Browsing by Subject "Orographic precipitation"

In high elevated regions, ground-based precipitation measurements are scarce or non-existing. Remote sensing estimates are prone to underestimation due to a lack of sensitivity to light precipitation and ground-clutter contamination of radar signals by steep terrain. The latter creates a blind zone extending up a few kilometers above the ground for the vertical profiling radars onboard the Global Precipitation Measurement mission (GPM) core satellite that serves as a reference standard to unify precipitation measurements from research and operational satellites. Ground radars which are not affected by the blind zone, show an increase in effective reflectivity factor (ERF) from the melting layer below the isotherm of zero towards the surface. The blind zone is critical as rainfall characteristics at the ground results from the vertical evolution of the rain drop-size distributions (RDSDs). Explicit modeling of the rainfall microphysics from the top of the blind zone, if located below the zero-degree isotherm, towards the surface can improve rainfall estimation at the ground, as demonstrated in the Southern Appalachian Mountains. However, if the top of the blind zone is located above the zero-degree isotherm, ice and mix-phase hydrometeors coexist at the top boundary condition precluding explicit modeling because the microphysics of these hydrometeors is yet an active research topic. This work has three parts. The first part is devoted to characterizing precipitating systems and the blind zone in the Central Andes of Peru. Twenty years of TRMM and GPM radar measurements reveal that stratiform precipitation is the most frequent type in the Central Andes. Long-lasting precipitating systems (LDPSs) having a stratiform structure with embedded convection determine the interannual variability of the diurnal cycle of precipitation. Moisture availability at high elevations in the Central Andes is scarce. Moisture sources that sustain LDPSs are located in the adjacent eastern foothills of the Andes (Western Amazon basin) that result from enhanced moisture convergence and low and mid-levels due to the interplay of the South American low-level jet and cold air incursions. In the Central Andes of Peru, the top of the GPM blind zone is 46 % of the time above the zero-degree isotherm located at 5000 masl missing liquid precipitation, and a comparison with ground disdrometer data shows that GPM DPR does not capture the variability of the rain drop size distribution. These findings show that rainfall in the Central Andes depends on the dynamics of atmospheric circulation and microphysics at multiple spatial and temporal scales. These scales cannot be modeled explicitly by one single model nor validated with current observations. However, progress in that direction can be made by using different models to simulate different weather regimes to elucidate the role of different microphysics and dynamic processes. Observations needed to validate the models are lacking in the Andes but available in the Southern Appalachian Mountains (SAM). So, the modeling part of this work is focused on the SAM, and the knowledge gained by modeling the SAM region can be used to improve the representation of dynamical and microphysical processes in numerical models to be used in the Andes.Changes in the cloud drop size distribution ultimately capture the interplay of microphysics, dynamics, and thermodynamics in clouds. The chain of microphysical mechanisms from aerosol activation to cloud drop size distribution (CDSD) evolution during cloud development to the raindrop size distribution (RDSD) dynamics spans a scale range of four orders of magnitude from fractions of micrometers to millimeters. It can be modeled explicitly in cloud parcel and rain-shaft models (for liquid hydrometeors), and it is described using different parameterizations with varying degrees of complexity in Numerical Weather Prediction (NWP) models. However, in the Weather Research and Forecast (WRF) model, parameterized microphysics often fail to capture the diurnal cycle and spatial