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

Turk, F. Joseph; Ringerud, Sarah; Camplani, Alberto; Casella, Daniele; Chase, Randy J.; Ebtehaj, Ardeshir M.; Gong, Jie; Kulie, Mark S.; Liu, Guosheng; Milani, Lisa; Panegrossi, Giulia; Padullés, Ramon; Rysman, Jean François; Sanò, Paolo; Vahedizade, Sajad; Wood, Norman B.

doi:10.3390/rs13122264

Cited by 26 publications

(16 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fact that GMI and the DPR are flying onboard the same platform allows for a high number of colocated observations at all latitudes; however, we acknowledge potential errors and inaccuracies in DPR measurements regarding light precipitation intensities. An additional investigation, if it does not require high space-time coverage, should consider measurements from CloudSat Cloud Profiling Radar (CPR), which is known to more accurately capture low-intensity snowfall [71,72].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

et al. 2021

Self Cite

View full text Add to dashboard Cite

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Brightness temperature information of multi‐sensors comes from 2B‐CSATGPM product. The 2B‐CSATGPM (V03B) selects coincident samples from both CloudSat and the GPM Core Observatory, with a maximum time difference of ±15 min (Turk et al., 2021). The 2B‐CSATGPM (V03B) incorporates data from GMI, DPR, and CPR, as well as some auxiliary datasets.…”

Section: Methodsmentioning

confidence: 99%

PCSSR‐DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Yan

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Precipitation is a critical component in water, energy and biogeochemical cycles (Kidd & Huffman, 2011;Ma, Xu, et al., 2022;Xu et al., 2022) and snowfall predominates over other types of precipitation in high-latitude regions (Skofronick-Jackson et al., 2015;Xiong et al., 2022). Today, satellite remote sensing is the primary observational source for gathering data on worldwide rainfall and snowfall since meteorological stations, ocean buoys, and weather radars have limited coverage (Hong et al., 2007;Ma, Zhu, & Yang, 2022;. Retrieving surface snowfall rate (SSR) using space-borne passive microwave sensors equipped with high-frequency channels (90-190 GHz) remains a very challenging task (Levizzani et al., 2011;Skofronick-Jackson et al., 2017), despite their high sensitivity to the radiation scattering by ice hydrometeors. The complexity of surface snowfall rate estimation is due to several factors: (a) the presence of snow cover on the ground complicates the separation between atmospheric snowfall and snow cover contributions to the satellite-observed brightness temperature (Kongoli et al., 2003); (b) the wide variation in particle size and shape of snowflakes suggests complicated radiative signatures (Liu & Seo, 2013); (c) the diversity of weather systems found in higher latitudes also contributes to the complexity of surface snowfall rate estimation (Kongoli et al., 2015).Over the past years, great efforts have been paid to develop algorithms for the detection and retrieval of snowfall rate (

show abstract

“…Brightness temperature information of multi-sensors comes from 2B-CSATGPM product. The 2B-CSATGPM (V03B) selects coincident samples from both CloudSat and the GPM Core Observatory, with a maximum time difference of ±15 minutes (Turk et al, 2021). The 2B-CSATGPM (V03B) incorporates data from GMI, DPR, and CPR, as well as some auxiliary datasets.…”

Section: Cloudsat-gpm Coincidence Data Setmentioning

confidence: 99%

PCSSR-DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Yan

et al. 2023

Preprint

View full text Add to dashboard Cite

Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. We present a novel approach to comprehensively consider passive microwave, infrared and physical constraints using deep neural networks with attention module for retrieving surface snowfall rate, namely PCSSR-DNNWA. PCSSR-DNNWA outperforms traditional approaches in predicting surface snowfall rate with CC ~ 0.75, ME ~ -0.03 mm/h, and RMSE ~ 0.21 mm/h. In addition, we found that graupel water path (GWP) is of vital importance with largest contributions in retrieving surface snowfall rate. Integrating the physical constraints, PCSSR-DNNWA paves a new avenue for retrieving satellite-borne surface snowfall rate by intelligently considering the varying importance of the multiple predictors, resulting in increased accuracy, interpretability, and computational efficiency.

show abstract

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

Cited by 26 publications

References 77 publications

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

PCSSR‐DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

PCSSR-DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Contact Info

Product

Resources

About