Remote Sensing of Water-Related Hazards. Группа авторов

       Critical flash flood data

      Critical rainfall is defined as the value when the flow reaches the early warning flow. In this work, the critical rainfall in Yunnan Province is collected from the China rainstorm parameters atlas. However, it should be noted that this critical rainfall may not agree with the spatial resolution of gridded IMERG data, which could to some extent affect the flood warning. In total, 120 flash floods were recorded during 2015–2018.

       Satellite and reanalysis precipitation products

There are seven satellite precipitation data sets and three reanalysis data sets evaluated, as shown in Table 2.1. The reanalysis products are involved because they are important sources of global precipitation data, and comparison with reanalysis products can deepen our understanding of satellite products.

IMERG V06, as the latest release version, has undergone major improvements compared with previous versions. First, IMERG V06 uses total precipitable water vapor to replace geostationary infrared data to calculate the morphing vectors of precipitation systems. Second, IMERG V06 includes data from more sensors such as the TRMM microwave imager (TMI) and the Megha‐Tropiques SAPHIR. Third, passive microwave estimates are morphed at high latitudes to diminish spatial gaps. Please refer to official documents for a more detailed description. IMERG generates three products, i.e., the near‐real‐time Early and Late runs and the post‐real‐time Final run.

Figure 2.2 Topographical map and locations of flash flood events from 2011 to 2018 in Yunnan province,

      Source: Based on Tang et al. (2020), Figure 01B, p 03 / Elsevier. based on Ma et al. (2020), Figure 01, p 04 / MDPI / CC BY 4.0.

Table 2.1 Summary of satellite and reanalysis precipitation data sets used in this study.

Data set	Full name of the data set	Resolution	Period	Reference
	Satellite Precipitation Data Set
IMERG	Integrated Multi‐satellitE Retrievals for GPM Fina run V06B	0.1°/0.5 h	2006–present	Huffman et al., (2019)
T3B42	TRMM Multi‐satellite Precipitation Analysis (TMPA) 3B42 V7	0.25°/3 h	1998–present	Huffman et al. (2007)
CMORPH	Climate Prediction Center (CPC) MORPHing technique bias corrected (CRT)	0.25°/3 h	1998–present	Joyce et al. (2004)
PCDR	PERSIANNClimate Data Record	0.25°/1 d	1983–present	Ashouri et al. (2015)
GSMaP	Gauge‐adjusted Global Satellite Mapping of Precipitation V6/V7	0.1°/1 h	2000–present	Mega et al. (2014)
CHIRPS	Climate Hazards group Infrared Precipitation with Stations	0.05°/1 d	1981–present	Funk et al. (2015)
SM2RAIN	SM2RAIN based on ESA Climate Change Initiative (CCI)	0.25°/1 d	1998–2015	Ciabatta et al. (2015)
	Reanalysis Precipitation Data Set
ERA5	Fifth generation of ECMWF atmospheric reanalyses of the global climate	~0.25°/1 h	1979–present	Hersbach et al. (2020)
ERA‐Interim	ECMWF ReAnalysis Interim	∼0.75°/3 h	1979–present	Dee et al. (2011)
MERRA2	The Modern‐Era Retrospective Analysis for Research and Applications, Version 2	~0.5°×0.625°/1 h	1980–present	Gelaro et al. (2017)

      TMPA 3B42 V7 (T3B42) is the precursor of IMERG. Both IMERG Final run and T3B42 are corrected using the station‐based Global Precipitation Climatology Center (GPCC). CMORPH and GSMaP are corrected using CPC daily analysis. The PERSIANN Climate Data Record (PCDR) is obtained by adjusting the PERSIANN estimate using GPCC monthly precipitation. CHIRPS (Climate Hazards group Infrared Precipitation with Stations) uses five public metering data streams and several private files detailed in Funk et al. (2015). SM2RAIN is produced using satellite soil moisture data.

      The three reanalysis products involved are ERA‐Interim, ERA5, and MERRA‐2. Among them, ERA5 is the fifth generation of global reanalysis conducted by the European Center for Medium‐Range Weather Forecast (ECMWF).

      All satellites and reanalysis products are resampled to 0.25° resolution and accumulated to a daily scale to facilitate the evaluation based on CGDPA.

2.3. METHODOLOGY

      2.3.1. Statistic Metrics

      Different types of statistical metrics are applied to evaluate the accuracy of various precipitation products, including the Pearson correlation coefficient (CC), mean error (ME), and root mean square error (RMSE), which are widely used in previous studies. We also used the Kling‐Gupta efficiency (KGE') statistics proposed by Gupta et al. (2009) and modified by Kling et al. (2012). KGE' considers the contribution of correlation, deviation, and variability, and its calculation formula is as follows:

      (1)

      (2)

      (3)

      where r is the CC between reference (abbreviated as ref) and target (abbreviated as tar), β is the bias ratio, γ is the variability ratio, μ is the mean precipitation, CV is the coefficient of variation, and σ is the standard deviation of precipitation. KGE' ranges