Evaluation of CHIRPS rainfall estimates over Iran

International Journal of Climatology, 2019

This study provides a comprehensive evaluation of a great variety of state-ofthe- art precipitation datasets against gauge observations over the Karun basin in southwestern Iran. In particular, we consider (a) gauge-interpolated datasets (GPCCv8, CRU TS4.01, PREC/L, and CPC-Unified), (b) multi-source products (PERSIANN-CDR, CHIRPS2.0, MSWEP V2, HydroGFD2.0, and SM2RAIN-CCI), and (c) reanalyses (ERA-Interim, ERA5, CFSR, and JRA-55). The spatiotemporal performance of each product is evaluated against monthly precipitation observations from 155 gauges distributed across the basin during the period 2000–2015. This way, we find that overall the GPCCv8 dataset agrees best with the measurements. Most datasets show significant underestimations, which are largest for the interpolated datasets. These underestimations are usually smallest at low altitudes and increase towards more mountainous areas, although there is large spread across the products. Interestingly, no overall performance difference can be found between precipitation datasets for which gauge observations from Karun basin were used, versus products that were derived without these measurements, except in the case of GPCCv8. In general, our findings highlight remarkable differences between state-of-the-art precipitation products over regions with comparatively sparse gauge density, such as Iran. Revealing the best-performing datasets and their remaining weaknesses, we provide guidance for monitoring and modelling applications which rely on high-quality precipitation input.

Remote Sensing, 2021

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed...

Advances in Geosciences, 2010

To evaluate satellite rainfall estimates of Tropical Rain Measurement Mission (TRMM) level 3 output (3B42) (TRMM 3B42) over Iran (20 • -45 • N, 40 • -65 • E), we compared these data with high-resolution gridded precipitation datasets (0.25 • ×0.25 • latitude/longitude) based on rain gauges (Iran Synoptic gauges Version 0902 (IS0902)). Spatial distribution of mean annual and mean seasonal rainfall in both IS0902 and TRMM 3B42 from 1998 to 2006 shows two main rainfall patterns along the Caspian Sea and over the Zagros Mountains. Scatter plots of annual average rainfall from IS0902 versus TRMM 3B42 for each 0.25 • ×0.25 • grid cell over the entire country (25 • -40 • N, 45 • -60 • E), along the Caspian Sea (35 • -40 • N, 48 • -56 • E), and over the Zagros Mountains (28 • -37 • N, 46 • -55 • E) were derived.

Journal of Arid Environments, 2020

The present study assessed the accuracy of twenty-three available globally and regionally gridded precipitation products over Iran. The datasets were classified into five different categories based on the data sources used: gauge-only, gauge-satellite, gauge-reanalysis, reanalysis-only, and gauge-satellite-reanalysis. By considering monthly rain gauge observation data as reference, the above precipitation products were assessed based on a variety of metrics over the period 1979 to 2013. The results indicated that the time-series of the spatial averaged precipitation of all the products do reasonably well in tracking the month-to-month variations with the majority of the products underestimating precipitation amounts with respect to the reference rain gauge observations. Overall, the best performances were demonstrated by Asfazari and APHRODITE followed by CHIRPS and GPCC. WFDEI-CRU was the worst performer among the twenty-three gridded products in revealing precipitation tempo-spatial patterns. The results demonstrate the high performance for all products in the west of the country (the Zagros Range) and in all seasons except the summer. The results also indicate that there is a better agreement between rain gauge observations and the gridded products during the rainy seasons (winter, spring and autumn) compared to the dry season (summer). This study improved our knowledge of the suitability of different precipitation gridded product estimates to capture the temporal and spatial variability of precipitation. Furthermore, it is applicable for developing precipitation products and provides valuable guidance for selecting alternative precipitation products to in situ observations.

Remote Sensing, 2020

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects o...

Hydrological Sciences Journal, 2014

High-resolution satellite-retrieved precipitation products are useful input data for hydrologic predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. In this study, four widely used satellite rainfall estimates (TMPA-3B42V7, TMPA-3B42RT, PERSIANN, and CMORPH) are evaluated with a dense raingauge network over six regions with various physiographic and climate conditions in Iran. Assessments are implemented at daily scale for different seasons during the five years period from 2003 to 2008. Overall, the results show that 3B42V7 leads to better performance than the other three products over different terrains. According to the value of relative bias (RBias) as one of the verification metric used in this study, 3B42V7 with an average value of 13.43% over all the regions matches best with the raingauge observations, while both PERSIANN and 3B42RT overestimate precipitation by 78.13% and 31%,respectively. On the other hand, CMORPH with RBias of -17.6% tends to underestimate the rainfall amount. Furthermore, the evaluations over different seasons indicate that the best performance for PERSIANN and both TMPA products is during the winter, while for CMORPH is during the autumn season. With respect to the critical success index (CSI) in order to assess the rain detecting skill of satellite products, one can conclude that PERSIANN leads to better estimations during the winter and summer, 3B42RT during the spring, and CMORPH during the autumn season. Generally, the implemented analyses in this research provide quantitative information of error characteristics associated with satellite precipitation products over different parts of Iran and thus will offer hydrologic users a better understanding of satellite rainfall estimates applicability in this area.