distribution of precipitation, rainfall intensity, and duration depending on the weather regime, regional topography, and regional aerosol characteristics. Besides, the representation of the dynamical processes affecting the aerosol activation and the CDSDs evolution during cloud formation and of the cloud-droplet-raindrop continuum in precipitating clouds is lacking in NWP models. In the second part of this work, a two-moment bulk microphysics scheme in WRF is modified to add an aerosol activation spectrum from in-situ measurement and compared to the default activation spectrum to characterize the impact of aerosol activation in the onset of precipitation in different weather conditions. WRF simulations show that using the in-situ aerosol activation spectrum yields higher cloud droplet number concentrations (CDNC) than the default WRF aerosol activation spectrum, with smaller cloud droplets and delayed onset of rainfall under weak synoptic forcing conditions. For large-scale systems with strong and sustained moisture convergence at low levels (frontal and tropical systems), mechanically forced rainfall efficiency is enhanced despite high CDNC, there is no delay in the onset of precipitation, and the impact of ACPI on the spatial and temporal variability of rainfall is negligible (significant) at onset (hours later) consistent with rainfall observations. The simulated cloud vertical structure from CDNC indicates that convective development is more intense in the inner SAM region than in the adjacent plains. In the inner region, valley-ridge circulations organize the spatial patterns of cloudiness under weak synoptic forcing conditions. The formation of early afternoon low-level clouds over the ridges in the summertime reflects the high sensitivity of cloud mixing ratios and cloud droplet concentrations to aerosol activation properties. In the third part of this work, the dynamical and microphysical processes not resolved by WRF are modeled using a large eddy simulation coupled with a spectrum bin microphysics that permits aerosol replenishment to characterize the heterogeneity of cloud microstructure associated with cloud circulations and entrainment in non-precipitating cumulus clouds. Furthermore, the sensitivity of cloud variables to different vertical profiles of aerosol loading is tested and validated with aircraft observations. The coupled model simulated a convective case under weak synoptic conditions, and the spatial variability of the LWC, CDNC, and CDSD was characterized. Simulations show large differences between the region around the updraft and regions of entrainment located at cloud edges and preferred locations. The interplay of environmental wind and cloud circulations explains these locations. More and larger droplets towards the location of the updraft and towards but before the cloud's top result in large LWC in the upper half of the cloud. The opposite in regions of entrainment, having small droplets in minor concentrations resulting in low LWC. Different initial vertical profiles of aerosol concentrations result in significantly different values of CDNC; as larger the aerosol loading, the larger the CDNC. The updraft transport aerosols and moisture up to the cloud altitude from near the ground elevation. In the updraft, a sharp CDSD with a small standard deviation has small values of relative dispersion. In regions of entrainment, the CDSD has smaller droplets in minor concentrations due to the evaporation of droplets. Its shape resembles the distribution of interstitial aerosols with a large standard deviation resulting in large values of relative dispersion and smaller values of LWC. Aircraft measurements show agreement with the simulated CDNC and relative dispersion for the simulation with an initial vertical profile of aerosol concentration that decays exponentially with height and has a scale height of 500m. The significant impact of aerosol loading in the CDNC affects the cloud optical thickness (COT). The COT in the cloud mature stage for a scale height of 2000m is approximately 1.75 times the COT for a scale height of 500m.

