Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

Abstract

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998-2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316-0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983-2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.

The PERSIANN family of global satellite precipitation data: a review and evaluation of products

“…PERSIANN products have been used frequently for different studies by researchers in the fields of hydrology, water resource management, and climate. Such studies include the evaluation of PERSIANN products against ground observations, other satellite-based products, and model simulations (Sorooshian et al, 2002;Yilmaz et al, 2005;Li et al, 2003;Miao et al, 2015;Nguyen et al, 2017;Mehran and AghaKouchak, 2014), application of PERSIANN products for modeling soil moisture (Juglea et al, 2010), prediction of runoff (Behrangi et al, 2011;Ashouri et al, 2016;Liu et al, 2017;AghaKouchak et al, 2010;Hsu et al, 2013), rainfall frequency analysis (Gado et al, 2017), tracking typhoons (Nguyen et al, 2014), monitoring drought (Katiraie-Boroujerdy et al, 2016;AghaKouchak and Nakhjiri, 2012), assimilation into climate models (Yi, 1996), precipitation forecasting (Zahraei et al, 2013), and trend analysis Damberg and AghaKouchak, 2014).…”

“…The PERSIANN algorithm relies primarily on infrared imagery from GEO satellites as an input to the ANN model. Similarly, PERSIANN-CDR uses infrared imagery data from different international GEO satellites which are available starting from 1979 at 10 km spatial resolution and 3 h temporal resolution (Rossow and Schiffer, 1991;Rossow and Garder, 1993;Knapp, 2008) and maintained by NOAA under the International Satellite Cloud Climatological Project (ISCCP). However, unlike the PERSIANN algorithm, where passive microwave imagery is used to update the parameters of the network, PERSIANN-CDR alternatively uses the National Centers for Environmental Prediction (NCEP) Stage IV hourly precipitation to train the ANN model.…”

Evaluation of Six Satellite-Based Precipitation Products and Their Ability for Capturing Characteristics of Extreme Precipitation Events over a Climate Transition Area in China

“…However, relatively few studies have focused on the ability of SPPs to detect extreme precipitation. Previous studies regarding extreme precipitation have mainly focused on using TRMM [27,39,40] and PERSIANN CDR [31,39,41] data and have primarily concentrated on heavy precipitation [39,40]. However, the ability of other precipitation products to detect extreme precipitation (including heavy precipitation that may cause floods and light precipitation that may cause droughts) is not clear, especially in China.…”

Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China

“…Compared with other popular precipitation data sets, such as daily precipitation data sets from Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (Huffman et al ., ), the Integrated Multisatellite Retrievals for Global Precipitation Mission (Huffman et al ., ), and the Climate Prediction Center morphing technique (Joyce et al ., ), PERSIANN‐CDR covers from 60°S to 60°N at 0.25 ∘ × 0.25 ∘ latitude–longitude spatial resolution and provides daily time series from 1983 to near present time. Although it is a newly developed data set, some evaluation and applications have shown PERSIANN‐CDR has its potential for hydroclimate studies (Casse and Gosset, ; Miao et al ., ; Tan et al ., ; Ashouri et al ., ; Katiraie‐Boroujerdy et al ., ; Zhu et al ., ). Miao et al .…”