*Article* **Evaluation of GPM-Era Satellite Precipitation Products on the Southern Slopes of the Central Himalayas Against Rain Gauge Data**

**Shankar Sharma 1,2, Yingying Chen 1,3,\*, Xu Zhou 1, Kun Yang 3,4, Xin Li 1,3, Xiaolei Niu 1, Xin Hu 1,2 and Nitesh Khadka 2,5**


Received: 21 May 2020; Accepted: 4 June 2020; Published: 5 June 2020

**Abstract:** The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.

**Keywords:** precipitation; GPM; IMERG; GSMaP; Nepal

#### **1. Introduction**

Precipitation is a vital component of the water cycle, and understanding the characteristics of precipitation is essential for hydro-meteorological applications [1,2]. In mountainous regions, water resource management is further challenging due to the complex climate associated with topographic variance [3]. In these regions, the occurrences of hydrological hazards such as floods, landslides and soil erosion are very sensitive to precipitation amounts. Thus, reliable and precise estimates of precipitation are a prerequisite for hydro-meteorological and natural disaster studies [4,5].

Nepal lies on the south-central part of the main Himalayan range, with more than 80% of the country covered by mountains; in this environment, there is a high probability of landslides and debris flows during the monsoon season. Precipitation in the country is extremely variable due to the complex topography. The seasonal cycle is predominantly governed by the monsoon system [6,7] with maximum (~80%) precipitation occurring in summer. Rain gauge-based measurements provide relatively accurate measurements of precipitation on the ground surface [8,9]. These observations developed by the Department of Hydrology and Meteorology (hereafter, DHM) in Nepal are relatively dense in the lowlands but sparse in high mountain areas [10,11]. The scarcity of rain gauge observations is a major challenge in hydro-meteorological studies and for effective water and disaster management. This scarcity of measurements also limits knowledge of precipitation patterns across the country [12]. Fortunately, high-resolution satellite-based precipitation (hereafter, SBP) products provide potential alternatives for monitoring precipitation on regular high-resolution grids, yielding unprecedented levels of detail especially over remote areas and mountainous regions where stations are very sparse. However, these estimates are indirect measurements and must be verified and calibrated using gauge observations before further application [13,14].

SBP estimates are based on various remotely sensed characteristics of clouds, such as cloud-top temperature (IR imagery), reflectivity (visible) or from the scattering effects of ice particles on passive microwave (PMW) radiation [15–18]. In the post- Tropical Rainfall Measuring Mission (TRMM) era, the Global Precipitation Measurement (GPM) Core Observatory spacecraft, equipped with advanced sensors and channels, like the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) which had capabilities to sense light rain and snowfall, was launched on 27 February 2014 in a collaboration between NASA and the Japan Aerospace Exploration Agency (JAXA) [16]. New SBP products were introduced after the GPM mission: the Integrated Multi-satellite Retrievals for GPM (IMERG) [19,20]; meanwhile, JAXA updated to a newer version of the Global Satellite Mapping of Precipitation (GSMaP) product (GSMaP Version 07) with orographic rainfall correction [21].

Several studies have already evaluated SBP products around the globe for different hydro-meteorological applications [22–28]. They found that the new generation SBP products (GPM-Era) were improved than their previous version (TRMM-Era). For example, [29–31] found that the GPM-Era (IMERG-V3) precipitation product outperforms the TRMM-Era (TRMM 3B42V7, TMPA-RT) precipitation products. Meanwhile, studies conducted in Myanmar found the GSMaP-V07 product had the lowest accuracy when compared with the IMERG-05B product [32]. Similarly, Wang and Yong [33] also mentioned that IMERG-V05 performed better than GSMaP-V07, especially in high-elevation areas. A study conducted in Northwestern China did not found any significant difference in estimating the precipitation by IMERG-V06 and IMERG-V05 [34]. Nevertheless, several studies have concluded that new generation SBPs products can represent either the spatial pattern or the overall amount and general characteristics of extreme precipitation events over China [35,36], Cyprus [37], Austria [38], Africa [39] and India [40].

Besides several global studies, only a few studies have evaluated the SBPs in a topographically challenging region like Nepal. For example, the TRMM precipitation product shows negative bias (underestimation) as compared to gauge observations over the Himalayan region of the country [41]. Similarly, Islam et al. [12] found comparable results for 15 stations across the country. In contrast, Duncan and Biggs, [42] indicated that the TRMM product generally overestimated (positive bias) the precipitation as compared to a gauge-based gridded product (the Asian Precipitation

Highly Resolved Observational Data Integration Towards Evaluation: APHRODITE) over Nepal. In mountainous regions the TRMM (3B43) precipitation product shows reasonable skill, while the GSMaP, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and the Climate Prediction Center Morphing Method (CMORPH) products showed considerably weaker performances in reproducing gauge-observed precipitation amounts [6]. A study in a high-elevation area (Khumbu Himalayas) of Nepal compared seasonal and diurnal variations of precipitation in TRMM (3B42), PERSIANN, CMORPH, and GSMaP products using hourly gauge observed precipitation [43]. They found that GSMaP performed poorly, while TRMM, PERSIANN and CMORPH had good agreement with rain-gauge data. Recently, Derin et al. [44] evaluated the GPM-era SBP products over different complex terrain areas, including ten stations from Nepal. They found that GSMaP-V07 was better for measuring the orographic precipitation and precipitation amount as compared to IMERG-V06B after the orographic rainfall classification ensemble in the GSMaP algorithm. The authors also noticed the better performance of IMERG-V05B to capture the light and heavy precipitation amount as compared to IMERG-V06B for the evaluated regions.

Most of the past studies in Nepal were based on previous-generation satellite products, which showed that errors in SBP estimates were partially related to the rugged topography as their algorithms could not detect orographically-induced precipitation appropriately. Additionally, the local climate and nature of the topography are some of the dominant factors to characterize the uncertainty of SBP products [35,45–47]. However, a systematic evaluation of the new-generation SBP products, and a intercomparison between these products, has not yet been performed at the national scale. Thus, in this study, we aimed to comprehensively evaluate four precipitation datasets from the two SBP products, i.e., GPM-era IMERG (V06B) and GSMaP (V07), against 388 gauge observations concerning their spatial and seasonal accuracy over Nepal. Their performances are analyzed for their tendencies and discrepancies depending on the different elevation range, and relative intensities on a daily and monthly timescale from March 2014 to December 2016. Moreover, the accuracy of East to West diversion of monsoon and extreme wet events on these SBP products are also analyzed. The result of this study will help to provide critical scientific references to choose the appropriate product for future scientific research.