Light rainfall (< 3 mm/hr) amounts to 30–70% of the annual water budget in the Southern Appalachian Mountains (SAM), a mid-latitude mid-mountain system in the SE CONUS. Topographic complexity favors the diurnal development of regional-scale convergence patterns that provide the moisture source for low-level clouds and fog (LLCF). Low-level moisture and cloud condensation nuclei (CCN) are distributed by ridge-valley circulations favoring LLCF formation that modulate the diurnal cycle of rainfall especially the mid-day peak. The overarching objective of this dissertation is to advance the quantitative understanding of the indirect effect of aerosols on the diurnal cycle of LLCF and warm-season precipitation in mountainous regions generally, and in the SAM in particular, for the purpose of improving the representation of orographic precipitation processes in remote sensing retrievals and physically-based models.

The research approach consists of integrating analysis of in situ observations from long-term observation networks and an intensive field campaign, multi-sensor satellite data, and modeling studies. In the first part of this dissertation, long-term satellite observations are analyzed to characterize the spatial and temporal variability of LLCF and to elucidate the physical basis of the space-time error structure in precipitation retrievals. Significantly underestimated precipitation errors are attributed to variations in low-level rainfall microstructure undetected by satellites. Column model simulations including observed LLCF microphysics demonstrate that seeder-feeder interactions (SFI) among upper-level precipitation and LLCF contribute to an three-fold increase in observed rainfall accumulation and can enhance surface rainfall by up to ten-fold. The second part of this dissertation examines the indirect effect of aerosols on cloud formation and warm-season daytime precipitation in the SAM. A new entraining spectral cloud parcel model was developed and applied to provide the first assessment of aerosol-cloud interactions in the early development of mid-day cumulus congestus over the inner SAM. Leveraging comprehensive measurements from the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, model results indicate that simulated spectra with a low value of condensation coefficient (0.01) are in good agreement with IPHEx aircraft observations. Further, to explore sensitivity of warm-season precipitation processes to CCN characteristics, detailed intercomparisons of Weather Research and Forecasting (WRF) model simulations using IPHEx and standard continental CCN spectra were conducted. The simulated CDNC using the local spectrum show better agreement with IPHEx airborne observations and better replicate the widespread low-level cloudiness around mid-day over the inner region. The local spectrum simulation also indicate suppressed early precipitation, enhanced ice processes tied to more vigorous vertical development of individual storm cells. The studied processes here are representative of dominant moist atmospheric processes in complex terrain and cloud forests in the humid tropics and extra-tropics, thus findings from this research in the SAM are transferable to mountainous areas elsewhere.

Quantitative precipitation estimation (QPE) in mountainous regions remains a challenging task owing to its high spatial and temporal variability. Satellite-based radar observations at high resolution have the best potential to capture the spatial patterns of precipitation, but there is high uncertainty in the interpretation of low-level measurements due to ground clutter effects, observing geometry, and sub-grid scale vertical and horizontal heterogeneity of precipitation systems that result from interactions among orographic clouds and propagating storm systems. In the high elevation tropics and in middle mountains everywhere, the landscape is often immersed in multi-layered cloud systems that modify precipitation significantly at low levels in a complex manner depending on time of day and location that is very different from the classical understanding of orographic precipitation enhancement with elevation, and are not easily parameterized or corrected for in QPE algorithms. The overall objective of this proposal is to characterize and elucidate the physical basis of uncertainty in Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR) QPE in mountainous regions and develop an improved retrieval framework for orographic precipitation. The following science objectives will be addressed specifically: i) to characterize the dependencies among the spatial and temporal variability of errors in orographic QPE and associated hydrometeorological regimes; ii) to characterize the vertical structure of radar reflectivity associated with QPE retrieval error and establish a physics-based retrieval model; and iii) to develop an operational framework to integrate DPR observations and Numerical Weather Prediction (NWP) model toward improving the retrieval of orographic QPE. The research hypothesis are two folds: a) satellite QPE errors (false alarms, missed detections, underestimations and overestimations) exhibit a robust spatial and temporal organization that is explained by the spatial and temporal variability in the vertical microstructure of precipitation; and b) current satellite-based QPE algorithms fail because the vertical structure of precipitation cannot be detected. If the vertical structure of precipitation systems can be predicted, then the key microphysical processes can be modelled to improve QPE. The study will be conducted using observations from the Southern Appalachian Mountains (SAM) region including a high-density rain gauge network, Micro Rain Radars (MRR), and Parsivel disdrometers since the launch of GPM as well as Integrated Precipitation and Hydrology Experiment (IPHEx) 2014 data.

This research approach consists of integrating ground-based point measurements from long-term observation networks, fields campaign (IPHEx), multi-satellite data, and modeling studies to develop a physically-based retrieval framework for orographic precipitation. To characterize the dependencies among the spatial and temporal variability of errors in orographic QPE, GPM estimations were evaluated using the ground-based precipitation observations and investigated for the robust organization of uncertainty. The physics-based retrieval of near-surface rain-rates was demonstrated through explicit modeling of rain shaft microphysical processes constrained by GPM-DPR observations. Finally, a general data-driven operational framework was developed to improve the detection and to predict the vertical structure of precipitation systems by integrating GPM observations with NWP model simulations.