This study examines the implications of Tropical Montane Forests (TMFs) loss on orographic precipitation in the Eastern slopes of the Andes (EADS). The focus is on moist processes for synoptic regimes associated with significant EADS precipitation: 1) monsoon rainfall for weak and strong South America Low-Level Jet (LLJ) conditions, and 2) heavy rainfall associated with cold air intrusions (CAI) in the dry season. High-resolution simulations using the Weather Research and Forecasting (WRF) model were conducted for realistic and modified land-cover resulting from the conversion of TMFs to savannah. The deforestation scenarios result in 50-100% decrease (up to ~ 400 J kg-1) in Convective Available Potential Energy (CAPE) spatially organized by land-cover change along the EADS. Analysis of the differences in simulated frequency distributions of rainfall intensity show robust daytime increases in light rainfall (less than 2 mm hr-1) and decreases in moderate rainfall rates (2-10 mm hr-1) in the altitudinal band 500-2,000 m where orographic enhancement is dominant. Whereas there are negligible changes in the spatial patterns of precipitation and hydrologic response for monsoon conditions, rainfall accumulations decrease for all cases, and the precipitation maxima shift downslope into the Amazon lowlands. Changes in rainfall amount and intensity result in runoff decreases of 5-10% at the event-scale for the CAI case. Sensitivity simulations for lower initial soil moisture conditions indicate a strong positive feedback of forest loss to hydrologic drought along the EADS foothills in the austral spring when CAIs play a key role in the tropical EADS dry season hydrometeorology.

Measurements at the high-elevation Lamar Observatory in the Mantaro Valley in the Central Andes of Peru demonstrate a diurnal cycle of precipitation characterized by convective rainfall during the afternoon and nighttime stratiform rainfall with embedded convection. Based on 15 years of TRMM precipitation radar (PR) swath product 2A25, the area and rain type of precipitation features (PFs) over the Mantaro Valley showing PFs with areas smaller than 25,000 km and a mean daily ratio of convective to stratiform rainfall of 40/60. Data from three wet seasons 2016-2018 reveal long-duration (6-12 hours) precipitating systems (LDPS) that produce about 17% of monsoon rainfall for warming/cooling of Sea Surface Temperature (SST) in 2016/2018 during the El Niño/La Niña in the regions 3.4 and 1.2 of the Pacific. The LPDS fraction of monsoon rainfall doubles to 34% with weekly recurrence under warm and cool conditions in the region 1.2 and 3.4 respectively, that is the El Niño Costero. Backward trajectory analysis shows that precipitable water sustaining > 80% of seasonal precipitation and LPDS originate from the western Amazon. The analysis further shows that LDPS are associated with terrain-following moisture transport at low levels from the eastern foothills of the Andes under favorable weak South America Low Level Jet (SALLJ) conditions. LDPSs consist of late afternoon shallow embedded convection in the valley with trailing stratiform rainfall that persists until the early morning of the next day. The increase in the frequency of LDPSs explains the 30% increase in rainfall during 2017.

Ground-clutter is a major cause of large detection and underestimation errors in satellite-based (e.g. Global Precipitation Measurement Dual Polarization Radar, GPM DPR) precipitation radar retrievals in complex terrain. Here, an Artificial Intelligence (AI) framework consisting of sequential precipitation detection and vertical structure prediction algorithms is proposed to mitigate these errors using machine learning techniques to uncover predictive associations among satellite- and ground-based measurements aided by Numerical Weather Prediction model analysis, specifically the High-Resolution Rapid Refresh (HRRR) model. The framework is implemented and tested for quantitative estimation of orographic precipitation in the Southern Appalachian Mountains (SAM). Precipitation detection relies on a Random Forest Classifier to identify rainfall based on GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and HRRR mixing ratios in the lower troposphere (~ 1.5 km above ground level). The vertical structure of precipitation prediction algorithm is a Convolution Neural Network trained to learn associations among GPM DPR Ku-band reflectivity profiles, GMI Tbs, and orographic precipitation regimes in the SAM including low level light rainfall, shallow rainfall with low-level enhancement, stratiform rainfall with bright band, and deep heavy rainfall with low- and mid-level enhancement. Vertical structure classes corresponding to the distinct orographic precipitation regimes were isolated through k-means clustering of ground-based Multi-Radar/Multi-Sensor radar reflectivity profiles. The AI framework is demonstrated for automatic retrieval of warm season precipitation in the SAM over a 3-year period (2016-2019) achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products, and significant rain-rate corrections (up to one order of magnitude) by using a physically-based model to capture the microphysics of low-level enhancement (i.e. seeder-feeder interactions).

Ensemble predictions of the seasonal snowpack over Grand Mesa, CO were conducted for the hydrologic year 2016-2017 using a multilayer snow hydrology model. Ensembles were generated from gridded atmospheric reanalysis, model predictions were evaluated against SnowEx’17 measurements, and the signatures of the weather-dependent variability of snow physics in the behavior of multi-frequency microwave brightness temperatures and backscattering were examined through forward modeling. At sub-daily timescales , the ensemble standard deviation due to atmospheric forcing (i.e., mesoscale spatial variability of weather within the Grand Mesa) is < 3 dB for dry snow, and increases to 8-10 dB at midday when there is surficial melt that also explains the wide ensemble range (~20 dB). Further, the ensemble mean backscatter exhibits robust (R 2 > 0.95) time-varying, weather-dependent linear heuristic relationships with SWE (e.g., 5-6 cm/dB/month in January; 2-2.5 cm/dB/month in late February) as melt-refreeze cycles modify the microphysical structure in the top 50 cm of the snowpack. The nonlinear evolution of ensemble snow physics translates into seasonal hysteresis in the microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the Land C-bands and collapse for wet shallow snow at Ku-band. The ensemble mean emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer) and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.