| Abstract: |
Retrievals of falling snow from space represent an important data set for understanding and linking the Earth’s atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the NASA-JAXA Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014. Comparisons are done between GPM snow products and the snow estimates from the NASA CloudSat satellite that was launched in April 2006.
We use the GPM-CO Level 2 instantaneous swath retrievals of precipitation classified as falling snow from data from both the GMI, from the Dual-frequency Precipitation Radar (DPR), and the combined DPR-GMI algorithms. The 166V, 166H, 183±3, and 183±7 GHz channels on the GMI were added to TRMM’s 9 channels from 10-89 GHz and designed to observe the smaller precipitation particles associated with light rain and falling snow found in the mid-latitudes. The Ka-band (36 GHz) channel on the DPR is also useful for light rain and falling snow and is sensitive to different particle size distributions. Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. CloudSat’s W-band (94 GHz) radar also provides estimates of instantaneous snow rates.
While satellite-based remote sensing provides global coverage of falling snow events, there are considerable uncertainties in the process of retrieving snowfall rate from the direct instrument measurements of reflectivity and brightness temperature. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes.
The differences between GPM and CloudSat falling snow products result from four main categories: classification-related, sampling, instrumentation (resolution/sensitivity), and algorithm. Classification refers to the method used to determine if a given profile is rain or snow at the surface. Sampling due to differing swath widths and orbits causes additional disparities between the products. The instruments have different design features, most notably minimum detectable reflectivity and frequency sensitivities. Furthermore, DPR and CPR are radars with vertical range gates whereas GMI is a radiometer that measures a column-integrated signal. Even when all of these factors are considered, algorithm assumptions lead to dissimilarities that are more difficult to reconcile, but one approach to put DPR and CPR estimates on a common basis will be presented. Here we highlight factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.
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