to both identify and quantify different water phases in mixed-phase cloud (Shupe et al, 2004), while multi-frequency radars have shown capabilities to retrieve information related to the shape of the ice particles (Kulie et al, 2014;Kneifel et al, 2015;Yin et al, 2017;Leinonen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

2020

View full text Add to dashboard Cite

Abstract. Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) will provide unprecedented measurements at sub-millimetre frequencies, employing channels up to 664 GHz. However, the utilization of such measurements requires detailed data on how individual ice particles scatter and absorb radiation, i.e. single scattering data. Several single scattering databases are currently available, with the one by Eriksson et al. (2018) specifically tailored to ICI. This study attempts to validate and constrain the large set of particle models available in this database to a smaller and more manageable set. A combined active and passive model framework is developed and employed, which converts CloudSat observations to simulated brightness temperatures (TBs) measured by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and ICI. Simulations covering about 1 month in the tropical Pacific Ocean are performed, assuming different microphysical settings realized as combinations of the particle model and particle size distribution (PSD). Firstly, it is found that when the CloudSat inversions and the passive forward model are considered separately, the assumed particle model and PSD have a considerable impact on both radar-retrieved ice water content (IWC) and simulated TBs. Conversely, when the combined active and passive framework is employed instead, the uncertainty due to the assumed particle model is significantly reduced. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (166, 186.31, and 190.31 GHz), indicating the robustness of the simulations. However, it is difficult to identify a particle model that outperforms any other. One aggregate particle model, composed of columns, yields marginally better agreement with GMI compared to the other particles, mainly for the most severe cases of deep convection. Of the tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement with GMI and also yields radar dBZ–IWC relationships closely matching measurements by Protat et al. (2016). Only one particle, modelled as an air–ice mixture spheroid, performs poorly overall. On the other hand, simulations at the higher ICI frequencies (328.65, 334.65, and 668.2 GHz) show significantly higher sensitivity to the assumed particle model. This study thus points to the potential use of combined ICI and 94 GHz radar measurements to constrain ice hydrometeor properties in radiative transfer (RT) using the method demonstrated in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

2020

View full text Add to dashboard Cite

show abstract

“…In addition, correctly simulating brightness temperatures is needed for physical snowfall retrievals as well as data assimilation of radiance observations in numerical weather prediction models. Yin and Liu (2019) has studied the bias characteristics of observed minus simulated brightness temperatures at high frequency channels of Global Precipitation Measurement Microwave Imager (GPM/GMI) under snowfall conditions. In their study, a radiative transfer model that includes single-scattering properties of nonspherical snow particles is used to simulate brightness temperatures at 89 through 183 GHz.…”

Section: Introductionmentioning

confidence: 99%

Microphysical Properties of Three Types of Snow Clouds: Implication to Satellite Snowfall Retrievals

Jeoung¹,

Liu²,

Kim³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Ground-based radar and radiometer data observed during the 2017–18 winter were used to simultaneously estimate both cloud liquid water path and snowfall rate for three types of snowing clouds: near-surface, shallow and deep. Surveying all the observed data, it is found that near-surface cloud is the most frequently observed cloud type with an area fraction of over 60 %, while deep cloud contributes the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with vast majority hardly reaching to 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, liquid water path in the three types of clouds all has substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as a priori database. Under idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.33, 0.48 and 0.74, respectively, for near-surface, shallow and deep snowing clouds over land surface; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increased from 0.33 to 0.54, while virtually no change in skills is found for deep clouds and only marginal improvement is found for shallow clouds. This study provides a general picture of the microphysical characteristics of the different types of snowing clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

show abstract

“…Also, numerical weather prediction can typically not predict small-scale cloud structures, making comparisons between simulated and real observations difficult at such scales. This issue can be circumvented by using radar reflectivity fields as input to the forward model (Bennartz and Bauer, 2003;Skofronick-Jackson et al, 2008;Kulie et al, 2010;Yin and Liu, 2019;Ringerud et al, 2019), ensuring that small-scale cloud structures are properly resolved and represented. Lidar measurements can be used in a similar way, as the already mentioned study by Fox et al (2019) is an example of.…”

Section: Introductionmentioning

confidence: 99%

“…The utilization of measurements by radar requires that the reflectivity fields are converted to fields of ice water content (IWC) (i.e., mass density of ice hydrometeors). The recent study by Yin and Liu (2019) assessed GMI measurements by comparing them to forward simulations derived from collocated CloudSat measurements, using a selection of different non-spheroidal particle models. In general, good agreement was attained, especially for the aggregate particle type used.…”

Section: Introductionmentioning

confidence: 99%

“…In general, good agreement was attained, especially for the aggregate particle type used. However, Yin and Liu (2019) only derive the IWC field once and used it as input to the forward simulations regardless of the particle type assumed in the latter procedure. This implies that the microphysical assumptions in the forward simulations and the active retrievals are not necessarily consistent.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using passive and active microwave observations to constrain ice particle models

Ekelund

Eriksson

Pfreundschuh

2019

Preprint

View full text Add to dashboard Cite

Abstract. Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) sensor will provide unprecedented measurements at sub-millimetre frequencies, employing channels up to 664 GHz. However, the utilization of such measurements requires detailed data on how individual ice particles scatter and absorb radiation, i.e., single scattering data. Several single scattering databases are currently available, with the one by Eriksson et al. (2018) specifically tailored to ICI. This study attempts to validate and constrain the large set of particle models available in this database, to a smaller and more manageable set. A combined active and passive model framework is developed and employed, which converts CloudSat observations to simulated brightness temperatures (TBs) measured by the GPM Microwave Imager (GMI) and ICI. Simulations covering about one month in the tropic pacific ocean are performed, assuming different microphysical settings realized as combinations of particle model and particle size distribution (PSD). Firstly, it is found that when the CloudSat inversions and passive forward model are considered separately, assumed particle model and PSD have a considerable impact on both radar retrieved ice water content (IWC) and simulated TBs. Conversely, when the combined active and passive framework is employed instead, the uncertainty due to assumed particle model is significantly reduced, essentially due to a compensation effect between bulk extinction at passive frequencies and radar reflectivity. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (186.31 and 190.31 GHz), indicating the robustness of the simulations. However, it is difficult to identify a particle model that outperforms any other. One aggregate particle model, composed of columns, yields marginally better agreement to GMI compared to the other particles, mainly for the most severe cases of deep convection. Of tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement to GMI and also yields radar dBZ-IWC relationships closely matching measurements by Protat et al. (2016). Only one particle, modelled as an air-ice mixture spheroid, performs poorly overall. On the other hand, simulations at the higher ICI frequencies (328.65, 334.65, and 668.2 GHz) show significantly higher sensitivity to the assumed particle model. This study thus points to the potential use of combined ICI and 94 GHz radar measurements to constrain ice hydrometeor properties in radiative transfer (RT), using the method demonstrated in this paper.

show abstract

Assessment of GPM high‐frequency microwave measurements with radiative transfer simulation under snowfall conditions

Cited by 10 publications

References 50 publications

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

Microphysical Properties of Three Types of Snow Clouds: Implication to Satellite Snowfall Retrievals

Using passive and active microwave observations to constrain ice particle models

Contact Info

Product

Resources

About