A Statistical Approach to generate ensembles of observation data of precipitation using hindcast data over the Northwest Himalayas

Abstract

<p>Availability of precipitation data at fine spatial resolution is highly desirable for hydroclimatic studies. Rain gauges are often considered as the primary source of precipitation data due to its reliability. However, due to either physical, climatic or economic constraints, setting up networks of rain gauges becomes unfeasible in many isolated terrains such as the Himalayan region. In the absence of gauge data, other alternate sources of weather information such as Satellite based Precipitation Products (SPPs) and Reanalysis precipitation Datasets (RPDs) are generally used. In this study, we aim to utilise 18 years of precipitation data (2001-2018) derived from the Integrated Multi-Satellite Retrievals for GPM (IMERG) at 10km spatial resolution as input to a Multiple-Point Statistics (MPS) based statistical model to obtain corresponding data for the year 2019 at 10km over the North-west Himalayan region. MPS is capable of generating fine scale data using the available coarse scale hindcast data by reproducing spatially connected spatial patterns. It requires data to be split into two parts. First part is called the training image and it requires both coarse and fine scale data. Second part is called the conditioning data which requires data only at coarse scale for the year 2019. In the attempt of using MPS as the tool for this study, the spatial field of Original IMERG data at 10 km (O_IMERG) is smoothen (S_IMERG) in order to transform the data features to a coarse scale reference data. The reference data used for this purpose is the High Asia Refined analysis (HAR) available at 30km spatial resolution over the South-Central Asia and Tibetan Plateau region. The variograms of both O_IMERG and S_IMERG are used to evaluate error frequency between the two data at specific distances followed by bias correction of S_IMERG. The bias corrected S_IMERG (BCS_IMERG) acts as the conditioning data for the MPS model. Training Image is composed of both BCS_IMERG and O_IMERG. Both the training image (year 2001-2018) and the conditioning data (2019) are provided to the MPS model. In addition to the variable of precipitation, the model also employs static parameters such as locational and topographical variables to help in identification of true patterns between training image and conditioning data. The study is significant in its ability to generate future precipitation information by utilising the available hindcast data observation data (10 km spatial resolution) by overcoming the spatial heterogeneity involved with observation data.</p>