Hydrological Sciences Journal, 2014

High-resolution satellite-retrieved precipitation products are useful input data for hydrologic predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. In this study, four widely used satellite rainfall estimates (TMPA-3B42V7, TMPA-3B42RT, PERSIANN, and CMORPH) are evaluated with a dense raingauge network over six regions with various physiographic and climate conditions in Iran. Assessments are implemented at daily scale for different seasons during the five years period from 2003 to 2008. Overall, the results show that 3B42V7 leads to better performance than the other three products over different terrains. According to the value of relative bias (RBias) as one of the verification metric used in this study, 3B42V7 with an average value of 13.43% over all the regions matches best with the raingauge observations, while both PERSIANN and 3B42RT overestimate precipitation by 78.13% and 31%,respectively. On the other hand, CMORPH with RBias of -17.6% tends to underestimate the rainfall amount. Furthermore, the evaluations over different seasons indicate that the best performance for PERSIANN and both TMPA products is during the winter, while for CMORPH is during the autumn season. With respect to the critical success index (CSI) in order to assess the rain detecting skill of satellite products, one can conclude that PERSIANN leads to better estimations during the winter and summer, 3B42RT during the spring, and CMORPH during the autumn season. Generally, the implemented analyses in this research provide quantitative information of error characteristics associated with satellite precipitation products over different parts of Iran and thus will offer hydrologic users a better understanding of satellite rainfall estimates applicability in this area.

Journal of Hydrometeorology, 2020

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility ...

Bulletin of the American Meteorological Society, 2020

R ain gauges and radars are the most popular sources for precipitation measurement and estimation, but their distribution is limited. In recent decades, satellite-based precipitation products have proven to be an effective alternative. They provide the first quasi-global estimations, except for the polar regions. Near-real-time, satellite-based precipitation data are essential in weather, climate, and hydrological modeling-including managing drought and flash floods. Satellite-based precipitation estimation algorithms primarily utilize input from longwave infrared (IR), visible (VIS), and passive microwave (PMW) sensors. Satellite observations do not read rain rate (RR) directly. An indirect relationship links the occurrence and intensity of rainfall to data from one or multiple sensors. Although PMW sensors are reliable sources for instantaneous precipitation estimations, their spatial and temporal resolutions are far coarser than IR/VIS readings. The spatiotemporal richness of IR and/or VIS helps balance Precipitation Rate Estimates Satellite Infrared Imagery

2018

Over the past two decades, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products have been incorporated in a wide range of studies. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely, PERSIANN, PERSIANN-CCS and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the Contiguous United States at different spatial and temporal scales using Climate Prediction Center (CPC) Unified gauge-based analysis as a benchmark. Finally, the available products are intercompared at a quasi-global scale. Furthermore, we highlight strength and limitations of the PERSIANN products and briefly discuss the expected future developments. Precipitation is an integral part of the Earth's hydrologic cycle, playing a foremost role in its water and energy balance. Accurate, uninterrupted and uniform observation of precipitation represent important inputs for research and operational applications. The resilience and capacity of societies to react and adapt to climate extremes such as storms, floods and droughts are greatly enhanced with a long term historical record of precipitation. Practical applications include use of precipitation Intensity-Duration-Frequency (IDF) information for infrastructure design and use of near real-time precipitation data in development of early warning systems and disaster management planning. Moreover, the observation of precipitation is essential for understanding Earth's climate, its underlying variabilities and trends. In turn, climatic understanding can improve our ability to forecast extreme events and enables informative strategic planning and decision making on issues related to water supply, both in quantity and quality. Precipitation measurement continues to represent a great challenge for the scientific community mainly due to its spatiotemporal variations in intensity and duration . The three primary instruments used for measurement of precipitation are gauges, radar and satellite. Rain gauges provides direct measurement of precipitation; however, it suffers from intermittent coverage over most continents. Radar technology is not available in many countries and even in places where the technology is available, radar blockage by mountains is a major challenge. Both rain gauges and radar do not provide measurements over oceans. On the other hand, satellite-based precipitation measurements seem to be the most promising method to accurately observe precipitation over both land and ocean.