Hamed Ashouri
University of California, Irvine, Civil and Environmental Engineering, Graduate Student
Research Interests:
Research Interests:
Understanding the ocean-atmospheric interactions that results in climate modes will not only improve weather and climate forecasting capabilities, but also could greatly enhance hydrological forecast skills. Therefore, identifying the... more
Understanding the ocean-atmospheric interactions that results in climate modes will not only improve weather and climate forecasting capabilities, but also could greatly enhance hydrological forecast skills. Therefore, identifying the effects of these phenomena on hydrological and meteorological parameters of different basins throughout the world is of great importance. On the other hand, Ensemble Streamflow Prediction (ESP) -part of the US National Weather Service River Forecasting System (NWSRFS)- is a well-known advanced hydrological forecasting technique that considers the forecast uncertainty in terms of occurrence probability and provides probabilistic forecast information rather than deterministic. This probabilistic approach, together with large-scale climate information could beneficially lead us to more accurate hydrological forecasts with longer forecasting lead times. In this research, as the first step, two of the most prominent known sources of interannual and interdecadal climate variability in the form of El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are analyzed to assess the influences of these large-scale climate phenomena on water supply in the Zayande-rood River Basin. For this purpose, any shifts in the mean and variance of the inflow volume of the Zayande-rood Dam in different climate conditions (resulted by different combination of ENSO and PDO phases) has been analyzed and compared with similar statistics in neutral condition to find out if the differences are statistically significant. Correlation analysis indicates that the inflow volume has a direct relation with Pacific Decadal Oscillation Index (PDO Index) and an inverse relation with Southern Oscillation Index (SOI). Also, it was found that any significant shift in the mean January through July inflow volume arises only when El Niño and La Niña events occurs respectively in the positive and negative phase of the PDO. To give some physical justification to the results, precipitation and temperature patterns in the above basin of Zayande-rood Dam are analyzed. As the second step, the hydrologic model is initialized based on meteorological and hydrological data of a year similar to the actual water year preceding the forecast year. Currently, forecast of ENSO condition with lead times of about 6 months up to a year are available. Having known the ENSO condition of the coming water year and the persistent phase of PDO, the climate condition of the coming water year is determined and then, the initialized hydrological model is driven to produce the ensemble members. The central tendency of the forecasts i.e. ensemble mean represents the forecasts hydrograph. Retrospective forecasts of the historic record are prepared to evaluate the technique, as well as applying probabilistic verification methods such as Skill Score.
Research Interests:
This study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network–Climate Data Record (PERSIANN-CDR), in... more
This study evaluates the performance of a newly developed daily precipitation climate data record, called Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network–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 data set. 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 data sets in dry regions such as the Tibetan Plateau in the west and the Taklamakan Desert in the northwest is not strong. An important factor that may have influenced the results is that the ground-based stations from which EA gridded data were produced are very sparse. In the station-rich regions in the eastern China, the performance of PERSIANN-CDR is significant. PERSIANN-CDR slightly underestimates the values of extreme heavy precipitation.
Research Interests:
A 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... more
A 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 01/01/1983 to 12/31/2012 (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 against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the Stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the Probability Density Function (PDF) of PERSIANN CDR over the Contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydro-climate studies in regional and global scales.
Research Interests:
Research Interests:
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... more
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.