SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

Rysman, Jean‐François; Panegrossi, Giulia; Sanò, Paolo; Marra, Anna Cinzia; Dietrich, Stefano; Milani, Lisa; Kulie, Mark S.

doi:10.3390/rs10081278

Cited by 36 publications

(52 citation statements)

References 60 publications

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“…Thirdly, comparisons of real and simulated observations using collocated datasets would have been ideal. However, because the number of collocations between CloudSat and GMI is limited around the Equator (Rysman et al, 2018;Yin and Liu, 2019) and the different observation geometries present difficulties, the statistical approach was selected instead.…”

Section: Methodsmentioning

confidence: 99%

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

2020

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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.

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Section: Methodsmentioning

confidence: 99%

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

2020

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“…The resulting findings indicate huge improvements compared to the TRMM era. Yet, the need for future improvements of the algorithm to further enhance the IMERG abilities in freezing conditions still persists [22,34,40,44,45].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

Ramsauer¹,

Weiß²,

Marzahn³

2018

Remote Sensing

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Precipitation measurements provide crucial information for hydrometeorological applications. In regions where typical precipitation measurement gauges are sparse, gridded products aim to provide alternative data sources. This study examines the performance of NASA’s Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement Mission (IMERG, GPM) satellite precipitation dataset in capturing the spatio-temporal variability of weather events compared to the German weather radar dataset RADOLAN RW. Besides quantity, also timing of rainfall is of very high importance when modeling or monitoring the hydrologic cycle. Therefore, detection metrics are evaluated along with standard statistical measures to test both datasets. Using indices like “probability of detection” allows a binary evaluation showing the basic categorical accordance of the radar and satellite data. Furthermore, a pixel-by-pixel comparison is performed to assess the ability to represent the spatial variability of rainfall and precipitation quantity. All calculations are additionally carried out for seasonal subsets of the data to assess potentially different behavior due to differences in precipitation schemes. The results indicate significant differences between the datasets. Overall, GPM IMERG overestimates the quantity of precipitation compared to RADOLAN, especially in the winter season. Moreover, shortcomings in detection performance arise in this season with significant erroneously-detected, yet also missed precipitation events compared to the weather radar data. Additionally, along secondary mountain ranges and the Alps, topographically-induced precipitation is not represented in GPM data, which generally shows a lack of spatial variability in rainfall and snowfall estimates due to lower resolution.

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“…The purpose was to develop a computationally efficient global precipitation retrieval algorithm able to handle the extremely large and rich observational database available from GPM-CO observations, meeting, at the same time, the H SAF requirement of delivering products useful to near-real time operations. It is worth noting that only liquid precipitation is considered in this work, while a separate module dedicated to snowfall retrieval from GMI measurements has been recently developed [35] and will be soon incorporated in PNPR v3.…”

Section: Introductionmentioning

confidence: 99%

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

Sanò

Panegrossi

Casella³

et al. 2018

Remote Sensing

Self Cite

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This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

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SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

Cited by 36 publications

References 60 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

Comparison of the GPM IMERG Final Precipitation Product to RADOLAN Weather Radar Data over the Topographically and Climatically Diverse Germany

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

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