Understanding How Low-Level Clouds and Fog Modify the Diurnal Cycle of Orographic Precipitation Using In Situ and Satellite Observations

Abstract

Satellite orographic precipitation estimates exhibit large errors with space-time structure tied to landform. Observations in the Southern Appalachian Mountains (SAM) suggest that low-level clouds and fog (LLCF) amplify mid-day rainfall via seeder-feeder interactions (SFI) at both high and low elevations. Here, a rainfall microphysics model constrained by fog observations was used first to reveal that fast SFI (2-5 min time-scales) modify the rain drop size distributions by increasing coalescence efficiency among small drops (<0.7 mm diameter), whereas competition between coalescence and filament-only breakup dominates for larger drops (3-5 mm diameter). The net result is a large increase in the number concentrations of intermediate size raindrops in the 0.7-3 mm range and up to a ten-fold increase in rainfall intensity. Next, a 10-year climatology of satellite observations was developed to map LLCF. Combined estimates from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) and CloudSat products reveal persistent shallower cloud base heights at high elevations enveloping the terrain. The regional cloud top height climatology derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) shows high-frequency daytime LLCF over mountain ridges in the warm season shifting to river valleys at nighttime. In fall and winter, LLCF patterns define a cloud-shadow region east of the continental divide, consistent with downwind rain-shadow effects. Optical and microphysical properties from collocated MODIS and ground ceilometers indicate small values of vertically integrated cloud water path (CWP < 100 g/m 2 ), optical thickness (COT < 15), and particle effective radius (CER) < 15 µm near cloud top whereas surface observed CER~25 µm changes to~150 µm and higher prior to the mid-day rainfall. The vertical stratification of LLCF microphysics and SFI at low levels pose a significant challenge to satellite-based remote sensing in complex topography.

Understanding aerosol–cloud interactions through modeling the development of orographic cumulus congestus during IPHEx

“…Barros et al (2018) showed that model simulations using aerosol activation spectra from local sources and activation spectra from remote aerosol sources resulted in a significant spatial and temporal redistribution of precipitation in the central Himalayas, including changes in cloud dynamics and the vertical distribution of hydrometeors. The latter is the basis for remote sensing measurements of precipitation, and therefore understanding how ACIs modify precipitation structure is key to improving retrievals in mountainous regions (e.g., Duan et al, 2015).…”

Modeling Aerosol-Cloud-Precipitation Interactions in Mountainous Regions: Challenges in the Representation of Indirect Microphysical Effects with Impacts at Subregional Scales

“…Indeed, [25] showed how IGP aerosol can remain sequestered to form pools over low lying areas and valleys in the Middle Himalaya after there is a full retreat of the pollution over the IGP. The aerosol pool is eventually scavenged by the formation of low-level clouds and fog, and washed out by rainfall similar to subregional scale forcing in the inner region of the Southern Appalachians investigated by [21,22]. Specifically, [26] showed that, in the presence of regional scale aerosol clouds and during dry periods, the mean volume aerosol concentration increased, and so did the aerosol mass concentrations in two different valleys of Central Nepal, the Marshyangdi and the Kathmandu, followed by rain-out.…”

“…Intercomparison modeling studies using CN with different activation characteristics suggest that the timing and intensity of precipitation are tightly linked to regional and subregional scale aerosol characteristics. Recent NWP (Numerical Weather Prediction) simulations in the Southern Appalachians Mountains (complex terrain with moderate elevation <2500 m) show that using regional CCN activation characteristics obtained from field measurements [20] has strong impact on rainfall structure as compared to standard continental aerosol by reducing unrealistic light rainfall on the one hand, and by intensifying convection on the other due to strong modification of cloud microphysics, even more so in the case of local vis-a-vis synoptic forcing [21,22]. This begs the question of whether the characterization of regional aerosol is not only desirable, but indeed necessary toward achieving a substantial improvement in NWP's predictive skill at high spatial resolution and short time-scales (< 24 hr) toward decreasing phase errors in storm arrival and improving rainfall intensity [2,23,24].…”

Toward Optimal Rainfall for Flood Prediction in Headwater Basins - Orographic QPE error modeling using machine learning

“…A high-density high-elevation network of rain gauges has been operating for 15 years in this region. Previous research using ground observations (Prat & Barros, 2007;Wilson & Barros, 2014;Duan & Barros, 2017;Miller et al, 2019) elucidated the dominant precipitation regimes in the SAM. The Cataloochee Creek Basin (CCB, 128 km 2 ), a USGS (United States Geological Survey) benchmark watershed, is selected for the end-to-end demonstration due to the high quality of streamflow observations (Figure 2), and nearby catchments are used to explore model transferability.…”