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Contrasting the co-variability of daytime cloud and precipitation over tropical...

Jin, D., L. Oreopoulos, D. Lee, N. Cho, and J. Tan (2018), Contrasting the co-variability of daytime cloud and precipitation over tropical land and ocean, Atmos. Chem. Phys., 18, 3065-3082, doi:10.5194/acp-18-3065-2018.

The co-variability of cloud and precipitation in the extended tropics (35◦ N–35◦ S) is investigated using contemporaneous data sets for a 13-year period. The goal is to quantify potential relationships between cloud type fractions and precipitation events of particular strength. Particular attention is paid to whether the relationships exhibit different characteristics over tropical land and ocean. A primary analysis metric is the correlation coefficient between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in 1◦ grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) data set aggregated to the same grid.

It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs over ocean for cumulonimbus clouds and the heaviest rainfall. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found that both over ocean and land the anti-correlation of strong precipitation with “weak” (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud–precipitation anti-correlations. Weak correlations between weaker rainfall and clouds indicate poor predictability for precipitation when cloud types are known, and this is even more true over land than over ocean.

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Research Program: 
Modeling Analysis and Prediction Program (MAP)
Energy & Water Cycle Program (EWCP)
Atmospheric Dynamics and Precipitation Program (ADP)