Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

Abstract

This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

“…However, V05, with a real enhancement, estimates precipitation with more accuracy in comparison with V03 and V04. Sharifi et al (2019) and Anjum et al (2019) could not find any improvement in V06 compared to the V05 across Austria and northwestern China, respectively. However, it is reported that the retrieval algorithm of IMERG V06 has benefited from several major improvements compared to V05, that is, a new morphing approach, advancements in the calibration, and refinement in the CMORPH-Kalman filter (Anjum et al 2019).…”

“…Sharifi et al (2019) and Anjum et al (2019) could not find any improvement in V06 compared to the V05 across Austria and northwestern China, respectively. However, it is reported that the retrieval algorithm of IMERG V06 has benefited from several major improvements compared to V05, that is, a new morphing approach, advancements in the calibration, and refinement in the CMORPH-Kalman filter (Anjum et al 2019). It should be noted that, in the new morphing scheme, the MERRA-2 variables are used instead of infrared data (Huffman et al 2015).…”

“…Currently, the Integrated Multisatellite Retrievals for GPM (IMERG) version 6 as the newest GPM precipitation product, with a half-hourly temporal resolution and 0.18 3 0.18 spatial resolution has a full coverage of the globe, whereas TRMM products cover a smaller latitude band (508N-508S) with a lower spatial (0.258 3 0.258) and temporal (3-hourly) resolution (Huffman et al 2007). Furthermore, in terms of algorithm progress, IMERG utilizes a combination of three SPPs features, including PERSIANN, CMORPH, and TMPA (Anjum et al 2018;Prakash 2019). It is worth mentioning that PERSIANN estimates the precipitation based on infrared brightness temperature image (as input) and artificial neural network (as a model) (Ashouri et al 2015), while CMORPH is mainly based on microwave data and only uses infrared data for transporting the microwavebased precipitation features within periods that microwave data are not available at a location (Joyce et al 2004).…”

“…It is worth mentioning that PERSIANN estimates the precipitation based on infrared brightness temperature image (as input) and artificial neural network (as a model) (Ashouri et al 2015), while CMORPH is mainly based on microwave data and only uses infrared data for transporting the microwavebased precipitation features within periods that microwave data are not available at a location (Joyce et al 2004). Because of these improvements, IMERG precipitation products (IPPs) appeared to be better than TMPA and other SPPs in many regions, for example, India (Prakash et al 2018), Pakistan (Anjum et al 2018), Brazil (Rozante et al 2018), and East Asia (Lee et al 2019).…”

Ground Validation of GPM IMERG Precipitation Products over Iran

“…As the spatiotemporal resolution and coverage of GPM have been extended beyond the TRMM resolution and coverage, the performance of the GPM IMERG products needs to be evaluated and validated globally.Several studies have compared the GPM IMERG and TRMM products with ground-based measurements, i.e., rain gauge and weather radar [4,6,10,[15][16][17][18][19], considering their hydrological applications [14,[20][21][22]. Also, different GPM IMERG products regarding temporal resolutions have been evaluated considering various climatic and topographic conditions using various statistical measures across the world [5,[23][24][25][26][27][28][29][30]. Although most of these studies confirmed the improvement of the IMERG products relative to those of the TRMM Multi-satellite Precipitation Analysis (TMPA), a more comprehensive investigation is still essential to better understand the IMERG performance in various regions of the world taking into consideration different products' versions and temporal resolution.…”