Aerosol-cloud-precipitation interaction based on remote sensing and cloud-resolving modeling over the Central Himalayas

The Central Himalayan region experiences pronounced orographic precipitation related to the South Asian summer monsoon, typically occurring from June to September. Atmospheric aerosols can influence regional and global climate through aerosol-radiation (ARI) and aerosol-cloud interactions (ACI). The study of the aerosol-precipitation relationship over the Central Himalayan region during the summer monsoon season is important due to extreme pollution over the upwind Indo-Gangetic Plains, enhanced moisture supply through monsoonal flow, and steep terrain of the Himalayas modulating the orographic forcing. This dissertation aims to study the impact of atmospheric aerosols, from natural and anthropogenic sources, in modulating the monsoonal precipitation, cloud processes, and freezing isotherm over the central Himalayas. The long-term (2002 – 2017) satellite-retrieved and reanalysis datasets showed regardless of the meteorological forcing, compared to relatively cleaner days, polluted days with higher aerosol optical depth is characterized by the invigorated clouds and enhanced precipitation over the southern slopes and foothills of the Himalayas. The mean freezing isotherm increased by 136.2 meters in a polluted environment, which can be crucial and significantly impact the hydroclimate of the Himalayas. Due to the limitations of satellite-retrieved observational data, these results underlined the need for state-of-the-art Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in a cloud-resolving scale to better represent and study the impact of the aerosols from different sources through radiation and microphysics pathways over the complex terrain of the Central Himalayas. A cloud-resolving WRF-Chem simulation is performed to assess the impact of anthropogenic and remotely transported dust aerosols on the convective processes and elevation-dependent precipitation. Long-range transported dust aerosols significantly impacted cloud microphysical properties and enhanced the precipitation by 9.3% over the southern slopes of the Nepal Himalayas. The mid-elevation of the Central Himalayas, generally between 1000 and 3000 meters, acted as the region below and above which the diurnal variation and precipitation of various intensities (light, moderate, and heavy) responded differently for ARI, ACI, and the combined effect of aerosols. Due to the ARI effect of aerosols, the light precipitation is suppressed by 17% over the Central Himalayas. The ACI effect dominated and resulted in enhanced heavy precipitation by 12% below 2000 m ASL, which can potentially increase the risk for extreme events (floods and landslides). In contrast, above 2000 m ASL, the suppression of precipitation due to aerosols can be critical for the regional supply of water resources. The overview of the study suggests that the natural and anthropogenic aerosols significantly modulate the convective processes, monsoonal precipitation, and freezing isotherm over the Central Himalayan region, which could pose significant consequences to the changing Himalayan hydroclimate

Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.Peer Reviewe

Aerosol–precipitation elevation dependence over the central Himalayas using cloud-resolving WRF-Chem numerical modeling

Atmospheric aerosols can modulate the orographic precipitation impacting the evolution of clouds through radiation and microphysical pathways. This study implements the cloud-resolving Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to study the response of the central Himalayan elevation-dependent precipitation to the atmospheric aerosols. The first monsoonal month of 2013 is simulated to assess the effect of aerosols through radiation and cloud interactions. The results show that the response of diurnal variation and precipitation intensities (light, moderate, and heavy) to aerosol radiation and cloud interaction depended on the different elevational ranges of the central Himalayan region. Below 2000 m a.s.l., the total effect of aerosols resulted in suppressed mean light precipitation by 19 % while enhancing the moderate and heavy precipitation by 3 % and 12 %, respectively. In contrast, above 2000 m a.s.l., a significant reduction of all three categories of precipitation intensity occurred with the 11 % reduction in mean precipitation. These contrasting altitudinal precipitation responses to the increased anthropogenic aerosols can significantly impact the hydroclimate of the central Himalayas, increasing the risk for extreme events and influencing the regional supply of water resources.</p

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Evaluation of the Potential of NASA Multi-satellite Precipitation Analysis in Global Landslide Hazard Assessment

Landslides are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage every year. In the U.S. alone landslides occur in every state, causing an estimated $2 billion in damage and 25- 50 deaths each year. Annual average loss of life from landslide hazards in Japan is 170. The situation is much worse in developing countries and remote mountainous regions due to lack of financial resources and inadequate disaster management ability. Recently, a landslide buried an entire village on the Philippines Island of Leyte on Feb 17,2006, with at least 1800 reported deaths and only 3 houses left standing of the original 300. Intense storms with high-intensity , long-duration rainfall have great potential to trigger rapidly moving landslides, resulting in casualties and property damage across the world. In recent years, through the availability of remotely sensed datasets, it has become possible to conduct global-scale landslide hazard assessment. This paper evaluates the potential of the real-time NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA) system to advance our understanding of and predictive ability for rainfall-triggered landslides. Early results show that the landslide occurrences are closely associated with the spatial patterns and temporal distribution of rainfall characteristics. Particularly, the number of landslide occurrences and the relative importance of rainfall in triggering landslides rely on the influence of rainfall attributes [e.g. rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms). TMPA precipitation data are available in both real-time and post-real-time versions, which are useful to assess the location and timing of rainfall-triggered landslide hazards by monitoring landslide-prone areas while receiving heavy rainfall. For the purpose of identifying rainfall-triggered landslides, an empirical global rainfall intensity-duration threshold is developed by examining a number of landslide occurrences and their corresponding TMPA precipitation characteristics across the world. These early results , in combination with TRMM real-time precipitation estimation system, may form a starting point for developing an operational early warning system for rainfall-triggered landslides around the globe

Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events

Precipitation Trends over the Indus Basin

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in many cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management