Hamed Ashouri

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

AbstractA new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset ...

Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China

AbstractThis study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), in capturing the behavior of daily extreme precipitation events in China during the period of 1983–2006. Different extreme precipitation indices, in the three categories of percentile, absolute threshold, and maximum indices, are studied and compared with the same indices from the East Asia (EA) ground-based gridded daily precipitation dataset. The results show that PERSIANN-CDR depicts similar precipitation behavior as the ground-based EA product in terms of capturing the spatial and temporal patterns of daily precipitation extremes, particularly in the eastern China monsoon region, where the intensity and frequency of heavy rainfall events are very high. However, the agreement between the datasets in dry regions such as the Tibetan Plateau in the west and the Taklama...

Satellites Track Precipitation of Super Typhoon Haiyan

Typhoon Haiyan, which struck Southeast Asia in November 2013, might be the strongest storm on record, with a 10-minute sustained wind speed of 230 kilometers per hour. In the Philippines alone, the damage was immense—the storm killed more than 6000 and completely leveled cities and towns, particularly on the island of Leyte.

Assessing the Efficacy of High-Resolution Satellite-Based PERSIANN-CDR Precipitation Product in Simulating Streamflow

AbstractThis study aims to investigate the performance of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in a rainfall–runoff modeling application over the past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal and spatial resolutions from 1983 to present for the 60°S–60°N latitude band and 0°–360° longitude. The study is conducted in two phases over three test basins from the Distributed Hydrologic Model Intercomparison Project, phase 2 (DMIP2). In phase 1, a more recent period of time (2003–10) when other high-resolution satellite-based precipitation products are available is chosen. Precipitation evaluation analysis, conducted against stage IV gauge-adjusted radar data, shows that PERSIANN-CDR and TRMM Multisatellite Precipitation Analysis (TMPA) have close performances with a higher correlation coefficient for TMPA (~0.8 vs 0.75 for PERSIANN-CDR) and almost the same root-mean-square deviati...

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

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.

Exploring Trends through “RainSphere”: Research data transformed into public knowledge

C limate is an area of interest that both influences and is influenced by the global population, and yet the transfer of critical basic knowledge about our climate system from scientists who specialize in the field to the general public is severely deficient. To bridge this gap and make understanding historical climate and climate projections accessible, an intuitive and user-friendly analysis tool called CHRS (Center for Hydrometeorology and Remote Sensing) RainSphere has been developed (hosted at http:// rainsphere.eng.uci.edu; a YouTube video tutorial on CHRS RainSphere is available at www.youtube.com /watch?v=eI2-f88iGlY&feature=youtu.be). CHRS RainSphere was designed as an educational tool that allows users to quickly and easily conduct analyses of historical and future precipitation at spatial scales that range from highly local to global and at daily, monthly, or annual time scales. With automatically generated time series, spatial plots, and basic trend analysis, users can swiftly explore historical precipitation estimates and future projections tailored to their specific interests. Allowing the data to speak for themselves in a way that the public can understand not only helps to spread the comprehension of climate and climate variability but also encourages independent inquisition and discovery of climate studies. At the heart of CHRS RainSphere is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) (Ashouri et al. 2015). This satellite-based precipitation product provides daily precipitation estimates from 60°S to 60°N latitude at a 0.25° spatial resolution. PERSIANN-CDR is derived from the parent PERSIANN algorithm (Hsu et al. 1997), which utilizes an artificial neural network to assign a surface rain rate based on brightness temperature retrievals of infrared information from geostationary Earth-orbiting satellites and passive microwave information from low-Earthorbiting satellites. Validation of the PERSIANN product has been performed in several studies (i.e., Sorooshian et al. 2000; Miao et al. 2015, Ashouri et al. 2016). PERSIANN-CDR provides the ability to study extreme hydrometeorological phenomena. The record begins on 1 January 1983 and continues to the present date. This 30+-year retrospective look at global precipitation lends itself to a host of historical precipitation-related questions such as “How does this June’s total precipitation compare to the average monthly total precipitation for June?” or “What is the trend in annual precipitation for my country?” These and countless other questions can be answered using PERSIANN-CDR facilitated through CHRS RainSphere. Complementing the past precipitation estimates from PERSIANN-CDR, CHRS RainSphere features global precipitation projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) based on three carbon emission scenarios (RCP2.6, RCP4.5, and RCP8.5 for low, stabilized, and high emissions scenarios, respectively) from the Intergovernmental Panel on Climate Change (IPCC). More details on CMIP5 can be found in Taylor et al. (2012). Ensemble mean IPCC projected precipitation data from 29 CMIP5 models were obtained from the Canadian Climate Data and Scenarios site (http:// ccds-dscc.ec.gc.ca). CMIP5 model results were interpolated to a common 1 × 1 degree grid (see more AFFILIATIONS: NguyeN—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, and Nong Lam University, Ho Chi Minh City, Vietnam; SorooShiaN, ThorSTeNSeN, TraN, huyNh, Pham, aShouri, hSu, aghaKouchaK, aNd BraiThwaiTe—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California

Global Precipitation Trends across Spatial Scales Using Satellite Observations

AbstractLittle dispute surrounds the observed global temperature changes over the past decades. As a result, there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in precipitation across spatial scales. We show that warm climate regions exhibit decreasing precipitation trends, while arid and polar climate regions show increasing trends. At the country scale, precipitation seems to have increased in 96 countries, and decreased in 104. We also explore precipitation changes over 237 global major basins. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset, P...

Evaluation of CMIP5 Model Precipitation Using PERSIANN-CDR

AbstractThe purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes ...